From a37c9af968aaf35b75fd2b8a5fa7003295f58702 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 19 Oct 2017 13:42:19 +0200 Subject: [PATCH 01/61] add potential solution to #360 to the docs --- doc/installation.rst | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/doc/installation.rst b/doc/installation.rst index c314617d48..3c60842509 100644 --- a/doc/installation.rst +++ b/doc/installation.rst @@ -96,7 +96,12 @@ issues holding us back from actively supporting OSX: *auto-sklearn* and its dependencies `SMAC3 `_ and `ConfigSpace `_. -Possible solutions (not tested): +In case you're having issues installing the +`pyrfr package `_, check out this +`installation suggestion on github`_ +. + +Possible other solutions (not tested): * virtual machine * docker image From ffb49af50c20430864b0230ee53c672e0a8c8d43 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 19 Oct 2017 14:05:21 +0200 Subject: [PATCH 02/61] do not include dummyclassifier in winning ensemble. --- autosklearn/ensemble_builder.py | 4 ++-- doc/releases.rst | 6 ++++++ 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/autosklearn/ensemble_builder.py b/autosklearn/ensemble_builder.py index 682e52a205..cd1dd1dca0 100644 --- a/autosklearn/ensemble_builder.py +++ b/autosklearn/ensemble_builder.py @@ -203,7 +203,7 @@ def main(self): num_run = int(match.group(2)) if self.ensemble_nbest is not None: - if score <= 0.001: + if score <= 0.001 or num_run == 1: self.logger.info('Model only predicts at random: ' + model_name + ' has score: ' + str(score)) backup_num_runs.append((automl_seed, num_run)) @@ -243,7 +243,7 @@ def main(self): else: # Load all predictions that are better than random - if score <= 0.001: + if score <= 0.001 or num_run == 1: # include_num_runs.append(True) self.logger.info('Model only predicts at random: ' + model_name + ' has score: ' + diff --git a/doc/releases.rst b/doc/releases.rst index 50ba0ca3be..35062350ea 100644 --- a/doc/releases.rst +++ b/doc/releases.rst @@ -11,6 +11,12 @@ Releases ======== +Version 0.2.2 +============= + +* Do not use the ``DummyClassifier`` or ``DummyRegressor`` as part of an + ensemble. Fixes `#140 `_. + Version 0.2.1 ============= From 23a02d67bebdb0b19802906dd1838492c6c9cae4 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 19 Oct 2017 14:15:23 +0200 Subject: [PATCH 03/61] FIX #297, fix typos in examples --- example/example_crossvalidation.py | 2 +- example/example_holdout.py | 2 +- example/example_regression.py | 2 +- example/example_sequential.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/example/example_crossvalidation.py b/example/example_crossvalidation.py index 8e454a8893..73f67ff9d9 100644 --- a/example/example_crossvalidation.py +++ b/example/example_crossvalidation.py @@ -13,7 +13,7 @@ def main(): automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autoslearn_cv_example_tmp', + tmp_folder='/tmp/autosklearn_cv_example_tmp', output_folder='/tmp/autosklearn_cv_example_out', delete_tmp_folder_after_terminate=False, resampling_strategy='cv', diff --git a/example/example_holdout.py b/example/example_holdout.py index 41431e0ec2..0dbff8efea 100644 --- a/example/example_holdout.py +++ b/example/example_holdout.py @@ -12,7 +12,7 @@ def main(): automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autoslearn_holdout_example_tmp', + tmp_folder='/tmp/autosklearn_holdout_example_tmp', output_folder='/tmp/autosklearn_holdout_example_out', disable_evaluator_output=False) automl.fit(X_train, y_train, dataset_name='digits') diff --git a/example/example_regression.py b/example/example_regression.py index 146c4bae44..1cc6c367d3 100644 --- a/example/example_regression.py +++ b/example/example_regression.py @@ -14,7 +14,7 @@ def main(): automl = autosklearn.regression.AutoSklearnRegressor( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autoslearn_regression_example_tmp', + tmp_folder='/tmp/autosklearn_regression_example_tmp', output_folder='/tmp/autosklearn_regression_example_out') automl.fit(X_train, y_train, dataset_name='boston', feat_type=feature_types) diff --git a/example/example_sequential.py b/example/example_sequential.py index c4d8ec17d4..fcd976ff52 100644 --- a/example/example_sequential.py +++ b/example/example_sequential.py @@ -12,7 +12,7 @@ def main(): automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autoslearn_sequential_example_tmp', + tmp_folder='/tmp/autosklearn_sequential_example_tmp', output_folder='/tmp/autosklearn_sequential_example_out', # Do not construct ensembles in parallel to avoid using more than one # core at a time. The ensemble will be constructed after auto-sklearn From 5ea15c89ae61441e1d02e1b700d669d287942058 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 19 Oct 2017 18:28:15 +0200 Subject: [PATCH 04/61] Update SMAC and ConfigSpace dependencies --- autosklearn/__init__.py | 7 ++- autosklearn/automl.py | 2 +- autosklearn/pipeline/classification.py | 8 ++-- .../components/classification/__init__.py | 2 +- .../components/classification/adaboost.py | 8 ++-- .../components/classification/bernoulli_nb.py | 4 +- .../classification/decision_tree.py | 8 ++-- .../components/classification/extra_trees.py | 10 ++-- .../classification/gradient_boosting.py | 14 +++--- .../classification/k_nearest_neighbors.py | 6 +-- .../pipeline/components/classification/lda.py | 6 +-- .../classification/liblinear_svc.py | 8 ++-- .../components/classification/libsvm_svc.py | 14 +++--- .../classification/multinomial_nb.py | 4 +- .../classification/passive_aggressive.py | 4 +- .../pipeline/components/classification/qda.py | 2 +- .../classification/random_forest.py | 10 ++-- .../pipeline/components/classification/sgd.py | 20 ++++---- .../classification/xgradient_boosting.py | 10 ++-- .../data_preprocessing/balancing/balancing.py | 2 +- .../imputation/imputation.py | 2 +- .../one_hot_encoding/one_hot_encoding.py | 4 +- .../data_preprocessing/rescaling/__init__.py | 2 +- .../feature_preprocessing/__init__.py | 2 +- .../extra_trees_preproc_for_classification.py | 10 ++-- .../extra_trees_preproc_for_regression.py | 8 ++-- .../feature_preprocessing/fast_ica.py | 2 +- .../feature_preprocessing/kernel_pca.py | 6 +-- .../feature_preprocessing/kitchen_sinks.py | 4 +- .../liblinear_svc_preprocessor.py | 6 +-- .../feature_preprocessing/nystroem_sampler.py | 6 +-- .../components/feature_preprocessing/pca.py | 4 +- .../random_trees_embedding.py | 8 ++-- .../select_percentile_classification.py | 4 +- .../select_percentile_regression.py | 2 +- .../feature_preprocessing/select_rates.py | 4 +- .../feature_preprocessing/truncatedSVD.py | 2 +- .../components/regression/__init__.py | 2 +- .../components/regression/adaboost.py | 8 ++-- .../components/regression/ard_regression.py | 12 ++--- .../components/regression/decision_tree.py | 6 +-- .../components/regression/extra_trees.py | 10 ++-- .../components/regression/gaussian_process.py | 6 +-- .../regression/gradient_boosting.py | 18 ++++---- .../regression/k_nearest_neighbors.py | 6 +-- .../components/regression/liblinear_svr.py | 8 ++-- .../components/regression/libsvm_svr.py | 16 +++---- .../components/regression/random_forest.py | 8 ++-- .../components/regression/ridge_regression.py | 4 +- .../pipeline/components/regression/sgd.py | 20 ++++---- .../regression/xgradient_boosting.py | 10 ++-- autosklearn/pipeline/regression.py | 6 +-- autosklearn/smbo.py | 46 +++++++++++-------- requirements.txt | 6 +-- setup.py | 6 +-- test/test_evaluation/test_evaluation.py | 8 ++-- test/test_pipeline/test_classification.py | 12 +++-- test/test_pipeline/test_regression.py | 12 +++-- 58 files changed, 235 insertions(+), 220 deletions(-) diff --git a/autosklearn/__init__.py b/autosklearn/__init__.py index ad2c39be8a..64ae355ccf 100644 --- a/autosklearn/__init__.py +++ b/autosklearn/__init__.py @@ -7,10 +7,9 @@ numpy>=1.9 scikit-learn>=0.18.1,<0.19 lockfile>=0.10 -smac>=0.6.0,<0.7 -pyrfr>=0.6.1,<0.7 -ConfigSpace>=0.3.3,<0.4 -pyrfr>=0.6.0,<0.7 +smac>=0.7.1,<0.8 +pyrfr>=0.6.1,<0.8 +ConfigSpace>=0.4.0,<0.5 ''' dependencies.verify_packages(__MANDATORY_PACKAGES__) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 58f4fdf8de..470b001bd4 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -7,7 +7,7 @@ import warnings -from ConfigSpace.io import pcs +from ConfigSpace.read_and_write import pcs import numpy as np import numpy.ma as ma import scipy.stats diff --git a/autosklearn/pipeline/classification.py b/autosklearn/pipeline/classification.py index c163145ef4..ad1ed8b182 100644 --- a/autosklearn/pipeline/classification.py +++ b/autosklearn/pipeline/classification.py @@ -186,7 +186,7 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, possible_default_classifier = copy.copy(list( available_classifiers.keys())) - default = cs.get_hyperparameter('classifier:__choice__').default + default = cs.get_hyperparameter('classifier:__choice__').default_value del possible_default_classifier[possible_default_classifier.index(default)] # A classifier which can handle sparse data after the densifier is @@ -214,7 +214,7 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, except IndexError: raise ValueError("Cannot find a legal default configuration.") cs.get_hyperparameter( - 'classifier:__choice__').default = default + 'classifier:__choice__').default_value = default # which would take too long # Combinations of non-linear models with feature learning: @@ -247,7 +247,7 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, raise ValueError( "Cannot find a legal default configuration.") cs.get_hyperparameter( - 'classifier:__choice__').default = default + 'classifier:__choice__').default_value = default # Won't work # Multinomial NB etc don't use with features learning, pca etc @@ -278,7 +278,7 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, raise ValueError( "Cannot find a legal default configuration.") cs.get_hyperparameter( - 'classifier:__choice__').default = default + 'classifier:__choice__').default_value = default self.configuration_space_ = cs self.dataset_properties_ = dataset_properties diff --git a/autosklearn/pipeline/components/classification/__init__.py b/autosklearn/pipeline/components/classification/__init__.py index 6d5aec3b85..3ddcadd53c 100644 --- a/autosklearn/pipeline/components/classification/__init__.py +++ b/autosklearn/pipeline/components/classification/__init__.py @@ -107,7 +107,7 @@ def get_hyperparameter_search_space(self, dataset_properties=None, estimator = CategoricalHyperparameter('__choice__', list(available_estimators.keys()), - default=default) + default_value=default) cs.add_hyperparameter(estimator) for estimator_name in available_estimators.keys(): estimator_configuration_space = available_estimators[estimator_name].\ diff --git a/autosklearn/pipeline/components/classification/adaboost.py b/autosklearn/pipeline/components/classification/adaboost.py index bc55bcee5e..524339a6c1 100644 --- a/autosklearn/pipeline/components/classification/adaboost.py +++ b/autosklearn/pipeline/components/classification/adaboost.py @@ -66,13 +66,13 @@ def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_estimators = UniformIntegerHyperparameter( - name="n_estimators", lower=50, upper=500, default=50, log=False) + name="n_estimators", lower=50, upper=500, default_value=50, log=False) learning_rate = UniformFloatHyperparameter( - name="learning_rate", lower=0.01, upper=2, default=0.1, log=True) + name="learning_rate", lower=0.01, upper=2, default_value=0.1, log=True) algorithm = CategoricalHyperparameter( - name="algorithm", choices=["SAMME.R", "SAMME"], default="SAMME.R") + name="algorithm", choices=["SAMME.R", "SAMME"], default_value="SAMME.R") max_depth = UniformIntegerHyperparameter( - name="max_depth", lower=1, upper=10, default=1, log=False) + name="max_depth", lower=1, upper=10, default_value=1, log=False) cs.add_hyperparameters([n_estimators, learning_rate, algorithm, max_depth]) return cs diff --git a/autosklearn/pipeline/components/classification/bernoulli_nb.py b/autosklearn/pipeline/components/classification/bernoulli_nb.py index 2542856b94..d955b03e59 100644 --- a/autosklearn/pipeline/components/classification/bernoulli_nb.py +++ b/autosklearn/pipeline/components/classification/bernoulli_nb.py @@ -106,11 +106,11 @@ def get_hyperparameter_search_space(dataset_properties=None): # I will limit it to 1000 and put it on a logarithmic scale. (SF) # Please adjust that, if you know a proper range, this is just a guess. alpha = UniformFloatHyperparameter(name="alpha", lower=1e-2, upper=100, - default=1, log=True) + default_value=1, log=True) fit_prior = CategoricalHyperparameter(name="fit_prior", choices=["True", "False"], - default="True") + default_value="True") cs.add_hyperparameters([alpha, fit_prior]) diff --git a/autosklearn/pipeline/components/classification/decision_tree.py b/autosklearn/pipeline/components/classification/decision_tree.py index 33459a7fe1..d510899656 100644 --- a/autosklearn/pipeline/components/classification/decision_tree.py +++ b/autosklearn/pipeline/components/classification/decision_tree.py @@ -85,15 +85,15 @@ def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() criterion = CategoricalHyperparameter( - "criterion", ["gini", "entropy"], default="gini") + "criterion", ["gini", "entropy"], default_value="gini") splitter = Constant("splitter", "best") max_features = Constant('max_features', 1.0) max_depth = UniformFloatHyperparameter( - 'max_depth', 0., 2., default=0.5) + 'max_depth', 0., 2., default_value=0.5) min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default=2) + "min_samples_split", 2, 20, default_value=2) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default=1) + "min_samples_leaf", 1, 20, default_value=1) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") diff --git a/autosklearn/pipeline/components/classification/extra_trees.py b/autosklearn/pipeline/components/classification/extra_trees.py index 27ae524c71..70a501f3bb 100644 --- a/autosklearn/pipeline/components/classification/extra_trees.py +++ b/autosklearn/pipeline/components/classification/extra_trees.py @@ -135,20 +135,20 @@ def get_hyperparameter_search_space(dataset_properties=None): n_estimators = Constant("n_estimators", 100) criterion = CategoricalHyperparameter( - "criterion", ["gini", "entropy"], default="gini") + "criterion", ["gini", "entropy"], default_value="gini") max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default=1) + "max_features", 0.5, 5, default_value=1) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default=2) + "min_samples_split", 2, 20, default_value=2) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default=1) + "min_samples_leaf", 1, 20, default_value=1) min_weight_fraction_leaf = Constant('min_weight_fraction_leaf', 0.) bootstrap = CategoricalHyperparameter( - "bootstrap", ["True", "False"], default="False") + "bootstrap", ["True", "False"], default_value="False") cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, bootstrap]) diff --git a/autosklearn/pipeline/components/classification/gradient_boosting.py b/autosklearn/pipeline/components/classification/gradient_boosting.py index c7fb734fd8..e0e325fd3e 100644 --- a/autosklearn/pipeline/components/classification/gradient_boosting.py +++ b/autosklearn/pipeline/components/classification/gradient_boosting.py @@ -131,20 +131,20 @@ def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() loss = Constant("loss", "deviance") learning_rate = UniformFloatHyperparameter( - name="learning_rate", lower=0.01, upper=1, default=0.1, log=True) + name="learning_rate", lower=0.01, upper=1, default_value=0.1, log=True) n_estimators = UniformIntegerHyperparameter( - "n_estimators", 50, 500, default=100) + "n_estimators", 50, 500, default_value=100) max_depth = UniformIntegerHyperparameter( - name="max_depth", lower=1, upper=10, default=3) + name="max_depth", lower=1, upper=10, default_value=3) min_samples_split = UniformIntegerHyperparameter( - name="min_samples_split", lower=2, upper=20, default=2, log=False) + name="min_samples_split", lower=2, upper=20, default_value=2, log=False) min_samples_leaf = UniformIntegerHyperparameter( - name="min_samples_leaf", lower=1, upper=20, default=1, log=False) + name="min_samples_leaf", lower=1, upper=20, default_value=1, log=False) min_weight_fraction_leaf = UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.) subsample = UniformFloatHyperparameter( - name="subsample", lower=0.01, upper=1.0, default=1.0, log=False) + name="subsample", lower=0.01, upper=1.0, default_value=1.0, log=False) max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default=1) + "max_features", 0.5, 5, default_value=1) max_leaf_nodes = UnParametrizedHyperparameter( name="max_leaf_nodes", value="None") cs.add_hyperparameters([loss, learning_rate, n_estimators, max_depth, diff --git a/autosklearn/pipeline/components/classification/k_nearest_neighbors.py b/autosklearn/pipeline/components/classification/k_nearest_neighbors.py index 9ff8400cb4..991fc33a21 100644 --- a/autosklearn/pipeline/components/classification/k_nearest_neighbors.py +++ b/autosklearn/pipeline/components/classification/k_nearest_neighbors.py @@ -58,10 +58,10 @@ def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_neighbors = UniformIntegerHyperparameter( - name="n_neighbors", lower=1, upper=100, log=True, default=1) + name="n_neighbors", lower=1, upper=100, log=True, default_value=1) weights = CategoricalHyperparameter( - name="weights", choices=["uniform", "distance"], default="uniform") - p = CategoricalHyperparameter(name="p", choices=[1, 2], default=2) + name="weights", choices=["uniform", "distance"], default_value="uniform") + p = CategoricalHyperparameter(name="p", choices=[1, 2], default_value=2) cs.add_hyperparameters([n_neighbors, weights, p]) return cs diff --git a/autosklearn/pipeline/components/classification/lda.py b/autosklearn/pipeline/components/classification/lda.py index cd6fa1a84f..560df09705 100644 --- a/autosklearn/pipeline/components/classification/lda.py +++ b/autosklearn/pipeline/components/classification/lda.py @@ -76,11 +76,11 @@ def get_properties(dataset_properties=None): def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() shrinkage = CategoricalHyperparameter( - "shrinkage", ["None", "auto", "manual"], default="None") + "shrinkage", ["None", "auto", "manual"], default_value="None") shrinkage_factor = UniformFloatHyperparameter( "shrinkage_factor", 0., 1., 0.5) - n_components = UniformIntegerHyperparameter('n_components', 1, 250, default=10) - tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default=1e-4, log=True) + n_components = UniformIntegerHyperparameter('n_components', 1, 250, default_value=10) + tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, log=True) cs.add_hyperparameters([shrinkage, shrinkage_factor, n_components, tol]) cs.add_condition(EqualsCondition(shrinkage_factor, shrinkage, "manual")) diff --git a/autosklearn/pipeline/components/classification/liblinear_svc.py b/autosklearn/pipeline/components/classification/liblinear_svc.py index a083169766..e5fad9850e 100644 --- a/autosklearn/pipeline/components/classification/liblinear_svc.py +++ b/autosklearn/pipeline/components/classification/liblinear_svc.py @@ -88,15 +88,15 @@ def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() penalty = CategoricalHyperparameter( - "penalty", ["l1", "l2"], default="l2") + "penalty", ["l1", "l2"], default_value="l2") loss = CategoricalHyperparameter( - "loss", ["hinge", "squared_hinge"], default="squared_hinge") + "loss", ["hinge", "squared_hinge"], default_value="squared_hinge") dual = Constant("dual", "False") # This is set ad-hoc tol = UniformFloatHyperparameter( - "tol", 1e-5, 1e-1, default=1e-4, log=True) + "tol", 1e-5, 1e-1, default_value=1e-4, log=True) C = UniformFloatHyperparameter( - "C", 0.03125, 32768, log=True, default=1.0) + "C", 0.03125, 32768, log=True, default_value=1.0) multi_class = Constant("multi_class", "ovr") # These are set ad-hoc fit_intercept = Constant("fit_intercept", "True") diff --git a/autosklearn/pipeline/components/classification/libsvm_svc.py b/autosklearn/pipeline/components/classification/libsvm_svc.py index 78bec211ec..7487fd9834 100644 --- a/autosklearn/pipeline/components/classification/libsvm_svc.py +++ b/autosklearn/pipeline/components/classification/libsvm_svc.py @@ -107,20 +107,20 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): C = UniformFloatHyperparameter("C", 0.03125, 32768, log=True, - default=1.0) + default_value=1.0) # No linear kernel here, because we have liblinear kernel = CategoricalHyperparameter(name="kernel", choices=["rbf", "poly", "sigmoid"], - default="rbf") - degree = UniformIntegerHyperparameter("degree", 1, 5, default=3) + default_value="rbf") + degree = UniformIntegerHyperparameter("degree", 1, 5, default_value=3) gamma = UniformFloatHyperparameter("gamma", 3.0517578125e-05, 8, - log=True, default=0.1) + log=True, default_value=0.1) # TODO this is totally ad-hoc - coef0 = UniformFloatHyperparameter("coef0", -1, 1, default=0) + coef0 = UniformFloatHyperparameter("coef0", -1, 1, default_value=0) # probability is no hyperparameter, but an argument to the SVM algo shrinking = CategoricalHyperparameter("shrinking", ["True", "False"], - default="True") - tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default=1e-4, + default_value="True") + tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, log=True) # cache size is not a hyperparameter, but an argument to the program! max_iter = UnParametrizedHyperparameter("max_iter", -1) diff --git a/autosklearn/pipeline/components/classification/multinomial_nb.py b/autosklearn/pipeline/components/classification/multinomial_nb.py index 2268c2740e..8066875795 100644 --- a/autosklearn/pipeline/components/classification/multinomial_nb.py +++ b/autosklearn/pipeline/components/classification/multinomial_nb.py @@ -114,11 +114,11 @@ def get_hyperparameter_search_space(dataset_properties=None): # I will limit it to 100 and put it on a logarithmic scale. (SF) # Please adjust that, if you know a proper range, this is just a guess. alpha = UniformFloatHyperparameter(name="alpha", lower=1e-2, upper=100, - default=1, log=True) + default_value=1, log=True) fit_prior = CategoricalHyperparameter(name="fit_prior", choices=["True", "False"], - default="True") + default_value="True") cs.add_hyperparameters([alpha, fit_prior]) diff --git a/autosklearn/pipeline/components/classification/passive_aggressive.py b/autosklearn/pipeline/components/classification/passive_aggressive.py index 1f206441e2..77cf87dfdf 100644 --- a/autosklearn/pipeline/components/classification/passive_aggressive.py +++ b/autosklearn/pipeline/components/classification/passive_aggressive.py @@ -98,9 +98,9 @@ def get_properties(dataset_properties=None): def get_hyperparameter_search_space(dataset_properties=None): loss = CategoricalHyperparameter("loss", ["hinge", "squared_hinge"], - default="hinge") + default_value="hinge") fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") - n_iter = UniformIntegerHyperparameter("n_iter", 5, 1000, default=20, + n_iter = UniformIntegerHyperparameter("n_iter", 5, 1000, default_value=20, log=True) C = UniformFloatHyperparameter("C", 1e-5, 10, 1, log=True) cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/classification/qda.py b/autosklearn/pipeline/components/classification/qda.py index 45b8296fb6..2444c307b1 100644 --- a/autosklearn/pipeline/components/classification/qda.py +++ b/autosklearn/pipeline/components/classification/qda.py @@ -70,7 +70,7 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): reg_param = UniformFloatHyperparameter('reg_param', 0.0, 1.0, - default=0.0) + default_value=0.0) cs = ConfigurationSpace() cs.add_hyperparameter(reg_param) return cs diff --git a/autosklearn/pipeline/components/classification/random_forest.py b/autosklearn/pipeline/components/classification/random_forest.py index 3080b6eed5..c2ee245370 100644 --- a/autosklearn/pipeline/components/classification/random_forest.py +++ b/autosklearn/pipeline/components/classification/random_forest.py @@ -122,18 +122,18 @@ def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_estimators = Constant("n_estimators", 100) criterion = CategoricalHyperparameter( - "criterion", ["gini", "entropy"], default="gini") + "criterion", ["gini", "entropy"], default_value="gini") max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default=1) + "max_features", 0.5, 5, default_value=1) max_depth = UnParametrizedHyperparameter("max_depth", "None") min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default=2) + "min_samples_split", 2, 20, default_value=2) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default=1) + "min_samples_leaf", 1, 20, default_value=1) min_weight_fraction_leaf = UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") bootstrap = CategoricalHyperparameter( - "bootstrap", ["True", "False"], default="True") + "bootstrap", ["True", "False"], default_value="True") cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, diff --git a/autosklearn/pipeline/components/classification/sgd.py b/autosklearn/pipeline/components/classification/sgd.py index d8e448ea38..49755a0d46 100644 --- a/autosklearn/pipeline/components/classification/sgd.py +++ b/autosklearn/pipeline/components/classification/sgd.py @@ -119,26 +119,26 @@ def get_hyperparameter_search_space(dataset_properties=None): loss = CategoricalHyperparameter("loss", ["hinge", "log", "modified_huber", "squared_hinge", "perceptron"], - default="log") + default_value="log") penalty = CategoricalHyperparameter( - "penalty", ["l1", "l2", "elasticnet"], default="l2") + "penalty", ["l1", "l2", "elasticnet"], default_value="l2") alpha = UniformFloatHyperparameter( - "alpha", 10e-7, 1e-1, log=True, default=0.0001) + "alpha", 10e-7, 1e-1, log=True, default_value=0.0001) l1_ratio = UniformFloatHyperparameter( - "l1_ratio", 1e-9, 1, log=True, default=0.15) + "l1_ratio", 1e-9, 1, log=True, default_value=0.15) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") n_iter = UniformIntegerHyperparameter("n_iter", 5, 1000, log=True, - default=20) + default_value=20) epsilon = UniformFloatHyperparameter( - "epsilon", 1e-5, 1e-1, default=1e-4, log=True) + "epsilon", 1e-5, 1e-1, default_value=1e-4, log=True) learning_rate = CategoricalHyperparameter( "learning_rate", ["optimal", "invscaling", "constant"], - default="optimal") + default_value="optimal") eta0 = UniformFloatHyperparameter( - "eta0", 10**-7, 0.1, default=0.01) - power_t = UniformFloatHyperparameter("power_t", 1e-5, 1, default=0.25) + "eta0", 10**-7, 0.1, default_value=0.01) + power_t = UniformFloatHyperparameter("power_t", 1e-5, 1, default_value=0.25) average = CategoricalHyperparameter( - "average", ["False", "True"], default="False") + "average", ["False", "True"], default_value="False") cs.add_hyperparameters([loss, penalty, alpha, l1_ratio, fit_intercept, n_iter, epsilon, learning_rate, eta0, power_t, average]) diff --git a/autosklearn/pipeline/components/classification/xgradient_boosting.py b/autosklearn/pipeline/components/classification/xgradient_boosting.py index d6ae101e73..d946e466b4 100644 --- a/autosklearn/pipeline/components/classification/xgradient_boosting.py +++ b/autosklearn/pipeline/components/classification/xgradient_boosting.py @@ -158,15 +158,15 @@ def get_hyperparameter_search_space(dataset_properties=None): # Parameterized Hyperparameters max_depth = UniformIntegerHyperparameter( - name="max_depth", lower=1, upper=10, default=3) + name="max_depth", lower=1, upper=10, default_value=3) learning_rate = UniformFloatHyperparameter( - name="learning_rate", lower=0.01, upper=1, default=0.1, log=True) + name="learning_rate", lower=0.01, upper=1, default_value=0.1, log=True) n_estimators = UniformIntegerHyperparameter( - "n_estimators", 50, 500, default=100) + "n_estimators", 50, 500, default_value=100) subsample = UniformFloatHyperparameter( - name="subsample", lower=0.01, upper=1.0, default=1.0, log=False) + name="subsample", lower=0.01, upper=1.0, default_value=1.0, log=False) min_child_weight = UniformIntegerHyperparameter( - name="min_child_weight", lower=1, upper=20, default=1, log=False) + name="min_child_weight", lower=1, upper=20, default_value=1, log=False) # Unparameterized Hyperparameters max_delta_step = UnParametrizedHyperparameter( diff --git a/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py b/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py index 6bf4582042..3ad99da76c 100644 --- a/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py +++ b/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py @@ -107,7 +107,7 @@ def get_properties(dataset_properties=None): def get_hyperparameter_search_space(dataset_properties=None): # TODO add replace by zero! strategy = CategoricalHyperparameter( - "strategy", ["none", "weighting"], default="none") + "strategy", ["none", "weighting"], default_value="none") cs = ConfigurationSpace() cs.add_hyperparameter(strategy) return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/imputation/imputation.py b/autosklearn/pipeline/components/data_preprocessing/imputation/imputation.py index 13774eb521..f50aa22644 100644 --- a/autosklearn/pipeline/components/data_preprocessing/imputation/imputation.py +++ b/autosklearn/pipeline/components/data_preprocessing/imputation/imputation.py @@ -48,7 +48,7 @@ def get_properties(dataset_properties=None): def get_hyperparameter_search_space(dataset_properties=None): # TODO add replace by zero! strategy = CategoricalHyperparameter( - "strategy", ["mean", "median", "most_frequent"], default="mean") + "strategy", ["mean", "median", "most_frequent"], default_value="mean") cs = ConfigurationSpace() cs.add_hyperparameter(strategy) return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py b/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py index 7aa296ba55..cf85507422 100644 --- a/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py @@ -79,9 +79,9 @@ def get_properties(dataset_properties=None): def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() use_minimum_fraction = CategoricalHyperparameter( - "use_minimum_fraction", ["True", "False"], default="True") + "use_minimum_fraction", ["True", "False"], default_value="True") minimum_fraction = UniformFloatHyperparameter( - "minimum_fraction", lower=.0001, upper=0.5, default=0.01, log=True) + "minimum_fraction", lower=.0001, upper=0.5, default_value=0.01, log=True) cs.add_hyperparameters([use_minimum_fraction, minimum_fraction]) cs.add_condition(EqualsCondition(minimum_fraction, use_minimum_fraction, 'True')) diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py index 75b7098a81..c4b2c098d6 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py @@ -53,7 +53,7 @@ def get_hyperparameter_search_space(self, dataset_properties=None, preprocessor = CategoricalHyperparameter('__choice__', list( available_preprocessors.keys()), - default=default) + default_value=default) cs.add_hyperparameter(preprocessor) for name in available_preprocessors: preprocessor_configuration_space = available_preprocessors[name]. \ diff --git a/autosklearn/pipeline/components/feature_preprocessing/__init__.py b/autosklearn/pipeline/components/feature_preprocessing/__init__.py index d6fa15f421..df274b596c 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/__init__.py +++ b/autosklearn/pipeline/components/feature_preprocessing/__init__.py @@ -111,7 +111,7 @@ def get_hyperparameter_search_space(self, dataset_properties=None, preprocessor = CategoricalHyperparameter('__choice__', list( available_preprocessors.keys()), - default=default) + default_value=default) cs.add_hyperparameter(preprocessor) for name in available_preprocessors: preprocessor_configuration_space = available_preprocessors[name]. \ diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py index 73b8794329..e91742136e 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py @@ -106,20 +106,20 @@ def get_hyperparameter_search_space(dataset_properties=None): n_estimators = Constant("n_estimators", 100) criterion = CategoricalHyperparameter( - "criterion", ["gini", "entropy"], default="gini") - max_features = UniformFloatHyperparameter("max_features", 0.5, 5, default=1) + "criterion", ["gini", "entropy"], default_value="gini") + max_features = UniformFloatHyperparameter("max_features", 0.5, 5, default_value=1) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default=2) + "min_samples_split", 2, 20, default_value=2) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default=1) + "min_samples_leaf", 1, 20, default_value=1) min_weight_fraction_leaf = Constant( 'min_weight_fraction_leaf', 0.) bootstrap = CategoricalHyperparameter( - "bootstrap", ["True", "False"], default="False") + "bootstrap", ["True", "False"], default_value="False") cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py index 01c8aa28c0..401dbfac0c 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py @@ -107,18 +107,18 @@ def get_hyperparameter_search_space(dataset_properties=None): n_estimators = Constant("n_estimators", 100) criterion = Constant("criterion", "mse") max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default=1) + "max_features", 0.5, 5, default_value=1) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default=2) + "min_samples_split", 2, 20, default_value=2) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default=1) + "min_samples_leaf", 1, 20, default_value=1) min_weight_fraction_leaf = Constant('min_weight_fraction_leaf', 0.) bootstrap = CategoricalHyperparameter( - "bootstrap", ["True", "False"], default="False") + "bootstrap", ["True", "False"], default_value="False") cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, diff --git a/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py b/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py index bdae44b167..96ed15b92c 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py +++ b/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py @@ -59,7 +59,7 @@ def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_components = UniformIntegerHyperparameter( - "n_components", 10, 2000, default=100) + "n_components", 10, 2000, default_value=100) algorithm = CategoricalHyperparameter('algorithm', ['parallel', 'deflation'], 'parallel') whiten = CategoricalHyperparameter('whiten', diff --git a/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py b/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py index f1374343bb..13bc607e10 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py +++ b/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py @@ -69,13 +69,13 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): n_components = UniformIntegerHyperparameter( - "n_components", 10, 2000, default=100) + "n_components", 10, 2000, default_value=100) kernel = CategoricalHyperparameter('kernel', ['poly', 'rbf', 'sigmoid', 'cosine'], 'rbf') degree = UniformIntegerHyperparameter('degree', 2, 5, 3) gamma = UniformFloatHyperparameter("gamma", 3.0517578125e-05, 8, - log=True, default=1.0) - coef0 = UniformFloatHyperparameter("coef0", -1, 1, default=0) + log=True, default_value=1.0) + coef0 = UniformFloatHyperparameter("coef0", -1, 1, default_value=0) cs = ConfigurationSpace() cs.add_hyperparameters([n_components, kernel, degree, gamma, coef0]) diff --git a/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py b/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py index d156d155ff..ea165936d1 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py +++ b/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py @@ -47,9 +47,9 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): gamma = UniformFloatHyperparameter( - "gamma", 0.3, 2., default=1.0) + "gamma", 0.3, 2., default_value=1.0) n_components = UniformIntegerHyperparameter( - "n_components", 50, 10000, default=100, log=True) + "n_components", 50, 10000, default_value=100, log=True) cs = ConfigurationSpace() cs.add_hyperparameters([gamma, n_components]) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py b/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py index 0d5894b660..a7b4845f50 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py +++ b/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py @@ -80,11 +80,11 @@ def get_hyperparameter_search_space(dataset_properties=None): penalty = Constant("penalty", "l1") loss = CategoricalHyperparameter( - "loss", ["hinge", "squared_hinge"], default="squared_hinge") + "loss", ["hinge", "squared_hinge"], default_value="squared_hinge") dual = Constant("dual", "False") # This is set ad-hoc - tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default=1e-4, log=True) - C = UniformFloatHyperparameter("C", 0.03125, 32768, log=True, default=1.0) + tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, log=True) + C = UniformFloatHyperparameter("C", 0.03125, 32768, log=True, default_value=1.0) multi_class = Constant("multi_class", "ovr") # These are set ad-hoc fit_intercept = Constant("fit_intercept", "True") diff --git a/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py b/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py index 25ef107639..d753a2d3df 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py +++ b/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py @@ -88,10 +88,10 @@ def get_hyperparameter_search_space(dataset_properties=None): kernel = CategoricalHyperparameter('kernel', possible_kernels, 'rbf') degree = UniformIntegerHyperparameter('degree', 2, 5, 3) gamma = UniformFloatHyperparameter("gamma", 3.0517578125e-05, 8, - log=True, default=0.1) - coef0 = UniformFloatHyperparameter("coef0", -1, 1, default=0) + log=True, default_value=0.1) + coef0 = UniformFloatHyperparameter("coef0", -1, 1, default_value=0) n_components = UniformIntegerHyperparameter( - "n_components", 50, 10000, default=100, log=True) + "n_components", 50, 10000, default_value=100, log=True) cs = ConfigurationSpace() cs.add_hyperparameters([kernel, degree, gamma, coef0, n_components]) diff --git a/autosklearn/pipeline/components/feature_preprocessing/pca.py b/autosklearn/pipeline/components/feature_preprocessing/pca.py index 02deab3fe4..7d8e760db8 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/pca.py +++ b/autosklearn/pipeline/components/feature_preprocessing/pca.py @@ -48,9 +48,9 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): keep_variance = UniformFloatHyperparameter( - "keep_variance", 0.5, 0.9999, default=0.9999) + "keep_variance", 0.5, 0.9999, default_value=0.9999) whiten = CategoricalHyperparameter( - "whiten", ["False", "True"], default="False") + "whiten", ["False", "True"], default_value="False") cs = ConfigurationSpace() cs.add_hyperparameters([keep_variance, whiten]) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py index 8934cc1862..10d4ea87e7 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py +++ b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py @@ -67,16 +67,16 @@ def get_properties(dataset_properties=None): def get_hyperparameter_search_space(dataset_properties=None): n_estimators = UniformIntegerHyperparameter(name="n_estimators", lower=10, upper=100, - default=10) + default_value=10) max_depth = UniformIntegerHyperparameter(name="max_depth", lower=2, upper=10, - default=5) + default_value=5) min_samples_split = UniformIntegerHyperparameter(name="min_samples_split", lower=2, upper=20, - default=2) + default_value=2) min_samples_leaf = UniformIntegerHyperparameter(name="min_samples_leaf", lower=1, upper=20, - default=1) + default_value=1) min_weight_fraction_leaf = Constant('min_weight_fraction_leaf', 1.0) max_leaf_nodes = UnParametrizedHyperparameter(name="max_leaf_nodes", value="None") diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py index 2e0c6f4442..1c57c556e9 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py @@ -89,10 +89,10 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): percentile = UniformFloatHyperparameter( - name="percentile", lower=1, upper=99, default=50) + name="percentile", lower=1, upper=99, default_value=50) score_func = CategoricalHyperparameter( - name="score_func", choices=["chi2", "f_classif"], default="chi2") + name="score_func", choices=["chi2", "f_classif"], default_value="chi2") if dataset_properties is not None: # Chi2 can handle sparse data, so we respect this if 'sparse' in dataset_properties and dataset_properties['sparse']: diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py index cfc3164c5d..c68ea6d306 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py @@ -40,7 +40,7 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): percentile = UniformFloatHyperparameter( - "percentile", lower=1, upper=99, default=50) + "percentile", lower=1, upper=99, default_value=50) score_func = UnParametrizedHyperparameter( name="score_func", value="f_regression") diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_rates.py b/autosklearn/pipeline/components/feature_preprocessing/select_rates.py index bfa964c76c..4549db41ea 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_rates.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_rates.py @@ -94,10 +94,10 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): alpha = UniformFloatHyperparameter( - name="alpha", lower=0.01, upper=0.5, default=0.1) + name="alpha", lower=0.01, upper=0.5, default_value=0.1) score_func = CategoricalHyperparameter( - name="score_func", choices=["chi2", "f_classif"], default="chi2") + name="score_func", choices=["chi2", "f_classif"], default_value="chi2") if dataset_properties is not None: # Chi2 can handle sparse data, so we respect this if 'sparse' in dataset_properties and dataset_properties['sparse']: diff --git a/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py b/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py index f7f40f7c17..73f3491652 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py +++ b/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py @@ -47,7 +47,7 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): target_dim = UniformIntegerHyperparameter( - "target_dim", 10, 256, default=128) + "target_dim", 10, 256, default_value=128) cs = ConfigurationSpace() cs.add_hyperparameter(target_dim) return cs diff --git a/autosklearn/pipeline/components/regression/__init__.py b/autosklearn/pipeline/components/regression/__init__.py index 99570d6884..b26d1eb5f9 100644 --- a/autosklearn/pipeline/components/regression/__init__.py +++ b/autosklearn/pipeline/components/regression/__init__.py @@ -95,7 +95,7 @@ def get_hyperparameter_search_space(self, dataset_properties, estimator = CategoricalHyperparameter('__choice__', list(available_estimators.keys()), - default=default) + default_value=default) cs.add_hyperparameter(estimator) for estimator_name in available_estimators.keys(): estimator_configuration_space = available_estimators[estimator_name].\ diff --git a/autosklearn/pipeline/components/regression/adaboost.py b/autosklearn/pipeline/components/regression/adaboost.py index 4352ac2c2d..c193fb8ba7 100644 --- a/autosklearn/pipeline/components/regression/adaboost.py +++ b/autosklearn/pipeline/components/regression/adaboost.py @@ -61,14 +61,14 @@ def get_hyperparameter_search_space(dataset_properties=None): # base_estimator = Constant(name="base_estimator", value="None") n_estimators = UniformIntegerHyperparameter( - name="n_estimators", lower=50, upper=500, default=50, log=False) + name="n_estimators", lower=50, upper=500, default_value=50, log=False) learning_rate = UniformFloatHyperparameter( - name="learning_rate", lower=0.01, upper=2, default=0.1, log=True) + name="learning_rate", lower=0.01, upper=2, default_value=0.1, log=True) loss = CategoricalHyperparameter( name="loss", choices=["linear", "square", "exponential"], - default="linear") + default_value="linear") max_depth = UniformIntegerHyperparameter( - name="max_depth", lower=1, upper=10, default=1, log=False) + name="max_depth", lower=1, upper=10, default_value=1, log=False) cs.add_hyperparameters([n_estimators, learning_rate, loss, max_depth]) return cs diff --git a/autosklearn/pipeline/components/regression/ard_regression.py b/autosklearn/pipeline/components/regression/ard_regression.py index 26a6d766ef..3b4349e9a8 100644 --- a/autosklearn/pipeline/components/regression/ard_regression.py +++ b/autosklearn/pipeline/components/regression/ard_regression.py @@ -64,23 +64,23 @@ def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_iter = UnParametrizedHyperparameter("n_iter", value=300) tol = UniformFloatHyperparameter("tol", 10 ** -5, 10 ** -1, - default=10 ** -4, log=True) + default_value=10 ** -4, log=True) alpha_1 = UniformFloatHyperparameter(name="alpha_1", lower=10 ** -10, - upper=10 ** -3, default=10 ** -6) + upper=10 ** -3, default_value=10 ** -6) alpha_2 = UniformFloatHyperparameter(name="alpha_2", log=True, lower=10 ** -10, upper=10 ** -3, - default=10 ** -6) + default_value=10 ** -6) lambda_1 = UniformFloatHyperparameter(name="lambda_1", log=True, lower=10 ** -10, upper=10 ** -3, - default=10 ** -6) + default_value=10 ** -6) lambda_2 = UniformFloatHyperparameter(name="lambda_2", log=True, lower=10 ** -10, upper=10 ** -3, - default=10 ** -6) + default_value=10 ** -6) threshold_lambda = UniformFloatHyperparameter(name="threshold_lambda", log=True, lower=10 ** 3, upper=10 ** 5, - default=10 ** 4) + default_value=10 ** 4) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") cs.add_hyperparameters([n_iter, tol, alpha_1, alpha_2, lambda_1, diff --git a/autosklearn/pipeline/components/regression/decision_tree.py b/autosklearn/pipeline/components/regression/decision_tree.py index 7a921991e1..0e78441758 100644 --- a/autosklearn/pipeline/components/regression/decision_tree.py +++ b/autosklearn/pipeline/components/regression/decision_tree.py @@ -78,11 +78,11 @@ def get_hyperparameter_search_space(dataset_properties=None): splitter = Constant("splitter", "best") max_features = Constant('max_features', 1.0) max_depth = UniformFloatHyperparameter( - 'max_depth', 0., 2., default=0.5) + 'max_depth', 0., 2., default_value=0.5) min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default=2) + "min_samples_split", 2, 20, default_value=2) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default=1) + "min_samples_leaf", 1, 20, default_value=1) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") diff --git a/autosklearn/pipeline/components/regression/extra_trees.py b/autosklearn/pipeline/components/regression/extra_trees.py index 0cc7599e6c..c78e4fdd57 100644 --- a/autosklearn/pipeline/components/regression/extra_trees.py +++ b/autosklearn/pipeline/components/regression/extra_trees.py @@ -129,27 +129,27 @@ def get_hyperparameter_search_space(dataset_properties=None): n_estimators = Constant("n_estimators", 100) criterion = Constant("criterion", "mse") max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default=1) + "max_features", 0.5, 5, default_value=1) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default=2) + "min_samples_split", 2, 20, default_value=2) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default=1) + "min_samples_leaf", 1, 20, default_value=1) # Unparametrized, we use min_samples as regularization # max_leaf_nodes_or_max_depth = UnParametrizedHyperparameter( # name="max_leaf_nodes_or_max_depth", value="max_depth") # CategoricalHyperparameter("max_leaf_nodes_or_max_depth", - # choices=["max_leaf_nodes", "max_depth"], default="max_depth") + # choices=["max_leaf_nodes", "max_depth"], default_value="max_depth") # min_weight_fraction_leaf = UniformFloatHyperparameter( # "min_weight_fraction_leaf", 0.0, 0.1) # max_leaf_nodes = UnParametrizedHyperparameter(name="max_leaf_nodes", # value="None") bootstrap = CategoricalHyperparameter( - "bootstrap", ["True", "False"], default="False") + "bootstrap", ["True", "False"], default_value="False") cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, diff --git a/autosklearn/pipeline/components/regression/gaussian_process.py b/autosklearn/pipeline/components/regression/gaussian_process.py index e3d7a0dcb6..9b62122ea6 100644 --- a/autosklearn/pipeline/components/regression/gaussian_process.py +++ b/autosklearn/pipeline/components/regression/gaussian_process.py @@ -58,11 +58,11 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): alpha = UniformFloatHyperparameter( - name="alpha", lower=1e-14, upper=1.0, default=1e-8, log=True) + name="alpha", lower=1e-14, upper=1.0, default_value=1e-8, log=True) thetaL = UniformFloatHyperparameter( - name="thetaL", lower=1e-10, upper=1e-3, default=1e-6, log=True) + name="thetaL", lower=1e-10, upper=1e-3, default_value=1e-6, log=True) thetaU = UniformFloatHyperparameter( - name="thetaU", lower=1.0, upper=100000, default=100000.0, log=True) + name="thetaU", lower=1.0, upper=100000, default_value=100000.0, log=True) cs = ConfigurationSpace() cs.add_hyperparameters([alpha, thetaL, thetaU]) diff --git a/autosklearn/pipeline/components/regression/gradient_boosting.py b/autosklearn/pipeline/components/regression/gradient_boosting.py index fc611a8a96..e18b2964a0 100644 --- a/autosklearn/pipeline/components/regression/gradient_boosting.py +++ b/autosklearn/pipeline/components/regression/gradient_boosting.py @@ -124,27 +124,27 @@ def get_properties(dataset_properties=None): def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() loss = CategoricalHyperparameter( - "loss", ["ls", "lad", "huber", "quantile"], default="ls") + "loss", ["ls", "lad", "huber", "quantile"], default_value="ls") learning_rate = UniformFloatHyperparameter( - name="learning_rate", lower=0.01, upper=1, default=0.1, log=True) + name="learning_rate", lower=0.01, upper=1, default_value=0.1, log=True) n_estimators = UniformIntegerHyperparameter( - "n_estimators", 50, 500, default=100) + "n_estimators", 50, 500, default_value=100) max_depth = UniformIntegerHyperparameter( - name="max_depth", lower=1, upper=10, default=3) + name="max_depth", lower=1, upper=10, default_value=3) min_samples_split = UniformIntegerHyperparameter( - name="min_samples_split", lower=2, upper=20, default=2, log=False) + name="min_samples_split", lower=2, upper=20, default_value=2, log=False) min_samples_leaf = UniformIntegerHyperparameter( - name="min_samples_leaf", lower=1, upper=20, default=1, log=False) + name="min_samples_leaf", lower=1, upper=20, default_value=1, log=False) min_weight_fraction_leaf = UnParametrizedHyperparameter( "min_weight_fraction_leaf", 0.) subsample = UniformFloatHyperparameter( - name="subsample", lower=0.01, upper=1.0, default=1.0, log=False) + name="subsample", lower=0.01, upper=1.0, default_value=1.0, log=False) max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default=1) + "max_features", 0.5, 5, default_value=1) max_leaf_nodes = UnParametrizedHyperparameter( name="max_leaf_nodes", value="None") alpha = UniformFloatHyperparameter( - "alpha", lower=0.75, upper=0.99, default=0.9) + "alpha", lower=0.75, upper=0.99, default_value=0.9) cs.add_hyperparameters([loss, learning_rate, n_estimators, max_depth, min_samples_split, min_samples_leaf, diff --git a/autosklearn/pipeline/components/regression/k_nearest_neighbors.py b/autosklearn/pipeline/components/regression/k_nearest_neighbors.py index e33414ddf7..64dfa1de01 100644 --- a/autosklearn/pipeline/components/regression/k_nearest_neighbors.py +++ b/autosklearn/pipeline/components/regression/k_nearest_neighbors.py @@ -46,10 +46,10 @@ def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_neighbors = UniformIntegerHyperparameter( - name="n_neighbors", lower=1, upper=100, log=True, default=1) + name="n_neighbors", lower=1, upper=100, log=True, default_value=1) weights = CategoricalHyperparameter( - name="weights", choices=["uniform", "distance"], default="uniform") - p = CategoricalHyperparameter(name="p", choices=[1, 2], default=2) + name="weights", choices=["uniform", "distance"], default_value="uniform") + p = CategoricalHyperparameter(name="p", choices=[1, 2], default_value=2) cs.add_hyperparameters([n_neighbors, weights, p]) diff --git a/autosklearn/pipeline/components/regression/liblinear_svr.py b/autosklearn/pipeline/components/regression/liblinear_svr.py index 23629a96d9..66f103c8a6 100644 --- a/autosklearn/pipeline/components/regression/liblinear_svr.py +++ b/autosklearn/pipeline/components/regression/liblinear_svr.py @@ -65,17 +65,17 @@ def get_properties(dataset_properties=None): def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() C = UniformFloatHyperparameter( - "C", 0.03125, 32768, log=True, default=1.0) + "C", 0.03125, 32768, log=True, default_value=1.0) loss = CategoricalHyperparameter( "loss", ["epsilon_insensitive", "squared_epsilon_insensitive"], - default="squared_epsilon_insensitive") + default_value="squared_epsilon_insensitive") # Random Guess epsilon = UniformFloatHyperparameter( - name="epsilon", lower=0.001, upper=1, default=0.1, log=True) + name="epsilon", lower=0.001, upper=1, default_value=0.1, log=True) dual = Constant("dual", "False") # These are set ad-hoc tol = UniformFloatHyperparameter( - "tol", 1e-5, 1e-1, default=1e-4, log=True) + "tol", 1e-5, 1e-1, default_value=1e-4, log=True) fit_intercept =Constant("fit_intercept", "True") intercept_scaling = Constant("intercept_scaling", 1) diff --git a/autosklearn/pipeline/components/regression/libsvm_svr.py b/autosklearn/pipeline/components/regression/libsvm_svr.py index 40a5c58147..96a19341c2 100644 --- a/autosklearn/pipeline/components/regression/libsvm_svr.py +++ b/autosklearn/pipeline/components/regression/libsvm_svr.py @@ -101,31 +101,31 @@ def get_properties(dataset_properties=None): def get_hyperparameter_search_space(dataset_properties=None): # Copied from libsvm_c C = UniformFloatHyperparameter( - name="C", lower=0.03125, upper=32768, log=True, default=1.0) + name="C", lower=0.03125, upper=32768, log=True, default_value=1.0) kernel = CategoricalHyperparameter( name="kernel", choices=['linear', 'poly', 'rbf', 'sigmoid'], - default="rbf") + default_value="rbf") degree = UniformIntegerHyperparameter( - name="degree", lower=1, upper=5, default=3) + name="degree", lower=1, upper=5, default_value=3) # Changed the gamma value to 0.0 (is 0.1 for classification) gamma = UniformFloatHyperparameter( - name="gamma", lower=3.0517578125e-05, upper=8, log=True, default=0.1) + name="gamma", lower=3.0517578125e-05, upper=8, log=True, default_value=0.1) # TODO this is totally ad-hoc coef0 = UniformFloatHyperparameter( - name="coef0", lower=-1, upper=1, default=0) + name="coef0", lower=-1, upper=1, default_value=0) # probability is no hyperparameter, but an argument to the SVM algo shrinking = CategoricalHyperparameter( - name="shrinking", choices=["True", "False"], default="True") + name="shrinking", choices=["True", "False"], default_value="True") tol = UniformFloatHyperparameter( - name="tol", lower=1e-5, upper=1e-1, default=1e-3, log=True) + name="tol", lower=1e-5, upper=1e-1, default_value=1e-3, log=True) max_iter = UnParametrizedHyperparameter("max_iter", -1) # Random Guess epsilon = UniformFloatHyperparameter(name="epsilon", lower=0.001, - upper=1, default=0.1, log=True) + upper=1, default_value=0.1, log=True) cs = ConfigurationSpace() cs.add_hyperparameters([C, kernel, degree, gamma, coef0, shrinking, tol, max_iter, epsilon]) diff --git a/autosklearn/pipeline/components/regression/random_forest.py b/autosklearn/pipeline/components/regression/random_forest.py index ecc6c835c9..28efd64047 100644 --- a/autosklearn/pipeline/components/regression/random_forest.py +++ b/autosklearn/pipeline/components/regression/random_forest.py @@ -113,17 +113,17 @@ def get_hyperparameter_search_space(dataset_properties=None): n_estimators = Constant("n_estimators", 100) criterion = Constant("criterion", "mse") max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default=1) + "max_features", 0.5, 5, default_value=1) max_depth = UnParametrizedHyperparameter("max_depth", "None") min_samples_split = UniformIntegerHyperparameter( - "min_samples_split", 2, 20, default=2) + "min_samples_split", 2, 20, default_value=2) min_samples_leaf = UniformIntegerHyperparameter( - "min_samples_leaf", 1, 20, default=1) + "min_samples_leaf", 1, 20, default_value=1) min_weight_fraction_leaf = \ UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") bootstrap = CategoricalHyperparameter( - "bootstrap", ["True", "False"], default="True") + "bootstrap", ["True", "False"], default_value="True") cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, diff --git a/autosklearn/pipeline/components/regression/ridge_regression.py b/autosklearn/pipeline/components/regression/ridge_regression.py index 005b279926..cb1f86d858 100644 --- a/autosklearn/pipeline/components/regression/ridge_regression.py +++ b/autosklearn/pipeline/components/regression/ridge_regression.py @@ -49,8 +49,8 @@ def get_properties(dataset_properties=None): def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() alpha = UniformFloatHyperparameter( - "alpha", 10 ** -5, 10., log=True, default=1.) + "alpha", 10 ** -5, 10., log=True, default_value=1.) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") - tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default=1e-4, log=True) + tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, log=True) cs.add_hyperparameters([alpha, fit_intercept, tol]) return cs diff --git a/autosklearn/pipeline/components/regression/sgd.py b/autosklearn/pipeline/components/regression/sgd.py index f39babe941..c281aedf1f 100644 --- a/autosklearn/pipeline/components/regression/sgd.py +++ b/autosklearn/pipeline/components/regression/sgd.py @@ -123,28 +123,28 @@ def get_hyperparameter_search_space(dataset_properties=None): loss = CategoricalHyperparameter("loss", ["squared_loss", "huber", "epsilon_insensitive", "squared_epsilon_insensitive"], - default="squared_loss") + default_value="squared_loss") penalty = CategoricalHyperparameter( - "penalty", ["l1", "l2", "elasticnet"], default="l2") + "penalty", ["l1", "l2", "elasticnet"], default_value="l2") alpha = UniformFloatHyperparameter( - "alpha", 10e-7, 1e-1, log=True, default=0.01) + "alpha", 10e-7, 1e-1, log=True, default_value=0.01) l1_ratio = UniformFloatHyperparameter( - "l1_ratio", 1e-9, 1., log=True, default=0.15) + "l1_ratio", 1e-9, 1., log=True, default_value=0.15) fit_intercept = UnParametrizedHyperparameter( "fit_intercept", "True") n_iter = UniformIntegerHyperparameter( - "n_iter", 5, 1000, log=True, default=20) + "n_iter", 5, 1000, log=True, default_value=20) epsilon = UniformFloatHyperparameter( - "epsilon", 1e-5, 1e-1, default=1e-4, log=True) + "epsilon", 1e-5, 1e-1, default_value=1e-4, log=True) learning_rate = CategoricalHyperparameter( "learning_rate", ["optimal", "invscaling", "constant"], - default="optimal") + default_value="optimal") eta0 = UniformFloatHyperparameter( - "eta0", 10 ** -7, 0.1, default=0.01) + "eta0", 10 ** -7, 0.1, default_value=0.01) power_t = UniformFloatHyperparameter( - "power_t", 1e-5, 1, default=0.5) + "power_t", 1e-5, 1, default_value=0.5) average = CategoricalHyperparameter( - "average", ["False", "True"], default="False") + "average", ["False", "True"], default_value="False") cs.add_hyperparameters([loss, penalty, alpha, l1_ratio, fit_intercept, n_iter, epsilon, learning_rate, eta0, diff --git a/autosklearn/pipeline/components/regression/xgradient_boosting.py b/autosklearn/pipeline/components/regression/xgradient_boosting.py index b7a31cfd9d..eab6ca653e 100644 --- a/autosklearn/pipeline/components/regression/xgradient_boosting.py +++ b/autosklearn/pipeline/components/regression/xgradient_boosting.py @@ -149,14 +149,14 @@ def get_hyperparameter_search_space(dataset_properties=None): # Parameterized Hyperparameters max_depth = UniformIntegerHyperparameter( - name="max_depth", lower=1, upper=10, default=3) + name="max_depth", lower=1, upper=10, default_value=3) learning_rate = UniformFloatHyperparameter( - name="learning_rate", lower=0.01, upper=1, default=0.1, log=True) - n_estimators = UniformIntegerHyperparameter("n_estimators", 50, 500, default=100) + name="learning_rate", lower=0.01, upper=1, default_value=0.1, log=True) + n_estimators = UniformIntegerHyperparameter("n_estimators", 50, 500, default_value=100) subsample = UniformFloatHyperparameter( - name="subsample", lower=0.01, upper=1.0, default=1.0, log=False) + name="subsample", lower=0.01, upper=1.0, default_value=1.0, log=False) min_child_weight = UniformIntegerHyperparameter( - name="min_child_weight", lower=1, upper=20, default=1, log=False) + name="min_child_weight", lower=1, upper=20, default_value=1, log=False) # Unparameterized Hyperparameters max_delta_step = UnParametrizedHyperparameter( diff --git a/autosklearn/pipeline/regression.py b/autosklearn/pipeline/regression.py index 0fa235e203..b208fd87cd 100644 --- a/autosklearn/pipeline/regression.py +++ b/autosklearn/pipeline/regression.py @@ -188,7 +188,7 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, possible_default_regressor = copy.copy(list( available_regressors.keys())) - default = cs.get_hyperparameter('regressor:__choice__').default + default = cs.get_hyperparameter('regressor:__choice__').default_value del possible_default_regressor[ possible_default_regressor.index(default)] @@ -216,7 +216,7 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, raise ValueError( "Cannot find a legal default configuration.") cs.get_hyperparameter( - 'regressor:__choice__').default = default + 'regressor:__choice__').default_value = default # which would take too long # Combinations of tree-based models with feature learning: @@ -248,7 +248,7 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, raise ValueError( "Cannot find a legal default configuration.") cs.get_hyperparameter( - 'regressor:__choice__').default = default + 'regressor:__choice__').default_value = default self.configuration_space_ = cs self.dataset_properties_ = dataset_properties diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index 8ff8382148..c9760f70b7 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -477,16 +477,19 @@ def run_smbo(self): startup_time = self.watcher.wall_elapsed(self.dataset_name) total_walltime_limit = self.total_walltime_limit - startup_time - 5 - scenario_dict = {'cs': self.config_space, - 'cutoff_time': self.func_eval_time_limit, - 'memory_limit': self.memory_limit, - 'wallclock_limit': total_walltime_limit, - 'output-dir': - self.backend.get_smac_output_directory(self.seed), - 'shared-model': self.shared_mode, - 'run_obj': 'quality', - 'deterministic': 'true', - 'instances': instances} + scenario_dict = { + 'cs': self.config_space, + 'cutoff_time': self.func_eval_time_limit, + 'memory_limit': self.memory_limit, + 'wallclock_limit': total_walltime_limit, + 'output-dir': + self.backend.get_smac_output_directory(self.seed), + 'shared-model': self.shared_mode, + 'run_obj': 'quality', + 'deterministic': 'true', + 'instances': instances, + 'abort_on_first_run_crash': False, + } if self.configuration_mode == 'RANDOM': scenario_dict['minR'] = len(instances) if instances is not None else 1 @@ -553,7 +556,11 @@ def run_smbo(self): scenario=self.scenario, success_states=[StatusType.SUCCESS, StatusType.MEMOUT, - StatusType.TIMEOUT], + StatusType.TIMEOUT, + # As long as we + # don't have a model + # for crashes yet! + StatusType.CRASHED], impute_censored_data=False, impute_state=None) _smac_arguments = dict(scenario=self.scenario, @@ -567,7 +574,11 @@ def run_smbo(self): scenario=self.scenario, success_states=[StatusType.SUCCESS, StatusType.MEMOUT, - StatusType.TIMEOUT], + StatusType.TIMEOUT, + # As long as we + # don't have a model + # for crashes yet! + StatusType.CRASHED], impute_censored_data=False, impute_state=None) model = UncorrelatedMultiObjectiveRandomForestWithInstances( @@ -657,7 +668,6 @@ def run_smbo(self): return self.runhistory, self.trajectory, self.fANOVA_input def choose_next(self, smac): - challengers = [] if len(smac.solver.runhistory.data) == 0: raise ValueError('Cannot use SMBO algorithm on empty runhistory.') @@ -676,12 +686,8 @@ def choose_next(self, smac): self.logger.info('Using %d training points for SMAC.' % X_cfg.shape[0]) - next_configs_tmp = smac.solver.choose_next( + return smac.solver.choose_next( X_cfg, Y_cfg, num_configurations_by_local_search=10, - num_configurations_by_random_search_sorted=500) - - challengers.extend(next_configs_tmp) - - return challengers - + num_configurations_by_random_search_sorted=1000 + ) diff --git a/requirements.txt b/requirements.txt index 528a12b365..8ddaed0b18 100644 --- a/requirements.txt +++ b/requirements.txt @@ -16,7 +16,7 @@ pyyaml liac-arff pandas -ConfigSpace>=0.3.3,<0.4 +ConfigSpace>=0.4.0,<0.5 pynisher>=0.4 -pyrfr>=0.6.1,<0.7 -smac>=0.6.0,<0.7 +pyrfr>=0.6.1,<0.8 +smac>=0.7.1,<0.8 diff --git a/setup.py b/setup.py index 1ccea1a2ed..8bd6c91668 100644 --- a/setup.py +++ b/setup.py @@ -25,10 +25,10 @@ "pyyaml", "liac-arff", "pandas", - "ConfigSpace>=0.3.3,<0.4", + "ConfigSpace>=0.4.0,<0.5", "pynisher>=0.4,<0.5", - "pyrfr>=0.6.1,<0.7", - "smac>=0.6.0,<0.7" + "pyrfr>=0.6.1,<0.8", + "smac>=0.7.1,<0.8" ] with open("autosklearn/__version__.py") as fh: diff --git a/test/test_evaluation/test_evaluation.py b/test/test_evaluation/test_evaluation.py index 68bdfd46bb..9aa7345094 100644 --- a/test/test_evaluation/test_evaluation.py +++ b/test/test_evaluation/test_evaluation.py @@ -132,9 +132,11 @@ def test_eval_with_limits_holdout_fail_silent(self, pynisher_mock): metric=accuracy) self.assertRaisesRegex(FirstRunCrashedException, - "First run crashed, abort. \(To prevent this, " - "toggle the " - "'abort_on_first_run_crash'-option!\)", + "First run crashed, abort. Please check your " + "setup -- we assume that your " + "defaultconfiguration does not crashes. \(To " + "deactivate this exception, use the SMAC " + "scenario option 'abort_on_first_run_crash'\)", ta.start, config=None, instance=None, cutoff=30) self.stats.ta_runs += 1 diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index bc0a2a03c7..5b2fa7c844 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -387,13 +387,17 @@ def test_get_hyperparameter_search_space_preprocessor_contradicts_default_classi cs = SimpleClassificationPipeline( include={'preprocessor': ['densifier']}, dataset_properties={'sparse': True}).\ get_hyperparameter_search_space() - self.assertEqual(cs.get_hyperparameter('classifier:__choice__').default, - 'qda') + self.assertEqual(cs.get_hyperparameter( + 'classifier:__choice__').default_value, + 'qda' + ) cs = SimpleClassificationPipeline(include={'preprocessor': ['nystroem_sampler']}).\ get_hyperparameter_search_space() - self.assertEqual(cs.get_hyperparameter('classifier:__choice__').default, - 'sgd') + self.assertEqual(cs.get_hyperparameter( + 'classifier:__choice__').default_value, + 'sgd' + ) def test_get_hyperparameter_search_space_only_forbidden_combinations(self): self.assertRaisesRegexp(AssertionError, "No valid pipeline found.", diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 347ca8f8c9..edd84e7f7f 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -249,13 +249,17 @@ def test_get_hyperparameter_search_space_preprocessor_contradicts_default_classi cs = SimpleRegressionPipeline(include={'preprocessor': ['densifier']}, dataset_properties={'sparse': True}).\ get_hyperparameter_search_space() - self.assertEqual(cs.get_hyperparameter('regressor:__choice__').default, - 'gradient_boosting') + self.assertEqual( + cs.get_hyperparameter('regressor:__choice__').default_value, + 'gradient_boosting' + ) cs = SimpleRegressionPipeline(include={'preprocessor': ['nystroem_sampler']}).\ get_hyperparameter_search_space() - self.assertEqual(cs.get_hyperparameter('regressor:__choice__').default, - 'sgd') + self.assertEqual( + cs.get_hyperparameter('regressor:__choice__').default_value, + 'sgd' + ) def test_get_hyperparameter_search_space_only_forbidden_combinations(self): self.assertRaisesRegexp(ValueError, "Cannot find a legal default " From 0e9e18efb5ab01ce178acc4e68f947eae7ddf9fb Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 20 Oct 2017 13:26:00 +0200 Subject: [PATCH 05/61] Adapt #random configs to original SMAC value --- autosklearn/smbo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index c9760f70b7..315594e64d 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -689,5 +689,5 @@ def choose_next(self, smac): return smac.solver.choose_next( X_cfg, Y_cfg, num_configurations_by_local_search=10, - num_configurations_by_random_search_sorted=1000 + num_configurations_by_random_search_sorted=10000 ) From 336823128dfb1c6443100fd542550629711eaff7 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 20 Oct 2017 13:26:48 +0200 Subject: [PATCH 06/61] FIX output of #successful/failed runs --- autosklearn/automl.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 58f4fdf8de..f738ed8cfb 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -703,18 +703,18 @@ def cv_results_(self): mean_test_score.append(1 - run_value.cost) mean_fit_time.append(run_value.time) s = run_value.status - if s == 1: + if s == StatusType.SUCCESS: status.append('Success') - elif s == 2: + elif s == StatusType.TIMEOUT: status.append('Timeout') - elif s == 3: + elif s == StatusType.CRASHED: status.append('Crash') - elif s == 4: + elif s == StatusType.ABORT: status.append('Abort') - elif s == 5: + elif s == StatusType.MEMOUT: status.append('Memout') else: - status.append('Unknown') + raise NotImplementedError(s) for hp_name in hp_names: if hp_name in param_dict: From 5b9b732370c16d6110e4a892d69659e73b0eb5b5 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 20 Oct 2017 13:38:41 +0200 Subject: [PATCH 07/61] FIX issue #290, cast array to c contiguous before hashing --- autosklearn/util/hash.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/autosklearn/util/hash.py b/autosklearn/util/hash.py index 7c2b81dbf8..af25e9b8b2 100644 --- a/autosklearn/util/hash.py +++ b/autosklearn/util/hash.py @@ -1,5 +1,6 @@ import hashlib +import numpy as np import scipy.sparse @@ -16,8 +17,9 @@ def hash_array_or_matrix(X): m.update(X.data) m.update(str(X.shape).encode('utf8')) else: - m.update(X.T.data) - m.update(str(X.T.shape).encode('utf8')) + X_tmp = np.ascontiguousarray(X.T) + m.update(X_tmp.data) + m.update(str(X_tmp.shape).encode('utf8')) hash = m.hexdigest() return hash \ No newline at end of file From 7c52152f5cc1e2aa69bf02944b38ae16fc0e2532 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 20 Oct 2017 14:33:41 +0200 Subject: [PATCH 08/61] ADD #205, add VarianceThreshold to pipelines --- autosklearn/pipeline/classification.py | 3 ++ .../variance_threshold/__init__.py | 0 .../variance_threshold/variance_threshold.py | 45 +++++++++++++++++++ autosklearn/pipeline/regression.py | 5 ++- test/test_evaluation/test_train_evaluator.py | 8 ++-- .../test_variance_threshold.py | 32 +++++++++++++ 6 files changed, 88 insertions(+), 5 deletions(-) create mode 100644 autosklearn/pipeline/components/data_preprocessing/variance_threshold/__init__.py create mode 100644 autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py create mode 100644 test/test_pipeline/components/data_preprocessing/test_variance_threshold.py diff --git a/autosklearn/pipeline/classification.py b/autosklearn/pipeline/classification.py index ad1ed8b182..549dc146e7 100644 --- a/autosklearn/pipeline/classification.py +++ b/autosklearn/pipeline/classification.py @@ -20,6 +20,8 @@ .one_hot_encoding import OneHotEncoder from autosklearn.pipeline.components import feature_preprocessing as \ feature_preprocessing_components +from autosklearn.pipeline.components.data_preprocessing.variance_threshold.variance_threshold \ + import VarianceThreshold from autosklearn.pipeline.base import BasePipeline from autosklearn.pipeline.constants import SPARSE @@ -294,6 +296,7 @@ def _get_pipeline(self): steps.extend( [["one_hot_encoding", OneHotEncoder()], ["imputation", Imputation()], + ["variance_threshold", VarianceThreshold()], ["rescaling", rescaling_components.RescalingChoice(default_dataset_properties)], ["balancing", Balancing()]]) diff --git a/autosklearn/pipeline/components/data_preprocessing/variance_threshold/__init__.py b/autosklearn/pipeline/components/data_preprocessing/variance_threshold/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py b/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py new file mode 100644 index 0000000000..c242ef4778 --- /dev/null +++ b/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py @@ -0,0 +1,45 @@ +from ConfigSpace.configuration_space import ConfigurationSpace + +from autosklearn.pipeline.components.base import \ + AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import * + +import sklearn.feature_selection + + +class VarianceThreshold(AutoSklearnPreprocessingAlgorithm): + def __init__(self, random_state=None): + pass + + def fit(self, X, y=None): + self.preprocessor = sklearn.feature_selection.VarianceThreshold( + threshold=0.0 + ) + self.preprocessor = self.preprocessor.fit(X) + return self + + def transform(self, X): + if self.preprocessor is None: + raise NotImplementedError() + return self.preprocessor.transform(X) + + @staticmethod + def get_properties(dataset_properties=None): + return { + 'shortname': 'Variance Threshold', + 'name': 'Variance Threshold (constant feature removal)', + 'handles_regression': True, + 'handles_classification': True, + 'handles_multiclass': True, + 'handles_multilabel': True, + 'is_deterministic': True, + 'handles_sparse': True, + 'handles_dense': True, + 'input': (DENSE, SPARSE, UNSIGNED_DATA), + 'output': (INPUT,), + } + + @staticmethod + def get_hyperparameter_search_space(dataset_properties=None): + cs = ConfigurationSpace() + return cs diff --git a/autosklearn/pipeline/regression.py b/autosklearn/pipeline/regression.py index b208fd87cd..cc532ffa29 100644 --- a/autosklearn/pipeline/regression.py +++ b/autosklearn/pipeline/regression.py @@ -17,6 +17,8 @@ .one_hot_encoding import OneHotEncoder from autosklearn.pipeline.components import feature_preprocessing as \ feature_preprocessing_components +from autosklearn.pipeline.components.data_preprocessing.variance_threshold.variance_threshold \ + import VarianceThreshold from autosklearn.pipeline.base import BasePipeline from autosklearn.pipeline.constants import SPARSE @@ -272,7 +274,8 @@ def _get_pipeline(self, init_params=None): steps.extend( [["one_hot_encoding", OneHotEncoder(categorical_features=categorical_features)], - ["imputation", Imputation()], + ["imputation", Imputation()], + ["variance_threshold", VarianceThreshold()], ["rescaling", rescaling_components.RescalingChoice( default_dataset_properties)]]) diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index d626b4c27a..e935ae43c7 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -634,8 +634,8 @@ def test_eval_holdout_all_loss_functions(self): 'f1_macro': 0.0636363636364, 'f1_micro': 0.0606060606061, 'f1_weighted': 0.0606060606061, - 'log_loss': 1.14529191037, - 'pac_score': 0.203125867166, + 'log_loss': 1.1322787511303569, + 'pac_score': 0.18611197712364891, 'precision_macro': 0.0636363636364, 'precision_micro': 0.0606060606061, 'precision_weighted': 0.0606060606061, @@ -715,8 +715,8 @@ def test_eval_cv_all_loss_functions(self): 'f1_macro': 0.0423387096774, 'f1_micro': 0.04, 'f1_weighted': 0.040020161290, - 'log_loss': 1.11651433976, - 'pac_score': 0.165226664054, + 'log_loss': 1.1170492260716856, + 'pac_score': 0.16594165340740463, 'precision_macro': 0.0414141414141, 'precision_micro': 0.04, 'precision_weighted': 0.0388484848485, diff --git a/test/test_pipeline/components/data_preprocessing/test_variance_threshold.py b/test/test_pipeline/components/data_preprocessing/test_variance_threshold.py new file mode 100644 index 0000000000..4e2e2d5e7e --- /dev/null +++ b/test/test_pipeline/components/data_preprocessing/test_variance_threshold.py @@ -0,0 +1,32 @@ +from scipy import sparse + +from autosklearn.pipeline.components.data_preprocessing.variance_threshold.variance_threshold \ + import VarianceThreshold +from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase + + +class VarianceThresholdTest(PreprocessingTestCase): + def test_default_configuration(self): + transformations = [] + for i in range(2): + transformation, original = _test_preprocessing(VarianceThreshold) + self.assertEqual(transformation.shape, original.shape) + self.assertTrue((transformation == original).all()) + transformations.append(transformation) + if len(transformations) > 1: + self.assertTrue( + (transformations[-1] == transformations[-2]).all()) + + def test_default_configuration_sparse_data(self): + transformations = [] + transformation, original = _test_preprocessing(VarianceThreshold, + make_sparse=True) + self.assertEqual(transformation.shape, (100, 3)) + self.assertTrue((transformation.toarray() == original.toarray()[:, 1:]).all()) + self.assertIsInstance(transformation, sparse.csr_matrix) + transformations.append(transformation) + + def test_preprocessing_dtype(self): + super(VarianceThresholdTest, self)._test_preprocessing_dtype( + VarianceThreshold, add_NaNs=False + ) \ No newline at end of file From 432331c2ea292f2f8b756901ea3c8bda04817ec5 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 29 Sep 2017 11:30:58 +0200 Subject: [PATCH 09/61] WIP simplify frontend --- autosklearn/estimators.py | 145 +++++++++++++++++++++++--------------- 1 file changed, 90 insertions(+), 55 deletions(-) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 37c6d12eb7..d9a5acf236 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -313,7 +313,7 @@ def build_automl(self): def fit(self, *args, **kwargs): self._automl = self.build_automl() - super(AutoSklearnEstimator, self).fit(*args, **kwargs) + super().fit(*args, **kwargs) def fit_ensemble(self, y, task=None, metric=None, precision='32', dataset_name=None, ensemble_nbest=None, @@ -517,33 +517,68 @@ def predict(self, X, batch_size=None, n_jobs=1): X, batch_size=batch_size, n_jobs=n_jobs) -class AutoMLClassifier(AutoMLDecorator): + +class BaseAutoML(AutoMLDecorator): + """Base class for AutoML objects to hold abstract functions for both + regression and classification.""" def __init__(self, automl): - self._classes = [] - self._n_classes = [] self._n_outputs = 0 + super().__init__(automl) + + def _perform_input_checks(self, X, y): + X = self._check_X(X) + y = self._check_y(y) + return X, y + + def _check_X(self, X): + X = sklearn.utils.check_array(X, accept_sparse="csr", + force_all_finite=False) + if scipy.sparse.issparse(X): + X.sort_indices() + return X + + def _check_y(self, y): + y = sklearn.utils.check_array(y, ensure_2d=False) + + y = np.atleast_1d(y) + if y.ndim == 2 and y.shape[1] == 1: + warnings.warn("A column-vector y was passed when a 1d array was" + " expected. Will change shape via np.ravel().", + sklearn.utils.DataConversionWarning, stacklevel=2) + y = np.ravel(y) + + return y + + def refit(self, X, y): + X, y = self._perform_input_checks(X, y) + _n_outputs = y.shape[1] + if self._n_outputs != _n_outputs: + raise ValueError('Number of outputs changed from %d to %d!' % + (self._n_outputs, _n_outputs)) + + return super().refit(X, y) + - super(AutoMLClassifier, self).__init__(automl) +class AutoMLClassifier(BaseAutoML): + + def __init__(self, automl): + + super().__init__(automl) + + self._task_mapping = {'multilabel-indicator': MULTILABEL_CLASSIFICATION, + 'multiclass': MULTICLASS_CLASSIFICATION, + 'binary': BINARY_CLASSIFICATION} def fit(self, X, y, metric=None, loss=None, feat_type=None, dataset_name=None): - X = sklearn.utils.check_array(X, accept_sparse="csr", - force_all_finite=False) - y = sklearn.utils.check_array(y, ensure_2d=False) - - if scipy.sparse.issparse(X): - X.sort_indices() + X, y = self._perform_input_checks(X, y) y_task = type_of_target(y) - task_mapping = {'multilabel-indicator': MULTILABEL_CLASSIFICATION, - 'multiclass': MULTICLASS_CLASSIFICATION, - 'binary': BINARY_CLASSIFICATION} - - task = task_mapping.get(y_task) + task = self._task_mapping.get(y_task) if task is None: raise ValueError('Cannot work on data of type %s' % y_task) @@ -553,52 +588,48 @@ def fit(self, X, y, else: metric = accuracy - y = self._process_target_classes(y) + y, self._classes, self._n_classes = self._process_target_classes(y) - return self._automl.fit(X, y, task, metric, feat_type, dataset_name) + return super().fit(X, y, task, metric, feat_type, dataset_name) def fit_ensemble(self, y, task=None, metric=None, precision='32', dataset_name=None, ensemble_nbest=None, ensemble_size=None): - self._process_target_classes(y) - return self._automl.fit_ensemble(y, task, metric, precision, dataset_name, - ensemble_nbest, ensemble_size) + y, _classes, _n_classes = self._process_target_classes(y) + if not hasattr(self, '_classes'): + self._classes = _classes + if not hasattr(self, '_n_classes'): + self._n_classes = _n_classes - def _process_target_classes(self, y): - y = np.atleast_1d(y) - if y.ndim == 2 and y.shape[1] == 1: - warnings.warn("A column-vector y was passed when a 1d array was" - " expected. Please change the shape of y to " - "(n_samples,), for example using ravel().", - sklearn.utils.DataConversionWarning, stacklevel=2) + return super().fit_ensemble(y, task, metric, precision, dataset_name, + ensemble_nbest, ensemble_size) - if y.ndim == 1: - # reshape is necessary to preserve the data contiguity against vs - # [:, np.newaxis] that does not. - y = np.reshape(y, (-1, 1)) - - self._n_outputs = y.shape[1] + def _process_target_classes(self, y): + y = super()._check_y(y) + self._n_outputs = 1 if len(y.shape) == 1 else y.shape[1] y = np.copy(y) - self._classes = [] - self._n_classes = [] - - for k in range(self._n_outputs): - classes_k, y[:, k] = np.unique(y[:, k], return_inverse=True) - self._classes.append(classes_k) - self._n_classes.append(classes_k.shape[0]) + _classes = [] + _n_classes = [] - self._n_classes = np.array(self._n_classes, dtype=np.int) + if self._n_outputs == 1: + classes_k, y = np.unique(y, return_inverse=True) + _classes.append(classes_k) + _n_classes.append(classes_k.shape[0]) + else: + for k in range(self._n_outputs): + classes_k, y[:, k] = np.unique(y[:, k], return_inverse=True) + _classes.append(classes_k) + _n_classes.append(classes_k.shape[0]) - if y.shape[1] == 1: - y = y.flatten() + self._n_classes = np.array(_n_classes, dtype=np.int) - return y + return y, _classes, _n_classes def predict(self, X, batch_size=None, n_jobs=1): - predicted_probabilities = self._automl.predict( - X, batch_size=batch_size, n_jobs=n_jobs) + predicted_probabilities = super().predict(X, batch_size=batch_size, + n_jobs=n_jobs) if self._n_outputs == 1: predicted_indexes = np.argmax(predicted_probabilities, axis=1) @@ -620,14 +651,18 @@ def predict_proba(self, X, batch_size=None, n_jobs=1): return self._automl.predict(X, batch_size=batch_size, n_jobs=n_jobs) -class AutoMLRegressor(AutoMLDecorator): +class AutoMLRegressor(BaseAutoML): - def fit(self, X, y, - metric=None, - feat_type=None, - dataset_name=None, - ): + def fit(self, X, y, metric=None, feat_type=None, dataset_name=None): + X, y = super()._perform_input_checks(X, y) if metric is None: metric = r2 - return self._automl.fit(X=X, y=y, task=REGRESSION, metric=metric, - feat_type=feat_type, dataset_name=dataset_name) + return super().fit(X=X, y=y, task=REGRESSION, metric=metric, + feat_type=feat_type, dataset_name=dataset_name) + + def fit_ensemble(self, y, task=None, metric=None, precision='32', + dataset_name=None, ensemble_nbest=None, + ensemble_size=None): + y = super()._check_y(y) + return super().fit_ensemble(y, task, metric, precision, dataset_name, + ensemble_nbest, ensemble_size) From 5a8aaf2fdf7cfbdeb8d3f340930d39d9cdd5f6e1 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 20 Oct 2017 15:55:18 +0200 Subject: [PATCH 10/61] test fix for #301 --- autosklearn/estimators.py | 22 ++++++++++++--- test/test_automl/test_estimators.py | 42 +++++++++++++++++++++++++++-- 2 files changed, 59 insertions(+), 5 deletions(-) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index d9a5acf236..415b5e28e5 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -523,7 +523,7 @@ class BaseAutoML(AutoMLDecorator): regression and classification.""" def __init__(self, automl): - self._n_outputs = 0 + self._n_outputs = 1 super().__init__(automl) def _perform_input_checks(self, X, y): @@ -552,13 +552,26 @@ def _check_y(self, y): def refit(self, X, y): X, y = self._perform_input_checks(X, y) - _n_outputs = y.shape[1] + _n_outputs = 1 if len(y.shape) == 1 else y.shape[1] if self._n_outputs != _n_outputs: raise ValueError('Number of outputs changed from %d to %d!' % (self._n_outputs, _n_outputs)) return super().refit(X, y) + def fit_ensemble(self, y, task=None, metric=None, precision='32', + dataset_name=None, ensemble_nbest=None, + ensemble_size=None): + _n_outputs = 1 if len(y.shape) == 1 else y.shape[1] + if self._n_outputs != _n_outputs: + raise ValueError('Number of outputs changed from %d to %d!' % + (self._n_outputs, _n_outputs)) + + return super().fit_ensemble( + y, task=task, metric=metric, precision=precision, + dataset_name=dataset_name, ensemble_nbest=ensemble_nbest, + ensemble_size=ensemble_size + ) class AutoMLClassifier(BaseAutoML): @@ -655,9 +668,12 @@ class AutoMLRegressor(BaseAutoML): def fit(self, X, y, metric=None, feat_type=None, dataset_name=None): X, y = super()._perform_input_checks(X, y) + _n_outputs = 1 if len(y.shape) == 1 else y.shape[1] + if _n_outputs > 1: + raise NotImplementedError('Multi-output regression is not implemented.') if metric is None: metric = r2 - return super().fit(X=X, y=y, task=REGRESSION, metric=metric, + return super().fit(X, y, task=REGRESSION, metric=metric, feat_type=feat_type, dataset_name=dataset_name) def fit_ensemble(self, y, task=None, metric=None, precision='32', diff --git a/test/test_automl/test_estimators.py b/test/test_automl/test_estimators.py index 4dfada97a7..9af9d91725 100644 --- a/test/test_automl/test_estimators.py +++ b/test/test_automl/test_estimators.py @@ -1,4 +1,3 @@ -import collections import os import pickle import sys @@ -15,6 +14,7 @@ from autosklearn.regression import AutoSklearnRegressor from autosklearn.metrics import accuracy, f1_macro, mean_squared_error from autosklearn.estimators import AutoMLClassifier +from autosklearn.automl import AutoML from autosklearn.util.backend import Backend, BackendContext from autosklearn.constants import * sys.path.append(os.path.dirname(__file__)) @@ -315,6 +315,25 @@ def test_binary(self): score = accuracy(Y_test, predictions) self.assertGreaterEqual(score, 0.9) + @unittest.mock.patch.object(AutoML, 'fit') + @unittest.mock.patch.object(AutoML, 'refit') + @unittest.mock.patch.object(AutoML, 'fit_ensemble') + def test_conversion_of_list_to_np(self, fit_ensemble, refit, fit): + automl = AutoSklearnClassifier() + X = [[1], [2], [3]] + y = [1, 2, 3] + automl.fit(X, y) + self.assertEqual(fit.call_count, 1) + self.assertIsInstance(fit.call_args[0][0], np.ndarray) + self.assertIsInstance(fit.call_args[0][1], np.ndarray) + automl.refit(X, y) + self.assertEqual(refit.call_count, 1) + self.assertIsInstance(refit.call_args[0][0], np.ndarray) + self.assertIsInstance(refit.call_args[0][1], np.ndarray) + automl.fit_ensemble(y) + self.assertEqual(fit_ensemble.call_count, 1) + self.assertIsInstance(fit_ensemble.call_args[0][0], np.ndarray) + class AutoMLRegressorTest(Base, unittest.TestCase): def test_regression(self): @@ -332,4 +351,23 @@ def test_regression(self): self.assertEqual(predictions.shape, (356,)) score = mean_squared_error(Y_test, predictions) # On average np.sqrt(30) away from the target -> ~5.5 on average - self.assertGreaterEqual(score, -30) \ No newline at end of file + self.assertGreaterEqual(score, -30) + + @unittest.mock.patch.object(AutoML, 'fit') + @unittest.mock.patch.object(AutoML, 'refit') + @unittest.mock.patch.object(AutoML, 'fit_ensemble') + def test_conversion_of_list_to_np(self, fit_ensemble, refit, fit): + automl = AutoSklearnRegressor() + X = [[1], [2], [3]] + y = [1, 2, 3] + automl.fit(X, y) + self.assertEqual(fit.call_count, 1) + self.assertIsInstance(fit.call_args[0][0], np.ndarray) + self.assertIsInstance(fit.call_args[0][1], np.ndarray) + automl.refit(X, y) + self.assertEqual(refit.call_count, 1) + self.assertIsInstance(refit.call_args[0][0], np.ndarray) + self.assertIsInstance(refit.call_args[0][1], np.ndarray) + automl.fit_ensemble(y) + self.assertEqual(fit_ensemble.call_count, 1) + self.assertIsInstance(fit_ensemble.call_args[0][0], np.ndarray) \ No newline at end of file From 1930fbeb71dcb36594ecddded829a6585312f4ca Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 20 Oct 2017 16:45:00 +0200 Subject: [PATCH 11/61] FIX issues #250 and #326 --- .../pipeline/components/classification/decision_tree.py | 4 ++-- autosklearn/pipeline/components/classification/extra_trees.py | 4 ++-- .../pipeline/components/classification/gradient_boosting.py | 4 ++-- autosklearn/pipeline/components/classification/lda.py | 2 +- .../pipeline/components/classification/liblinear_svc.py | 2 +- autosklearn/pipeline/components/classification/libsvm_svc.py | 2 +- .../extra_trees_preproc_for_classification.py | 4 ++-- .../extra_trees_preproc_for_regression.py | 4 ++-- .../feature_preprocessing/liblinear_svc_preprocessor.py | 2 +- .../feature_preprocessing/random_trees_embedding.py | 4 ++-- autosklearn/pipeline/components/regression/decision_tree.py | 4 ++-- autosklearn/pipeline/components/regression/extra_trees.py | 4 ++-- .../pipeline/components/regression/gradient_boosting.py | 4 ++-- 13 files changed, 22 insertions(+), 22 deletions(-) diff --git a/autosklearn/pipeline/components/classification/decision_tree.py b/autosklearn/pipeline/components/classification/decision_tree.py index d510899656..ea37172806 100644 --- a/autosklearn/pipeline/components/classification/decision_tree.py +++ b/autosklearn/pipeline/components/classification/decision_tree.py @@ -31,7 +31,7 @@ def fit(self, X, y, sample_weight=None): from sklearn.tree import DecisionTreeClassifier self.max_features = float(self.max_features) - if self.max_depth == "None": + if self.max_depth == "None" or self.max_depth is None: max_depth = self.max_depth = None else: num_features = X.shape[1] @@ -39,7 +39,7 @@ def fit(self, X, y, sample_weight=None): max_depth = max(1, int(np.round(self.max_depth * num_features, 0))) self.min_samples_split = int(self.min_samples_split) self.min_samples_leaf = int(self.min_samples_leaf) - if self.max_leaf_nodes == "None": + if self.max_leaf_nodes == "None" or self.max_leaf_nodes is None: self.max_leaf_nodes = None else: self.max_leaf_nodes = int(self.max_leaf_nodes) diff --git a/autosklearn/pipeline/components/classification/extra_trees.py b/autosklearn/pipeline/components/classification/extra_trees.py index 70a501f3bb..a24d67c8af 100644 --- a/autosklearn/pipeline/components/classification/extra_trees.py +++ b/autosklearn/pipeline/components/classification/extra_trees.py @@ -28,7 +28,7 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, if max_leaf_nodes_or_max_depth == "max_depth": self.max_leaf_nodes = None - if max_depth == "None": + if max_depth == "None" or max_depth is None: self.max_depth = None else: self.max_depth = int(max_depth) @@ -37,7 +37,7 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, #elif use_max_depth == "False": # self.max_depth = None else: - if max_leaf_nodes == "None": + if max_leaf_nodes == "None" or max_leaf_nodes is None: self.max_leaf_nodes = None else: self.max_leaf_nodes = int(max_leaf_nodes) diff --git a/autosklearn/pipeline/components/classification/gradient_boosting.py b/autosklearn/pipeline/components/classification/gradient_boosting.py index e0e325fd3e..8a81e6a39b 100644 --- a/autosklearn/pipeline/components/classification/gradient_boosting.py +++ b/autosklearn/pipeline/components/classification/gradient_boosting.py @@ -53,7 +53,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.min_samples_split = int(self.min_samples_split) self.min_samples_leaf = int(self.min_samples_leaf) self.min_weight_fraction_leaf = float(self.min_weight_fraction_leaf) - if self.max_depth == "None": + if self.max_depth == "None" or self.max_depth is None: self.max_depth = None else: self.max_depth = int(self.max_depth) @@ -62,7 +62,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): float(self.max_features) * (np.log(num_features) + 1)) # Use at most half of the features max_features = max(1, min(int(X.shape[1] / 2), max_features)) - if self.max_leaf_nodes == "None": + if self.max_leaf_nodes == "None" or self.max_leaf_nodes is None: self.max_leaf_nodes = None else: self.max_leaf_nodes = int(self.max_leaf_nodes) diff --git a/autosklearn/pipeline/components/classification/lda.py b/autosklearn/pipeline/components/classification/lda.py index 560df09705..62effe761d 100644 --- a/autosklearn/pipeline/components/classification/lda.py +++ b/autosklearn/pipeline/components/classification/lda.py @@ -22,7 +22,7 @@ def fit(self, X, Y): import sklearn.discriminant_analysis import sklearn.multiclass - if self.shrinkage == "None": + if self.shrinkage == "None" or self.shrinkage is None: self.shrinkage = None solver = 'svd' elif self.shrinkage == "auto": diff --git a/autosklearn/pipeline/components/classification/liblinear_svc.py b/autosklearn/pipeline/components/classification/liblinear_svc.py index e5fad9850e..d3b7954129 100644 --- a/autosklearn/pipeline/components/classification/liblinear_svc.py +++ b/autosklearn/pipeline/components/classification/liblinear_svc.py @@ -37,7 +37,7 @@ def fit(self, X, Y): self.fit_intercept = self.fit_intercept == 'True' self.intercept_scaling = float(self.intercept_scaling) - if self.class_weight == "None": + if self.class_weight == "None" or self.class_weight is None: self.class_weight = None estimator = sklearn.svm.LinearSVC(penalty=self.penalty, diff --git a/autosklearn/pipeline/components/classification/libsvm_svc.py b/autosklearn/pipeline/components/classification/libsvm_svc.py index 7487fd9834..aff91dd2d1 100644 --- a/autosklearn/pipeline/components/classification/libsvm_svc.py +++ b/autosklearn/pipeline/components/classification/libsvm_svc.py @@ -57,7 +57,7 @@ def fit(self, X, Y): self.max_iter = float(self.max_iter) self.shrinking = self.shrinking == 'True' - if self.class_weight == "None": + if self.class_weight == "None" or self.class_weight is None: self.class_weight = None self.estimator = sklearn.svm.SVC(C=self.C, diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py index e91742136e..d3111358dd 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py @@ -29,7 +29,7 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, if max_leaf_nodes_or_max_depth == "max_depth": self.max_leaf_nodes = None - if max_depth == "None": + if max_depth == "None" or max_depth is None: self.max_depth = None else: self.max_depth = int(max_depth) @@ -38,7 +38,7 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, #elif use_max_depth == "False": # self.max_depth = None else: - if max_leaf_nodes == "None": + if max_leaf_nodes == "None" or max_leaf_nodes is None: self.max_leaf_nodes = None else: self.max_leaf_nodes = int(max_leaf_nodes) diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py index 401dbfac0c..768c604213 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py @@ -28,7 +28,7 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, if max_leaf_nodes_or_max_depth == "max_depth": self.max_leaf_nodes = None - if max_depth == "None": + if max_depth == "None" or max_depth is None: self.max_depth = None else: self.max_depth = int(max_depth) @@ -37,7 +37,7 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, #elif use_max_depth == "False": # self.max_depth = None else: - if max_leaf_nodes == "None": + if max_leaf_nodes == "None" or max_leaf_nodes is None: self.max_leaf_nodes = None else: self.max_leaf_nodes = int(max_leaf_nodes) diff --git a/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py b/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py index a7b4845f50..78e615c056 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py +++ b/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py @@ -37,7 +37,7 @@ def fit(self, X, Y): self.fit_intercept = self.fit_intercept == 'True' self.intercept_scaling = float(self.intercept_scaling) - if self.class_weight == "None": + if self.class_weight == "None" or self.class_weight is None: self.class_weight = None estimator = sklearn.svm.LinearSVC(penalty=self.penalty, diff --git a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py index 10d4ea87e7..dfbe915c0d 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py +++ b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py @@ -24,11 +24,11 @@ def __init__(self, n_estimators, max_depth, min_samples_split, def fit(self, X, Y=None): import sklearn.ensemble - if self.max_depth == "None": + if self.max_depth == "None" or self.max_depth is None: self.max_depth = None else: self.max_depth = int(self.max_depth) - if self.max_leaf_nodes == "None": + if self.max_leaf_nodes == "None" or self.max_leaf_nodes is None: self.max_leaf_nodes = None else: self.max_leaf_nodes = int(self.max_leaf_nodes) diff --git a/autosklearn/pipeline/components/regression/decision_tree.py b/autosklearn/pipeline/components/regression/decision_tree.py index 0e78441758..0d57b0f741 100644 --- a/autosklearn/pipeline/components/regression/decision_tree.py +++ b/autosklearn/pipeline/components/regression/decision_tree.py @@ -29,7 +29,7 @@ def fit(self, X, y, sample_weight=None): from sklearn.tree import DecisionTreeRegressor self.max_features = float(self.max_features) - if self.max_depth == "None": + if self.max_depth == "None" or self.max_depth is None: max_depth = self.max_depth = None else: num_features = X.shape[1] @@ -37,7 +37,7 @@ def fit(self, X, y, sample_weight=None): max_depth = max(1, int(np.round(self.max_depth * num_features, 0))) self.min_samples_split = int(self.min_samples_split) self.min_samples_leaf = int(self.min_samples_leaf) - if self.max_leaf_nodes == "None": + if self.max_leaf_nodes == "None" or self.max_leaf_nodes is None: self.max_leaf_nodes = None else: self.max_leaf_nodes = int(self.max_leaf_nodes) diff --git a/autosklearn/pipeline/components/regression/extra_trees.py b/autosklearn/pipeline/components/regression/extra_trees.py index c78e4fdd57..f3437885fe 100644 --- a/autosklearn/pipeline/components/regression/extra_trees.py +++ b/autosklearn/pipeline/components/regression/extra_trees.py @@ -25,7 +25,7 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, if max_leaf_nodes_or_max_depth == "max_depth": self.max_leaf_nodes = None - if max_depth == "None": + if max_depth == "None" or max_depth is None: self.max_depth = None else: self.max_depth = int(max_depth) @@ -34,7 +34,7 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, #elif use_max_depth == "False": # self.max_depth = None else: - if max_leaf_nodes == "None": + if max_leaf_nodes == "None" or max_leaf_nodes is None: self.max_leaf_nodes = None else: self.max_leaf_nodes = int(max_leaf_nodes) diff --git a/autosklearn/pipeline/components/regression/gradient_boosting.py b/autosklearn/pipeline/components/regression/gradient_boosting.py index e18b2964a0..5b407c81a5 100644 --- a/autosklearn/pipeline/components/regression/gradient_boosting.py +++ b/autosklearn/pipeline/components/regression/gradient_boosting.py @@ -56,7 +56,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.min_samples_split = int(self.min_samples_split) self.min_samples_leaf = int(self.min_samples_leaf) self.min_weight_fraction_leaf = float(self.min_weight_fraction_leaf) - if self.max_depth == "None": + if self.max_depth == "None" or self.max_depth is None: self.max_depth = None else: self.max_depth = int(self.max_depth) @@ -65,7 +65,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): float(self.max_features) * (np.log(num_features) + 1)) # Use at most half of the features max_features = max(1, min(int(X.shape[1] / 2), max_features)) - if self.max_leaf_nodes == "None": + if self.max_leaf_nodes == "None" or self.max_leaf_nodes is None: self.max_leaf_nodes = None else: self.max_leaf_nodes = int(self.max_leaf_nodes) From 1374081a8f5c454b30b9a419c643c401812abc30 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Mon, 23 Oct 2017 16:44:11 +0200 Subject: [PATCH 12/61] remove one level of indirection in AutoML class hierarchy --- autosklearn/automl.py | 124 +++++++++- autosklearn/estimators.py | 366 +++++++--------------------- example/example_metrics.py | 6 +- test/test_automl/test_estimators.py | 32 ++- 4 files changed, 228 insertions(+), 300 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index fdd4479a92..12f52fed22 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -1,12 +1,10 @@ # -*- encoding: utf-8 -*- -from collections import defaultdict import io import json import os import unittest.mock import warnings - from ConfigSpace.read_and_write import pcs import numpy as np import numpy.ma as ma @@ -15,6 +13,9 @@ from smac.tae.execute_ta_run import StatusType from smac.stats.stats import Stats from sklearn.externals import joblib +import sklearn.utils +import scipy.sparse +from sklearn.metrics.classification import type_of_target from autosklearn.constants import * from autosklearn.metrics import Scorer @@ -27,6 +28,8 @@ from autosklearn.ensemble_builder import EnsembleBuilder from autosklearn.smbo import AutoMLSMBO from autosklearn.util.hash import hash_array_or_matrix +from autosklearn.metrics import f1_macro, accuracy, r2 +from autosklearn.constants import * def _model_predict(self, X, batch_size, identifier): @@ -808,3 +811,120 @@ def _create_search_space(self, tmp_dir, backend, datamanager, def configuration_space_created_hook(self, datamanager, configuration_space): return configuration_space + + +class AutoMLClassifier(AutoML): + def __init__(self, *args, **kwargs): + self._classes = [] + self._n_classes = [] + self._n_outputs = 0 + + super().__init__(*args, **kwargs) + + def fit(self, X, y, + task=MULTICLASS_CLASSIFICATION, + metric=None, + feat_type=None, + dataset_name=None): + X = sklearn.utils.check_array(X, accept_sparse="csr", + force_all_finite=False) + y = sklearn.utils.check_array(y, ensure_2d=False) + + if scipy.sparse.issparse(X): + X.sort_indices() + + y_task = type_of_target(y) + task_mapping = {'multilabel-indicator': MULTILABEL_CLASSIFICATION, + 'multiclass': MULTICLASS_CLASSIFICATION, + 'binary': BINARY_CLASSIFICATION} + + task = task_mapping.get(y_task) + if task is None: + raise ValueError('Cannot work on data of type %s' % y_task) + + if metric is None: + if task == MULTILABEL_CLASSIFICATION: + metric = f1_macro + else: + metric = accuracy + + y = self._process_target_classes(y) + + return super().fit(X, y, task, metric, feat_type, dataset_name) + + def fit_ensemble(self, y, task=None, metric=None, precision='32', + dataset_name=None, ensemble_nbest=None, + ensemble_size=None): + self._process_target_classes(y) + return super().fit_ensemble(y, task, metric, precision, + dataset_name, + ensemble_nbest, ensemble_size) + + def _process_target_classes(self, y): + y = np.atleast_1d(y) + if y.ndim == 2 and y.shape[1] == 1: + warnings.warn("A column-vector y was passed when a 1d array was" + " expected. Please change the shape of y to " + "(n_samples,), for example using ravel().", + sklearn.utils.DataConversionWarning, stacklevel=2) + + if y.ndim == 1: + # reshape is necessary to preserve the data contiguity against vs + # [:, np.newaxis] that does not. + y = np.reshape(y, (-1, 1)) + + self._n_outputs = y.shape[1] + + y = np.copy(y) + + self._classes = [] + self._n_classes = [] + + for k in range(self._n_outputs): + classes_k, y[:, k] = np.unique(y[:, k], return_inverse=True) + self._classes.append(classes_k) + self._n_classes.append(classes_k.shape[0]) + + self._n_classes = np.array(self._n_classes, dtype=np.int) + + if y.shape[1] == 1: + y = y.flatten() + + return y + + def predict(self, X, batch_size=None, n_jobs=1): + predicted_probabilities = super().predict( + X, batch_size=batch_size, n_jobs=n_jobs + ) + + if self._n_outputs == 1: + predicted_indexes = np.argmax(predicted_probabilities, axis=1) + predicted_classes = self._classes[0].take(predicted_indexes) + + return predicted_classes + else: + predicted_indices = (predicted_probabilities > 0.5).astype(int) + n_samples = predicted_probabilities.shape[0] + predicted_classes = np.zeros((n_samples, self._n_outputs)) + + for k in range(self._n_outputs): + output_predicted_indexes = predicted_indices[:, k].reshape(-1) + predicted_classes[:, k] = self._classes[k].take( + output_predicted_indexes) + + return predicted_classes + + def predict_proba(self, X, batch_size=None, n_jobs=1): + return super().predict(X, batch_size=batch_size, n_jobs=n_jobs) + + +class AutoMLRegressor(AutoML): + def fit(self, X, y, + task=MULTICLASS_CLASSIFICATION, + metric=None, + feat_type=None, + dataset_name=None): + if metric is None: + metric = r2 + return super().fit(X=X, y=y, task=REGRESSION, metric=metric, + feat_type=feat_type, dataset_name=dataset_name) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 415b5e28e5..5e0db81ee7 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -1,105 +1,11 @@ # -*- encoding: utf-8 -*- - -import numpy as np -import warnings - -from sklearn.metrics.classification import type_of_target from sklearn.base import BaseEstimator -import sklearn.utils -import scipy.sparse -import autosklearn.automl -from autosklearn.metrics import f1_macro, accuracy, r2 -from autosklearn.constants import * +from autosklearn.automl import AutoMLClassifier, AutoMLRegressor from autosklearn.util.backend import create -class AutoMLDecorator(object): - - def __init__(self, automl): - self._automl = automl - - def fit(self, *args, **kwargs): - self._automl.fit(*args, **kwargs) - - def refit(self, X, y): - """Refit all models found with fit to new data. - - Necessary when using cross-validation. During training, auto-sklearn - fits each model k times on the dataset, but does not keep any trained - model and can therefore not be used to predict for new data points. - This methods fits all models found during a call to fit on the data - given. This method may also be used together with holdout to avoid - only using 66% of the training data to fit the final model. - - Parameters - ---------- - - X : array-like or sparse matrix of shape = [n_samples, n_features] - The training input samples. - - y : array-like, shape = [n_samples] or [n_samples, n_outputs] - The targets. - - Returns - ------- - - self - - """ - return self._automl.refit(X, y) - - def fit_ensemble(self, y, task=None, metric=None, precision='32', - dataset_name=None, ensemble_nbest=None, - ensemble_size=None): - - return self._automl.fit_ensemble(y, task, metric, precision, - dataset_name, ensemble_nbest, - ensemble_size) - - def predict(self, X, batch_size=None, n_jobs=1): - return self._automl.predict(X, batch_size=batch_size, n_jobs=n_jobs) - - def score(self, X, y): - return self._automl.score(X, y) - - def show_models(self): - """Return a representation of the final ensemble found by auto-sklearn. - - Returns - ------- - str - - """ - return self._automl.show_models() - - def get_models_with_weights(self): - """Return a list of the final ensemble found by auto-sklearn. - - Returns - ------- - [(weight_1, model_1), ..., (weight_n, model_n)] - - """ - return self._automl.get_models_with_weights() - - @property - def cv_results_(self): - return self._automl.cv_results_ - - @property - def trajectory_(self): - return self._automl.trajectory_ - - @property - def fANOVA_input_(self): - return self._automl.fANOVA_input_ - - def sprint_statistics(self): - return self._automl.sprint_statistics() - - -class AutoSklearnEstimator(AutoMLDecorator, BaseEstimator): +class AutoSklearnEstimator(BaseEstimator): def __init__(self, time_left_for_this_task=3600, @@ -130,7 +36,6 @@ def __init__(self, models. By increasing this value, *auto-sklearn* has a higher chance of finding better models. - per_run_time_limit : int, optional (default=360) Time limit for a single call to the machine learning model. Model fitting will be terminated if the machine learning @@ -268,9 +173,11 @@ def __init__(self, self.shared_mode = shared_mode self.disable_evaluator_output = disable_evaluator_output self.configuration_mode = configuration_mode - super(AutoSklearnEstimator, self).__init__(None) - def build_automl(self): + self._automl = None + super().__init__() + + def build_automl(self, cls): if self.shared_mode: self.delete_output_folder_after_terminate = False self.delete_tmp_folder_after_terminate = False @@ -285,7 +192,7 @@ def build_automl(self): output_directory=self.output_folder, delete_tmp_folder_after_terminate=self.delete_tmp_folder_after_terminate, delete_output_folder_after_terminate=self.delete_output_folder_after_terminate) - automl = autosklearn.automl.AutoML( + automl = cls( backend=backend, time_left_for_this_task=self.time_left_for_this_task, per_run_time_limit=self.per_run_time_limit, @@ -313,7 +220,7 @@ def build_automl(self): def fit(self, *args, **kwargs): self._automl = self.build_automl() - super().fit(*args, **kwargs) + self._automl.fit(*args, **kwargs) def fit_ensemble(self, y, task=None, metric=None, precision='32', dataset_name=None, ensemble_nbest=None, @@ -364,6 +271,74 @@ def fit_ensemble(self, y, task=None, metric=None, precision='32', dataset_name, ensemble_nbest, ensemble_size) + def refit(self, X, y): + """Refit all models found with fit to new data. + + Necessary when using cross-validation. During training, auto-sklearn + fits each model k times on the dataset, but does not keep any trained + model and can therefore not be used to predict for new data points. + This methods fits all models found during a call to fit on the data + given. This method may also be used together with holdout to avoid + only using 66% of the training data to fit the final model. + + Parameters + ---------- + + X : array-like or sparse matrix of shape = [n_samples, n_features] + The training input samples. + + y : array-like, shape = [n_samples] or [n_samples, n_outputs] + The targets. + + Returns + ------- + + self + + """ + return self._automl.refit(X, y) + + def predict(self, X, batch_size=None, n_jobs=1): + return self._automl.predict(X, batch_size=batch_size, n_jobs=n_jobs) + + def score(self, X, y): + return self._automl.score(X, y) + + def show_models(self): + """Return a representation of the final ensemble found by auto-sklearn. + + Returns + ------- + str + + """ + return self._automl.show_models() + + def get_models_with_weights(self): + """Return a list of the final ensemble found by auto-sklearn. + + Returns + ------- + [(weight_1, model_1), ..., (weight_n, model_n)] + + """ + return self._automl.get_models_with_weights() + + @property + def cv_results_(self): + return self._automl.cv_results_ + + @property + def trajectory_(self): + return self._automl.trajectory_ + + @property + def fANOVA_input_(self): + return self._automl.fANOVA_input_ + + def sprint_statistics(self): + return self._automl.sprint_statistics() + class AutoSklearnClassifier(AutoSklearnEstimator): """ @@ -372,8 +347,7 @@ class AutoSklearnClassifier(AutoSklearnEstimator): """ def build_automl(self): - automl = super(AutoSklearnClassifier, self).build_automl() - return AutoMLClassifier(automl) + return super().build_automl(AutoMLClassifier) def fit(self, X, y, metric=None, @@ -412,9 +386,8 @@ def fit(self, X, y, self """ - return super(AutoSklearnClassifier, self).fit(X=X, y=y, metric=metric, - feat_type=feat_type, - dataset_name=dataset_name) + return super().fit(X=X, y=y, metric=metric, feat_type=feat_type, + dataset_name=dataset_name) def predict(self, X, batch_size=None, n_jobs=1): """Predict classes for X. @@ -429,8 +402,7 @@ def predict(self, X, batch_size=None, n_jobs=1): The predicted classes. """ - return super(AutoSklearnClassifier, self).predict( - X, batch_size=batch_size, n_jobs=n_jobs) + return super().predict(X, batch_size=batch_size, n_jobs=n_jobs) def predict_proba(self, X, batch_size=None, n_jobs=1): @@ -457,8 +429,7 @@ class AutoSklearnRegressor(AutoSklearnEstimator): """ def build_automl(self): - automl = super(AutoSklearnRegressor, self).build_automl() - return AutoMLRegressor(automl) + return super().build_automl(AutoMLRegressor) def fit(self, X, y, metric=None, @@ -496,9 +467,8 @@ def fit(self, X, y, """ # Fit is supposed to be idempotent! # But not if we use share_mode. - return super(AutoSklearnRegressor, self).fit(X=X, y=y, metric=metric, - feat_type=feat_type, - dataset_name=dataset_name) + return super().fit(X=X, y=y, metric=metric, feat_type=feat_type, + dataset_name=dataset_name) def predict(self, X, batch_size=None, n_jobs=1): """Predict regression target for X. @@ -513,172 +483,4 @@ def predict(self, X, batch_size=None, n_jobs=1): The predicted values. """ - return super(AutoSklearnRegressor, self).predict( - X, batch_size=batch_size, n_jobs=n_jobs) - - - -class BaseAutoML(AutoMLDecorator): - """Base class for AutoML objects to hold abstract functions for both - regression and classification.""" - - def __init__(self, automl): - self._n_outputs = 1 - super().__init__(automl) - - def _perform_input_checks(self, X, y): - X = self._check_X(X) - y = self._check_y(y) - return X, y - - def _check_X(self, X): - X = sklearn.utils.check_array(X, accept_sparse="csr", - force_all_finite=False) - if scipy.sparse.issparse(X): - X.sort_indices() - return X - - def _check_y(self, y): - y = sklearn.utils.check_array(y, ensure_2d=False) - - y = np.atleast_1d(y) - if y.ndim == 2 and y.shape[1] == 1: - warnings.warn("A column-vector y was passed when a 1d array was" - " expected. Will change shape via np.ravel().", - sklearn.utils.DataConversionWarning, stacklevel=2) - y = np.ravel(y) - - return y - - def refit(self, X, y): - X, y = self._perform_input_checks(X, y) - _n_outputs = 1 if len(y.shape) == 1 else y.shape[1] - if self._n_outputs != _n_outputs: - raise ValueError('Number of outputs changed from %d to %d!' % - (self._n_outputs, _n_outputs)) - - return super().refit(X, y) - - def fit_ensemble(self, y, task=None, metric=None, precision='32', - dataset_name=None, ensemble_nbest=None, - ensemble_size=None): - _n_outputs = 1 if len(y.shape) == 1 else y.shape[1] - if self._n_outputs != _n_outputs: - raise ValueError('Number of outputs changed from %d to %d!' % - (self._n_outputs, _n_outputs)) - - return super().fit_ensemble( - y, task=task, metric=metric, precision=precision, - dataset_name=dataset_name, ensemble_nbest=ensemble_nbest, - ensemble_size=ensemble_size - ) - -class AutoMLClassifier(BaseAutoML): - - def __init__(self, automl): - - super().__init__(automl) - - self._task_mapping = {'multilabel-indicator': MULTILABEL_CLASSIFICATION, - 'multiclass': MULTICLASS_CLASSIFICATION, - 'binary': BINARY_CLASSIFICATION} - - def fit(self, X, y, - metric=None, - loss=None, - feat_type=None, - dataset_name=None): - X, y = self._perform_input_checks(X, y) - - y_task = type_of_target(y) - task = self._task_mapping.get(y_task) - if task is None: - raise ValueError('Cannot work on data of type %s' % y_task) - - if metric is None: - if task == MULTILABEL_CLASSIFICATION: - metric = f1_macro - else: - metric = accuracy - - y, self._classes, self._n_classes = self._process_target_classes(y) - - return super().fit(X, y, task, metric, feat_type, dataset_name) - - def fit_ensemble(self, y, task=None, metric=None, precision='32', - dataset_name=None, ensemble_nbest=None, - ensemble_size=None): - y, _classes, _n_classes = self._process_target_classes(y) - if not hasattr(self, '_classes'): - self._classes = _classes - if not hasattr(self, '_n_classes'): - self._n_classes = _n_classes - - return super().fit_ensemble(y, task, metric, precision, dataset_name, - ensemble_nbest, ensemble_size) - - def _process_target_classes(self, y): - y = super()._check_y(y) - self._n_outputs = 1 if len(y.shape) == 1 else y.shape[1] - - y = np.copy(y) - - _classes = [] - _n_classes = [] - - if self._n_outputs == 1: - classes_k, y = np.unique(y, return_inverse=True) - _classes.append(classes_k) - _n_classes.append(classes_k.shape[0]) - else: - for k in range(self._n_outputs): - classes_k, y[:, k] = np.unique(y[:, k], return_inverse=True) - _classes.append(classes_k) - _n_classes.append(classes_k.shape[0]) - - self._n_classes = np.array(_n_classes, dtype=np.int) - - return y, _classes, _n_classes - - def predict(self, X, batch_size=None, n_jobs=1): - predicted_probabilities = super().predict(X, batch_size=batch_size, - n_jobs=n_jobs) - - if self._n_outputs == 1: - predicted_indexes = np.argmax(predicted_probabilities, axis=1) - predicted_classes = self._classes[0].take(predicted_indexes) - - return predicted_classes - else: - predicted_indices = (predicted_probabilities > 0.5).astype(int) - n_samples = predicted_probabilities.shape[0] - predicted_classes = np.zeros((n_samples, self._n_outputs)) - - for k in range(self._n_outputs): - output_predicted_indexes = predicted_indices[:, k].reshape(-1) - predicted_classes[:, k] = self._classes[k].take(output_predicted_indexes) - - return predicted_classes - - def predict_proba(self, X, batch_size=None, n_jobs=1): - return self._automl.predict(X, batch_size=batch_size, n_jobs=n_jobs) - - -class AutoMLRegressor(BaseAutoML): - - def fit(self, X, y, metric=None, feat_type=None, dataset_name=None): - X, y = super()._perform_input_checks(X, y) - _n_outputs = 1 if len(y.shape) == 1 else y.shape[1] - if _n_outputs > 1: - raise NotImplementedError('Multi-output regression is not implemented.') - if metric is None: - metric = r2 - return super().fit(X, y, task=REGRESSION, metric=metric, - feat_type=feat_type, dataset_name=dataset_name) - - def fit_ensemble(self, y, task=None, metric=None, precision='32', - dataset_name=None, ensemble_nbest=None, - ensemble_size=None): - y = super()._check_y(y) - return super().fit_ensemble(y, task, metric, precision, dataset_name, - ensemble_nbest, ensemble_size) + return super().predict(X, batch_size=batch_size, n_jobs=n_jobs) diff --git a/example/example_metrics.py b/example/example_metrics.py index 3238bb9006..8e58137879 100644 --- a/example/example_metrics.py +++ b/example/example_metrics.py @@ -45,7 +45,7 @@ def main(): predictions = cls.predict(X_test) print("Accuracy score {:g} using {:s}". format(sklearn.metrics.accuracy_score(y_test, predictions), - cls._automl._automl._metric.name)) + cls._automl._metric.name)) # Second example: Use own accuracy metric print("#"*80) @@ -63,7 +63,7 @@ def main(): predictions = cls.predict(X_test) print("Accuracy score {:g} using {:s}". format(sklearn.metrics.accuracy_score(y_test, predictions), - cls._automl._automl._metric.name)) + cls._automl._metric.name)) # Third example: Use own accuracy metric with additional argument print("#"*80) @@ -82,7 +82,7 @@ def main(): predictions = cls.predict(X_test) print("Accuracy score {:g} using {:s}". format(sklearn.metrics.accuracy_score(y_test, predictions), - cls._automl._automl._metric.name)) + cls._automl._metric.name)) if __name__ == "__main__": diff --git a/test/test_automl/test_estimators.py b/test/test_automl/test_estimators.py index 9af9d91725..0ba9d68233 100644 --- a/test/test_automl/test_estimators.py +++ b/test/test_automl/test_estimators.py @@ -13,8 +13,7 @@ from autosklearn.classification import AutoSklearnClassifier from autosklearn.regression import AutoSklearnRegressor from autosklearn.metrics import accuracy, f1_macro, mean_squared_error -from autosklearn.estimators import AutoMLClassifier -from autosklearn.automl import AutoML +from autosklearn.automl import AutoMLClassifier, AutoML from autosklearn.util.backend import Backend, BackendContext from autosklearn.constants import * sys.path.append(os.path.dirname(__file__)) @@ -155,9 +154,9 @@ def test_fit_pSMAC(self): self.assertEqual(len(os.listdir(os.path.join(output, '.auto-sklearn', 'ensembles'))), 1) self.assertGreaterEqual(score, 0.90) - self.assertEqual(automl._automl._automl._task, MULTICLASS_CLASSIFICATION) + self.assertEqual(automl._automl._task, MULTICLASS_CLASSIFICATION) - models = automl._automl._automl.models_ + models = automl._automl.models_ classifier_types = [type(c) for c in models.values()] self.assertIn(ArrayReturningDummyPredictor, classifier_types) @@ -193,18 +192,21 @@ def test_cv_results(self): class AutoMLClassifierTest(Base, unittest.TestCase): - - def test_multiclass_prediction(self): + @unittest.mock.patch('autosklearn.automl.AutoML.predict') + def test_multiclass_prediction(self, predict_mock): classes = [['a', 'b', 'c']] predicted_probabilities = [[0, 0, 0.99], [0, 0.99, 0], [0.99, 0, 0], [0, 0.99, 0], [0, 0, 0.99]] predicted_indexes = [2, 1, 0, 1, 2] expected_result = ['c', 'b', 'a', 'b', 'c'] - automl_mock = unittest.mock.Mock() - automl_mock.predict.return_value = np.array(predicted_probabilities) + predict_mock.return_value = np.array(predicted_probabilities) - classifier = AutoMLClassifier(automl_mock) + classifier = AutoMLClassifier( + time_left_for_this_task=1, + per_run_time_limit=1, + backend=None, + ) classifier._classes = [np.array(classes)] classifier._n_outputs = 1 classifier._n_classes = np.array([3]) @@ -213,7 +215,8 @@ def test_multiclass_prediction(self): np.testing.assert_array_equal(expected_result, actual_result) - def test_multilabel_prediction(self): + @unittest.mock.patch('autosklearn.automl.AutoML.predict') + def test_multilabel_prediction(self, predict_mock): classes = [[1, 2], [13, 17]] predicted_probabilities = [[0.99, 0], [0.99, 0], @@ -223,10 +226,13 @@ def test_multilabel_prediction(self): predicted_indexes = np.array([[1, 0], [1, 0], [0, 1], [1, 1], [1, 1]]) expected_result = np.array([[2, 13], [2, 13], [1, 17], [2, 17], [2, 17]]) - automl_mock = unittest.mock.Mock() - automl_mock.predict.return_value = np.array(predicted_probabilities) + predict_mock.return_value = np.array(predicted_probabilities) - classifier = AutoMLClassifier(automl_mock) + classifier = AutoMLClassifier( + time_left_for_this_task=1, + per_run_time_limit=1, + backend=None, + ) classifier._classes = list(map(np.array, classes)) classifier._n_outputs = 2 classifier._n_classes = np.array([3, 2]) From 9f064b12701d517b3135bebe73092fee63abca21 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 24 Oct 2017 14:40:02 +0200 Subject: [PATCH 13/61] update to sklearn==0.19.1, do obvious changes --- autosklearn/pipeline/base.py | 1 + autosklearn/pipeline/components/base.py | 1 + .../classification/decision_tree.py | 2 +- .../classification/gradient_boosting.py | 13 +++-- .../components/classification/libsvm_svc.py | 9 +--- .../classification/passive_aggressive.py | 17 +++++-- .../data_preprocessing/rescaling/__init__.py | 4 +- .../rescaling/quantile_transformer.py | 45 +++++++++++++++++ .../rescaling/robust_scaler.py | 41 +++++++++++++++ .../extra_trees_preproc_for_regression.py | 9 ++-- .../feature_preprocessing/kernel_pca.py | 2 +- .../feature_preprocessing/kitchen_sinks.py | 2 +- .../feature_preprocessing/nystroem_sampler.py | 6 +-- .../random_trees_embedding.py | 10 ++-- .../select_percentile_classification.py | 9 +++- .../select_percentile_regression.py | 9 ++-- .../feature_preprocessing/select_rates.py | 6 ++- .../components/regression/ard_regression.py | 2 +- .../components/regression/decision_tree.py | 3 +- .../components/regression/extra_trees.py | 3 +- .../components/regression/libsvm_svr.py | 11 ++-- .../components/regression/random_forest.py | 3 +- .../components/regression/ridge_regression.py | 5 +- test/test_evaluation/test_train_evaluator.py | 34 ++++++------- .../test_meta_base_data/configurations.csv | 50 +++++++++---------- .../components/classification/test_base.py | 8 +-- .../classification/test_bernoulli_nb.py | 4 +- .../classification/test_decision_tree.py | 4 +- .../classification/test_extra_trees.py | 4 +- .../classification/test_gaussian_nb.py | 4 +- .../classification/test_k_nearest_neighbor.py | 4 +- .../components/classification/test_lda.py | 4 +- .../classification/test_liblinear.py | 2 +- .../classification/test_libsvm_svc.py | 6 +-- .../classification/test_multinomial_nb.py | 4 +- .../classification/test_passive_aggressive.py | 4 +- .../classification/test_random_forest.py | 4 +- .../regression/test_ard_regression.py | 4 +- .../components/regression/test_extra_trees.py | 16 +++--- .../regression/test_random_forests.py | 12 ++--- .../regression/test_ridge_regression.py | 6 +-- test/test_pipeline/test_classification.py | 16 +----- test/test_pipeline/test_regression.py | 2 +- 43 files changed, 253 insertions(+), 152 deletions(-) create mode 100644 autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py create mode 100644 autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py diff --git a/autosklearn/pipeline/base.py b/autosklearn/pipeline/base.py index 2feda04b03..473b714b7e 100644 --- a/autosklearn/pipeline/base.py +++ b/autosklearn/pipeline/base.py @@ -58,6 +58,7 @@ def __init__(self, config=None, pipeline=None, dataset_properties=None, self.random_state = check_random_state(1) else: self.random_state = check_random_state(random_state) + super().__init__(steps=self.steps) def fit(self, X, y, fit_params=None): """Fit the selected algorithm to the training data. diff --git a/autosklearn/pipeline/components/base.py b/autosklearn/pipeline/components/base.py index 612609ac59..a66e99e61a 100644 --- a/autosklearn/pipeline/components/base.py +++ b/autosklearn/pipeline/components/base.py @@ -363,6 +363,7 @@ def set_hyperparameters(self, configuration, init_params=None): new_params['random_state'] = self.random_state self.new_params = new_params + print(choice) self.choice = self.get_components()[choice](**new_params) return self diff --git a/autosklearn/pipeline/components/classification/decision_tree.py b/autosklearn/pipeline/components/classification/decision_tree.py index ea37172806..e06c6aefa5 100644 --- a/autosklearn/pipeline/components/classification/decision_tree.py +++ b/autosklearn/pipeline/components/classification/decision_tree.py @@ -87,7 +87,6 @@ def get_hyperparameter_search_space(dataset_properties=None): criterion = CategoricalHyperparameter( "criterion", ["gini", "entropy"], default_value="gini") splitter = Constant("splitter", "best") - max_features = Constant('max_features', 1.0) max_depth = UniformFloatHyperparameter( 'max_depth', 0., 2., default_value=0.5) min_samples_split = UniformIntegerHyperparameter( @@ -95,6 +94,7 @@ def get_hyperparameter_search_space(dataset_properties=None): min_samples_leaf = UniformIntegerHyperparameter( "min_samples_leaf", 1, 20, default_value=1) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0) + max_features = Constant('max_features', 1.0) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") cs.add_hyperparameters([criterion, splitter, max_features, max_depth, diff --git a/autosklearn/pipeline/components/classification/gradient_boosting.py b/autosklearn/pipeline/components/classification/gradient_boosting.py index 8a81e6a39b..2732f4496b 100644 --- a/autosklearn/pipeline/components/classification/gradient_boosting.py +++ b/autosklearn/pipeline/components/classification/gradient_boosting.py @@ -13,8 +13,8 @@ class GradientBoostingClassifier(AutoSklearnClassificationAlgorithm): def __init__(self, loss, learning_rate, n_estimators, subsample, min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, max_depth, max_features, - max_leaf_nodes, init=None, random_state=None, verbose=0): + min_weight_fraction_leaf, max_depth, criterion, max_features, + max_leaf_nodes, random_state=None, verbose=0): self.loss = loss self.learning_rate = learning_rate self.n_estimators = n_estimators @@ -23,9 +23,9 @@ def __init__(self, loss, learning_rate, n_estimators, subsample, self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.max_depth = max_depth + self.criterion=criterion self.max_features = max_features self.max_leaf_nodes = max_leaf_nodes - self.init = init self.random_state = random_state self.verbose = verbose self.estimator = None @@ -77,9 +77,9 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): min_samples_leaf=self.min_samples_leaf, min_weight_fraction_leaf=self.min_weight_fraction_leaf, max_depth=self.max_depth, + criterion=self.criterion, max_features=max_features, max_leaf_nodes=self.max_leaf_nodes, - init=self.init, random_state=self.random_state, verbose=self.verbose, warm_start=True, @@ -136,6 +136,9 @@ def get_hyperparameter_search_space(dataset_properties=None): "n_estimators", 50, 500, default_value=100) max_depth = UniformIntegerHyperparameter( name="max_depth", lower=1, upper=10, default_value=3) + criterion = CategoricalHyperparameter( + 'criterion', ['friedman_mse', 'mse', 'mae'], + default_value='friedman_mse') min_samples_split = UniformIntegerHyperparameter( name="min_samples_split", lower=2, upper=20, default_value=2, log=False) min_samples_leaf = UniformIntegerHyperparameter( @@ -148,7 +151,7 @@ def get_hyperparameter_search_space(dataset_properties=None): max_leaf_nodes = UnParametrizedHyperparameter( name="max_leaf_nodes", value="None") cs.add_hyperparameters([loss, learning_rate, n_estimators, max_depth, - min_samples_split, min_samples_leaf, + criterion, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, subsample, max_features, max_leaf_nodes]) diff --git a/autosklearn/pipeline/components/classification/libsvm_svc.py b/autosklearn/pipeline/components/classification/libsvm_svc.py index aff91dd2d1..21b0ff8cc3 100644 --- a/autosklearn/pipeline/components/classification/libsvm_svc.py +++ b/autosklearn/pipeline/components/classification/libsvm_svc.py @@ -72,7 +72,6 @@ def fit(self, X, Y): random_state=self.random_state, cache_size=cache_size, decision_function_shape='ovr') - #probability=True) self.estimator.fit(X, Y) return self @@ -84,11 +83,7 @@ def predict(self, X): def predict_proba(self, X): if self.estimator is None: raise NotImplementedError() - # return self.estimator.predict_proba(X) decision = self.estimator.decision_function(X) - #if len(self.estimator.classes_) > 2: - # decision = _ovr_decision_function(decision < 0, decision, - # len(self.estimator.classes_)) return softmax(decision) @@ -112,7 +107,7 @@ def get_hyperparameter_search_space(dataset_properties=None): kernel = CategoricalHyperparameter(name="kernel", choices=["rbf", "poly", "sigmoid"], default_value="rbf") - degree = UniformIntegerHyperparameter("degree", 1, 5, default_value=3) + degree = UniformIntegerHyperparameter("degree", 2, 5, default_value=3) gamma = UniformFloatHyperparameter("gamma", 3.0517578125e-05, 8, log=True, default_value=0.1) # TODO this is totally ad-hoc @@ -120,7 +115,7 @@ def get_hyperparameter_search_space(dataset_properties=None): # probability is no hyperparameter, but an argument to the SVM algo shrinking = CategoricalHyperparameter("shrinking", ["True", "False"], default_value="True") - tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, + tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-3, log=True) # cache size is not a hyperparameter, but an argument to the program! max_iter = UnParametrizedHyperparameter("max_iter", -1) diff --git a/autosklearn/pipeline/components/classification/passive_aggressive.py b/autosklearn/pipeline/components/classification/passive_aggressive.py index 77cf87dfdf..91b38d207c 100644 --- a/autosklearn/pipeline/components/classification/passive_aggressive.py +++ b/autosklearn/pipeline/components/classification/passive_aggressive.py @@ -38,9 +38,14 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self._iterations = 0 self.estimator = PassiveAggressiveClassifier( - C=self.C, fit_intercept=self.fit_intercept, n_iter=1, - loss=self.loss, shuffle=True, random_state=self.random_state, - warm_start=True) + C=self.C, + fit_intercept=self.fit_intercept, + n_iter=1, + loss=self.loss, + shuffle=True, + random_state=self.random_state, + warm_start=True + ) self.classes_ = np.unique(y.astype(int)) # Fallback for multilabel classification @@ -96,13 +101,15 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): + C = UniformFloatHyperparameter("C", 1e-5, 10, 1, log=True) + fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") loss = CategoricalHyperparameter("loss", ["hinge", "squared_hinge"], default_value="hinge") - fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") + n_iter = UniformIntegerHyperparameter("n_iter", 5, 1000, default_value=20, log=True) - C = UniformFloatHyperparameter("C", 1e-5, 10, 1, log=True) + cs = ConfigurationSpace() cs.add_hyperparameters([loss, fit_intercept, n_iter, C]) return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py index c4b2c098d6..891129571a 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py @@ -58,7 +58,9 @@ def get_hyperparameter_search_space(self, dataset_properties=None, for name in available_preprocessors: preprocessor_configuration_space = available_preprocessors[name]. \ get_hyperparameter_search_space(dataset_properties) - cs.add_configuration_space(name, preprocessor_configuration_space) + parent_hyperparameter = {'parent': preprocessor, 'value': name} + cs.add_configuration_space(name, preprocessor_configuration_space, + parent_hyperparameter=parent_hyperparameter) self.configuration_space_ = cs self.dataset_properties_ = dataset_properties diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py new file mode 100644 index 0000000000..57ffd2eac1 --- /dev/null +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py @@ -0,0 +1,45 @@ +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, \ + CategoricalHyperparameter + +from autosklearn.pipeline.constants import * +from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling \ + import Rescaling +from autosklearn.pipeline.components.base import \ + AutoSklearnPreprocessingAlgorithm + + +class QuantileTransformerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): + def __init__(self, n_quantiles, output_distribution, random_state): + from sklearn.preprocessing import QuantileTransformer + self.preprocessor = QuantileTransformer( + n_quantiles=n_quantiles, + output_distribution=output_distribution, + ) + + @staticmethod + def get_properties(dataset_properties=None): + return {'shortname': 'QuantileTransformer', + 'name': 'QuantileTransformer', + 'handles_regression': True, + 'handles_classification': True, + 'handles_multiclass': True, + 'handles_multilabel': True, + 'is_deterministic': True, + # TODO find out of this is right! + 'handles_sparse': True, + 'handles_dense': True, + 'input': (DENSE, UNSIGNED_DATA), + 'output': (INPUT, SIGNED_DATA), + 'preferred_dtype': None} + + def get_hyperparameter_search_space(dataset_properties=None): + cs = ConfigurationSpace() + n_quantiles = UniformIntegerHyperparameter( + 'n_quantiles', lower=10, upper=int(1e5), default_value=1000 + ) + output_distribution = CategoricalHyperparameter( + 'output_distribution', ['uniform', 'normal'] + ) + cs.add_hyperparameters((n_quantiles, output_distribution)) + return cs \ No newline at end of file diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py new file mode 100644 index 0000000000..4b36eee44b --- /dev/null +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py @@ -0,0 +1,41 @@ +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import UniformFloatHyperparameter + +from autosklearn.pipeline.constants import * +from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling \ + import Rescaling +from autosklearn.pipeline.components.base import \ + AutoSklearnPreprocessingAlgorithm + + +class RobustScalerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): + def __init__(self, q_min, q_max, random_state): + from sklearn.preprocessing import RobustScaler + self.preprocessor = RobustScaler(quantile_range=(q_min, q_max)) + + @staticmethod + def get_properties(dataset_properties=None): + return {'shortname': 'RobustScaler', + 'name': 'RobustScaler', + 'handles_regression': True, + 'handles_classification': True, + 'handles_multiclass': True, + 'handles_multilabel': True, + 'is_deterministic': True, + # TODO find out of this is right! + 'handles_sparse': True, + 'handles_dense': True, + 'input': (DENSE, UNSIGNED_DATA), + 'output': (INPUT, SIGNED_DATA), + 'preferred_dtype': None} + + def get_hyperparameter_search_space(dataset_properties=None): + cs = ConfigurationSpace() + q_min = UniformFloatHyperparameter( + 'q_min', 0.001, 0.3, default_value=0.25 + ) + q_max = UniformFloatHyperparameter( + 'q_max', 0.7, 0.999, default_value=0.75 + ) + cs.add_hyperparameters((q_min, q_max)) + return cs \ No newline at end of file diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py index 768c604213..2792911442 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py @@ -21,9 +21,9 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, self.n_estimators = int(n_estimators) self.estimator_increment = 10 - if criterion not in ("mse", ): - raise ValueError("'criterion' is not in ('mse', ): " - "%s" % criterion) + if criterion not in ("mse", "friedman_mse", "mae"): + raise ValueError("'criterion' is not in ('mse', 'friedman_mse', " + "'mae'): %s" % criterion) self.criterion = criterion if max_leaf_nodes_or_max_depth == "max_depth": @@ -105,7 +105,8 @@ def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_estimators = Constant("n_estimators", 100) - criterion = Constant("criterion", "mse") + criterion = CategoricalHyperparameter("criterion", + ["mse", 'friedman_mse', 'mae']) max_features = UniformFloatHyperparameter( "max_features", 0.5, 5, default_value=1) diff --git a/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py b/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py index 13bc607e10..2fae161600 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py +++ b/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py @@ -72,9 +72,9 @@ def get_hyperparameter_search_space(dataset_properties=None): "n_components", 10, 2000, default_value=100) kernel = CategoricalHyperparameter('kernel', ['poly', 'rbf', 'sigmoid', 'cosine'], 'rbf') - degree = UniformIntegerHyperparameter('degree', 2, 5, 3) gamma = UniformFloatHyperparameter("gamma", 3.0517578125e-05, 8, log=True, default_value=1.0) + degree = UniformIntegerHyperparameter('degree', 2, 5, 3) coef0 = UniformFloatHyperparameter("coef0", -1, 1, default_value=0) cs = ConfigurationSpace() cs.add_hyperparameters([n_components, kernel, degree, gamma, coef0]) diff --git a/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py b/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py index ea165936d1..fb6027d677 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py +++ b/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py @@ -47,7 +47,7 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): gamma = UniformFloatHyperparameter( - "gamma", 0.3, 2., default_value=1.0) + "gamma", 3.0517578125e-05, 8, default_value=1.0, log=True) n_components = UniformIntegerHyperparameter( "n_components", 50, 10000, default_value=100, log=True) cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py b/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py index d753a2d3df..42fea09f59 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py +++ b/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py @@ -86,12 +86,12 @@ def get_hyperparameter_search_space(dataset_properties=None): if allow_chi2: possible_kernels.append("chi2") kernel = CategoricalHyperparameter('kernel', possible_kernels, 'rbf') - degree = UniformIntegerHyperparameter('degree', 2, 5, 3) + n_components = UniformIntegerHyperparameter( + "n_components", 50, 10000, default_value=100, log=True) gamma = UniformFloatHyperparameter("gamma", 3.0517578125e-05, 8, log=True, default_value=0.1) + degree = UniformIntegerHyperparameter('degree', 2, 5, 3) coef0 = UniformFloatHyperparameter("coef0", -1, 1, default_value=0) - n_components = UniformIntegerHyperparameter( - "n_components", 50, 10000, default_value=100, log=True) cs = ConfigurationSpace() cs.add_hyperparameters([kernel, degree, gamma, coef0, n_components]) diff --git a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py index dfbe915c0d..5656f5127e 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py +++ b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py @@ -1,6 +1,6 @@ from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, \ - UnParametrizedHyperparameter, Constant + UnParametrizedHyperparameter, Constant, CategoricalHyperparameter from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.constants import * @@ -10,13 +10,14 @@ class RandomTreesEmbedding(AutoSklearnPreprocessingAlgorithm): def __init__(self, n_estimators, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, - sparse_output=True, n_jobs=1, random_state=None): + bootstrap, sparse_output=True, n_jobs=1, random_state=None): self.n_estimators = n_estimators self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.max_leaf_nodes = max_leaf_nodes self.min_weight_fraction_leaf = min_weight_fraction_leaf + self.bootstrap = bootstrap self.sparse_output = sparse_output self.n_jobs = n_jobs self.random_state = random_state @@ -32,6 +33,8 @@ def fit(self, X, Y=None): self.max_leaf_nodes = None else: self.max_leaf_nodes = int(self.max_leaf_nodes) + if self.bootstrap in ['true', 'false']: + self.bootstrap = bool(self.bootstrap) self.preprocessor = sklearn.ensemble.RandomTreesEmbedding( n_estimators=self.n_estimators, @@ -80,8 +83,9 @@ def get_hyperparameter_search_space(dataset_properties=None): min_weight_fraction_leaf = Constant('min_weight_fraction_leaf', 1.0) max_leaf_nodes = UnParametrizedHyperparameter(name="max_leaf_nodes", value="None") + bootstrap = CategoricalHyperparameter('bootstrap', ['True', 'False']) cs = ConfigurationSpace() cs.add_hyperparameters([n_estimators, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, - max_leaf_nodes]) + max_leaf_nodes, bootstrap]) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py index 1c57c556e9..e34254b2c1 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py @@ -24,8 +24,10 @@ def __init__(self, percentile, score_func="chi2", random_state=None): self.score_func = sklearn.feature_selection.chi2 elif score_func == "f_classif": self.score_func = sklearn.feature_selection.f_classif + elif score_func == "mutual_info": + self.score_func = sklearn.feature_selection.mutual_info_classif else: - raise ValueError("score_func must be in ('chi2, 'f_classif'), " + raise ValueError("score_func must be in ('chi2, 'f_classif', 'mutual_info'), " "but is: %s" % score_func) def fit(self, X, y): @@ -92,7 +94,10 @@ def get_hyperparameter_search_space(dataset_properties=None): name="percentile", lower=1, upper=99, default_value=50) score_func = CategoricalHyperparameter( - name="score_func", choices=["chi2", "f_classif"], default_value="chi2") + name="score_func", + choices=["chi2", "f_classif", "mutual_info"], + default_value="chi2" + ) if dataset_properties is not None: # Chi2 can handle sparse data, so we respect this if 'sparse' in dataset_properties and dataset_properties['sparse']: diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py index c68ea6d306..734a6c1705 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py @@ -1,5 +1,6 @@ from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.hyperparameters import UniformFloatHyperparameter, UnParametrizedHyperparameter +from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ + CategoricalHyperparameter from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.components.feature_preprocessing.select_percentile import SelectPercentileBase @@ -22,6 +23,8 @@ def __init__(self, percentile, score_func="f_classif", random_state=None): self.percentile = int(float(percentile)) if score_func == "f_regression": self.score_func = sklearn.feature_selection.f_regression + elif score_func == "mutual_info": + self.score_func = sklearn.feature_selection.mutual_info_regression else: raise ValueError("Don't know this scoring function: %s" % score_func) @@ -42,8 +45,8 @@ def get_hyperparameter_search_space(dataset_properties=None): percentile = UniformFloatHyperparameter( "percentile", lower=1, upper=99, default_value=50) - score_func = UnParametrizedHyperparameter( - name="score_func", value="f_regression") + score_func = CategoricalHyperparameter( + name="score_func", choices=["f_regression", "mutual_info"]) cs = ConfigurationSpace() cs.add_hyperparameters([percentile, score_func]) diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_rates.py b/autosklearn/pipeline/components/feature_preprocessing/select_rates.py index 4549db41ea..2b919abf3f 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_rates.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_rates.py @@ -20,7 +20,7 @@ def __init__(self, alpha, mode='fpr', elif score_func == "f_classif": self.score_func = sklearn.feature_selection.f_classif else: - raise ValueError("score_func must be in ('chi2, 'f_classif'), " + raise ValueError("score_func must be in ('chi2, 'f_classif', 'mutual_info'), " "but is: %s" % score_func) self.mode = mode @@ -97,7 +97,9 @@ def get_hyperparameter_search_space(dataset_properties=None): name="alpha", lower=0.01, upper=0.5, default_value=0.1) score_func = CategoricalHyperparameter( - name="score_func", choices=["chi2", "f_classif"], default_value="chi2") + name="score_func", + choices=["chi2", "f_classif"], + default_value="chi2") if dataset_properties is not None: # Chi2 can handle sparse data, so we respect this if 'sparse' in dataset_properties and dataset_properties['sparse']: diff --git a/autosklearn/pipeline/components/regression/ard_regression.py b/autosklearn/pipeline/components/regression/ard_regression.py index 3b4349e9a8..c764723b81 100644 --- a/autosklearn/pipeline/components/regression/ard_regression.py +++ b/autosklearn/pipeline/components/regression/ard_regression.py @@ -64,7 +64,7 @@ def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_iter = UnParametrizedHyperparameter("n_iter", value=300) tol = UniformFloatHyperparameter("tol", 10 ** -5, 10 ** -1, - default_value=10 ** -4, log=True) + default_value=10 ** -3, log=True) alpha_1 = UniformFloatHyperparameter(name="alpha_1", lower=10 ** -10, upper=10 ** -3, default_value=10 ** -6) alpha_2 = UniformFloatHyperparameter(name="alpha_2", log=True, diff --git a/autosklearn/pipeline/components/regression/decision_tree.py b/autosklearn/pipeline/components/regression/decision_tree.py index 0d57b0f741..77bb3cf0e3 100644 --- a/autosklearn/pipeline/components/regression/decision_tree.py +++ b/autosklearn/pipeline/components/regression/decision_tree.py @@ -74,7 +74,8 @@ def get_properties(dataset_properties=None): def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() - criterion = Constant('criterion', 'mse') + criterion = CategoricalHyperparameter('criterion', + ['mse', 'friedman_mse', 'mae']) splitter = Constant("splitter", "best") max_features = Constant('max_features', 1.0) max_depth = UniformFloatHyperparameter( diff --git a/autosklearn/pipeline/components/regression/extra_trees.py b/autosklearn/pipeline/components/regression/extra_trees.py index f3437885fe..aa010f312e 100644 --- a/autosklearn/pipeline/components/regression/extra_trees.py +++ b/autosklearn/pipeline/components/regression/extra_trees.py @@ -127,7 +127,8 @@ def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_estimators = Constant("n_estimators", 100) - criterion = Constant("criterion", "mse") + criterion = CategoricalHyperparameter("criterion", + ['mse', 'friedman_mse', 'mae']) max_features = UniformFloatHyperparameter( "max_features", 0.5, 5, default_value=1) diff --git a/autosklearn/pipeline/components/regression/libsvm_svr.py b/autosklearn/pipeline/components/regression/libsvm_svr.py index 96a19341c2..da7c18d36b 100644 --- a/autosklearn/pipeline/components/regression/libsvm_svr.py +++ b/autosklearn/pipeline/components/regression/libsvm_svr.py @@ -99,17 +99,19 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): - # Copied from libsvm_c C = UniformFloatHyperparameter( name="C", lower=0.03125, upper=32768, log=True, default_value=1.0) + # Random Guess + epsilon = UniformFloatHyperparameter(name="epsilon", lower=0.001, + upper=1, default_value=0.1, + log=True) kernel = CategoricalHyperparameter( name="kernel", choices=['linear', 'poly', 'rbf', 'sigmoid'], default_value="rbf") degree = UniformIntegerHyperparameter( - name="degree", lower=1, upper=5, default_value=3) + name="degree", lower=2, upper=5, default_value=3) - # Changed the gamma value to 0.0 (is 0.1 for classification) gamma = UniformFloatHyperparameter( name="gamma", lower=3.0517578125e-05, upper=8, log=True, default_value=0.1) @@ -123,9 +125,6 @@ def get_hyperparameter_search_space(dataset_properties=None): name="tol", lower=1e-5, upper=1e-1, default_value=1e-3, log=True) max_iter = UnParametrizedHyperparameter("max_iter", -1) - # Random Guess - epsilon = UniformFloatHyperparameter(name="epsilon", lower=0.001, - upper=1, default_value=0.1, log=True) cs = ConfigurationSpace() cs.add_hyperparameters([C, kernel, degree, gamma, coef0, shrinking, tol, max_iter, epsilon]) diff --git a/autosklearn/pipeline/components/regression/random_forest.py b/autosklearn/pipeline/components/regression/random_forest.py index 28efd64047..1616970fd7 100644 --- a/autosklearn/pipeline/components/regression/random_forest.py +++ b/autosklearn/pipeline/components/regression/random_forest.py @@ -111,7 +111,8 @@ def get_properties(dataset_properties=None): def get_hyperparameter_search_space(dataset_properties=None): cs = ConfigurationSpace() n_estimators = Constant("n_estimators", 100) - criterion = Constant("criterion", "mse") + criterion = CategoricalHyperparameter("criterion", + ['mse', 'friedman_mse', 'mae']) max_features = UniformFloatHyperparameter( "max_features", 0.5, 5, default_value=1) max_depth = UnParametrizedHyperparameter("max_depth", "None") diff --git a/autosklearn/pipeline/components/regression/ridge_regression.py b/autosklearn/pipeline/components/regression/ridge_regression.py index cb1f86d858..238326dfff 100644 --- a/autosklearn/pipeline/components/regression/ridge_regression.py +++ b/autosklearn/pipeline/components/regression/ridge_regression.py @@ -21,7 +21,7 @@ def fit(self, X, Y): self.estimator = sklearn.linear_model.Ridge(alpha=self.alpha, fit_intercept=self.fit_intercept, tol=self.tol, - copy_X=False, + copy_X=True, normalize=False, random_state=self.random_state) self.estimator.fit(X, Y) @@ -51,6 +51,7 @@ def get_hyperparameter_search_space(dataset_properties=None): alpha = UniformFloatHyperparameter( "alpha", 10 ** -5, 10., log=True, default_value=1.) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") - tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, log=True) + tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, + default_value=1e-3, log=True) cs.add_hyperparameters([alpha, fit_intercept, tol]) return cs diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index e935ae43c7..42bb820f45 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -696,7 +696,7 @@ def test_eval_cv(self): disable_file_output=False, instance=self.dataset_name, metric=accuracy) rval = get_last_result(self.queue) - self.assertAlmostEqual(rval['loss'], 0.040000000000000036) + self.assertAlmostEqual(rval['loss'], 0.06) self.assertEqual(rval['status'], StatusType.SUCCESS) self.assertNotIn('bac_metric', rval['additional_run_info']) @@ -710,19 +710,19 @@ def test_eval_cv_all_loss_functions(self): metric=accuracy) rval = get_last_result(self.queue) - fixture = {'accuracy': 0.04, - 'balanced_accuracy': 0.042002688172, - 'f1_macro': 0.0423387096774, - 'f1_micro': 0.04, - 'f1_weighted': 0.040020161290, - 'log_loss': 1.1170492260716856, - 'pac_score': 0.16594165340740463, - 'precision_macro': 0.0414141414141, - 'precision_micro': 0.04, - 'precision_weighted': 0.0388484848485, - 'recall_macro': 0.042002688172, - 'recall_micro': 0.04, - 'recall_weighted': 0.04, + fixture = {'accuracy': 0.06, + 'balanced_accuracy': 0.063508064516129004, + 'f1_macro': 0.063508064516129004, + 'f1_micro': 0.06, + 'f1_weighted': 0.06, + 'log_loss': 1.4434709423780441, + 'pac_score': 0.35696863167504633, + 'precision_macro': 0.063508064516129004, + 'precision_micro': 0.06, + 'precision_weighted': 0.06, + 'recall_macro': 0.063508064516129004, + 'recall_micro': 0.06, + 'recall_weighted': 0.06, 'num_run': 1} additional_run_info = rval['additional_run_info'] @@ -732,7 +732,7 @@ def test_eval_cv_all_loss_functions(self): self.assertEqual(len(additional_run_info), len(fixture) + 1, msg=sorted(additional_run_info.items())) - self.assertAlmostEqual(rval['loss'], 0.040000000000000036) + self.assertAlmostEqual(rval['loss'], 0.06) self.assertEqual(rval['status'], StatusType.SUCCESS) # def test_eval_cv_on_subset(self): @@ -748,8 +748,8 @@ def test_eval_cv_all_loss_functions(self): def test_eval_partial_cv(self): cv = StratifiedKFold(shuffle=True, random_state=1, n_splits=5) - results = [0.045454545454545414, - 0.095238095238095233, + results = [0.090909090909090939, + 0.047619047619047672, 0.052631578947368474, 0.10526315789473684, 0.0] diff --git a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv index c3c5d45b9e..fb61844743 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv +++ b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv @@ -1,25 +1,25 @@ 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+24,none,adaboost,SAMME,0.723185509,5,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0011288364,True,extra_trees_preproc_for_classification,True,gini,None,0.6953159413,5,8,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/test/test_pipeline/components/classification/test_base.py b/test/test_pipeline/components/classification/test_base.py index 3c0f9cddc2..365fcdcab9 100644 --- a/test/test_pipeline/components/classification/test_base.py +++ b/test/test_pipeline/components/classification/test_base.py @@ -115,8 +115,8 @@ def test_default_digits_multilabel(self): dataset='digits', make_multilabel=True) self.assertAlmostEqual(self.res["default_digits_multilabel"], - sklearn.metrics.average_precision_score( - targets, predictions), + sklearn.metrics.precision_score( + targets, predictions, average='macro'), places=self.res.get( "default_digits_multilabel_places", 7)) @@ -130,8 +130,8 @@ def test_default_digits_multilabel_predict_proba(self): make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual(self.res["default_digits_multilabel_proba"], - sklearn.metrics.average_precision_score( - targets, predictions), + sklearn.metrics.roc_auc_score( + targets, predictions, average='macro'), places=self.res.get( "default_digits_multilabel_proba_places", 7)) diff --git a/test/test_pipeline/components/classification/test_bernoulli_nb.py b/test/test_pipeline/components/classification/test_bernoulli_nb.py index b86604b1ee..dd6f13cf83 100644 --- a/test/test_pipeline/components/classification/test_bernoulli_nb.py +++ b/test/test_pipeline/components/classification/test_bernoulli_nb.py @@ -18,8 +18,8 @@ class BernoulliNBComponentTest(BaseClassificationComponentTest): res["default_digits"] = 0.81238615664845171 res["default_digits_iterative"] = 0.81238615664845171 res["default_digits_binary"] = 0.99392835458409234 - res["default_digits_multilabel"] = 0.73112394623587451 - res["default_digits_multilabel_proba"] = 0.66666666666666663 + res["default_digits_multilabel"] = 0.67651391068802913 + res["default_digits_multilabel_proba"] = 0.5 sk_mod = sklearn.naive_bayes.BernoulliNB module = BernoulliNB \ No newline at end of file diff --git a/test/test_pipeline/components/classification/test_decision_tree.py b/test/test_pipeline/components/classification/test_decision_tree.py index 0a1c9cab22..281754d0c8 100644 --- a/test/test_pipeline/components/classification/test_decision_tree.py +++ b/test/test_pipeline/components/classification/test_decision_tree.py @@ -18,8 +18,8 @@ class DecisionTreetComponentTest(BaseClassificationComponentTest): res["default_digits"] = 0.15057680631451123 res["default_digits_iterative"] = -1 res["default_digits_binary"] = 0.92167577413479052 - res["default_digits_multilabel"] = 0.56326230155706081 - res["default_digits_multilabel_proba"] = 0.83333333333333337 + res["default_digits_multilabel"] = 0.076521739130434779 + res["default_digits_multilabel_proba"] = 0.80426747311827962 sk_mod = sklearn.tree.DecisionTreeClassifier module = DecisionTree \ No newline at end of file diff --git a/test/test_pipeline/components/classification/test_extra_trees.py b/test/test_pipeline/components/classification/test_extra_trees.py index f878fb9607..72e83cd08b 100644 --- a/test/test_pipeline/components/classification/test_extra_trees.py +++ b/test/test_pipeline/components/classification/test_extra_trees.py @@ -18,8 +18,8 @@ class ExtraTreesComponentTest(BaseClassificationComponentTest): res["default_digits"] = 0.8986035215543412 res["default_digits_iterative"] = 0.81785063752276865 res["default_digits_binary"] = 0.99392835458409234 - res["default_digits_multilabel"] = 0.82229416703109792 - res["default_digits_multilabel_proba"] = 0.99401797442008899 + res["default_digits_multilabel"] = 0.99746415200960636 + res["default_digits_multilabel_proba"] = 0.99628313054140716 sk_mod = sklearn.ensemble.ExtraTreesClassifier module = ExtraTreesClassifier \ No newline at end of file diff --git a/test/test_pipeline/components/classification/test_gaussian_nb.py b/test/test_pipeline/components/classification/test_gaussian_nb.py index 4010c04bc3..950c2c3636 100644 --- a/test/test_pipeline/components/classification/test_gaussian_nb.py +++ b/test/test_pipeline/components/classification/test_gaussian_nb.py @@ -18,8 +18,8 @@ class GaussianNBComponentTest(BaseClassificationComponentTest): res["default_digits"] = 0.80692167577413476 res["default_digits_iterative"] = 0.80692167577413476 res["default_digits_binary"] = 0.98664238008500305 - res["default_digits_multilabel"] = 0.71507312748717466 - res["default_digits_multilabel_proba"] = 0.98533237262174234 + res["default_digits_multilabel"] = 0.54135471896765841 + res["default_digits_multilabel_proba"] = 0.99028976450984096 sk_mod = sklearn.naive_bayes.GaussianNB module = GaussianNB diff --git a/test/test_pipeline/components/classification/test_k_nearest_neighbor.py b/test/test_pipeline/components/classification/test_k_nearest_neighbor.py index 1348370e82..9b1730e4d7 100644 --- a/test/test_pipeline/components/classification/test_k_nearest_neighbor.py +++ b/test/test_pipeline/components/classification/test_k_nearest_neighbor.py @@ -18,8 +18,8 @@ class KNearestNeighborsComponentTest(BaseClassificationComponentTest): res["default_digits"] = 0.93321190042501523 res["default_digits_iterative"] = -1 res["default_digits_binary"] = 0.99574984820886459 - res["default_digits_multilabel"] = 0.93733794389823633 - res["default_digits_multilabel_proba"] = 0.97060428849902536 + res["default_digits_multilabel"] = 0.93433756191199024 + res["default_digits_multilabel_proba"] = 0.9713841334968244 sk_mod = sklearn.neighbors.KNeighborsClassifier module = KNearestNeighborsClassifier \ No newline at end of file diff --git a/test/test_pipeline/components/classification/test_lda.py b/test/test_pipeline/components/classification/test_lda.py index daeb849f97..936efc81b7 100644 --- a/test/test_pipeline/components/classification/test_lda.py +++ b/test/test_pipeline/components/classification/test_lda.py @@ -17,8 +17,8 @@ class LDAComponentTest(BaseClassificationComponentTest): res["default_digits"] = 0.88585306618093507 res["default_digits_iterative"] = -1 res["default_digits_binary"] = 0.9811778992106861 - res["default_digits_multilabel"] = 0.83890327969812117 - res["default_digits_multilabel_proba"] = 0.96639166748245653 + res["default_digits_multilabel"] = 0.82204896441795205 + res["default_digits_multilabel_proba"] = 0.98272829811985163 sk_mod = sklearn.discriminant_analysis.LinearDiscriminantAnalysis module = LDA diff --git a/test/test_pipeline/components/classification/test_liblinear.py b/test/test_pipeline/components/classification/test_liblinear.py index ca61b3f44e..c8ea58a046 100644 --- a/test/test_pipeline/components/classification/test_liblinear.py +++ b/test/test_pipeline/components/classification/test_liblinear.py @@ -19,7 +19,7 @@ class LibLinearComponentTest(BaseClassificationComponentTest): res['default_digits_places'] = 2 res["default_digits_iterative"] = -1 res["default_digits_binary"] = 0.98907103825136611 - res["default_digits_multilabel"] = 0.89539354612444322 + res["default_digits_multilabel"] = 0.89889188078944637 res["default_digits_multilabel_proba"] = 0.99999999999999989 sk_mod = sklearn.svm.LinearSVC diff --git a/test/test_pipeline/components/classification/test_libsvm_svc.py b/test/test_pipeline/components/classification/test_libsvm_svc.py index 0c77ce4888..8ae1d3603d 100644 --- a/test/test_pipeline/components/classification/test_libsvm_svc.py +++ b/test/test_pipeline/components/classification/test_libsvm_svc.py @@ -15,7 +15,7 @@ class LibSVM_SVCComponentTest(BaseClassificationComponentTest): res = dict() res["default_iris"] = 0.96 res["default_iris_iterative"] = -1 - res["default_iris_proba"] = 0.32243463915795706 + res["default_iris_proba"] = 0.32242983456012941 res["default_iris_sparse"] = 0.64 res["default_digits"] = 0.096539162112932606 res["default_digits_iterative"] = -1 @@ -39,7 +39,7 @@ def test_default_configuration_predict_proba_individual(self): for i in range(2): predictions, targets = _test_classifier_predict_proba( LibSVM_SVC, sparse=True, dataset='iris') - self.assertAlmostEqual(0.84333924656905945, + self.assertAlmostEqual(0.84336416900751887, sklearn.metrics.log_loss(targets, predictions)) @@ -64,4 +64,4 @@ def test_default_configuration_predict_proba_individual(self): cls = cls.fit(X_train, Y_train) prediction = cls.predict_proba(X_test) self.assertAlmostEqual(sklearn.metrics.log_loss(Y_test, prediction), - 0.69323680119641773) \ No newline at end of file + 0.6932, places=4) \ No newline at end of file diff --git a/test/test_pipeline/components/classification/test_multinomial_nb.py b/test/test_pipeline/components/classification/test_multinomial_nb.py index fae721b1ef..263918ec90 100644 --- a/test/test_pipeline/components/classification/test_multinomial_nb.py +++ b/test/test_pipeline/components/classification/test_multinomial_nb.py @@ -22,8 +22,8 @@ class MultinomialNBComponentTest(BaseClassificationComponentTest): res["default_digits"] = 0.89496053430479661 res["default_digits_iterative"] = 0.89496053430479661 res["default_digits_binary"] = 0.98967820279295693 - res["default_digits_multilabel"] = 0.81239938943608647 - res["default_digits_multilabel_proba"] = 0.76548981051208942 + res["default_digits_multilabel"] = 0.70484946987667163 + res["default_digits_multilabel_proba"] = 0.80324074074074081 sk_mod = sklearn.naive_bayes.MultinomialNB module = MultinomialNB diff --git a/test/test_pipeline/components/classification/test_passive_aggressive.py b/test/test_pipeline/components/classification/test_passive_aggressive.py index 7c3358cf82..e846b17853 100644 --- a/test/test_pipeline/components/classification/test_passive_aggressive.py +++ b/test/test_pipeline/components/classification/test_passive_aggressive.py @@ -18,8 +18,8 @@ class PassiveAggressiveComponentTest(BaseClassificationComponentTest): res["default_digits"] = 0.92046144505160898 res["default_digits_iterative"] = 0.92349726775956287 res["default_digits_binary"] = 0.99574984820886459 - res["default_digits_multilabel"] = 0.8975269956947447 - res["default_digits_multilabel_proba"] = 0.99703892466326138 + res["default_digits_multilabel"] = 0.93337431764214218 + res["default_digits_multilabel_proba"] = 0.99827666163616924 sk_mod = sklearn.linear_model.PassiveAggressiveClassifier module = PassiveAggressive diff --git a/test/test_pipeline/components/classification/test_random_forest.py b/test/test_pipeline/components/classification/test_random_forest.py index 0b4e17356a..3846ba9567 100644 --- a/test/test_pipeline/components/classification/test_random_forest.py +++ b/test/test_pipeline/components/classification/test_random_forest.py @@ -18,8 +18,8 @@ class RandomForestComponentTest(BaseClassificationComponentTest): res["default_digits"] = 0.88585306618093507 res["default_digits_iterative"] = 0.78870673952641168 res["default_digits_binary"] = 0.98664238008500305 - res["default_digits_multilabel"] = 0.79662039405484386 - res["default_digits_multilabel_proba"] = 0.99252721833266977 + res["default_digits_multilabel"] = 0.89737735528772711 + res["default_digits_multilabel_proba"] = 0.99514488225492048 sk_mod = sklearn.ensemble.RandomForestClassifier module = RandomForest diff --git a/test/test_pipeline/components/regression/test_ard_regression.py b/test/test_pipeline/components/regression/test_ard_regression.py index b13319a26d..8ed0e66160 100644 --- a/test/test_pipeline/components/regression/test_ard_regression.py +++ b/test/test_pipeline/components/regression/test_ard_regression.py @@ -10,11 +10,11 @@ class ARDRegressionComponentTest(BaseRegressionComponentTest): __test__ = True res = dict() - res["default_boston"] = 0.70316694175513961 + res["default_boston"] = 0.70316707632060815 res["default_boston_iterative"] = None res["default_boston_sparse"] = None res["default_boston_iterative_sparse"] = None - res["default_diabetes"] = 0.4172236487551515 + res["default_diabetes"] = 0.41720078991053211 res["default_diabetes_iterative"] = None res["default_diabetes_sparse"] = None res["default_diabetes_iterative_sparse"] = None diff --git a/test/test_pipeline/components/regression/test_extra_trees.py b/test/test_pipeline/components/regression/test_extra_trees.py index 1cf3cfb72c..fd4d434b4e 100644 --- a/test/test_pipeline/components/regression/test_extra_trees.py +++ b/test/test_pipeline/components/regression/test_extra_trees.py @@ -10,14 +10,14 @@ class ExtraTreesComponentTest(BaseRegressionComponentTest): __test__ = True res = dict() - res["default_boston"] = 0.77744792837581866 - res["default_boston_iterative"] = 0.77744792837581866 - res["default_boston_sparse"] = 0.32936702992375644 - res["default_boston_iterative_sparse"] = 0.32936702992375644 - res["default_diabetes"] = 0.43258995365114405 - res["default_diabetes_iterative"] = 0.43258995365114405 - res["default_diabetes_sparse"] = 0.28016012771570553 - res["default_diabetes_iterative_sparse"] = 0.28016012771570553 + res["default_boston"] = 0.77812069925853511 + res["default_boston_iterative"] = 0.77812069925853511 + res["default_boston_sparse"] = 0.33175043290947837 + res["default_boston_iterative_sparse"] = 0.33175043290947837 + res["default_diabetes"] = 0.43359660671062761 + res["default_diabetes_iterative"] = 0.43359660671062761 + res["default_diabetes_sparse"] = 0.28040986328860906 + res["default_diabetes_iterative_sparse"] = 0.28040986328860906 sk_mod = sklearn.ensemble.ExtraTreesRegressor diff --git a/test/test_pipeline/components/regression/test_random_forests.py b/test/test_pipeline/components/regression/test_random_forests.py index 1bbd5bd173..6bfbf1ede3 100644 --- a/test/test_pipeline/components/regression/test_random_forests.py +++ b/test/test_pipeline/components/regression/test_random_forests.py @@ -9,14 +9,14 @@ class RandomForestComponentTest(BaseRegressionComponentTest): __test__ = True res = dict() - res["default_boston"] = 0.78435242169129116 - res["default_boston_iterative"] = 0.78435242169129116 - res["default_boston_sparse"] = 0.42374643794982714 - res["default_boston_iterative_sparse"] = 0.42374643794982714 + res["default_boston"] = 0.78511903295069552 + res["default_boston_iterative"] = 0.78511903295069552 + res["default_boston_sparse"] = 0.42511238171276622 + res["default_boston_iterative_sparse"] = 0.42511238171276622 res["default_diabetes"] = 0.41795829411621988 res["default_diabetes_iterative"] = 0.41795829411621988 - res["default_diabetes_sparse"] = 0.24225685933770469 - res["default_diabetes_iterative_sparse"] = 0.24225685933770469 + res["default_diabetes_sparse"] = 0.24346318857157412 + res["default_diabetes_iterative_sparse"] = 0.24346318857157412 sk_mod = sklearn.ensemble.RandomForestRegressor diff --git a/test/test_pipeline/components/regression/test_ridge_regression.py b/test/test_pipeline/components/regression/test_ridge_regression.py index 4d98e299bd..42f734aee8 100644 --- a/test/test_pipeline/components/regression/test_ridge_regression.py +++ b/test/test_pipeline/components/regression/test_ridge_regression.py @@ -9,13 +9,13 @@ class RidgeComponentTest(BaseRegressionComponentTest): __test__ = True res = dict() - res["default_boston"] = 0.70337988453496891 + res["default_boston"] = 0.70337994943813897 res["default_boston_iterative"] = None - res["default_boston_sparse"] = 0.11243478302989141 + res["default_boston_sparse"] = 0.1086535440527433 res["default_boston_iterative_sparse"] = None res["default_diabetes"] = 0.32614416980439365 res["default_diabetes_iterative"] = None - res["default_diabetes_sparse"] = 0.12989713186102791 + res["default_diabetes_sparse"] = 0.1298840412368325 res["default_diabetes_iterative_sparse"] = None sk_mod = sklearn.linear_model.Ridge diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 5b2fa7c844..391b357bd7 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -345,14 +345,14 @@ def test_get_hyperparameter_search_space(self): conditions = cs.get_conditions() self.assertEqual(len(cs.get_hyperparameter( - 'rescaling:__choice__').choices), 4) + 'rescaling:__choice__').choices), 6) self.assertEqual(len(cs.get_hyperparameter( 'classifier:__choice__').choices), 15) self.assertEqual(len(cs.get_hyperparameter( 'preprocessor:__choice__').choices), 13) hyperparameters = cs.get_hyperparameters() - self.assertEqual(141, len(hyperparameters)) + self.assertEqual(147, len(hyperparameters)) #for hp in sorted([str(h) for h in hyperparameters]): # print hp @@ -543,18 +543,6 @@ def test_predict_proba_batched_sparse(self): self.assertEqual(84, cls_predict.call_count) assert_array_almost_equal(prediction_, prediction) - @unittest.skip("test_check_random_state Not yet Implemented") - def test_check_random_state(self): - raise NotImplementedError() - - @unittest.skip("test_validate_input_X Not yet Implemented") - def test_validate_input_X(self): - raise NotImplementedError() - - @unittest.skip("test_validate_input_Y Not yet Implemented") - def test_validate_input_Y(self): - raise NotImplementedError() - def test_set_params(self): pass diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index edd84e7f7f..19f1f7c412 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -221,7 +221,7 @@ def test_get_hyperparameter_search_space(self): self.assertIsInstance(cs, ConfigurationSpace) conditions = cs.get_conditions() hyperparameters = cs.get_hyperparameters() - self.assertEqual(130, len(hyperparameters)) + self.assertEqual(135, len(hyperparameters)) self.assertEqual(len(hyperparameters) - 5, len(conditions)) def test_get_hyperparameter_search_space_include_exclude_models(self): From 0f5cf963e29b86ec0a51fc60970212a2defea706 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 24 Oct 2017 17:25:15 +0200 Subject: [PATCH 14/61] FIX minor issues from porting to 0.19 --- autosklearn/automl.py | 159 ++++++++++++------ .../select_percentile_regression.py | 2 +- .../components/regression/extra_trees.py | 10 +- test/test_pipeline/test_regression.py | 5 +- 4 files changed, 110 insertions(+), 66 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 12f52fed22..242f597a2d 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -813,32 +813,79 @@ def configuration_space_created_hook(self, datamanager, configuration_space): return configuration_space -class AutoMLClassifier(AutoML): +class BaseAutoML(AutoML): + """Base class for AutoML objects to hold abstract functions for both + regression and classification.""" + def __init__(self, *args, **kwargs): - self._classes = [] - self._n_classes = [] - self._n_outputs = 0 + self._n_outputs = 1 + super().__init__(*args, **kwargs) + + def _perform_input_checks(self, X, y): + X = self._check_X(X) + y = self._check_y(y) + return X, y + + def _check_X(self, X): + X = sklearn.utils.check_array(X, accept_sparse="csr", + force_all_finite=False) + if scipy.sparse.issparse(X): + X.sort_indices() + return X + + def _check_y(self, y): + y = sklearn.utils.check_array(y, ensure_2d=False) + + y = np.atleast_1d(y) + if y.ndim == 2 and y.shape[1] == 1: + warnings.warn("A column-vector y was passed when a 1d array was" + " expected. Will change shape via np.ravel().", + sklearn.utils.DataConversionWarning, stacklevel=2) + y = np.ravel(y) + return y + + def refit(self, X, y): + X, y = self._perform_input_checks(X, y) + _n_outputs = 1 if len(y.shape) == 1 else y.shape[1] + if self._n_outputs != _n_outputs: + raise ValueError('Number of outputs changed from %d to %d!' % + (self._n_outputs, _n_outputs)) + + return super().refit(X, y) + + def fit_ensemble(self, y, task=None, metric=None, precision='32', + dataset_name=None, ensemble_nbest=None, + ensemble_size=None): + _n_outputs = 1 if len(y.shape) == 1 else y.shape[1] + if self._n_outputs != _n_outputs: + raise ValueError('Number of outputs changed from %d to %d!' % + (self._n_outputs, _n_outputs)) + + return super().fit_ensemble( + y, task=task, metric=metric, precision=precision, + dataset_name=dataset_name, ensemble_nbest=ensemble_nbest, + ensemble_size=ensemble_size + ) + + +class AutoMLClassifier(BaseAutoML): + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) + self._task_mapping = {'multilabel-indicator': MULTILABEL_CLASSIFICATION, + 'multiclass': MULTICLASS_CLASSIFICATION, + 'binary': BINARY_CLASSIFICATION} + def fit(self, X, y, - task=MULTICLASS_CLASSIFICATION, metric=None, + loss=None, feat_type=None, dataset_name=None): - X = sklearn.utils.check_array(X, accept_sparse="csr", - force_all_finite=False) - y = sklearn.utils.check_array(y, ensure_2d=False) - - if scipy.sparse.issparse(X): - X.sort_indices() + X, y = self._perform_input_checks(X, y) y_task = type_of_target(y) - task_mapping = {'multilabel-indicator': MULTILABEL_CLASSIFICATION, - 'multiclass': MULTICLASS_CLASSIFICATION, - 'binary': BINARY_CLASSIFICATION} - - task = task_mapping.get(y_task) + task = self._task_mapping.get(y_task) if task is None: raise ValueError('Cannot work on data of type %s' % y_task) @@ -848,54 +895,48 @@ def fit(self, X, y, else: metric = accuracy - y = self._process_target_classes(y) + y, self._classes, self._n_classes = self._process_target_classes(y) return super().fit(X, y, task, metric, feat_type, dataset_name) def fit_ensemble(self, y, task=None, metric=None, precision='32', dataset_name=None, ensemble_nbest=None, ensemble_size=None): - self._process_target_classes(y) - return super().fit_ensemble(y, task, metric, precision, - dataset_name, + y, _classes, _n_classes = self._process_target_classes(y) + if not hasattr(self, '_classes'): + self._classes = _classes + if not hasattr(self, '_n_classes'): + self._n_classes = _n_classes + + return super().fit_ensemble(y, task, metric, precision, dataset_name, ensemble_nbest, ensemble_size) def _process_target_classes(self, y): - y = np.atleast_1d(y) - if y.ndim == 2 and y.shape[1] == 1: - warnings.warn("A column-vector y was passed when a 1d array was" - " expected. Please change the shape of y to " - "(n_samples,), for example using ravel().", - sklearn.utils.DataConversionWarning, stacklevel=2) - - if y.ndim == 1: - # reshape is necessary to preserve the data contiguity against vs - # [:, np.newaxis] that does not. - y = np.reshape(y, (-1, 1)) - - self._n_outputs = y.shape[1] + y = super()._check_y(y) + self._n_outputs = 1 if len(y.shape) == 1 else y.shape[1] y = np.copy(y) - self._classes = [] - self._n_classes = [] - - for k in range(self._n_outputs): - classes_k, y[:, k] = np.unique(y[:, k], return_inverse=True) - self._classes.append(classes_k) - self._n_classes.append(classes_k.shape[0]) + _classes = [] + _n_classes = [] - self._n_classes = np.array(self._n_classes, dtype=np.int) + if self._n_outputs == 1: + classes_k, y = np.unique(y, return_inverse=True) + _classes.append(classes_k) + _n_classes.append(classes_k.shape[0]) + else: + for k in range(self._n_outputs): + classes_k, y[:, k] = np.unique(y[:, k], return_inverse=True) + _classes.append(classes_k) + _n_classes.append(classes_k.shape[0]) - if y.shape[1] == 1: - y = y.flatten() + self._n_classes = np.array(_n_classes, dtype=np.int) - return y + return y, _classes, _n_classes def predict(self, X, batch_size=None, n_jobs=1): - predicted_probabilities = super().predict( - X, batch_size=batch_size, n_jobs=n_jobs - ) + predicted_probabilities = super().predict(X, batch_size=batch_size, + n_jobs=n_jobs) if self._n_outputs == 1: predicted_indexes = np.argmax(predicted_probabilities, axis=1) @@ -915,16 +956,24 @@ def predict(self, X, batch_size=None, n_jobs=1): return predicted_classes def predict_proba(self, X, batch_size=None, n_jobs=1): - return super().predict(X, batch_size=batch_size, n_jobs=n_jobs) + return self._automl.predict(X, batch_size=batch_size, n_jobs=n_jobs) -class AutoMLRegressor(AutoML): - def fit(self, X, y, - task=MULTICLASS_CLASSIFICATION, - metric=None, - feat_type=None, - dataset_name=None): +class AutoMLRegressor(BaseAutoML): + def fit(self, X, y, metric=None, feat_type=None, dataset_name=None): + X, y = super()._perform_input_checks(X, y) + _n_outputs = 1 if len(y.shape) == 1 else y.shape[1] + if _n_outputs > 1: + raise NotImplementedError( + 'Multi-output regression is not implemented.') if metric is None: metric = r2 - return super().fit(X=X, y=y, task=REGRESSION, metric=metric, + return super().fit(X, y, task=REGRESSION, metric=metric, feat_type=feat_type, dataset_name=dataset_name) + + def fit_ensemble(self, y, task=None, metric=None, precision='32', + dataset_name=None, ensemble_nbest=None, + ensemble_size=None): + y = super()._check_y(y) + return super().fit_ensemble(y, task, metric, precision, dataset_name, + ensemble_nbest, ensemble_size) diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py index 734a6c1705..1244ffdb30 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py @@ -37,7 +37,7 @@ def get_properties(dataset_properties=None): 'handles_multiclass': False, 'handles_multilabel': False, 'is_deterministic': True, - 'input': (DENSE, UNSIGNED_DATA), + 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (INPUT,)} @staticmethod diff --git a/autosklearn/pipeline/components/regression/extra_trees.py b/autosklearn/pipeline/components/regression/extra_trees.py index aa010f312e..ff6ce413e1 100644 --- a/autosklearn/pipeline/components/regression/extra_trees.py +++ b/autosklearn/pipeline/components/regression/extra_trees.py @@ -18,9 +18,9 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, self.n_estimators = int(n_estimators) self.estimator_increment = 10 - if criterion not in ("mse"): - raise ValueError("'criterion' is not in ('mse'): " - "%s" % criterion) + if criterion not in ("mse", "friedman_mse", "mae"): + raise ValueError("'criterion' is not in ('mse', 'friedman_mse', " + "'mae): %s" % criterion) self.criterion = criterion if max_leaf_nodes_or_max_depth == "max_depth": @@ -29,10 +29,6 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, self.max_depth = None else: self.max_depth = int(max_depth) - #if use_max_depth == "True": - # self.max_depth = int(max_depth) - #elif use_max_depth == "False": - # self.max_depth = None else: if max_leaf_nodes == "None" or max_leaf_nodes is None: self.max_leaf_nodes = None diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 19f1f7c412..68309f94c1 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -86,9 +86,8 @@ def test_configurations_signed_data(self): def test_configurations_sparse(self): dataset_properties = {'sparse': True} cs = SimpleRegressionPipeline( - # TODO remove in sklearn 0.18 - dataset_properties=dataset_properties, - exclude={'regressor': 'gaussian_process'}).get_hyperparameter_search_space() + dataset_properties=dataset_properties + ).get_hyperparameter_search_space() self._test_configurations(cs, make_sparse=True, dataset_properties=dataset_properties) From e0f67ebffc45b65d55d5755766e42c59a043503a Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 24 Oct 2017 17:26:07 +0200 Subject: [PATCH 15/61] update list of models --- misc/classifiers.csv | 18 ++++++++++-- misc/create_list_of_potential_models.py | 34 +++++++++++++++++++++ misc/regressors.csv | 8 +++-- misc/transformers.csv | 39 ++++++------------------- 4 files changed, 64 insertions(+), 35 deletions(-) create mode 100644 misc/create_list_of_potential_models.py diff --git a/misc/classifiers.csv b/misc/classifiers.csv index aad2610b00..01a2e6ed64 100644 --- a/misc/classifiers.csv +++ b/misc/classifiers.csv @@ -1,15 +1,17 @@ class,added,comment ,False,Mixin class which adds no functionality except the score function ,, -,False,Outlier detection +,True, +,True, ,False,Please read the module name ,False,This can blow up the configuration space; because we need to define a configured base object. Maybe consider later. ,True, ,False,Scikit-learn source code says: This class should not be used directly ,True, ,True, +,False,Meta-model ,True,Added with decision stumps(up to depth ten) -,True, +,False,not very scalable ,False,Mixin but no full model ,False,Is implemented using LibLinear ,, @@ -21,9 +23,12 @@ class,added,comment ,True, ,False,This classifier is in a test module ,False,This classifier is in a test module +,False,This classifier is in a test module ,False,Is a meta-estimator ,False,Is a meta-estimator ,False,Is a meta-estimator +,False,Is a meta-estimator +,False,Is a meta-estimator ,False,Abstract base class for naive Bayes estimators ,False,Abstract base class for naive Bayes on discrete/categorical data ,True, @@ -32,7 +37,7 @@ class,added,comment ,True, ,False,Has no predict_proba method, method cannot be easily added ,False,Has no predict_proba method, method cannot be easily added -,True, +,False,TODO ,False,semi-supervised learning ,False,semi-supervised learning ,False,semi-supervised learning @@ -40,6 +45,13 @@ class,added,comment ,True, ,False,Equivalent to SVC ,True, +,False,This classifier is in a test module +,False,This classifier is in a test module ,False,This classifier is in a test module ,True, ,FALSE,Extra-trees should only be used within ensemble methods. +,False,This classifier is in a test module +,False,This classifier is in a test module +,False,This classifier is in a test module +,False,This classifier is in a test module +,False,This classifier is in a test module \ No newline at end of file diff --git a/misc/create_list_of_potential_models.py b/misc/create_list_of_potential_models.py new file mode 100644 index 0000000000..8153c639e7 --- /dev/null +++ b/misc/create_list_of_potential_models.py @@ -0,0 +1,34 @@ +import os +import glob +import inspect +import importlib + +import sklearn.base + +files = glob.glob(os.path.join(os.path.dirname(sklearn.__file__), "**/*.py"), + recursive=True) + +def find_all(cls): + found = set() + for file in files: + parts = file.split('/') + parts[-1] = parts[-1].replace('.py', '') + sklearn_dir = parts.index('sklearn') + name = '.'.join(parts[sklearn_dir:]) + module = importlib.import_module(name) + for member in module.__dict__.values(): + if not inspect.isclass(member): + continue + if issubclass(member, cls): + found.add(member) + print('#####') + found = list(found) + found.sort(key=lambda t: str(t)) + for f in found: + print(f) + return found + +#classifiers = find_all(sklearn.base.ClassifierMixin) +#regressors = find_all(sklearn.base.RegressorMixin) +preprocs = find_all(sklearn.base.TransformerMixin) + diff --git a/misc/regressors.csv b/misc/regressors.csv index 9be39cd8bf..ed7718f52b 100644 --- a/misc/regressors.csv +++ b/misc/regressors.csv @@ -2,9 +2,9 @@ class,added,comment ,False,BaseClass ,, ,False,Is a preprocessing method -,False,Is a preprocessing method ,False,Is a preprocessing method ,False,Is a preprocessing method +,False,Is a preprocessing method ,False,See module name ,False,This can blow up the configuration space; because we need to define a configured base object. Maybe consider later. ,True, @@ -12,7 +12,8 @@ class,added,comment ,True, ,True, ,True, -,True,Crashes when getting two similar inputs +,False,Deprecated +,True, ,False,Calibration instead of prediction method ,False,Add ,False,No @@ -26,6 +27,7 @@ class,added,comment ,False,We want to perform CV ourselves ,False,MultiTask ,False,MultiTask +,False,No ,,No ,False,We want to perform CV ourselves ,,No @@ -41,8 +43,10 @@ class,added,comment ,True, ,FALSE,This regressor is inside a test module ,, +,, ,True, ,False,Can crash when there is no neighbour within the radius +,False,TODO ,True, ,False,Mathematical idental to SVR ,True, diff --git a/misc/transformers.csv b/misc/transformers.csv index 422bd67484..dd7aa4fdaa 100644 --- a/misc/transformers.csv +++ b/misc/transformers.csv @@ -3,14 +3,13 @@ class,added,comment ,FALSE,Mixin class for feature agglomeration. ,, ,TRUE,try out -,FALSE,deprecated ,FALSE,We expect this to not work better than PCA ,False,BaseClass ,FALSE,works only if there are known correlated data points -,FALSE,Base class; https://www.stat.washington.edu/research/reports/2000/tr371.pdf ,FALSE,Regression only ,FALSE,Regression only ,FALSE,Regression only +,FALSE,Base class; https://www.stat.washington.edu/research/reports/2000/tr371.pdf ,, ,TRUE,try out ,FALSE, @@ -21,24 +20,15 @@ class,added,comment ,, ,TRUE,try out ,FALSE,Special case of sparse coding -,FALSE,see above +,FALSE,too specific ,TRUE, -,FALSE,same output as above ,FALSE,? ,FALSE,? ,FALSE,should not use according to the scikit-learn docs ,True, -,FALSE,Base class -,TRUE,try out -,FALSE,Only classification so far -,FALSE,Base class -,FALSE,Base class -,FALSE,use ExtraTreesClassifier -,FALSE,Only classification so far -,True, -,FALSE,Base class -,FALSE,use ExtraTreesClassifier -,FALSE,use ExtraTreesClassifier +,FALSE,? +,FALSE,? +,FALSE,? ,FALSE,Similar to 1HotEncoding ,FALSE,Useful when working with strings ,FALSE,no text classification atm @@ -60,28 +50,24 @@ class,added,comment ,TRUE,try out ,True, ,FALSE,very specia case -,FALSE,Special case of GEM -,FALSE,same as LibLinear -,, -,FALSE,same as SGD ,FALSE,Base class ,FALSE,not right now ,FALSE,not right now -,FALSE,not right now -,FALSE,not right now -,FALSE,test class -,FALSE,test class ,FALSE,only look at if clustering helps ,FALSE,only look at if clustering helps ,, ,FALSE,handles only binary input ,FALSE,"Right now, we do not have different feature sources." +,FALSE,? ,FALSE,"Right now, we have no need to binarize data" ,FALSE,"Right now, we have no need to center a kernel" +,FALSE,Maybe? ,TRUE, ,TRUE, ,TRUE, ,TRUE, +,TRUE, +,TRUE, ,TRUE, ,TRUE, ,FALSE,"Right now, we have 1HotEncoding" @@ -90,10 +76,3 @@ class,added,comment ,FALSE,Base class ,FALSE,use kitchen sinks instead ,FALSE,use kitchen sinks -,TRUE, -,FALSE,Is in a test package -,FALSE,Base class -,FALSE,Use forests -,FALSE,Use forests -,FALSE,Use forests -,FALSE,Use forests From c0e183c1932860b96b9d261d0b71b78fbf5d2a87 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 25 Oct 2017 10:01:11 +0200 Subject: [PATCH 16/61] clarify docstring for issue #370 --- autosklearn/estimators.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 5e0db81ee7..761460fce5 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -230,6 +230,9 @@ def fit_ensemble(self, y, task=None, metric=None, precision='32', All parameters are ``None`` by default. If no other value is given, the default values which were set in a call to ``fit()`` are used. + Calling this function is only necessary if ``ensemble_size==0``, for + example when executing *auto-sklearn* in parallel. + Parameters ---------- y : array-like @@ -355,6 +358,9 @@ def fit(self, X, y, dataset_name=None): """Fit *auto-sklearn* to given training set (X, y). + Fit both optimizes the machine learning models and builds an ensemble + out of them. To disable ensembling, set ``ensemble_size==1``. + Parameters ---------- @@ -435,7 +441,10 @@ def fit(self, X, y, metric=None, feat_type=None, dataset_name=None): - """Fit *autosklearn* to given training set (X, y). + """Fit *Auto-sklearn* to given training set (X, y). + + Fit both optimizes the machine learning models and builds an ensemble + out of them. To disable ensembling, set ``ensemble_size==1``. Parameters ---------- From 30e93fa23adde9e9e36fede4552d2ff932497f6c Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 25 Oct 2017 12:48:29 +0200 Subject: [PATCH 17/61] Upgrade pipeline --- autosklearn/pipeline/base.py | 34 +++++++++++++------------- autosklearn/pipeline/classification.py | 18 ++++---------- autosklearn/pipeline/regression.py | 26 +++++--------------- 3 files changed, 28 insertions(+), 50 deletions(-) diff --git a/autosklearn/pipeline/base.py b/autosklearn/pipeline/base.py index 473b714b7e..904ec50b06 100644 --- a/autosklearn/pipeline/base.py +++ b/autosklearn/pipeline/base.py @@ -60,7 +60,7 @@ def __init__(self, config=None, pipeline=None, dataset_properties=None, self.random_state = check_random_state(random_state) super().__init__(steps=self.steps) - def fit(self, X, y, fit_params=None): + def fit(self, X, y, **fit_params): """Fit the selected algorithm to the training data. Parameters @@ -86,35 +86,36 @@ def fit(self, X, y, fit_params=None): NoModelException is raised if fit() is called without specifying a classification algorithm first. """ - X, fit_params = self.fit_transformer(X, y, fit_params=fit_params) + X, fit_params = self.fit_transformer(X, y, **fit_params) self.fit_estimator(X, y, **fit_params) return self def fit_transformer(self, X, y, fit_params=None): - if fit_params is None or not isinstance(fit_params, dict): - fit_params = dict() - else: - fit_params = {key.replace(":", "__"): value for key, value in - fit_params.items()} + self.num_targets = 1 if len(y.shape) == 1 else y.shape[1] + if fit_params is None: + fit_params = {} + fit_params = {key.replace(":", "__"): value for key, value in + fit_params.items()} X, fit_params = self._fit(X, y, **fit_params) + if fit_params is None: + fit_params = {} return X, fit_params def fit_estimator(self, X, y, **fit_params): - if fit_params is None: - fit_params = {} - self.steps[-1][-1].fit(X, y, **fit_params) + fit_params = {key.replace(":", "__"): value for key, value in + fit_params.items()} + self._final_estimator.fit(X, y, **fit_params) return self def iterative_fit(self, X, y, n_iter=1, **fit_params): - if fit_params is None: - fit_params = {} - self.steps[-1][-1].iterative_fit(X, y, n_iter=n_iter, **fit_params) + self._final_estimator.iterative_fit(X, y, n_iter=n_iter, + **fit_params) def estimator_supports_iterative_fit(self): - return self.steps[-1][-1].estimator_supports_iterative_fit() + return self.steps._final_estimator.estimator_supports_iterative_fit() def configuration_fully_fitted(self): - return self.steps[-1][-1].configuration_fully_fitted() + return self.steps._final_estimator.configuration_fully_fitted() def predict(self, X, batch_size=None): """Predict the classes using the selected model. @@ -132,10 +133,9 @@ def predict(self, X, batch_size=None): ------- array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes) Returns the predicted values""" - # TODO check if fit() was called before... if batch_size is None: - return super(BasePipeline, self).predict(X).astype(self._output_dtype) + return super().predict(X).astype(self._output_dtype) else: if not isinstance(batch_size, int): raise ValueError("Argument 'batch_size' must be of type int, " diff --git a/autosklearn/pipeline/classification.py b/autosklearn/pipeline/classification.py index 549dc146e7..b99ff2fbff 100644 --- a/autosklearn/pipeline/classification.py +++ b/autosklearn/pipeline/classification.py @@ -78,12 +78,11 @@ def __init__(self, config=None, pipeline=None, dataset_properties=None, include=None, exclude=None, random_state=None, init_params=None): self._output_dtype = np.int32 - super(SimpleClassificationPipeline, self).__init__( + super().__init__( config, pipeline, dataset_properties, include, exclude, random_state, init_params) def fit_transformer(self, X, y, fit_params=None): - self.num_targets = 1 if len(y.shape) == 1 else y.shape[1] if fit_params is None: fit_params = {} @@ -100,7 +99,7 @@ def fit_transformer(self, X, y, fit_params=None): if _fit_params is not None: fit_params.update(_fit_params) - X, fit_params = super(SimpleClassificationPipeline, self).fit_transformer( + X, fit_params = super().fit_transformer( X, y, fit_params=fit_params) return X, fit_params @@ -122,11 +121,7 @@ def predict_proba(self, X, batch_size=None): array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes) """ if batch_size is None: - Xt = X - for name, transform in self.steps[:-1]: - Xt = transform.transform(Xt) - - return self.steps[-1][-1].predict_proba(Xt) + return super().predict_proba(X) else: if not isinstance(batch_size, int): @@ -174,16 +169,13 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, if dataset_properties['target_type'] != 'classification': dataset_properties['target_type'] = 'classification' - pipeline = self.steps cs = self._get_base_search_space( cs=cs, dataset_properties=dataset_properties, - exclude=exclude, include=include, pipeline=pipeline) + exclude=exclude, include=include, pipeline=self.steps) classifiers = cs.get_hyperparameter('classifier:__choice__').choices preprocessors = cs.get_hyperparameter('preprocessor:__choice__').choices - available_classifiers = pipeline[-1][1].get_available_components( - dataset_properties) - available_preprocessors = pipeline[-2][1].get_available_components( + available_classifiers = self._final_estimator.get_available_components( dataset_properties) possible_default_classifier = copy.copy(list( diff --git a/autosklearn/pipeline/regression.py b/autosklearn/pipeline/regression.py index cc532ffa29..628e4c1ce9 100644 --- a/autosklearn/pipeline/regression.py +++ b/autosklearn/pipeline/regression.py @@ -74,37 +74,26 @@ def __init__(self, config=None, pipeline=None, dataset_properties=None, include=None, exclude=None, random_state=None, init_params=None): self._output_dtype = np.float32 - super(SimpleRegressionPipeline, self).__init__( + super().__init__( config=config, pipeline=pipeline, dataset_properties=dataset_properties, include=include, exclude=exclude, random_state=random_state, init_params=init_params) - def fit_transformer(self, X, Y, fit_params=None, init_params=None): - X, fit_params = super(SimpleRegressionPipeline, self).fit_transformer( - X, Y, fit_params=fit_params) - self.num_targets = 1 if len(Y.shape) == 1 else Y.shape[1] - return X, fit_params - - def fit_estimator(self, X, y, fit_params=None): + def fit_estimator(self, X, y, **fit_params): self.y_max_ = np.nanmax(y) self.y_min_ = np.nanmin(y) - if fit_params is None: - fit_params = {} return super(SimpleRegressionPipeline, self).fit_estimator( X, y, **fit_params) - def iterative_fit(self, X, y, fit_params=None, n_iter=1): + def iterative_fit(self, X, y, n_iter=1, **fit_params): self.y_max_ = np.nanmax(y) self.y_min_ = np.nanmin(y) - if fit_params is None: - fit_params = {} return super(SimpleRegressionPipeline, self).iterative_fit( X, y, n_iter=n_iter, **fit_params) def predict(self, X, batch_size=None): - y = super(SimpleRegressionPipeline, self).\ - predict(X, batch_size=batch_size) + y = super().predict(X, batch_size=batch_size) y[y > (2 * self.y_max_)] = 2 * self.y_max_ if self.y_min_ < 0: y[y < (2 * self.y_min_)] = 2 * self.y_min_ @@ -176,16 +165,13 @@ def _get_hyperparameter_search_space(self, include=None, exclude=None, # This dataset is probaby dense dataset_properties['sparse'] = False - pipeline = self.steps cs = self._get_base_search_space( cs=cs, dataset_properties=dataset_properties, - exclude=exclude, include=include, pipeline=pipeline) + exclude=exclude, include=include, pipeline=self.steps) regressors = cs.get_hyperparameter('regressor:__choice__').choices preprocessors = cs.get_hyperparameter('preprocessor:__choice__').choices - available_regressors = pipeline[-1][1].get_available_components( - dataset_properties) - available_preprocessors = pipeline[-2][1].get_available_components( + available_regressors = self._final_estimator.get_available_components( dataset_properties) possible_default_regressor = copy.copy(list( From 6bb01644661a901c67af27751beb5c65cb9c1fd4 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 25 Oct 2017 14:18:29 +0200 Subject: [PATCH 18/61] Fix weird parametrization in GradientBoosting --- .../classification/gradient_boosting.py | 18 +++++++----------- .../components/regression/gradient_boosting.py | 10 +++------- .../components/classification/test_base.py | 4 ++-- .../classification/test_gradient_boosting.py | 12 ++++++------ .../data_preprocessing/test_balancing.py | 2 +- .../regression/test_gradient_boosting.py | 8 ++++---- 6 files changed, 23 insertions(+), 31 deletions(-) diff --git a/autosklearn/pipeline/components/classification/gradient_boosting.py b/autosklearn/pipeline/components/classification/gradient_boosting.py index 2732f4496b..227e6440a8 100644 --- a/autosklearn/pipeline/components/classification/gradient_boosting.py +++ b/autosklearn/pipeline/components/classification/gradient_boosting.py @@ -57,11 +57,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.max_depth = None else: self.max_depth = int(self.max_depth) - num_features = X.shape[1] - max_features = int( - float(self.max_features) * (np.log(num_features) + 1)) - # Use at most half of the features - max_features = max(1, min(int(X.shape[1] / 2), max_features)) + self.max_features = float(self.max_features) if self.max_leaf_nodes == "None" or self.max_leaf_nodes is None: self.max_leaf_nodes = None else: @@ -78,7 +74,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): min_weight_fraction_leaf=self.min_weight_fraction_leaf, max_depth=self.max_depth, criterion=self.criterion, - max_features=max_features, + max_features=self.max_features, max_leaf_nodes=self.max_leaf_nodes, random_state=self.random_state, verbose=self.verbose, @@ -138,16 +134,16 @@ def get_hyperparameter_search_space(dataset_properties=None): name="max_depth", lower=1, upper=10, default_value=3) criterion = CategoricalHyperparameter( 'criterion', ['friedman_mse', 'mse', 'mae'], - default_value='friedman_mse') + default_value='mse') min_samples_split = UniformIntegerHyperparameter( - name="min_samples_split", lower=2, upper=20, default_value=2, log=False) + name="min_samples_split", lower=2, upper=20, default_value=2) min_samples_leaf = UniformIntegerHyperparameter( - name="min_samples_leaf", lower=1, upper=20, default_value=1, log=False) + name="min_samples_leaf", lower=1, upper=20, default_value=1) min_weight_fraction_leaf = UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.) subsample = UniformFloatHyperparameter( - name="subsample", lower=0.01, upper=1.0, default_value=1.0, log=False) + name="subsample", lower=0.01, upper=1.0, default_value=1.0) max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default_value=1) + "max_features", 0.1, 1.0 , default_value=1) max_leaf_nodes = UnParametrizedHyperparameter( name="max_leaf_nodes", value="None") cs.add_hyperparameters([loss, learning_rate, n_estimators, max_depth, diff --git a/autosklearn/pipeline/components/regression/gradient_boosting.py b/autosklearn/pipeline/components/regression/gradient_boosting.py index 5b407c81a5..06c3dc0a14 100644 --- a/autosklearn/pipeline/components/regression/gradient_boosting.py +++ b/autosklearn/pipeline/components/regression/gradient_boosting.py @@ -60,11 +60,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.max_depth = None else: self.max_depth = int(self.max_depth) - num_features = X.shape[1] - max_features = int( - float(self.max_features) * (np.log(num_features) + 1)) - # Use at most half of the features - max_features = max(1, min(int(X.shape[1] / 2), max_features)) + self.max_features = float(self.max_features) if self.max_leaf_nodes == "None" or self.max_leaf_nodes is None: self.max_leaf_nodes = None else: @@ -82,7 +78,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): min_samples_leaf=self.min_samples_leaf, min_weight_fraction_leaf=self.min_weight_fraction_leaf, max_depth=self.max_depth, - max_features=max_features, + max_features=self.max_features, max_leaf_nodes=self.max_leaf_nodes, init=self.init, random_state=self.random_state, @@ -140,7 +136,7 @@ def get_hyperparameter_search_space(dataset_properties=None): subsample = UniformFloatHyperparameter( name="subsample", lower=0.01, upper=1.0, default_value=1.0, log=False) max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default_value=1) + "max_features", 0.1, 1.0, default_value=1) max_leaf_nodes = UnParametrizedHyperparameter( name="max_leaf_nodes", value="None") alpha = UniformFloatHyperparameter( diff --git a/test/test_pipeline/components/classification/test_base.py b/test/test_pipeline/components/classification/test_base.py index 365fcdcab9..49c083aa53 100644 --- a/test/test_pipeline/components/classification/test_base.py +++ b/test/test_pipeline/components/classification/test_base.py @@ -100,8 +100,8 @@ def test_default_digits_iterative_fit(self): _test_classifier_iterative_fit(dataset="digits", classifier=self.module) self.assertAlmostEqual(self.res["default_digits_iterative"], - sklearn.metrics.accuracy_score(predictions, - targets), + sklearn.metrics.accuracy_score(targets, + predictions), places=self.res.get( "default_digits_iterative_places", 7)) diff --git a/test/test_pipeline/components/classification/test_gradient_boosting.py b/test/test_pipeline/components/classification/test_gradient_boosting.py index 528b41d85c..fdb65ff69c 100644 --- a/test/test_pipeline/components/classification/test_gradient_boosting.py +++ b/test/test_pipeline/components/classification/test_gradient_boosting.py @@ -11,13 +11,13 @@ class GradientBoostingComponentTest(BaseClassificationComponentTest): __test__ = True res = dict() - res["default_iris"] = 0.93999999999999995 - res["default_iris_iterative"] = 0.95999999999999996 - res["default_iris_proba"] = 0.36351844058108812 + res["default_iris"] = 0.92 + res["default_iris_iterative"] = 0.92 + res["default_iris_proba"] = 0.48109031836615801 res["default_iris_sparse"] = -1 - res["default_digits"] = 0.87795992714025506 - res["default_digits_iterative"] = 0.78324225865209474 - res["default_digits_binary"] = 0.99089253187613846 + res["default_digits"] = 0.80206435944140864 + res["default_digits_iterative"] = 0.74741955069823918 + res["default_digits_binary"] = 0.98178506375227692 res["default_digits_multilabel"] = -1 res["default_digits_multilabel_proba"] = -1 diff --git a/test/test_pipeline/components/data_preprocessing/test_balancing.py b/test/test_pipeline/components/data_preprocessing/test_balancing.py index 4a43dc07d8..c08d322a8e 100644 --- a/test/test_pipeline/components/data_preprocessing/test_balancing.py +++ b/test/test_pipeline/components/data_preprocessing/test_balancing.py @@ -72,7 +72,7 @@ def test_weighting_effect(self): ('decision_tree', DecisionTree, 0.780, 0.643), ('extra_trees', ExtraTreesClassifier, 0.75, 0.800), ('gradient_boosting', GradientBoostingClassifier, - 0.789, 0.762), + 0.737, 0.684), ('random_forest', RandomForest, 0.75, 0.821), ('libsvm_svc', LibSVM_SVC, 0.769, 0.72), ('liblinear_svc', LibLinear_SVC, 0.762, 0.735), diff --git a/test/test_pipeline/components/regression/test_gradient_boosting.py b/test/test_pipeline/components/regression/test_gradient_boosting.py index a70e5ebb2c..890698f22b 100644 --- a/test/test_pipeline/components/regression/test_gradient_boosting.py +++ b/test/test_pipeline/components/regression/test_gradient_boosting.py @@ -11,12 +11,12 @@ class GradientBoostingComponentTest(BaseRegressionComponentTest): __test__ = True res = dict() - res["default_boston"] = 0.83961954550470863 - res["default_boston_iterative"] = 0.83961954550470863 + res["default_boston"] = 0.83773015801058082 + res["default_boston_iterative"] = 0.83773015801058082 res["default_boston_sparse"] = None res["default_boston_iterative_sparse"] = None - res["default_diabetes"] = 0.37192663934006487 - res["default_diabetes_iterative"] = 0.37192663934006487 + res["default_diabetes"] = 0.33574377250653153 + res["default_diabetes_iterative"] = 0.33574377250653153 res["default_diabetes_sparse"] = None res["default_diabetes_iterative_sparse"] = None From 65e2a412982c51a4dbf102c4c8a1069235b20f0a Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 25 Oct 2017 15:12:19 +0200 Subject: [PATCH 19/61] swith from n_iter to tol (sgd & passive aggressive) --- autosklearn/pipeline/components/base.py | 1 - .../classification/passive_aggressive.py | 21 +++++++------- .../pipeline/components/classification/sgd.py | 21 +++++++------- .../pipeline/components/regression/sgd.py | 29 ++++++++----------- .../classification/test_passive_aggressive.py | 18 ++++++------ .../components/classification/test_sgd.py | 14 ++++----- .../data_preprocessing/test_balancing.py | 6 ++-- .../components/regression/test_base.py | 2 -- .../components/regression/test_sgd.py | 16 +++++----- test/test_pipeline/test_classification.py | 4 +-- test/test_pipeline/test_regression.py | 4 +-- 11 files changed, 61 insertions(+), 75 deletions(-) diff --git a/autosklearn/pipeline/components/base.py b/autosklearn/pipeline/components/base.py index a66e99e61a..612609ac59 100644 --- a/autosklearn/pipeline/components/base.py +++ b/autosklearn/pipeline/components/base.py @@ -363,7 +363,6 @@ def set_hyperparameters(self, configuration, init_params=None): new_params['random_state'] = self.random_state self.new_params = new_params - print(choice) self.choice = self.get_components()[choice](**new_params) return self diff --git a/autosklearn/pipeline/components/classification/passive_aggressive.py b/autosklearn/pipeline/components/classification/passive_aggressive.py index 91b38d207c..26eb90cdab 100644 --- a/autosklearn/pipeline/components/classification/passive_aggressive.py +++ b/autosklearn/pipeline/components/classification/passive_aggressive.py @@ -12,10 +12,10 @@ class PassiveAggressive(AutoSklearnClassificationAlgorithm): - def __init__(self, C, fit_intercept, n_iter, loss, random_state=None): + def __init__(self, C, fit_intercept, tol, loss, random_state=None): self.C = float(C) self.fit_intercept = fit_intercept == 'True' - self.n_iter = int(n_iter) + self.tol = float(tol) self.loss = loss self.random_state = random_state self.estimator = None @@ -35,12 +35,11 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.estimator = None if self.estimator is None: - self._iterations = 0 self.estimator = PassiveAggressiveClassifier( C=self.C, fit_intercept=self.fit_intercept, - n_iter=1, + max_iter=1, loss=self.loss, shuffle=True, random_state=self.random_state, @@ -51,7 +50,7 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): # Fallback for multilabel classification if len(y.shape) > 1 and y.shape[1] > 1: import sklearn.multiclass - self.estimator.n_iter = self.n_iter + self.estimator.max_iter = 50 self.estimator = sklearn.multiclass.OneVsRestClassifier( self.estimator, n_jobs=1) self.estimator.fit(X, y) @@ -59,11 +58,11 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): else: # In the first iteration, there is not yet an intercept - self.estimator.n_iter = n_iter - self.estimator.partial_fit(X, y, classes=np.unique(y)) - if self._iterations >= self.n_iter: + self.estimator.max_iter += n_iter + self.estimator.fit(X, y) + if self.estimator.max_iter >= 50 or \ + self.estimator.max_iter > self.estimator.n_iter_: self.fully_fit_ = True - self._iterations += n_iter return self @@ -107,9 +106,9 @@ def get_hyperparameter_search_space(dataset_properties=None): ["hinge", "squared_hinge"], default_value="hinge") - n_iter = UniformIntegerHyperparameter("n_iter", 5, 1000, default_value=20, + tol = UniformFloatHyperparameter("tol", 1e-4, 1e-1, default_value=1e-3, log=True) cs = ConfigurationSpace() - cs.add_hyperparameters([loss, fit_intercept, n_iter, C]) + cs.add_hyperparameters([loss, fit_intercept, tol, C]) return cs diff --git a/autosklearn/pipeline/components/classification/sgd.py b/autosklearn/pipeline/components/classification/sgd.py index 49755a0d46..966d5c1e8e 100644 --- a/autosklearn/pipeline/components/classification/sgd.py +++ b/autosklearn/pipeline/components/classification/sgd.py @@ -12,14 +12,14 @@ class SGD(AutoSklearnClassificationAlgorithm): - def __init__(self, loss, penalty, alpha, fit_intercept, n_iter, + def __init__(self, loss, penalty, alpha, fit_intercept, tol, learning_rate, l1_ratio=0.15, epsilon=0.1, eta0=0.01, power_t=0.5, average=False, random_state=None): self.loss = loss self.penalty = penalty self.alpha = alpha self.fit_intercept = fit_intercept - self.n_iter = n_iter + self.tol = tol self.learning_rate = learning_rate self.l1_ratio = l1_ratio self.epsilon = epsilon @@ -47,7 +47,6 @@ def iterative_fit(self, X, y, n_iter=1, refit=False, sample_weight=None): self.alpha = float(self.alpha) self.fit_intercept = self.fit_intercept == 'True' - self.n_iter = int(self.n_iter) self.l1_ratio = float(self.l1_ratio) if self.l1_ratio is not None else 0.15 self.epsilon = float(self.epsilon) if self.epsilon is not None else 0.1 self.eta0 = float(self.eta0) @@ -58,7 +57,7 @@ def iterative_fit(self, X, y, n_iter=1, refit=False, sample_weight=None): penalty=self.penalty, alpha=self.alpha, fit_intercept=self.fit_intercept, - n_iter=n_iter, + max_iter=1, learning_rate=self.learning_rate, l1_ratio=self.l1_ratio, epsilon=self.epsilon, @@ -68,12 +67,12 @@ def iterative_fit(self, X, y, n_iter=1, refit=False, sample_weight=None): average=self.average, random_state=self.random_state) else: - self.estimator.n_iter += n_iter + self.estimator.max_iter += n_iter - self.estimator.partial_fit(X, y, classes=np.unique(y), - sample_weight=sample_weight) + self.estimator.fit(X, y, sample_weight=sample_weight) - if self.estimator.n_iter >= self.n_iter: + if self.estimator.max_iter >= 50 or \ + self.estimator.max_iter > self.estimator.n_iter_: self.fully_fit_ = True return self @@ -127,8 +126,8 @@ def get_hyperparameter_search_space(dataset_properties=None): l1_ratio = UniformFloatHyperparameter( "l1_ratio", 1e-9, 1, log=True, default_value=0.15) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") - n_iter = UniformIntegerHyperparameter("n_iter", 5, 1000, log=True, - default_value=20) + tol = UniformFloatHyperparameter("tol", 1e-4, 1e-1, log=True, + default_value=10e-3) epsilon = UniformFloatHyperparameter( "epsilon", 1e-5, 1e-1, default_value=1e-4, log=True) learning_rate = CategoricalHyperparameter( @@ -140,7 +139,7 @@ def get_hyperparameter_search_space(dataset_properties=None): average = CategoricalHyperparameter( "average", ["False", "True"], default_value="False") cs.add_hyperparameters([loss, penalty, alpha, l1_ratio, fit_intercept, - n_iter, epsilon, learning_rate, eta0, power_t, + tol, epsilon, learning_rate, eta0, power_t, average]) # TODO add passive/aggressive here, although not properly documented? diff --git a/autosklearn/pipeline/components/regression/sgd.py b/autosklearn/pipeline/components/regression/sgd.py index c281aedf1f..7008caadd8 100644 --- a/autosklearn/pipeline/components/regression/sgd.py +++ b/autosklearn/pipeline/components/regression/sgd.py @@ -9,14 +9,14 @@ class SGD(AutoSklearnRegressionAlgorithm): - def __init__(self, loss, penalty, alpha, fit_intercept, n_iter, + def __init__(self, loss, penalty, alpha, fit_intercept, tol, learning_rate, l1_ratio=0.15, epsilon=0.1, eta0=0.01, power_t=0.5, average=False, random_state=None): self.loss = loss self.penalty = penalty self.alpha = alpha self.fit_intercept = fit_intercept - self.n_iter = n_iter + self.tol = tol self.learning_rate = learning_rate self.l1_ratio = l1_ratio self.epsilon = epsilon @@ -47,7 +47,7 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.alpha = float(self.alpha) self.fit_intercept = self.fit_intercept == 'True' - self.n_iter = int(self.n_iter) + self.tol = float(self.tol) self.l1_ratio = float( self.l1_ratio) if self.l1_ratio is not None else 0.15 self.epsilon = float( @@ -60,7 +60,7 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): penalty=self.penalty, alpha=self.alpha, fit_intercept=self.fit_intercept, - n_iter=n_iter, + max_iter=1, learning_rate=self.learning_rate, l1_ratio=self.l1_ratio, epsilon=self.epsilon, @@ -73,12 +73,13 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.scaler = sklearn.preprocessing.StandardScaler(copy=True) self.scaler.fit(y.reshape((-1, 1))) else: - self.estimator.n_iter += n_iter + self.estimator.max_iter += n_iter Y_scaled = self.scaler.transform(y.reshape((-1, 1))).ravel() - self.estimator.partial_fit(X, Y_scaled) + self.estimator.fit(X, Y_scaled) - if self.estimator.n_iter >= self.n_iter: + if self.estimator.max_iter >= 50 or \ + self.estimator.max_iter > self.estimator.n_iter_: self.fully_fit_ = True return self @@ -101,11 +102,6 @@ def predict(self, X): def get_properties(dataset_properties=None): return {'shortname': 'SGD Regressor', 'name': 'Stochastic Gradient Descent Regressor', - 'handles_missing_values': False, - 'handles_nominal_values': False, - 'handles_numerical_features': True, - 'prefers_data_scaled': True, - 'prefers_data_normalized': True, 'handles_regression': True, 'handles_classification': False, 'handles_multiclass': False, @@ -114,8 +110,7 @@ def get_properties(dataset_properties=None): 'handles_sparse': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,), - # TODO find out what is best used here! - 'preferred_dtype': None} + } @staticmethod def get_hyperparameter_search_space(dataset_properties=None): @@ -132,8 +127,8 @@ def get_hyperparameter_search_space(dataset_properties=None): "l1_ratio", 1e-9, 1., log=True, default_value=0.15) fit_intercept = UnParametrizedHyperparameter( "fit_intercept", "True") - n_iter = UniformIntegerHyperparameter( - "n_iter", 5, 1000, log=True, default_value=20) + tol = UniformFloatHyperparameter( + "tol", 1e-4, 1e-1, default_value=1e-3, log=True) epsilon = UniformFloatHyperparameter( "epsilon", 1e-5, 1e-1, default_value=1e-4, log=True) learning_rate = CategoricalHyperparameter( @@ -147,7 +142,7 @@ def get_hyperparameter_search_space(dataset_properties=None): "average", ["False", "True"], default_value="False") cs.add_hyperparameters([loss, penalty, alpha, l1_ratio, fit_intercept, - n_iter, epsilon, learning_rate, eta0, + tol, epsilon, learning_rate, eta0, power_t, average]) # TODO add passive/aggressive here, although not properly documented? diff --git a/test/test_pipeline/components/classification/test_passive_aggressive.py b/test/test_pipeline/components/classification/test_passive_aggressive.py index e846b17853..06c2a9c900 100644 --- a/test/test_pipeline/components/classification/test_passive_aggressive.py +++ b/test/test_pipeline/components/classification/test_passive_aggressive.py @@ -11,15 +11,15 @@ class PassiveAggressiveComponentTest(BaseClassificationComponentTest): __test__ = True res = dict() - res["default_iris"] = 0.71999999999999997 - res["default_iris_iterative"] = 0.81999999999999995 - res["default_iris_proba"] = 0.46713079533751622 - res["default_iris_sparse"] = 0.4 - res["default_digits"] = 0.92046144505160898 - res["default_digits_iterative"] = 0.92349726775956287 - res["default_digits_binary"] = 0.99574984820886459 - res["default_digits_multilabel"] = 0.93337431764214218 - res["default_digits_multilabel_proba"] = 0.99827666163616924 + res["default_iris"] = 0.92 + res["default_iris_iterative"] = 0.9 + res["default_iris_proba"] = 0.25869970129843273 + res["default_iris_sparse"] = 0.46 + res["default_digits"] = 0.9174256223436551 + res["default_digits_iterative"] = 0.9174256223436551 + res["default_digits_binary"] = 0.99332119004250152 + res["default_digits_multilabel"] = 0.91507635802108533 + res["default_digits_multilabel_proba"] = 0.99942129629629628 sk_mod = sklearn.linear_model.PassiveAggressiveClassifier module = PassiveAggressive diff --git a/test/test_pipeline/components/classification/test_sgd.py b/test/test_pipeline/components/classification/test_sgd.py index 5b8661f569..6ab2027233 100644 --- a/test/test_pipeline/components/classification/test_sgd.py +++ b/test/test_pipeline/components/classification/test_sgd.py @@ -10,13 +10,13 @@ class SGDComponentTest(BaseClassificationComponentTest): __test__ = True res = dict() - res["default_iris"] = 0.64 - res["default_iris_iterative"] = 0.76 - res["default_iris_proba"] = 12.433959502167845 - res["default_iris_sparse"] = 0.26 - res["default_digits"] = 0.89981785063752273 - res["default_digits_iterative"] = 0.89981785063752273 - res["default_digits_binary"] = 0.9927140255009107 + res["default_iris"] = 0.92 + res["default_iris_iterative"] = 0.68 + res["default_iris_proba"] = 0.32146556612186317 + res["default_iris_sparse"] = 0.41999999999999998 + res["default_digits"] = 0.88767455980570731 + res["default_digits_iterative"] = 0.91317547055251969 + res["default_digits_binary"] = 0.9884638737097754 res["default_digits_multilabel"] = -1 res["default_digits_multilabel_proba"] = -1 diff --git a/test/test_pipeline/components/data_preprocessing/test_balancing.py b/test/test_pipeline/components/data_preprocessing/test_balancing.py index c08d322a8e..f5d16b1e8b 100644 --- a/test/test_pipeline/components/data_preprocessing/test_balancing.py +++ b/test/test_pipeline/components/data_preprocessing/test_balancing.py @@ -76,7 +76,7 @@ def test_weighting_effect(self): ('random_forest', RandomForest, 0.75, 0.821), ('libsvm_svc', LibSVM_SVC, 0.769, 0.72), ('liblinear_svc', LibLinear_SVC, 0.762, 0.735), - ('sgd', SGD, 0.704, 0.667) + ('sgd', SGD, 0.632, 0.708) ]: for strategy, acc in [('none', acc_no_weighting), ('weighting', acc_weighting)]: @@ -121,9 +121,9 @@ def test_weighting_effect(self): for name, pre, acc_no_weighting, acc_weighting in \ [('extra_trees_preproc_for_classification', - ExtraTreesPreprocessorClassification, 0.691, 0.692), + ExtraTreesPreprocessorClassification, 0.818, 0.706), ('liblinear_svc_preprocessor', LibLinear_Preprocessor, - 0.692, 0.590)]: + 0.818, 0.735)]: for strategy, acc in [('none', acc_no_weighting), ('weighting', acc_weighting)]: data_ = copy.copy(data) diff --git a/test/test_pipeline/components/regression/test_base.py b/test/test_pipeline/components/regression/test_base.py index 508ece98cc..1c37d072b6 100644 --- a/test/test_pipeline/components/regression/test_base.py +++ b/test/test_pipeline/components/regression/test_base.py @@ -37,11 +37,9 @@ def test_default_boston(self): else: score = sklearn.metrics.r2_score(targets, predictions) fixture = self.res["default_boston"] - if score < -1e10: score = np.log(-score) fixture = np.log(-fixture) - self.assertAlmostEqual( fixture, score, diff --git a/test/test_pipeline/components/regression/test_sgd.py b/test/test_pipeline/components/regression/test_sgd.py index bacbb61797..d09b13b677 100644 --- a/test/test_pipeline/components/regression/test_sgd.py +++ b/test/test_pipeline/components/regression/test_sgd.py @@ -8,14 +8,14 @@ class SGDComponentTest(BaseRegressionComponentTest): __test__ = True res = dict() - res["default_boston"] = -5.4808512936980714e+31 - res["default_boston_iterative"] = -5.4808512936980714e+31 - res["default_boston_sparse"] = -9.432255366952963e+29 - res["default_boston_iterative_sparse"] = -9.432255366952963e+29 - res["default_diabetes"] = 0.066576586105546731 - res["default_diabetes_iterative"] = 0.066576586105546731 - res["default_diabetes_sparse"] = 0.098980579505685062 - res["default_diabetes_iterative_sparse"] = 0.098980579505685062 + res["default_boston"] = -7.76508935746e+30 + res["default_boston_iterative"] = -7.76508935746e+30 + res["default_boston_sparse"] = -8.4314088748670574e+30 + res["default_boston_iterative_sparse"] = -8.4314088748670574e+30 + res["default_diabetes"] = 0.28217342878579466 + res["default_diabetes_iterative"] = 0.28217342878579466 + res["default_diabetes_sparse"] = 0.099141102939435899 + res["default_diabetes_iterative_sparse"] = 0.099141102939435899 sk_mod = sklearn.linear_model.SGDRegressor diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 391b357bd7..bc634f8b2e 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -251,9 +251,7 @@ def _test_configurations(self, configurations_space, make_sparse=False, config._populate_values() # Restrict configurations which could take too long on travis-ci - restrictions = {'classifier:passive_aggressive:n_iter': 5, - 'classifier:sgd:n_iter': 5, - 'classifier:adaboost:n_estimators': 50, + restrictions = {'classifier:adaboost:n_estimators': 50, 'classifier:adaboost:max_depth': 1, 'preprocessor:kernel_pca:n_components': 10, 'preprocessor:kitchen_sinks:n_components': 50, diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 68309f94c1..2fd4eadf92 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -105,9 +105,7 @@ def _test_configurations(self, configurations_space, make_sparse=False, config._populate_values() # Restrict configurations which could take too long on travis-ci - restrictions = {'regressor:passive_aggressive:n_iter': 5, - 'regressor:sgd:n_iter': 5, - 'regressor:adaboost:n_estimators': 50, + restrictions = {'regressor:adaboost:n_estimators': 50, 'regressor:adaboost:max_depth': 1, 'preprocessor:kernel_pca:n_components': 10, 'preprocessor:kitchen_sinks:n_components': 50, From b942c9b3765a95e4fc1026427b036a2be63cde75 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 25 Oct 2017 15:21:57 +0200 Subject: [PATCH 20/61] Add average argument to passive aggressive --- .../components/classification/passive_aggressive.py | 9 ++++++--- .../feature_preprocessing/random_trees_embedding.py | 4 ++-- test/test_pipeline/test_classification.py | 6 ++++-- 3 files changed, 12 insertions(+), 7 deletions(-) diff --git a/autosklearn/pipeline/components/classification/passive_aggressive.py b/autosklearn/pipeline/components/classification/passive_aggressive.py index 26eb90cdab..9fb17eaa99 100644 --- a/autosklearn/pipeline/components/classification/passive_aggressive.py +++ b/autosklearn/pipeline/components/classification/passive_aggressive.py @@ -12,11 +12,12 @@ class PassiveAggressive(AutoSklearnClassificationAlgorithm): - def __init__(self, C, fit_intercept, tol, loss, random_state=None): + def __init__(self, C, fit_intercept, tol, loss, average, random_state=None): self.C = float(C) self.fit_intercept = fit_intercept == 'True' self.tol = float(tol) self.loss = loss + self.average = average == 'True' self.random_state = random_state self.estimator = None @@ -43,7 +44,8 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): loss=self.loss, shuffle=True, random_state=self.random_state, - warm_start=True + warm_start=True, + average=self.average, ) self.classes_ = np.unique(y.astype(int)) @@ -108,7 +110,8 @@ def get_hyperparameter_search_space(dataset_properties=None): tol = UniformFloatHyperparameter("tol", 1e-4, 1e-1, default_value=1e-3, log=True) + average = CategoricalHyperparameter('average', [False, True]) cs = ConfigurationSpace() - cs.add_hyperparameters([loss, fit_intercept, tol, C]) + cs.add_hyperparameters([loss, fit_intercept, tol, C, average]) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py index 5656f5127e..cd66033dfa 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py +++ b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py @@ -33,8 +33,8 @@ def fit(self, X, Y=None): self.max_leaf_nodes = None else: self.max_leaf_nodes = int(self.max_leaf_nodes) - if self.bootstrap in ['true', 'false']: - self.bootstrap = bool(self.bootstrap) + if self.bootstrap in ['True', 'False']: + self.bootstrap = self.bootstrap == 'True' self.preprocessor = sklearn.ensemble.RandomTreesEmbedding( n_estimators=self.n_estimators, diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index bc634f8b2e..cd436d0aeb 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -251,7 +251,9 @@ def _test_configurations(self, configurations_space, make_sparse=False, config._populate_values() # Restrict configurations which could take too long on travis-ci - restrictions = {'classifier:adaboost:n_estimators': 50, + restrictions = {'classifier:passive_aggressive:n_iter': 5, + 'classifier:sgd:n_iter': 5, + 'classifier:adaboost:n_estimators': 50, 'classifier:adaboost:max_depth': 1, 'preprocessor:kernel_pca:n_components': 10, 'preprocessor:kitchen_sinks:n_components': 50, @@ -350,7 +352,7 @@ def test_get_hyperparameter_search_space(self): 'preprocessor:__choice__').choices), 13) hyperparameters = cs.get_hyperparameters() - self.assertEqual(147, len(hyperparameters)) + self.assertEqual(148, len(hyperparameters)) #for hp in sorted([str(h) for h in hyperparameters]): # print hp From 98736e643f3e31e2eae5b12aa3946d2bf1b31b4f Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 26 Oct 2017 12:15:33 +0200 Subject: [PATCH 21/61] FIX/UPDATE hyperparameters of tree-based models --- .../classification/decision_tree.py | 19 ++++-- .../components/classification/extra_trees.py | 37 +++++----- .../classification/gradient_boosting.py | 10 ++- .../classification/random_forest.py | 9 ++- .../extra_trees_preproc_for_classification.py | 58 ++++++++-------- .../extra_trees_preproc_for_regression.py | 27 +++----- .../components/regression/decision_tree.py | 17 +++-- .../components/regression/extra_trees.py | 68 ++++++------------- .../regression/gradient_boosting.py | 11 ++- .../components/regression/random_forest.py | 22 +++--- .../data_preprocessing/test_balancing.py | 28 ++++---- .../feature_preprocessing/test_pca.py | 7 +- .../components/regression/test_extra_trees.py | 18 ++--- .../regression/test_random_forests.py | 18 ++--- test/test_pipeline/test_classification.py | 2 +- test/test_pipeline/test_regression.py | 4 +- 16 files changed, 173 insertions(+), 182 deletions(-) diff --git a/autosklearn/pipeline/components/classification/decision_tree.py b/autosklearn/pipeline/components/classification/decision_tree.py index e06c6aefa5..930b30ac98 100644 --- a/autosklearn/pipeline/components/classification/decision_tree.py +++ b/autosklearn/pipeline/components/classification/decision_tree.py @@ -12,17 +12,18 @@ class DecisionTree(AutoSklearnClassificationAlgorithm): - def __init__(self, criterion, splitter, max_features, max_depth, + def __init__(self, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, - max_leaf_nodes, class_weight=None, random_state=None): + max_leaf_nodes, min_impurity_decrease, class_weight=None, + random_state=None): self.criterion = criterion - self.splitter = splitter self.max_features = max_features self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.max_leaf_nodes = max_leaf_nodes self.min_weight_fraction_leaf = min_weight_fraction_leaf + self.min_impurity_decrease = min_impurity_decrease self.random_state = random_state self.class_weight = class_weight self.estimator = None @@ -44,6 +45,7 @@ def fit(self, X, y, sample_weight=None): else: self.max_leaf_nodes = int(self.max_leaf_nodes) self.min_weight_fraction_leaf = float(self.min_weight_fraction_leaf) + self.min_impurity_decrease = float(self.min_impurity_decrease) self.estimator = DecisionTreeClassifier( criterion=self.criterion, @@ -51,6 +53,8 @@ def fit(self, X, y, sample_weight=None): min_samples_split=self.min_samples_split, min_samples_leaf=self.min_samples_leaf, max_leaf_nodes=self.max_leaf_nodes, + min_weight_fraction_leaf=self.min_weight_fraction_leaf, + min_impurity_decrease=self.min_impurity_decrease, class_weight=self.class_weight, random_state=self.random_state) self.estimator.fit(X, y, sample_weight=sample_weight) @@ -86,7 +90,6 @@ def get_hyperparameter_search_space(dataset_properties=None): criterion = CategoricalHyperparameter( "criterion", ["gini", "entropy"], default_value="gini") - splitter = Constant("splitter", "best") max_depth = UniformFloatHyperparameter( 'max_depth', 0., 2., default_value=0.5) min_samples_split = UniformIntegerHyperparameter( @@ -94,11 +97,13 @@ def get_hyperparameter_search_space(dataset_properties=None): min_samples_leaf = UniformIntegerHyperparameter( "min_samples_leaf", 1, 20, default_value=1) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0) - max_features = Constant('max_features', 1.0) + max_features = UnParametrizedHyperparameter('max_features', 1.0) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") + min_impurity_decrease = UnParametrizedHyperparameter('min_impurity_decrease', 0.0) - cs.add_hyperparameters([criterion, splitter, max_features, max_depth, + cs.add_hyperparameters([criterion, max_features, max_depth, min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, max_leaf_nodes]) + min_weight_fraction_leaf, max_leaf_nodes, + min_impurity_decrease]) return cs diff --git a/autosklearn/pipeline/components/classification/extra_trees.py b/autosklearn/pipeline/components/classification/extra_trees.py index a24d67c8af..98f6635f7e 100644 --- a/autosklearn/pipeline/components/classification/extra_trees.py +++ b/autosklearn/pipeline/components/classification/extra_trees.py @@ -13,9 +13,8 @@ class ExtraTreesClassifier(AutoSklearnClassificationAlgorithm): def __init__(self, n_estimators, criterion, min_samples_leaf, - min_samples_split, max_features, max_leaf_nodes_or_max_depth="max_depth", - bootstrap=False, max_leaf_nodes=None, max_depth="None", - min_weight_fraction_leaf=0.0, + min_samples_split, max_features, bootstrap, max_leaf_nodes, + max_depth, min_weight_fraction_leaf, min_impurity_decrease, oob_score=False, n_jobs=1, random_state=None, verbose=0, class_weight=None): @@ -26,22 +25,14 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, "%s" % criterion) self.criterion = criterion - if max_leaf_nodes_or_max_depth == "max_depth": + if max_depth == "None" or max_depth is None: + self.max_depth = None + else: + self.max_depth = int(max_depth) + if max_leaf_nodes == "None" or max_leaf_nodes is None: self.max_leaf_nodes = None - if max_depth == "None" or max_depth is None: - self.max_depth = None - else: - self.max_depth = int(max_depth) - #if use_max_depth == "True": - # self.max_depth = int(max_depth) - #elif use_max_depth == "False": - # self.max_depth = None else: - if max_leaf_nodes == "None" or max_leaf_nodes is None: - self.max_leaf_nodes = None - else: - self.max_leaf_nodes = int(max_leaf_nodes) - self.max_depth = None + self.max_leaf_nodes = int(max_leaf_nodes) self.min_samples_leaf = int(min_samples_leaf) self.min_samples_split = int(min_samples_split) @@ -53,6 +44,9 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, elif bootstrap == "False": self.bootstrap = False + self.min_weight_fraction_leaf = float(min_weight_fraction_leaf) + self.min_impurity_decrease = float(min_impurity_decrease) + self.oob_score = oob_score self.n_jobs = int(n_jobs) self.random_state = random_state @@ -87,6 +81,8 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): bootstrap=self.bootstrap, max_features=max_features, max_leaf_nodes=self.max_leaf_nodes, + min_weight_fraction_leaf=self.min_weight_fraction_leaf, + min_impurity_decrease=self.min_impurity_decrease, oob_score=self.oob_score, n_jobs=self.n_jobs, verbose=self.verbose, @@ -145,12 +141,15 @@ def get_hyperparameter_search_space(dataset_properties=None): "min_samples_split", 2, 20, default_value=2) min_samples_leaf = UniformIntegerHyperparameter( "min_samples_leaf", 1, 20, default_value=1) - min_weight_fraction_leaf = Constant('min_weight_fraction_leaf', 0.) + min_weight_fraction_leaf = UnParametrizedHyperparameter('min_weight_fraction_leaf', 0.) + max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") + min_impurity_decrease = UnParametrizedHyperparameter('min_impurity_decrease', 0.0) bootstrap = CategoricalHyperparameter( "bootstrap", ["True", "False"], default_value="False") cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, bootstrap]) + min_weight_fraction_leaf, max_leaf_nodes, + min_impurity_decrease, bootstrap]) return cs diff --git a/autosklearn/pipeline/components/classification/gradient_boosting.py b/autosklearn/pipeline/components/classification/gradient_boosting.py index 227e6440a8..d0b36fcacb 100644 --- a/autosklearn/pipeline/components/classification/gradient_boosting.py +++ b/autosklearn/pipeline/components/classification/gradient_boosting.py @@ -14,7 +14,8 @@ class GradientBoostingClassifier(AutoSklearnClassificationAlgorithm): def __init__(self, loss, learning_rate, n_estimators, subsample, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_depth, criterion, max_features, - max_leaf_nodes, random_state=None, verbose=0): + max_leaf_nodes, min_impurity_decrease, random_state=None, + verbose=0): self.loss = loss self.learning_rate = learning_rate self.n_estimators = n_estimators @@ -26,6 +27,7 @@ def __init__(self, loss, learning_rate, n_estimators, subsample, self.criterion=criterion self.max_features = max_features self.max_leaf_nodes = max_leaf_nodes + self.min_impurity_decrease = min_impurity_decrease self.random_state = random_state self.verbose = verbose self.estimator = None @@ -62,6 +64,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.max_leaf_nodes = None else: self.max_leaf_nodes = int(self.max_leaf_nodes) + self.min_impurity_decrease = float(self.min_impurity_decrease) self.verbose = int(self.verbose) self.estimator = sklearn.ensemble.GradientBoostingClassifier( @@ -146,10 +149,13 @@ def get_hyperparameter_search_space(dataset_properties=None): "max_features", 0.1, 1.0 , default_value=1) max_leaf_nodes = UnParametrizedHyperparameter( name="max_leaf_nodes", value="None") + min_impurity_decrease = UnParametrizedHyperparameter( + name='min_impurity_decrease', value=0.0) cs.add_hyperparameters([loss, learning_rate, n_estimators, max_depth, criterion, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, subsample, - max_features, max_leaf_nodes]) + max_features, max_leaf_nodes, + min_impurity_decrease]) return cs diff --git a/autosklearn/pipeline/components/classification/random_forest.py b/autosklearn/pipeline/components/classification/random_forest.py index c2ee245370..4cd3b50e76 100644 --- a/autosklearn/pipeline/components/classification/random_forest.py +++ b/autosklearn/pipeline/components/classification/random_forest.py @@ -14,7 +14,8 @@ class RandomForest(AutoSklearnClassificationAlgorithm): def __init__(self, n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, bootstrap, max_leaf_nodes, - random_state=None, n_jobs=1, class_weight=None): + min_impurity_decrease, random_state=None, n_jobs=1, + class_weight=None): self.n_estimators = n_estimators self.criterion = criterion self.max_features = max_features @@ -24,6 +25,7 @@ def __init__(self, n_estimators, criterion, max_features, self.min_weight_fraction_leaf = min_weight_fraction_leaf self.bootstrap = bootstrap self.max_leaf_nodes = max_leaf_nodes + self.min_impurity_decrease = min_impurity_decrease self.random_state = random_state self.n_jobs = n_jobs self.class_weight = class_weight @@ -64,6 +66,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.bootstrap = False if self.max_leaf_nodes == "None" or self.max_leaf_nodes is None: self.max_leaf_nodes = None + self.min_impurity_decrease = float(self.min_impurity_decrease) # initial fit of only increment trees self.estimator = RandomForestClassifier( @@ -76,6 +79,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): min_weight_fraction_leaf=self.min_weight_fraction_leaf, bootstrap=self.bootstrap, max_leaf_nodes=self.max_leaf_nodes, + min_impurity_decrease=self.min_impurity_decrease, random_state=self.random_state, n_jobs=self.n_jobs, class_weight=self.class_weight, @@ -132,10 +136,11 @@ def get_hyperparameter_search_space(dataset_properties=None): "min_samples_leaf", 1, 20, default_value=1) min_weight_fraction_leaf = UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") + min_impurity_decrease = UnParametrizedHyperparameter('min_impurity_decrease', 0.0) bootstrap = CategoricalHyperparameter( "bootstrap", ["True", "False"], default_value="True") cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, - bootstrap]) + bootstrap, min_impurity_decrease]) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py index d3111358dd..6eb71eb87d 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py @@ -13,10 +13,8 @@ class ExtraTreesPreprocessorClassification(AutoSklearnPreprocessingAlgorithm): def __init__(self, n_estimators, criterion, min_samples_leaf, - min_samples_split, max_features, - max_leaf_nodes_or_max_depth="max_depth", - bootstrap=False, max_leaf_nodes=None, max_depth="None", - min_weight_fraction_leaf=0.0, + min_samples_split, max_features, bootstrap, max_leaf_nodes, + max_depth, min_weight_fraction_leaf, min_impurity_decrease, oob_score=False, n_jobs=1, random_state=None, verbose=0, class_weight=None): @@ -27,27 +25,21 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, "%s" % criterion) self.criterion = criterion - if max_leaf_nodes_or_max_depth == "max_depth": + if max_depth == "None" or max_depth is None: + self.max_depth = None + else: + self.max_depth = int(max_depth) + if max_leaf_nodes == "None" or max_leaf_nodes is None: self.max_leaf_nodes = None - if max_depth == "None" or max_depth is None: - self.max_depth = None - else: - self.max_depth = int(max_depth) - # if use_max_depth == "True": - # self.max_depth = int(max_depth) - #elif use_max_depth == "False": - # self.max_depth = None else: - if max_leaf_nodes == "None" or max_leaf_nodes is None: - self.max_leaf_nodes = None - else: - self.max_leaf_nodes = int(max_leaf_nodes) - self.max_depth = None + self.max_leaf_nodes = int(max_leaf_nodes) self.min_samples_leaf = int(min_samples_leaf) self.min_samples_split = int(min_samples_split) self.max_features = float(max_features) + self.min_weight_fraction_leaf = min_weight_fraction_leaf + self.min_impurity_decrease = float(min_impurity_decrease) if bootstrap == "True": self.bootstrap = True @@ -71,12 +63,20 @@ def fit(self, X, Y, sample_weight=None): # Use at most half of the features max_features = max(1, min(int(X.shape[1] / 2), max_features)) estimator = ExtraTreesClassifier( - n_estimators=self.n_estimators, criterion=self.criterion, - max_depth=self.max_depth, min_samples_split=self.min_samples_split, - min_samples_leaf=self.min_samples_leaf, bootstrap=self.bootstrap, - max_features=max_features, max_leaf_nodes=self.max_leaf_nodes, - oob_score=self.oob_score, n_jobs=self.n_jobs, verbose=self.verbose, - random_state=self.random_state, class_weight=self.class_weight) + n_estimators=self.n_estimators, + criterion=self.criterion, + max_depth=self.max_depth, + min_samples_split=self.min_samples_split, + min_samples_leaf=self.min_samples_leaf, + bootstrap=self.bootstrap, + max_features=max_features, + max_leaf_nodes=self.max_leaf_nodes, + min_impurity_decrease=self.min_impurity_decrease, + oob_score=self.oob_score, + n_jobs=self.n_jobs, + verbose=self.verbose, + random_state=self.random_state, + class_weight=self.class_weight) estimator.fit(X, Y, sample_weight=sample_weight) self.preprocessor = SelectFromModel(estimator=estimator, threshold='mean', @@ -110,19 +110,23 @@ def get_hyperparameter_search_space(dataset_properties=None): max_features = UniformFloatHyperparameter("max_features", 0.5, 5, default_value=1) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") + max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") min_samples_split = UniformIntegerHyperparameter( "min_samples_split", 2, 20, default_value=2) min_samples_leaf = UniformIntegerHyperparameter( "min_samples_leaf", 1, 20, default_value=1) - min_weight_fraction_leaf = Constant( + min_weight_fraction_leaf = UnParametrizedHyperparameter( 'min_weight_fraction_leaf', 0.) + min_impurity_decrease = UnParametrizedHyperparameter( + 'min_impurity_decrease', 0.) bootstrap = CategoricalHyperparameter( "bootstrap", ["True", "False"], default_value="False") cs.add_hyperparameters([n_estimators, criterion, max_features, - max_depth, min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, bootstrap]) + max_depth, max_leaf_nodes, min_samples_split, + min_samples_leaf, min_weight_fraction_leaf, + min_impurity_decrease, bootstrap]) return cs diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py index 2792911442..6e9dda6b25 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py @@ -14,7 +14,6 @@ class ExtraTreesPreprocessorRegression(AutoSklearnPreprocessingAlgorithm): def __init__(self, n_estimators, criterion, min_samples_leaf, min_samples_split, max_features, - max_leaf_nodes_or_max_depth="max_depth", bootstrap=False, max_leaf_nodes=None, max_depth="None", min_weight_fraction_leaf=0.0, oob_score=False, n_jobs=1, random_state=None, verbose=0): @@ -26,22 +25,14 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, "'mae'): %s" % criterion) self.criterion = criterion - if max_leaf_nodes_or_max_depth == "max_depth": + if max_depth == "None" or max_depth is None: + self.max_depth = None + else: + self.max_depth = int(max_depth) + if max_leaf_nodes == "None" or max_leaf_nodes is None: self.max_leaf_nodes = None - if max_depth == "None" or max_depth is None: - self.max_depth = None - else: - self.max_depth = int(max_depth) - # if use_max_depth == "True": - # self.max_depth = int(max_depth) - #elif use_max_depth == "False": - # self.max_depth = None else: - if max_leaf_nodes == "None" or max_leaf_nodes is None: - self.max_leaf_nodes = None - else: - self.max_leaf_nodes = int(max_leaf_nodes) - self.max_depth = None + self.max_leaf_nodes = int(max_leaf_nodes) self.min_samples_leaf = int(min_samples_leaf) self.min_samples_split = int(min_samples_split) @@ -111,6 +102,7 @@ def get_hyperparameter_search_space(dataset_properties=None): "max_features", 0.5, 5, default_value=1) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") + max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") min_samples_split = UniformIntegerHyperparameter( "min_samples_split", 2, 20, default_value=2) @@ -122,7 +114,8 @@ def get_hyperparameter_search_space(dataset_properties=None): "bootstrap", ["True", "False"], default_value="False") cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, - min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, bootstrap]) + max_leaf_nodes, min_samples_split, + min_samples_leaf, min_weight_fraction_leaf, + bootstrap]) return cs diff --git a/autosklearn/pipeline/components/regression/decision_tree.py b/autosklearn/pipeline/components/regression/decision_tree.py index 77bb3cf0e3..09ca473937 100644 --- a/autosklearn/pipeline/components/regression/decision_tree.py +++ b/autosklearn/pipeline/components/regression/decision_tree.py @@ -11,17 +11,17 @@ class DecisionTree(AutoSklearnRegressionAlgorithm): - def __init__(self, criterion, splitter, max_features, max_depth, + def __init__(self, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, - max_leaf_nodes, random_state=None): + max_leaf_nodes, min_impurity_decrease, random_state=None): self.criterion = criterion - self.splitter = splitter self.max_features = max_features self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.max_leaf_nodes = max_leaf_nodes self.min_weight_fraction_leaf = min_weight_fraction_leaf + self.min_impurity_decrease = min_impurity_decrease self.random_state = random_state self.estimator = None @@ -42,6 +42,7 @@ def fit(self, X, y, sample_weight=None): else: self.max_leaf_nodes = int(self.max_leaf_nodes) self.min_weight_fraction_leaf = float(self.min_weight_fraction_leaf) + self.min_impurity_decrease = float(self.min_impurity_decrease) self.estimator = DecisionTreeRegressor( criterion=self.criterion, @@ -49,6 +50,8 @@ def fit(self, X, y, sample_weight=None): min_samples_split=self.min_samples_split, min_samples_leaf=self.min_samples_leaf, max_leaf_nodes=self.max_leaf_nodes, + min_weight_fraction_leaf=self.min_weight_fraction_leaf, + min_impurity_decrease=self.min_impurity_decrease, random_state=self.random_state) self.estimator.fit(X, y, sample_weight=sample_weight) return self @@ -76,7 +79,6 @@ def get_hyperparameter_search_space(dataset_properties=None): criterion = CategoricalHyperparameter('criterion', ['mse', 'friedman_mse', 'mae']) - splitter = Constant("splitter", "best") max_features = Constant('max_features', 1.0) max_depth = UniformFloatHyperparameter( 'max_depth', 0., 2., default_value=0.5) @@ -86,9 +88,12 @@ def get_hyperparameter_search_space(dataset_properties=None): "min_samples_leaf", 1, 20, default_value=1) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") + min_impurity_decrease = UnParametrizedHyperparameter( + 'min_impurity_decrease', 0.0) - cs.add_hyperparameters([criterion, splitter, max_features, max_depth, + cs.add_hyperparameters([criterion, max_features, max_depth, min_samples_split, min_samples_leaf, - min_weight_fraction_leaf, max_leaf_nodes]) + min_weight_fraction_leaf, max_leaf_nodes, + min_impurity_decrease]) return cs diff --git a/autosklearn/pipeline/components/regression/extra_trees.py b/autosklearn/pipeline/components/regression/extra_trees.py index ff6ce413e1..2b42d86ab1 100644 --- a/autosklearn/pipeline/components/regression/extra_trees.py +++ b/autosklearn/pipeline/components/regression/extra_trees.py @@ -11,10 +11,9 @@ class ExtraTreesRegressor(AutoSklearnRegressionAlgorithm): def __init__(self, n_estimators, criterion, min_samples_leaf, - min_samples_split, max_features, - max_leaf_nodes_or_max_depth="max_depth", - bootstrap=False, max_leaf_nodes=None, max_depth="None", - oob_score=False, n_jobs=1, random_state=None, verbose=0): + min_samples_split, max_features, bootstrap, max_leaf_nodes, + max_depth, min_impurity_decrease, oob_score=False, n_jobs=1, + random_state=None, verbose=0): self.n_estimators = int(n_estimators) self.estimator_increment = 10 @@ -23,23 +22,20 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, "'mae): %s" % criterion) self.criterion = criterion - if max_leaf_nodes_or_max_depth == "max_depth": + if max_depth == "None" or max_depth is None: + self.max_depth = None + else: + self.max_depth = int(max_depth) + if max_leaf_nodes == "None" or max_leaf_nodes is None: self.max_leaf_nodes = None - if max_depth == "None" or max_depth is None: - self.max_depth = None - else: - self.max_depth = int(max_depth) else: - if max_leaf_nodes == "None" or max_leaf_nodes is None: - self.max_leaf_nodes = None - else: - self.max_leaf_nodes = int(max_leaf_nodes) - self.max_depth = None + self.max_leaf_nodes = int(max_leaf_nodes) self.min_samples_leaf = int(min_samples_leaf) self.min_samples_split = int(min_samples_split) self.max_features = float(max_features) + self.min_impurity_decrease = float(min_impurity_decrease) if bootstrap == "True": self.bootstrap = True @@ -66,19 +62,15 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.estimator = None if self.estimator is None: - num_features = X.shape[1] - max_features = int( - float(self.max_features) * (np.log(num_features) + 1)) - # Use at most half of the features - max_features = max(1, min(int(X.shape[1] / 2), max_features)) self.estimator = ETR(n_estimators=n_iter, criterion=self.criterion, max_depth=self.max_depth, min_samples_split=self.min_samples_split, min_samples_leaf=self.min_samples_leaf, bootstrap=self.bootstrap, - max_features=max_features, + max_features=self.max_features, max_leaf_nodes=self.max_leaf_nodes, + min_impurity_decrease=self.min_impurity_decrease, oob_score=self.oob_score, n_jobs=self.n_jobs, verbose=self.verbose, @@ -126,47 +118,25 @@ def get_hyperparameter_search_space(dataset_properties=None): criterion = CategoricalHyperparameter("criterion", ['mse', 'friedman_mse', 'mae']) max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default_value=1) + "max_features", 0.1, 1.0, default_value=1) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") + max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") min_samples_split = UniformIntegerHyperparameter( "min_samples_split", 2, 20, default_value=2) min_samples_leaf = UniformIntegerHyperparameter( "min_samples_leaf", 1, 20, default_value=1) - - # Unparametrized, we use min_samples as regularization - # max_leaf_nodes_or_max_depth = UnParametrizedHyperparameter( - # name="max_leaf_nodes_or_max_depth", value="max_depth") - # CategoricalHyperparameter("max_leaf_nodes_or_max_depth", - # choices=["max_leaf_nodes", "max_depth"], default_value="max_depth") - # min_weight_fraction_leaf = UniformFloatHyperparameter( - # "min_weight_fraction_leaf", 0.0, 0.1) - # max_leaf_nodes = UnParametrizedHyperparameter(name="max_leaf_nodes", - # value="None") + min_impurity_decrease = UnParametrizedHyperparameter( + 'min_impurity_decrease', 0.0 + ) bootstrap = CategoricalHyperparameter( "bootstrap", ["True", "False"], default_value="False") cs.add_hyperparameters([n_estimators, criterion, max_features, - max_depth, min_samples_split, min_samples_leaf, + max_depth, max_leaf_nodes, min_samples_split, + min_samples_leaf, min_impurity_decrease, bootstrap]) - # Conditions - # Not applicable because max_leaf_nodes is no legal value of the parent - #cond_max_leaf_nodes_or_max_depth = \ - # EqualsCondition(child=max_leaf_nodes, - # parent=max_leaf_nodes_or_max_depth, - # value="max_leaf_nodes") - #cond2_max_leaf_nodes_or_max_depth = \ - # EqualsCondition(child=use_max_depth, - # parent=max_leaf_nodes_or_max_depth, - # value="max_depth") - - #cond_max_depth = EqualsCondition(child=max_depth, parent=use_max_depth, - #value="True") - #cs.add_condition(cond_max_leaf_nodes_or_max_depth) - #cs.add_condition(cond2_max_leaf_nodes_or_max_depth) - #cs.add_condition(cond_max_depth) - return cs diff --git a/autosklearn/pipeline/components/regression/gradient_boosting.py b/autosklearn/pipeline/components/regression/gradient_boosting.py index 06c3dc0a14..919b59dd29 100644 --- a/autosklearn/pipeline/components/regression/gradient_boosting.py +++ b/autosklearn/pipeline/components/regression/gradient_boosting.py @@ -14,8 +14,8 @@ class GradientBoosting(AutoSklearnRegressionAlgorithm): def __init__(self, loss, learning_rate, n_estimators, subsample, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_depth, max_features, - max_leaf_nodes, alpha=None, init=None, random_state=None, - verbose=0): + max_leaf_nodes, min_impurity_decrease, alpha=None, init=None, + random_state=None, verbose=0): self.loss = loss self.learning_rate = learning_rate self.n_estimators = n_estimators @@ -26,6 +26,7 @@ def __init__(self, loss, learning_rate, n_estimators, subsample, self.max_depth = max_depth self.max_features = max_features self.max_leaf_nodes = max_leaf_nodes + self.min_impurity_decrease = min_impurity_decrease self.alpha = alpha self.init = init self.random_state = random_state @@ -65,6 +66,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.max_leaf_nodes = None else: self.max_leaf_nodes = int(self.max_leaf_nodes) + self.min_impurity_decrease = float(self.min_impurity_decrease) if self.alpha is not None: self.alpha = float(self.alpha) self.verbose = int(self.verbose) @@ -80,6 +82,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): max_depth=self.max_depth, max_features=self.max_features, max_leaf_nodes=self.max_leaf_nodes, + min_impurity_decrease=self.min_impurity_decrease, init=self.init, random_state=self.random_state, verbose=self.verbose, @@ -139,13 +142,15 @@ def get_hyperparameter_search_space(dataset_properties=None): "max_features", 0.1, 1.0, default_value=1) max_leaf_nodes = UnParametrizedHyperparameter( name="max_leaf_nodes", value="None") + min_impurity_decrease = UnParametrizedHyperparameter( + name='min_impurity_decrease', value=0.0) alpha = UniformFloatHyperparameter( "alpha", lower=0.75, upper=0.99, default_value=0.9) cs.add_hyperparameters([loss, learning_rate, n_estimators, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, subsample, max_features, - max_leaf_nodes, alpha]) + max_leaf_nodes, min_impurity_decrease, alpha]) cs.add_condition(InCondition(alpha, loss, ['huber', 'quantile'])) return cs diff --git a/autosklearn/pipeline/components/regression/random_forest.py b/autosklearn/pipeline/components/regression/random_forest.py index 1616970fd7..0721c82009 100644 --- a/autosklearn/pipeline/components/regression/random_forest.py +++ b/autosklearn/pipeline/components/regression/random_forest.py @@ -13,7 +13,7 @@ class RandomForest(AutoSklearnRegressionAlgorithm): def __init__(self, n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, bootstrap, max_leaf_nodes, - random_state=None, n_jobs=1): + min_impurity_decrease, random_state=None, n_jobs=1): self.n_estimators = n_estimators self.estimator_increment = 10 self.criterion = criterion @@ -24,6 +24,7 @@ def __init__(self, n_estimators, criterion, max_features, self.min_weight_fraction_leaf = min_weight_fraction_leaf self.bootstrap = bootstrap self.max_leaf_nodes = max_leaf_nodes + self.min_impurity_decrease = min_impurity_decrease self.random_state = random_state self.n_jobs = n_jobs self.estimator = None @@ -49,31 +50,26 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.max_depth = int(self.max_depth) self.min_samples_split = int(self.min_samples_split) self.min_samples_leaf = int(self.min_samples_leaf) - if self.max_features not in ("sqrt", "log2", "auto"): - num_features = X.shape[1] - max_features = int( - float(self.max_features) * (np.log(num_features) + 1)) - # Use at most half of the features - max_features = max(1, min(int(X.shape[1] / 2), max_features)) - else: - max_features = self.max_features + self.max_features = float(self.max_features) if self.bootstrap == "True": self.bootstrap = True else: self.bootstrap = False if self.max_leaf_nodes == "None" or self.max_leaf_nodes is None: self.max_leaf_nodes = None + self.min_impurity_decrease = float(self.min_impurity_decrease) self.estimator = RandomForestRegressor( n_estimators=n_iter, criterion=self.criterion, - max_features=max_features, + max_features=self.max_features, max_depth=self.max_depth, min_samples_split=self.min_samples_split, min_samples_leaf=self.min_samples_leaf, min_weight_fraction_leaf=self.min_weight_fraction_leaf, bootstrap=self.bootstrap, max_leaf_nodes=self.max_leaf_nodes, + min_impurity_decrease=self.min_impurity_decrease, random_state=self.random_state, n_jobs=self.n_jobs, warm_start=True) @@ -114,7 +110,7 @@ def get_hyperparameter_search_space(dataset_properties=None): criterion = CategoricalHyperparameter("criterion", ['mse', 'friedman_mse', 'mae']) max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default_value=1) + "max_features", 0.1, 1.0, default_value=1.0) max_depth = UnParametrizedHyperparameter("max_depth", "None") min_samples_split = UniformIntegerHyperparameter( "min_samples_split", 2, 20, default_value=2) @@ -123,12 +119,14 @@ def get_hyperparameter_search_space(dataset_properties=None): min_weight_fraction_leaf = \ UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.) max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") + min_impurity_decrease = UnParametrizedHyperparameter( + 'min_impurity_decrease', 0.0) bootstrap = CategoricalHyperparameter( "bootstrap", ["True", "False"], default_value="True") cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, - bootstrap]) + min_impurity_decrease, bootstrap]) return cs diff --git a/test/test_pipeline/components/data_preprocessing/test_balancing.py b/test/test_pipeline/components/data_preprocessing/test_balancing.py index f5d16b1e8b..979e4227dd 100644 --- a/test/test_pipeline/components/data_preprocessing/test_balancing.py +++ b/test/test_pipeline/components/data_preprocessing/test_balancing.py @@ -67,19 +67,21 @@ def test_weighting_effect(self): n_repeated=2, n_clusters_per_class=2, weights=[0.8, 0.2], random_state=1) - for name, clf, acc_no_weighting, acc_weighting in \ - [('adaboost', AdaboostClassifier, 0.810, 0.735), - ('decision_tree', DecisionTree, 0.780, 0.643), - ('extra_trees', ExtraTreesClassifier, 0.75, 0.800), + for name, clf, acc_no_weighting, acc_weighting, places in \ + [('adaboost', AdaboostClassifier, 0.810, 0.735, 3), + ('decision_tree', DecisionTree, 0.780, 0.643, 3), + ('extra_trees', ExtraTreesClassifier, 0.75, 0.800, 3), ('gradient_boosting', GradientBoostingClassifier, - 0.737, 0.684), - ('random_forest', RandomForest, 0.75, 0.821), - ('libsvm_svc', LibSVM_SVC, 0.769, 0.72), - ('liblinear_svc', LibLinear_SVC, 0.762, 0.735), - ('sgd', SGD, 0.632, 0.708) + 0.737, 0.684, 3), + ('random_forest', RandomForest, 0.75, 0.821, 3), + ('libsvm_svc', LibSVM_SVC, 0.769, 0.72, 3), + ('liblinear_svc', LibLinear_SVC, 0.762, 0.735, 3), + ('sgd', SGD, 0.632, 0.708, 1) ]: - for strategy, acc in [('none', acc_no_weighting), - ('weighting', acc_weighting)]: + for strategy, acc in [ + ('none', acc_no_weighting), + ('weighting', acc_weighting) + ]: # Fit data_ = copy.copy(data) X_train = data_[0][:100] @@ -100,7 +102,7 @@ def test_weighting_effect(self): predictions = predictor.predict(X_test) self.assertAlmostEqual( sklearn.metrics.f1_score(predictions, Y_test), acc, - places=3, msg=(name, strategy)) + places=places, msg=(name, strategy)) # fit_transformer and fit_estimator data_ = copy.copy(data) @@ -117,7 +119,7 @@ def test_weighting_effect(self): predictions = classifier.predict(X_test) self.assertAlmostEqual( sklearn.metrics.f1_score(predictions, Y_test), acc, - places=3) + places=places) for name, pre, acc_no_weighting, acc_weighting in \ [('extra_trees_preproc_for_classification', diff --git a/test/test_pipeline/components/feature_preprocessing/test_pca.py b/test/test_pipeline/components/feature_preprocessing/test_pca.py index 20ec2c2af0..02ab8bdd0e 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_pca.py +++ b/test/test_pipeline/components/feature_preprocessing/test_pca.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np from autosklearn.pipeline.components.feature_preprocessing.pca import PCA @@ -15,8 +13,9 @@ def test_default_configuration(self): self.assertFalse((transformation == original).all()) transformations.append(transformation) if len(transformations) > 1: - self.assertTrue((transformations[-1] == transformations[-2]).all()) + np.testing.assert_allclose(transformations[-1], + transformations[-2], rtol=1e-4) def test_preprocessing_dtype(self): super(PCAComponentTest, self)._test_preprocessing_dtype(PCA, - test_sparse=False) \ No newline at end of file + test_sparse=False) diff --git a/test/test_pipeline/components/regression/test_extra_trees.py b/test/test_pipeline/components/regression/test_extra_trees.py index fd4d434b4e..6b29316377 100644 --- a/test/test_pipeline/components/regression/test_extra_trees.py +++ b/test/test_pipeline/components/regression/test_extra_trees.py @@ -10,15 +10,15 @@ class ExtraTreesComponentTest(BaseRegressionComponentTest): __test__ = True res = dict() - res["default_boston"] = 0.77812069925853511 - res["default_boston_iterative"] = 0.77812069925853511 - res["default_boston_sparse"] = 0.33175043290947837 - res["default_boston_iterative_sparse"] = 0.33175043290947837 - res["default_diabetes"] = 0.43359660671062761 - res["default_diabetes_iterative"] = 0.43359660671062761 - res["default_diabetes_sparse"] = 0.28040986328860906 - res["default_diabetes_iterative_sparse"] = 0.28040986328860906 + res["default_boston"] = 0.84600084385083418 + res["default_boston_iterative"] = 0.84600084385083418 + res["default_boston_sparse"] = 0.4241336980285072 + res["default_boston_iterative_sparse"] = 0.4241336980285072 + res["default_diabetes"] = 0.38869684680884398 + res["default_diabetes_iterative"] = 0.38869684680884398 + res["default_diabetes_sparse"] = 0.25345808769763623 + res["default_diabetes_iterative_sparse"] = 0.25345808769763623 sk_mod = sklearn.ensemble.ExtraTreesRegressor - module = ExtraTreesRegressor \ No newline at end of file + module = ExtraTreesRegressor diff --git a/test/test_pipeline/components/regression/test_random_forests.py b/test/test_pipeline/components/regression/test_random_forests.py index 6bfbf1ede3..24be9a2d44 100644 --- a/test/test_pipeline/components/regression/test_random_forests.py +++ b/test/test_pipeline/components/regression/test_random_forests.py @@ -9,15 +9,15 @@ class RandomForestComponentTest(BaseRegressionComponentTest): __test__ = True res = dict() - res["default_boston"] = 0.78511903295069552 - res["default_boston_iterative"] = 0.78511903295069552 - res["default_boston_sparse"] = 0.42511238171276622 - res["default_boston_iterative_sparse"] = 0.42511238171276622 - res["default_diabetes"] = 0.41795829411621988 - res["default_diabetes_iterative"] = 0.41795829411621988 - res["default_diabetes_sparse"] = 0.24346318857157412 - res["default_diabetes_iterative_sparse"] = 0.24346318857157412 + res["default_boston"] = 0.83555736176073558 + res["default_boston_iterative"] = 0.83555736176073558 + res["default_boston_sparse"] = 0.40733598090972378 + res["default_boston_iterative_sparse"] = 0.40733598090972378 + res["default_diabetes"] = 0.34470637712379781 + res["default_diabetes_iterative"] = 0.34470637712379781 + res["default_diabetes_sparse"] = 0.22910562828064962 + res["default_diabetes_iterative_sparse"] = 0.22910562828064962 sk_mod = sklearn.ensemble.RandomForestRegressor - module = RandomForest \ No newline at end of file + module = RandomForest diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index cd436d0aeb..705566fdc0 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -352,7 +352,7 @@ def test_get_hyperparameter_search_space(self): 'preprocessor:__choice__').choices), 13) hyperparameters = cs.get_hyperparameters() - self.assertEqual(148, len(hyperparameters)) + self.assertEqual(154, len(hyperparameters)) #for hp in sorted([str(h) for h in hyperparameters]): # print hp diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 2fd4eadf92..7fe0fe9bbc 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -193,7 +193,7 @@ def test_default_configuration(self): predictions = auto.predict(copy.deepcopy(X_test)) # The lower the worse r2_score = sklearn.metrics.r2_score(Y_test, predictions) - self.assertAlmostEqual(0.417, r2_score, places=3) + self.assertAlmostEqual(0.343, r2_score, places=3) model_score = auto.score(copy.deepcopy(X_test), Y_test) self.assertAlmostEqual(model_score, r2_score, places=5) @@ -218,7 +218,7 @@ def test_get_hyperparameter_search_space(self): self.assertIsInstance(cs, ConfigurationSpace) conditions = cs.get_conditions() hyperparameters = cs.get_hyperparameters() - self.assertEqual(135, len(hyperparameters)) + self.assertEqual(140, len(hyperparameters)) self.assertEqual(len(hyperparameters) - 5, len(conditions)) def test_get_hyperparameter_search_space_include_exclude_models(self): From 90060f22a1beae0d7e7417d80fcfd15db1a11b13 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 26 Oct 2017 13:47:49 +0200 Subject: [PATCH 22/61] Use correct SMAC version, fix tests --- autosklearn/__init__.py | 4 +- autosklearn/pipeline/base.py | 4 +- autosklearn/smbo.py | 2 +- requirements.txt | 4 +- setup.py | 4 +- .../test_meta_base_data/configurations.csv | 45 +++++++++---------- 6 files changed, 29 insertions(+), 34 deletions(-) diff --git a/autosklearn/__init__.py b/autosklearn/__init__.py index 64ae355ccf..70cc59b116 100644 --- a/autosklearn/__init__.py +++ b/autosklearn/__init__.py @@ -5,9 +5,9 @@ __MANDATORY_PACKAGES__ = ''' numpy>=1.9 -scikit-learn>=0.18.1,<0.19 +scikit-learn>=0.19,<0.20 lockfile>=0.10 -smac>=0.7.1,<0.8 +smac>=0.8,<0.9 pyrfr>=0.6.1,<0.8 ConfigSpace>=0.4.0,<0.5 ''' diff --git a/autosklearn/pipeline/base.py b/autosklearn/pipeline/base.py index 904ec50b06..d6ceeee975 100644 --- a/autosklearn/pipeline/base.py +++ b/autosklearn/pipeline/base.py @@ -112,10 +112,10 @@ def iterative_fit(self, X, y, n_iter=1, **fit_params): **fit_params) def estimator_supports_iterative_fit(self): - return self.steps._final_estimator.estimator_supports_iterative_fit() + return self._final_estimator.estimator_supports_iterative_fit() def configuration_fully_fitted(self): - return self.steps._final_estimator.configuration_fully_fitted() + return self._final_estimator.configuration_fully_fitted() def predict(self, X, batch_size=None): """Predict the classes using the selected model. diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index 315594e64d..0f49bea362 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -674,7 +674,7 @@ def choose_next(self, smac): X_cfg, Y_cfg = smac.solver.rh2EPM.transform(smac.solver.runhistory) - if not smac.solver.runhistory.empty(): + if len(X_cfg) > 0: # Update costs by normalization dataset_minimum = np.min(Y_cfg[:, 0]) Y_cfg[:, 0] = 1 - ((1. - Y_cfg[:, 0]) / diff --git a/requirements.txt b/requirements.txt index 8ddaed0b18..a86c23bc2d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,7 +7,7 @@ Cython numpy>=1.9.0 scipy>=0.14.1 -scikit-learn>=0.18.1,<0.19 +scikit-learn>=0.19,<0.20 lockfile joblib @@ -19,4 +19,4 @@ pandas ConfigSpace>=0.4.0,<0.5 pynisher>=0.4 pyrfr>=0.6.1,<0.8 -smac>=0.7.1,<0.8 +git+https://github.com/automl/SMAC3@development diff --git a/setup.py b/setup.py index 8bd6c91668..359b5bbfbe 100644 --- a/setup.py +++ b/setup.py @@ -18,7 +18,7 @@ "Cython", "numpy>=1.9.0", "scipy>=0.14.1", - "scikit-learn>=0.18.1,<0.19.0", + "scikit-learn>=0.19,<0.20", "lockfile", "joblib", "psutil", @@ -28,7 +28,7 @@ "ConfigSpace>=0.4.0,<0.5", "pynisher>=0.4,<0.5", "pyrfr>=0.6.1,<0.8", - "smac>=0.7.1,<0.8" + "smac>=0.8,<0.9" ] with open("autosklearn/__version__.py") as fh: diff --git a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv index fb61844743..9823ec7e80 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv +++ b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv @@ -1,25 +1,20 @@ 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+16,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1,deviance,3,1,None,0,1,2,0,100,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,extra_trees_preproc_for_classification,False,gini,None,1,None,0,1,2,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +19,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,2.1084303493,None,0,5,6,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0139334889,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1580946925,fwe,chi2,none +20,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30848.1086905961,False,True,1,squared_hinge,ovr,l2,0.0554582945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.2647256282,True,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,105,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.0930229314,None,9,19,0,0,100,,,,,,,,,,,,mean,0.0683328961,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +23,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.4037066292,None,0,11,20,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0004586088,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +24,none,adaboost,SAMME,0.723185509,5,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0011288364,True,extra_trees_preproc_for_classification,True,gini,None,0.6953159413,None,0,5,8,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize From 1f070f018fbbf12d8f90f452d5615d604015c603 Mon Sep 17 00:00:00 2001 From: Jesper van Engelen Date: Thu, 26 Oct 2017 20:12:40 +0200 Subject: [PATCH 23/61] Specify max_features parameter in forest as the exponent of the total number of features, which is then used as the max_features parameter in the forest models --- .../components/classification/extra_trees.py | 13 +++++++------ .../components/classification/random_forest.py | 13 ++++++++----- 2 files changed, 15 insertions(+), 11 deletions(-) diff --git a/autosklearn/pipeline/components/classification/extra_trees.py b/autosklearn/pipeline/components/classification/extra_trees.py index 98f6635f7e..62df3eac38 100644 --- a/autosklearn/pipeline/components/classification/extra_trees.py +++ b/autosklearn/pipeline/components/classification/extra_trees.py @@ -68,11 +68,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.estimator = None if self.estimator is None: - num_features = X.shape[1] - max_features = int( - float(self.max_features) * (np.log(num_features) + 1)) - # Use at most half of the features - max_features = max(1, min(int(X.shape[1] / 2), max_features)) + max_features = int(X.shape[1] ** float(self.max_features)) self.estimator = ETC(n_estimators=n_iter, criterion=self.criterion, max_depth=self.max_depth, @@ -132,8 +128,13 @@ def get_hyperparameter_search_space(dataset_properties=None): n_estimators = Constant("n_estimators", 100) criterion = CategoricalHyperparameter( "criterion", ["gini", "entropy"], default_value="gini") + + # The maximum number of features used in the forest is calculated as m^max_features, where + # m is the total number of features, and max_features is the hyperparameter specified below. + # The default is 0.5, which yields sqrt(m) features as max_features in the estimator. This + # corresponds with Geurts' heuristic. max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default_value=1) + "max_features", 0., 1., default_value=0.5) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") diff --git a/autosklearn/pipeline/components/classification/random_forest.py b/autosklearn/pipeline/components/classification/random_forest.py index 4cd3b50e76..96fdc1f790 100644 --- a/autosklearn/pipeline/components/classification/random_forest.py +++ b/autosklearn/pipeline/components/classification/random_forest.py @@ -54,10 +54,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.min_samples_leaf = int(self.min_samples_leaf) self.min_weight_fraction_leaf = float(self.min_weight_fraction_leaf) if self.max_features not in ("sqrt", "log2", "auto"): - num_features = X.shape[1] - max_features = int(float(self.max_features) * (np.log(num_features) + 1)) - # Use at most half of the features - max_features = max(1, min(int(X.shape[1] / 2), max_features)) + max_features = int(X.shape[1] ** float(self.max_features)) else: max_features = self.max_features if self.bootstrap == "True": @@ -127,8 +124,14 @@ def get_hyperparameter_search_space(dataset_properties=None): n_estimators = Constant("n_estimators", 100) criterion = CategoricalHyperparameter( "criterion", ["gini", "entropy"], default_value="gini") + + # The maximum number of features used in the forest is calculated as m^max_features, where + # m is the total number of features, and max_features is the hyperparameter specified below. + # The default is 0.5, which yields sqrt(m) features as max_features in the estimator. This + # corresponds with Geurts' heuristic. max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default_value=1) + "max_features", 0., 1., default_value=0.5) + max_depth = UnParametrizedHyperparameter("max_depth", "None") min_samples_split = UniformIntegerHyperparameter( "min_samples_split", 2, 20, default_value=2) From 5bf6a3a617fdfeda97ff364e9e38da90c60621ec Mon Sep 17 00:00:00 2001 From: Jesper van Engelen Date: Fri, 27 Oct 2017 13:26:00 +0200 Subject: [PATCH 24/61] Update default result values for forest tests --- .../components/classification/test_extra_trees.py | 8 ++++---- .../components/classification/test_random_forest.py | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/test/test_pipeline/components/classification/test_extra_trees.py b/test/test_pipeline/components/classification/test_extra_trees.py index 72e83cd08b..a07b5ef7fd 100644 --- a/test/test_pipeline/components/classification/test_extra_trees.py +++ b/test/test_pipeline/components/classification/test_extra_trees.py @@ -15,10 +15,10 @@ class ExtraTreesComponentTest(BaseClassificationComponentTest): res["default_iris_iterative"] = 0.93999999999999995 res["default_iris_proba"] = 0.1086791056721286 res["default_iris_sparse"] = 0.71999999999999997 - res["default_digits"] = 0.8986035215543412 - res["default_digits_iterative"] = 0.81785063752276865 - res["default_digits_binary"] = 0.99392835458409234 - res["default_digits_multilabel"] = 0.99746415200960636 + res["default_digits"] = 0.91499696417729204 + res["default_digits_iterative"] = 0.85428051001821492 + res["default_digits_binary"] = 0.99453551912568305 + res["default_digits_multilabel"] = 0.99838668373879624 res["default_digits_multilabel_proba"] = 0.99628313054140716 sk_mod = sklearn.ensemble.ExtraTreesClassifier diff --git a/test/test_pipeline/components/classification/test_random_forest.py b/test/test_pipeline/components/classification/test_random_forest.py index 3846ba9567..47500d7837 100644 --- a/test/test_pipeline/components/classification/test_random_forest.py +++ b/test/test_pipeline/components/classification/test_random_forest.py @@ -15,10 +15,10 @@ class RandomForestComponentTest(BaseClassificationComponentTest): res["default_iris_iterative"] = 0.95999999999999996 res["default_iris_proba"] = 0.12735301219200268 res["default_iris_sparse"] = 0.85999999999999999 - res["default_digits"] = 0.88585306618093507 - res["default_digits_iterative"] = 0.78870673952641168 - res["default_digits_binary"] = 0.98664238008500305 - res["default_digits_multilabel"] = 0.89737735528772711 + res["default_digits"] = 0.88281724347298118 + res["default_digits_iterative"] = 0.77231329690346084 + res["default_digits_binary"] = 0.98907103825136611 + res["default_digits_multilabel"] = 0.99232171389942414 res["default_digits_multilabel_proba"] = 0.99514488225492048 sk_mod = sklearn.ensemble.RandomForestClassifier From 5cc46a250cda6fe8acb298b567ea90940c788038 Mon Sep 17 00:00:00 2001 From: Jesper van Engelen Date: Mon, 6 Nov 2017 21:36:33 +0100 Subject: [PATCH 25/61] Update meta learning test configurations --- .../test_meta_base_data/configurations.csv | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv index 9823ec7e80..46e1d06d89 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv +++ b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv @@ -1,20 +1,20 @@ idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:decision_tree:splitter,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:n_iter,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:n_iter,classifier:sgd:penalty,classifier:sgd:power_t,imputation:strategy,one_hot_encoding:minimum_fraction,one_hot_encoding:use_minimum_fraction,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:gem:N,preprocessor:gem:precond,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampl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1,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1,deviance,3,1,None,0,1,2,0,100,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1,None,1,2,0,0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -4,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.4221013841,None,11,13,0,0,100,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,standardize +2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,1,2,0,0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +4,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,11,13,0,0,100,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,standardize 5,none,adaboost,SAMME.R,0.0714763016,4,218,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.000180734,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1218512909,False,True,1,squared_hinge,ovr,l1,0.0003298504,,,,,,,,,,,,,,,,,,,,,,normalize -6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.4140810967,None,0,3,11,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0149859455,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -8,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.4037066292,None,0,11,20,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0004586088,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5,None,0,3,11,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0149859455,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +8,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5,None,0,11,20,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0004586088,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none 9,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.7693027214,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4170768642,fwe,chi2,normalize -10,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,3.1991078307,None,3,13,0,0,100,,,,,,,,,,,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.7303688903,chi2,,,,minmax +10,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,3,13,0,0,100,,,,,,,,,,,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.7303688903,chi2,,,,minmax 11,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1,deviance,3,1,None,0,1,2,0,100,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,none 12,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41.1755269651,False,True,1,squared_hinge,ovr,l2,0.0096773791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,gem,,,,,,,,,,,,,,,,,,,15,0.0445976731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,3.742296716,None,7,4,0,0,100,,,,,,,,,,,,median,0.0001889749,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -14,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20904.8417042538,False,True,1,squared_hinge,ovr,l2,0.0107702558,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0011021511,True,extra_trees_preproc_for_classification,True,gini,None,3.5016742505,None,0,18,6,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.0930229314,None,9,19,0,0,100,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5610623217,False,True,1,squared_hinge,ovr,l1,5.1665462635671E-05,,,,,,,,,,,,,,,,,,,,,,standardize -16,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1,deviance,3,1,None,0,1,2,0,100,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,extra_trees_preproc_for_classification,False,gini,None,1,None,0,1,2,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -19,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,2.1084303493,None,0,5,6,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0139334889,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1580946925,fwe,chi2,none +13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,7,4,0,0,100,,,,,,,,,,,,median,0.0001889749,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +14,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20904.8417042538,False,True,1,squared_hinge,ovr,l2,0.0107702558,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0011021511,True,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0,18,6,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,9,19,0,0,100,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5610623217,False,True,1,squared_hinge,ovr,l1,5.1665462635671E-05,,,,,,,,,,,,,,,,,,,,,,standardize +16,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1,deviance,3,1,None,0,1,2,0,100,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0,1,2,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +19,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5,None,0,5,6,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0139334889,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1580946925,fwe,chi2,none 20,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30848.1086905961,False,True,1,squared_hinge,ovr,l2,0.0554582945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.2647256282,True,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,105,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.0930229314,None,9,19,0,0,100,,,,,,,,,,,,mean,0.0683328961,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -23,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.4037066292,None,0,11,20,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0004586088,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -24,none,adaboost,SAMME,0.723185509,5,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0011288364,True,extra_trees_preproc_for_classification,True,gini,None,0.6953159413,None,0,5,8,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,9,19,0,0,100,,,,,,,,,,,,mean,0.0683328961,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +23,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5,None,0,11,20,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0004586088,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +24,none,adaboost,SAMME,0.723185509,5,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0011288364,True,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0,5,8,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize From b568e0fcb7d576ccf79b7d9010ae0a2bdf4a4fe9 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 7 Nov 2017 17:39:05 +0100 Subject: [PATCH 26/61] FIX unittest, adapt interfaces --- scripts/run_auto-sklearn_for_metadata_generation.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/scripts/run_auto-sklearn_for_metadata_generation.py b/scripts/run_auto-sklearn_for_metadata_generation.py index fe9a616c64..89005c1069 100644 --- a/scripts/run_auto-sklearn_for_metadata_generation.py +++ b/scripts/run_auto-sklearn_for_metadata_generation.py @@ -69,13 +69,13 @@ automl.fit(X_train, y_train, dataset_name=str(task_id), metric=metric, feat_type=cat) -data = automl._automl._automl._backend.load_datamanager() +data = automl._automl._backend.load_datamanager() # Data manager can't be replaced with save_datamanager, it has to be deleted # first -os.remove(automl._automl._automl._backend._get_datamanager_pickle_filename()) +os.remove(automl._automl._backend._get_datamanager_pickle_filename()) data.data['X_test'] = X_test data.data['Y_test'] = y_test -automl._automl._automl._backend.save_datamanager(data) +automl._automl._backend.save_datamanager(data) trajectory = automl.trajectory_ incumbent_id_to_model = {} @@ -103,7 +103,7 @@ stats.start_timing() # To avoid the output "first run crashed"... stats.ta_runs += 1 - ta = ExecuteTaFuncWithQueue(backend=automl._automl._automl._backend, + ta = ExecuteTaFuncWithQueue(backend=automl._automl._backend, autosklearn_seed=seed, resampling_strategy='test', memory_limit=memory_limit_factor * automl_arguments['ml_memory_limit'], From bfb1054c45ebc5afad94dc32e67f8f894444958f Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 26 Oct 2017 15:32:37 +0200 Subject: [PATCH 27/61] Refactor the building of the SMAC object into its own function --- autosklearn/automl.py | 77 +++--- autosklearn/estimators.py | 39 +-- autosklearn/smbo.py | 435 +++++++++++++------------------ example/example_eips.py | 82 ++++++ example/example_parallel.py | 8 +- example/example_random_search.py | 92 +++++++ 6 files changed, 406 insertions(+), 327 deletions(-) create mode 100644 example/example_eips.py create mode 100644 example/example_random_search.py diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 242f597a2d..052eb80f9b 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -61,7 +61,6 @@ def __init__(self, backend, time_left_for_this_task, per_run_time_limit, - log_dir=None, initial_configurations_via_metalearning=25, ensemble_size=1, ensemble_nbest=1, @@ -76,21 +75,18 @@ def __init__(self, exclude_preprocessors=None, resampling_strategy='holdout-iterative-fit', resampling_strategy_arguments=None, - delete_tmp_folder_after_terminate=False, - delete_output_folder_after_terminate=False, shared_mode=False, precision=32, - max_iter_smac=None, - acquisition_function='EI', disable_evaluator_output=False, - configuration_mode='SMAC'): + get_smac_object_callback=None, + smac_scenario_args=None, + ): super(AutoML, self).__init__() self._backend = backend #self._tmp_dir = tmp_dir #self._output_dir = output_dir self._time_for_task = time_left_for_this_task self._per_run_time_limit = per_run_time_limit - #self._log_dir = log_dir if log_dir is not None else self._tmp_dir self._initial_configurations_via_metalearning = \ initial_configurations_via_metalearning self._ensemble_size = ensemble_size @@ -107,16 +103,11 @@ def __init__(self, self._resampling_strategy = resampling_strategy self._resampling_strategy_arguments = resampling_strategy_arguments \ if resampling_strategy_arguments is not None else {} - self._max_iter_smac = max_iter_smac - #self.delete_tmp_folder_after_terminate = \ - # delete_tmp_folder_after_terminate - #self.delete_output_folder_after_terminate = \ - # delete_output_folder_after_terminate self._shared_mode = shared_mode self.precision = precision - self.acquisition_function = acquisition_function self._disable_evaluator_output = disable_evaluator_output - self._configuration_mode = configuration_mode + self._get_smac_object_callback = get_smac_object_callback + self._smac_scenario_args = smac_scenario_args self._datamanager = None self._dataset_name = None @@ -319,11 +310,6 @@ def _fit(self, datamanager, metric): not 'folds' in self._resampling_strategy_arguments: self._resampling_strategy_arguments['folds'] = 5 - acquisition_functions = ['EI', 'EIPS'] - if self.acquisition_function not in acquisition_functions: - raise ValueError('Illegal acquisition %s: Must be one of %s.' % - (self.acquisition_function, acquisition_functions)) - self._backend._make_internals_directory() if self._keep_models: try: @@ -423,32 +409,33 @@ def _fit(self, datamanager, metric): else: per_run_time_limit = self._per_run_time_limit - _proc_smac = AutoMLSMBO(config_space=self.configuration_space, - dataset_name=self._dataset_name, - backend=self._backend, - total_walltime_limit=time_left_for_smac, - func_eval_time_limit=per_run_time_limit, - memory_limit=self._ml_memory_limit, - data_memory_limit=self._data_memory_limit, - watcher=self._stopwatch, - start_num_run=num_run, - num_metalearning_cfgs=self._initial_configurations_via_metalearning, - config_file=configspace_path, - smac_iters=self._max_iter_smac, - seed=self._seed, - metadata_directory=self._metadata_directory, - metric=self._metric, - resampling_strategy=self._resampling_strategy, - resampling_strategy_args=self._resampling_strategy_arguments, - acquisition_function=self.acquisition_function, - shared_mode=self._shared_mode, - include_estimators=self._include_estimators, - exclude_estimators=self._exclude_estimators, - include_preprocessors=self._include_preprocessors, - exclude_preprocessors=self._exclude_preprocessors, - disable_file_output=self._disable_evaluator_output, - configuration_mode=self._configuration_mode) - self.runhistory_, self.trajectory_, self.fANOVA_input_ = \ + _proc_smac = AutoMLSMBO( + config_space=self.configuration_space, + dataset_name=self._dataset_name, + backend=self._backend, + total_walltime_limit=time_left_for_smac, + func_eval_time_limit=per_run_time_limit, + memory_limit=self._ml_memory_limit, + data_memory_limit=self._data_memory_limit, + watcher=self._stopwatch, + start_num_run=num_run, + num_metalearning_cfgs=self._initial_configurations_via_metalearning, + config_file=configspace_path, + seed=self._seed, + metadata_directory=self._metadata_directory, + metric=self._metric, + resampling_strategy=self._resampling_strategy, + resampling_strategy_args=self._resampling_strategy_arguments, + shared_mode=self._shared_mode, + include_estimators=self._include_estimators, + exclude_estimators=self._exclude_estimators, + include_preprocessors=self._include_preprocessors, + exclude_preprocessors=self._exclude_preprocessors, + disable_file_output=self._disable_evaluator_output, + get_smac_object_callback=self._get_smac_object_callback, + smac_scenario_args=self._smac_scenario_args, + ) + self.runhistory_, self.trajectory_ = \ _proc_smac.run_smbo() trajectory_filename = os.path.join( self._backend.get_smac_output_directory(self._seed) + '_run1', diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 761460fce5..5b157d73e4 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -27,7 +27,8 @@ def __init__(self, delete_output_folder_after_terminate=True, shared_mode=False, disable_evaluator_output=False, - configuration_mode='SMAC'): + get_smac_object_callback=None, + smac_scenario_args=None): """ Parameters ---------- @@ -132,16 +133,19 @@ def __init__(self, optimization/validation set, which would later on be used to build an ensemble. * ``'model'`` : do not save any model files - - configuration_mode : ``SMAC`` or ``ROAR`` - Defines the configuration mode as described in the paper - `Sequential Model-Based Optimization for General Algorithm - Configuration `_: - - * ``SMAC`` (default): Sequential Model-based Algorithm - Configuration, which is a Bayesian optimization algorithm - * ``ROAR``: Random Online Aggressive Racing, which is basically - random search + + smac_scenario_args : dict, optional (None) + Additional arguments inserted into the scenario of SMAC. See the + `SMAC documentation `_ + for a list of available arguments. + + get_smac_object_callback : callable + Callback function to create an object of class + `smac.optimizer.smbo.SMBO `_. + The function must accept the arguments ``scenario_dict``, + ``instances``, ``num_params``, ``runhistory``, ``seed`` and ``ta``. + This is an advanced feature. Use only if you are familiar with + `SMAC `_. Attributes ---------- @@ -172,7 +176,8 @@ def __init__(self, self.delete_output_folder_after_terminate = delete_output_folder_after_terminate self.shared_mode = shared_mode self.disable_evaluator_output = disable_evaluator_output - self.configuration_mode = configuration_mode + self.get_smac_object_callback = get_smac_object_callback + self.smac_scenario_args = smac_scenario_args self._automl = None super().__init__() @@ -196,7 +201,6 @@ def build_automl(self, cls): backend=backend, time_left_for_this_task=self.time_left_for_this_task, per_run_time_limit=self.per_run_time_limit, - log_dir=backend.temporary_directory, initial_configurations_via_metalearning= self.initial_configurations_via_metalearning, ensemble_size=self.ensemble_size, @@ -209,12 +213,11 @@ def build_automl(self, cls): exclude_preprocessors=self.exclude_preprocessors, resampling_strategy=self.resampling_strategy, resampling_strategy_arguments=self.resampling_strategy_arguments, - delete_tmp_folder_after_terminate=self.delete_tmp_folder_after_terminate, - delete_output_folder_after_terminate= - self.delete_output_folder_after_terminate, shared_mode=self.shared_mode, - configuration_mode=self.configuration_mode, - disable_evaluator_output=self.disable_evaluator_output) + get_smac_object_callback=self.get_smac_object_callback, + disable_evaluator_output=self.disable_evaluator_output, + smac_scenario_args=self.smac_scenario_args + ) return automl diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index 0f49bea362..ea5ddc94cc 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -9,19 +9,9 @@ import pynisher from smac.facade.smac_facade import SMAC -from smac.facade.roar_facade import ROAR -from smac.utils.util_funcs import get_types from smac.scenario.scenario import Scenario from smac.tae.execute_ta_run import StatusType -from smac.optimizer.objective import average_cost -from smac.runhistory.runhistory import RunHistory -from smac.runhistory.runhistory2epm import RunHistory2EPM4Cost, \ - RunHistory2EPM4EIPS -from smac.epm.uncorrelated_mo_rf_with_instances import \ - UncorrelatedMultiObjectiveRandomForestWithInstances -from smac.epm.rf_with_instances import RandomForestWithInstances -from smac.optimizer.acquisition import EIPS -from smac.optimizer import pSMAC +from smac.runhistory.runhistory2epm import RunHistory2EPM4Cost import autosklearn.metalearning from autosklearn.constants import * @@ -169,6 +159,42 @@ def _print_debug_info_of_init_configuration(initial_configurations, basename, basename, time_for_task - watcher.wall_elapsed(basename)) +def get_smac_object( + scenario_dict, + seed, + ta, + backend, + metalearning_configurations, +): + scenario_dict['input_psmac_dirs'] = backend.get_smac_output_glob() + scenario = Scenario(scenario_dict) + if len(metalearning_configurations) > 0: + default_config = scenario.cs.get_default_configuration() + initial_configurations = [default_config] + metalearning_configurations + else: + initial_configurations = None + rh2EPM = RunHistory2EPM4Cost( + num_params=len(scenario.cs.get_hyperparameters()), + scenario=scenario, + success_states=[ + StatusType.SUCCESS, + StatusType.MEMOUT, + StatusType.TIMEOUT, + # As long as we don't have a model for crashes yet! + StatusType.CRASHED, + ], + impute_censored_data=False, + impute_state=None, + ) + return SMAC( + scenario=scenario, + rng=seed, + runhistory2epm=rh2EPM, + tae_runner=ta, + initial_configurations=initial_configurations, + ) + + class AutoMLSMBO(object): def __init__(self, config_space, dataset_name, @@ -181,19 +207,18 @@ def __init__(self, config_space, dataset_name, data_memory_limit=None, num_metalearning_cfgs=25, config_file=None, - smac_iters=1000, seed=1, metadata_directory=None, resampling_strategy='holdout', resampling_strategy_args=None, - acquisition_function='EI', shared_mode=False, include_estimators=None, exclude_estimators=None, include_preprocessors=None, exclude_preprocessors=None, disable_file_output=False, - configuration_mode='SMAC'): + smac_scenario_args=None, + get_smac_object_callback=None): super(AutoMLSMBO, self).__init__() # data related self.dataset_name = dataset_name @@ -221,17 +246,15 @@ def __init__(self, config_space, dataset_name, self.config_file = config_file self.seed = seed self.metadata_directory = metadata_directory - self.smac_iters = smac_iters self.start_num_run = start_num_run - self.acquisition_function = acquisition_function self.shared_mode = shared_mode - self.runhistory = None self.include_estimators = include_estimators self.exclude_estimators = exclude_estimators self.include_preprocessors = include_preprocessors self.exclude_preprocessors = exclude_preprocessors self.disable_file_output = disable_file_output - self.configuration_mode = configuration_mode + self.smac_scenario_args = smac_scenario_args + self.get_smac_object_callback = get_smac_object_callback logger_name = '%s(%d):%s' % (self.__class__.__name__, self.seed, ":" + dataset_name if dataset_name is @@ -351,14 +374,131 @@ def run_smbo(self): num_run = self.start_num_run # Initialize some SMAC dependencies - runhistory = RunHistory(aggregate_func=average_cost) - # meta_runhistory = RunHistory(aggregate_func=average_cost) - # meta_runs_dataset_indices = {} + metalearning_configurations = self.get_metalearning_suggestions() + + if self.resampling_strategy in ['partial-cv', + 'partial-cv-iterative-fit']: + num_folds = self.resampling_strategy_args['folds'] + instances = [[json.dumps({'task_id': self.dataset_name, + 'fold': fold_number})] + for fold_number in range(num_folds)] + else: + instances = [[json.dumps({'task_id': self.dataset_name})]] + + # TODO rebuild target algorithm to be it's own target algorithm + # evaluator, which takes into account that a run can be killed prior + # to the model being fully fitted; thus putting intermediate results + # into a queue and querying them once the time is over + exclude = dict() + include = dict() + if self.include_preprocessors is not None and \ + self.exclude_preprocessors is not None: + raise ValueError('Cannot specify include_preprocessors and ' + 'exclude_preprocessors.') + elif self.include_preprocessors is not None: + include['preprocessor'] = self.include_preprocessors + elif self.exclude_preprocessors is not None: + exclude['preprocessor'] = self.exclude_preprocessors + + if self.include_estimators is not None and \ + self.exclude_estimators is not None: + raise ValueError('Cannot specify include_estimators and ' + 'exclude_estimators.') + elif self.include_estimators is not None: + if self.task in CLASSIFICATION_TASKS: + include['classifier'] = self.include_estimators + elif self.task in REGRESSION_TASKS: + include['regressor'] = self.include_estimators + else: + raise ValueError(self.task) + elif self.exclude_estimators is not None: + if self.task in CLASSIFICATION_TASKS: + exclude['classifier'] = self.exclude_estimators + elif self.task in REGRESSION_TASKS: + exclude['regressor'] = self.exclude_estimators + else: + raise ValueError(self.task) + + ta = ExecuteTaFuncWithQueue(backend=self.backend, + autosklearn_seed=seed, + resampling_strategy=self.resampling_strategy, + initial_num_run=num_run, + logger=self.logger, + include=include, + exclude=exclude, + metric=self.metric, + memory_limit=self.memory_limit, + disable_file_output=self.disable_file_output, + **self.resampling_strategy_args) + + startup_time = self.watcher.wall_elapsed(self.dataset_name) + total_walltime_limit = self.total_walltime_limit - startup_time - 5 + scenario_dict = { + 'abort_on_first_run_crash': False, + 'cs': self.config_space, + 'cutoff_time': self.func_eval_time_limit, + 'deterministic': 'true', + 'instances': instances, + 'memory_limit': self.memory_limit, + 'output-dir': + self.backend.get_smac_output_directory(self.seed), + 'run_obj': 'quality', + 'shared-model': self.shared_mode, + 'wallclock_limit': total_walltime_limit, + } + if self.smac_scenario_args is not None: + for arg in [ + 'abort_on_first_run_crash', + 'cs', + 'deterministic', + 'instances', + 'output-dir', + 'run_obj', + 'shared-model', + ]: + if arg in self.smac_scenario_args: + self.logger.warning('Cannot override scenario argument %s, ' + 'will ignore this.', arg) + del self.smac_scenario_args[arg] + for arg in [ + 'cutoff_time', + 'memory_limit', + 'wallclock_limit', + ]: + if arg in self.smac_scenario_args: + self.logger.warning( + 'Overriding scenario argument %s: %s with value %s', + arg, + scenario_dict[arg], + self.smac_scenario_args[arg] + ) + scenario_dict.update(self.smac_scenario_args) + + smac_args = { + 'scenario_dict': scenario_dict, + 'seed': seed, + 'ta': ta, + 'backend': self.backend, + 'metalearning_configurations': metalearning_configurations, + } + if self.get_smac_object_callback is not None: + smac = self.get_smac_object_callback(**smac_args) + else: + smac = get_smac_object(**smac_args) + + smac.optimize() + + self.runhistory = smac.solver.runhistory + self.trajectory = smac.solver.intensifier.traj_logger.trajectory + + return self.runhistory, self.trajectory + + + def get_metalearning_suggestions(self): # == METALEARNING suggestions # we start by evaluating the defaults on the full dataset again # and add the suggestions from metalearning behind it - if self.num_metalearning_cfgs > 0: if self.metadata_directory is None: metalearning_directory = os.path.dirname( @@ -377,7 +517,8 @@ def run_smbo(self): if os.path.exists(self.metadata_directory): - self.logger.info('Metadata directory: %s', self.metadata_directory) + self.logger.info('Metadata directory: %s', + self.metadata_directory) meta_base = MetaBase(self.config_space, self.metadata_directory) try: @@ -393,14 +534,15 @@ def run_smbo(self): metafeature_calculation_end_time = time.time() metafeature_calculation_time_limit = \ metafeature_calculation_time_limit - ( - metafeature_calculation_end_time - - metafeature_calculation_start_time) + metafeature_calculation_end_time - + metafeature_calculation_start_time) if metafeature_calculation_time_limit < 1: - self.logger.warning('Time limit for metafeature calculation less ' - 'than 1 seconds (%f). Skipping calculation ' - 'of metafeatures for encoded dataset.', - metafeature_calculation_time_limit) + self.logger.warning( + 'Time limit for metafeature calculation less ' + 'than 1 seconds (%f). Skipping calculation ' + 'of metafeatures for encoded dataset.', + metafeature_calculation_time_limit) meta_features_encoded = None else: with warnings.catch_warnings(): @@ -425,7 +567,8 @@ def run_smbo(self): # for each prediction model! all_metafeatures = meta_base.get_metafeatures( features=list(meta_features.keys())) - all_metafeatures.fillna(all_metafeatures.mean(), inplace=True) + all_metafeatures.fillna(all_metafeatures.mean(), + inplace=True) with warnings.catch_warnings(): warnings.showwarning = self._send_warnings_to_log @@ -457,237 +600,7 @@ def run_smbo(self): else: meta_features = None - if meta_features is None: - if self.acquisition_function == 'EIPS': - self.logger.critical('Reverting to acquisition function EI!') - self.acquisition_function = 'EI' meta_features_list = [] - meta_features_dict = {} metalearning_configurations = [] - - if self.resampling_strategy in ['partial-cv', - 'partial-cv-iterative-fit']: - num_folds = self.resampling_strategy_args['folds'] - instances = [[json.dumps({'task_id': self.dataset_name, - 'fold': fold_number})] - for fold_number in range(num_folds)] - else: - instances = [[json.dumps({'task_id': self.dataset_name})]] - - startup_time = self.watcher.wall_elapsed(self.dataset_name) - total_walltime_limit = self.total_walltime_limit - startup_time - 5 - scenario_dict = { - 'cs': self.config_space, - 'cutoff_time': self.func_eval_time_limit, - 'memory_limit': self.memory_limit, - 'wallclock_limit': total_walltime_limit, - 'output-dir': - self.backend.get_smac_output_directory(self.seed), - 'shared-model': self.shared_mode, - 'run_obj': 'quality', - 'deterministic': 'true', - 'instances': instances, - 'abort_on_first_run_crash': False, - } - - if self.configuration_mode == 'RANDOM': - scenario_dict['minR'] = len(instances) if instances is not None else 1 - scenario_dict['initial_incumbent'] = 'RANDOM' - - self.scenario = Scenario(scenario_dict) - - # TODO rebuild target algorithm to be it's own target algorithm - # evaluator, which takes into account that a run can be killed prior - # to the model being fully fitted; thus putting intermediate results - # into a queue and querying them once the time is over - exclude = dict() - include = dict() - if self.include_preprocessors is not None and \ - self.exclude_preprocessors is not None: - raise ValueError('Cannot specify include_preprocessors and ' - 'exclude_preprocessors.') - elif self.include_preprocessors is not None: - include['preprocessor'] = self.include_preprocessors - elif self.exclude_preprocessors is not None: - exclude['preprocessor'] = self.exclude_preprocessors - - if self.include_estimators is not None and \ - self.exclude_estimators is not None: - raise ValueError('Cannot specify include_estimators and ' - 'exclude_estimators.') - elif self.include_estimators is not None: - if self.task in CLASSIFICATION_TASKS: - include['classifier'] = self.include_estimators - elif self.task in REGRESSION_TASKS: - include['regressor'] = self.include_estimators - else: - raise ValueError(self.task) - elif self.exclude_estimators is not None: - if self.task in CLASSIFICATION_TASKS: - exclude['classifier'] = self.exclude_estimators - elif self.task in REGRESSION_TASKS: - exclude['regressor'] = self.exclude_estimators - else: - raise ValueError(self.task) - - ta = ExecuteTaFuncWithQueue(backend=self.backend, - autosklearn_seed=seed, - resampling_strategy=self.resampling_strategy, - initial_num_run=num_run, - logger=self.logger, - include=include, - exclude=exclude, - metric=self.metric, - memory_limit=self.memory_limit, - disable_file_output=self.disable_file_output, - **self.resampling_strategy_args) - - types, bounds = get_types(self.config_space, - self.scenario.feature_array) - - # TODO extract generation of SMAC object into it's own function for - # testing - if self.acquisition_function == 'EI': - model = RandomForestWithInstances(types=types, bounds=bounds, - #instance_features=meta_features_list, - seed=1, num_trees=10) - rh2EPM = RunHistory2EPM4Cost(num_params=num_params, - scenario=self.scenario, - success_states=[StatusType.SUCCESS, - StatusType.MEMOUT, - StatusType.TIMEOUT, - # As long as we - # don't have a model - # for crashes yet! - StatusType.CRASHED], - impute_censored_data=False, - impute_state=None) - _smac_arguments = dict(scenario=self.scenario, - model=model, - rng=seed, - runhistory2epm=rh2EPM, - tae_runner=ta, - runhistory=runhistory) - elif self.acquisition_function == 'EIPS': - rh2EPM = RunHistory2EPM4EIPS(num_params=num_params, - scenario=self.scenario, - success_states=[StatusType.SUCCESS, - StatusType.MEMOUT, - StatusType.TIMEOUT, - # As long as we - # don't have a model - # for crashes yet! - StatusType.CRASHED], - impute_censored_data=False, - impute_state=None) - model = UncorrelatedMultiObjectiveRandomForestWithInstances( - ['cost', 'runtime'], types=types, bounds=bounds, num_trees=10, - instance_features=meta_features_list, seed=1) - acquisition_function = EIPS(model) - _smac_arguments = dict(scenario=self.scenario, - model=model, - rng=seed, - tae_runner=ta, - runhistory2epm=rh2EPM, - runhistory=runhistory, - acquisition_function=acquisition_function) - else: - raise ValueError('Unknown acquisition function value %s!' % - self.acquisition_function) - - if self.configuration_mode == 'SMAC': - smac = SMAC(**_smac_arguments) - elif self.configuration_mode in ['ROAR', 'RANDOM']: - for not_in_roar in ['runhistory2epm', 'model']: - if not_in_roar in _smac_arguments: - del _smac_arguments[not_in_roar] - smac = ROAR(**_smac_arguments) - else: - raise ValueError(self.configuration_mode) - - smac.solver.stats.start_timing() - # == first, evaluate all metelearning and default configurations - smac.solver.incumbent = smac.solver.initial_design.run() - - for challenger in metalearning_configurations: - - smac.solver.incumbent, inc_perf = smac.solver.intensifier.intensify( - challengers=[challenger], - incumbent=smac.solver.incumbent, - run_history=smac.solver.runhistory, - aggregate_func=smac.solver.aggregate_func, - time_bound=self.total_walltime_limit) - - if smac.solver.scenario.shared_model: - pSMAC.write( - run_history=smac.solver.runhistory, - output_directory=smac.solver.scenario.output_dir, - ) - - if smac.solver.stats.is_budget_exhausted(): - break - - # == after metalearning run SMAC loop - while True: - - if smac.solver.scenario.shared_model: - pSMAC.read(run_history=smac.solver.runhistory, - output_dirs=glob.glob(self.backend.get_smac_output_glob()), - configuration_space=self.config_space, - logger=self.logger) - - choose_next_start_time = time.time() - challengers = self.choose_next(smac) - time_for_choose_next = time.time() - choose_next_start_time - self.logger.info('Used %g seconds to find next ' - 'configurations' % (time_for_choose_next)) - - time_for_choose_next = max(time_for_choose_next, 1.0) - smac.solver.incumbent, inc_perf = smac.solver.intensifier.intensify( - challengers=challengers, - incumbent=smac.solver.incumbent, - run_history=smac.solver.runhistory, - aggregate_func=smac.solver.aggregate_func, - time_bound=time_for_choose_next) - - if smac.solver.scenario.shared_model: - pSMAC.write( - run_history=smac.solver.runhistory, - output_directory=smac.solver.scenario.output_dir, - ) - - if smac.solver.stats.is_budget_exhausted(): - break - - self.runhistory = smac.solver.runhistory - self.trajectory = smac.solver.intensifier.traj_logger.trajectory - smac.runhistory = self.runhistory - self.fANOVA_input = smac.get_X_y() - - return self.runhistory, self.trajectory, self.fANOVA_input - - def choose_next(self, smac): - - if len(smac.solver.runhistory.data) == 0: - raise ValueError('Cannot use SMBO algorithm on empty runhistory.') - - X_cfg, Y_cfg = smac.solver.rh2EPM.transform(smac.solver.runhistory) - - if len(X_cfg) > 0: - # Update costs by normalization - dataset_minimum = np.min(Y_cfg[:, 0]) - Y_cfg[:, 0] = 1 - ((1. - Y_cfg[:, 0]) / - (1. - dataset_minimum)) - Y_cfg[:, 0][Y_cfg[:, 0] > 2] = 2 - - X_cfg = X_cfg.copy() - Y_cfg = Y_cfg.copy() - - self.logger.info('Using %d training points for SMAC.' % - X_cfg.shape[0]) - return smac.solver.choose_next( - X_cfg, Y_cfg, - num_configurations_by_local_search=10, - num_configurations_by_random_search_sorted=10000 - ) + return metalearning_configurations diff --git a/example/example_eips.py b/example/example_eips.py new file mode 100644 index 0000000000..854ccabea4 --- /dev/null +++ b/example/example_eips.py @@ -0,0 +1,82 @@ +import sklearn.model_selection +import sklearn.datasets +import sklearn.metrics + +from smac.epm.uncorrelated_mo_rf_with_instances import \ + UncorrelatedMultiObjectiveRandomForestWithInstances +from smac.utils.util_funcs import get_types +from smac.facade.smac_facade import SMAC +from smac.optimizer.acquisition import EIPS +from smac.runhistory.runhistory2epm import RunHistory2EPM4EIPS +from smac.scenario.scenario import Scenario +from smac.tae.execute_ta_run import StatusType + +import autosklearn.classification + + +def get_eips_object_callback( + scenario_dict, + seed, + ta, + **kwargs +): + scenario = Scenario(scenario_dict) + rh2EPM = RunHistory2EPM4EIPS( + num_params=len(scenario.cs.get_hyperparameters()), + scenario=scenario, + success_states=[ + StatusType.SUCCESS, + StatusType.MEMOUT, + StatusType.TIMEOUT, + StatusType.CRASHED + ], + impute_censored_data=False, + impute_state=None + ) + types, bounds = get_types(scenario.cs, + scenario.feature_array) + model = UncorrelatedMultiObjectiveRandomForestWithInstances( + ['cost', 'runtime'], + types=types, + bounds=bounds, + instance_features=scenario.feature_array, + rf_kwargs={'seed': 1,}, + ) + acquisition_function = EIPS(model) + return SMAC( + scenario=scenario, + rng=seed, + tae_runner=ta, + runhistory2epm=rh2EPM, + model=model, + acquisition_function=acquisition_function + ) + + +def main(): + X, y = sklearn.datasets.load_digits(return_X_y=True) + X_train, X_test, y_train, y_test = \ + sklearn.model_selection.train_test_split(X, y, random_state=1) + + automl = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=120, per_run_time_limit=30, + tmp_folder='/tmp/autosklearn_eips_example_tmp', + output_folder='/tmp/autosklearn_eips_example_out', + get_smac_object_callback=get_eips_object_callback, + delete_tmp_folder_after_terminate=False, + initial_configurations_via_metalearning=0, + ) + automl.fit(X_train, y_train, dataset_name='digits') + + # Print the final ensemble constructed by auto-sklearn via ROAR. + print(automl.show_models()) + predictions = automl.predict(X_test) + # Print statistics about the auto-sklearn run such as number of + # iterations, number of models failed with a time out. + print(automl.sprint_statistics()) + print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions)) + + + +if __name__ == '__main__': + main() diff --git a/example/example_parallel.py b/example/example_parallel.py index e5a0b54fda..a3db16d928 100644 --- a/example/example_parallel.py +++ b/example/example_parallel.py @@ -34,8 +34,10 @@ def spawn_classifier(seed, dataset_name): # the same configurations in four processes. if seed == 0: initial_configurations_via_metalearning = 25 + smac_scenario_args = {} else: initial_configurations_via_metalearning = 0 + smac_scenario_args = {'initial_incumbent': 'RANDOM'} # Arguments which are different to other runs of auto-sklearn: # 1. all classifiers write to the same output directory @@ -43,8 +45,7 @@ def spawn_classifier(seed, dataset_name): # models. # 3. all instances of the AutoSklearnClassifier must have a different seed! automl = AutoSklearnClassifier( - time_left_for_this_task=60, # sec., how long should this seed fit - # process run + time_left_for_this_task=60, # sec., how long should this seed fit process run per_run_time_limit=15, # sec., each model may only take this long before it's killed ml_memory_limit=1024, # MB, memory limit imposed on each call to a ML algorithm shared_mode=True, # tmp folder will be shared between seeds @@ -53,7 +54,8 @@ def spawn_classifier(seed, dataset_name): delete_tmp_folder_after_terminate=False, ensemble_size=0, # ensembles will be built when all optimization runs are finished initial_configurations_via_metalearning=initial_configurations_via_metalearning, - seed=seed) + seed=seed, + smac_scenario_args=smac_scenario_args) automl.fit(X_train, y_train, dataset_name=dataset_name) if __name__ == '__main__': diff --git a/example/example_random_search.py b/example/example_random_search.py new file mode 100644 index 0000000000..3d1ac10efc --- /dev/null +++ b/example/example_random_search.py @@ -0,0 +1,92 @@ +import sklearn.model_selection +import sklearn.datasets +import sklearn.metrics + +from smac.facade.roar_facade import ROAR +from smac.scenario.scenario import Scenario + +import autosklearn.classification + + +def get_roar_object_callback( + scenario_dict, + seed, + ta, + **kwargs +): + """Random online adaptive racing. + + http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf""" + scenario = Scenario(scenario_dict) + return ROAR( + scenario=scenario, + rng=seed, + tae_runner=ta, + ) + + +def get_random_search_object_callback( + scenario_dict, + seed, + ta, + **kwargs +): + """Random search. + + http://www.jmlr.org/papers/v13/bergstra12a.html""" + scenario_dict['minR'] = len(scenario_dict['instances']) + scenario_dict['initial_incumbent'] = 'RANDOM' + scenario = Scenario(scenario_dict) + return ROAR( + scenario=scenario, + rng=seed, + tae_runner=ta, + ) + + +def main(): + X, y = sklearn.datasets.load_digits(return_X_y=True) + X_train, X_test, y_train, y_test = \ + sklearn.model_selection.train_test_split(X, y, random_state=1) + + automl = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=120, per_run_time_limit=30, + tmp_folder='/tmp/autosklearn_random_search_example_tmp', + output_folder='/tmp/autosklearn_random_search_example_out', + get_smac_object_callback=get_roar_object_callback, + initial_configurations_via_metalearning=0, + ) + automl.fit(X_train, y_train, dataset_name='digits') + + print('#' * 80) + print('Results for ROAR.') + # Print the final ensemble constructed by auto-sklearn via ROAR. + print(automl.show_models()) + predictions = automl.predict(X_test) + # Print statistics about the auto-sklearn run such as number of + # iterations, number of models failed with a time out. + print(automl.sprint_statistics()) + print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions)) + + automl = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=120, per_run_time_limit=30, + tmp_folder='/tmp/autosklearn_random_search_example_tmp', + output_folder='/tmp/autosklearn_random_search_example_out', + get_smac_object_callback=get_random_search_object_callback, + initial_configurations_via_metalearning=0, + ) + automl.fit(X_train, y_train, dataset_name='digits') + + print('#' * 80) + print('Results for random search.') + # Print the final ensemble constructed by auto-sklearn via random search. + print(automl.show_models()) + predictions = automl.predict(X_test) + # Print statistics about the auto-sklearn run such as number of + # iterations, number of models failed with a time out. + print(automl.sprint_statistics()) + print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions)) + + +if __name__ == '__main__': + main() From 7b8e08c329bd0ab6e05fd5ecae3cd9d47004c1e6 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 8 Nov 2017 15:53:10 +0100 Subject: [PATCH 28/61] use partial_fit again --- .../classification/passive_aggressive.py | 61 +++++++++++++------ .../pipeline/components/classification/sgd.py | 53 +++++++++++----- .../pipeline/components/regression/sgd.py | 53 +++++++++++----- .../classification/test_passive_aggressive.py | 12 ++-- .../components/classification/test_sgd.py | 15 +++-- .../data_preprocessing/test_balancing.py | 6 +- .../components/regression/test_sgd.py | 16 ++--- 7 files changed, 143 insertions(+), 73 deletions(-) diff --git a/autosklearn/pipeline/components/classification/passive_aggressive.py b/autosklearn/pipeline/components/classification/passive_aggressive.py index 9fb17eaa99..4fac16bcba 100644 --- a/autosklearn/pipeline/components/classification/passive_aggressive.py +++ b/autosklearn/pipeline/components/classification/passive_aggressive.py @@ -22,25 +22,34 @@ def __init__(self, C, fit_intercept, tol, loss, average, random_state=None): self.estimator = None def fit(self, X, y): - self.iterative_fit(X, y, n_iter=1, refit=True) + self.iterative_fit(X, y, n_iter=2, refit=True) while not self.configuration_fully_fitted(): - self.iterative_fit(X, y, n_iter=1) + self.iterative_fit(X, y, n_iter=2) return self - def iterative_fit(self, X, y, n_iter=1, refit=False): + def iterative_fit(self, X, y, n_iter=2, refit=False): from sklearn.linear_model.passive_aggressive import \ PassiveAggressiveClassifier + # Need to fit at least two iterations, otherwise early stopping will not + # work because we cannot determine whether the algorithm actually + # converged. The only way of finding this out is if the sgd spends less + # iterations than max_iter. If max_iter == 1, it has to spend at least + # one iteration and will always spend at least one iteration, so we + # cannot know about convergence. + n_iter = max(n_iter, 2) + if refit: self.estimator = None if self.estimator is None: - + call_fit = True self.estimator = PassiveAggressiveClassifier( C=self.C, fit_intercept=self.fit_intercept, - max_iter=1, + max_iter=n_iter, + tol=self.tol, loss=self.loss, shuffle=True, random_state=self.random_state, @@ -48,6 +57,8 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): average=self.average, ) self.classes_ = np.unique(y.astype(int)) + else: + call_fit = False # Fallback for multilabel classification if len(y.shape) > 1 and y.shape[1] > 1: @@ -58,13 +69,29 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.estimator.fit(X, y) self.fully_fit_ = True else: - # In the first iteration, there is not yet an intercept - - self.estimator.max_iter += n_iter - self.estimator.fit(X, y) - if self.estimator.max_iter >= 50 or \ - self.estimator.max_iter > self.estimator.n_iter_: - self.fully_fit_ = True + if call_fit: + self.estimator.fit(X, y) + else: + self.estimator.max_iter += n_iter + self.estimator._validate_params() + lr = "pa1" if self.estimator.loss == "hinge" else "pa2" + self.estimator._partial_fit( + X, y, + alpha=1.0, + C=self.estimator.C, + loss="hinge", + learning_rate=lr, + max_iter=n_iter, + classes=None, + sample_weight=None, + coef_init=None, + intercept_init=None + ) + if ( + self.estimator._max_iter >= 1000 + or n_iter > self.estimator.n_iter_ + ): + self.fully_fit_ = True return self @@ -102,14 +129,14 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): - C = UniformFloatHyperparameter("C", 1e-5, 10, 1, log=True) + C = UniformFloatHyperparameter("C", 1e-5, 10, 1.0, log=True) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") - loss = CategoricalHyperparameter("loss", - ["hinge", "squared_hinge"], - default_value="hinge") + loss = CategoricalHyperparameter( + "loss", ["hinge", "squared_hinge"], default_value="hinge" + ) tol = UniformFloatHyperparameter("tol", 1e-4, 1e-1, default_value=1e-3, - log=True) + log=True) average = CategoricalHyperparameter('average', [False, True]) cs = ConfigurationSpace() diff --git a/autosklearn/pipeline/components/classification/sgd.py b/autosklearn/pipeline/components/classification/sgd.py index 966d5c1e8e..633b90729b 100644 --- a/autosklearn/pipeline/components/classification/sgd.py +++ b/autosklearn/pipeline/components/classification/sgd.py @@ -30,16 +30,24 @@ def __init__(self, loss, penalty, alpha, fit_intercept, tol, self.estimator = None def fit(self, X, y, sample_weight=None): - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, + self.iterative_fit(X, y, n_iter=2, sample_weight=sample_weight, refit=True) while not self.configuration_fully_fitted(): - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight) + self.iterative_fit(X, y, n_iter=2, sample_weight=sample_weight) return self - def iterative_fit(self, X, y, n_iter=1, refit=False, sample_weight=None): + def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None): from sklearn.linear_model.stochastic_gradient import SGDClassifier + # Need to fit at least two iterations, otherwise early stopping will not + # work because we cannot determine whether the algorithm actually + # converged. The only way of finding this out is if the sgd spends less + # iterations than max_iter. If max_iter == 1, it has to spend at least + # one iteration and will always spend at least one iteration, so we + # cannot know about convergence. + n_iter = max(n_iter, 2) + if refit: self.estimator = None @@ -52,12 +60,14 @@ def iterative_fit(self, X, y, n_iter=1, refit=False, sample_weight=None): self.eta0 = float(self.eta0) self.power_t = float(self.power_t) if self.power_t is not None else 0.25 self.average = self.average == 'True' + self.tol = float(self.tol) self.estimator = SGDClassifier(loss=self.loss, penalty=self.penalty, alpha=self.alpha, fit_intercept=self.fit_intercept, - max_iter=1, + max_iter=n_iter, + tol=self.tol, learning_rate=self.learning_rate, l1_ratio=self.l1_ratio, epsilon=self.epsilon, @@ -65,14 +75,26 @@ def iterative_fit(self, X, y, n_iter=1, refit=False, sample_weight=None): power_t=self.power_t, shuffle=True, average=self.average, - random_state=self.random_state) + random_state=self.random_state, + warm_start=True) + self.estimator.fit(X, y, sample_weight=sample_weight) else: self.estimator.max_iter += n_iter - - self.estimator.fit(X, y, sample_weight=sample_weight) - - if self.estimator.max_iter >= 50 or \ - self.estimator.max_iter > self.estimator.n_iter_: + self.estimator._validate_params() + self.estimator._partial_fit( + X, y, + alpha=self.estimator.alpha, + C=1.0, + loss=self.estimator.loss, + learning_rate=self.estimator.learning_rate, + max_iter=n_iter, + sample_weight=sample_weight, + classes=None, + coef_init=None, + intercept_init=None + ) + + if self.estimator._max_iter >= 1000 or n_iter > self.estimator.n_iter_: self.fully_fit_ = True return self @@ -122,20 +144,21 @@ def get_hyperparameter_search_space(dataset_properties=None): penalty = CategoricalHyperparameter( "penalty", ["l1", "l2", "elasticnet"], default_value="l2") alpha = UniformFloatHyperparameter( - "alpha", 10e-7, 1e-1, log=True, default_value=0.0001) + "alpha", 1e-7, 1e-1, log=True, default_value=0.0001) l1_ratio = UniformFloatHyperparameter( "l1_ratio", 1e-9, 1, log=True, default_value=0.15) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") tol = UniformFloatHyperparameter("tol", 1e-4, 1e-1, log=True, - default_value=10e-3) + default_value=1e-3) epsilon = UniformFloatHyperparameter( "epsilon", 1e-5, 1e-1, default_value=1e-4, log=True) learning_rate = CategoricalHyperparameter( "learning_rate", ["optimal", "invscaling", "constant"], - default_value="optimal") + default_value="invscaling") eta0 = UniformFloatHyperparameter( - "eta0", 10**-7, 0.1, default_value=0.01) - power_t = UniformFloatHyperparameter("power_t", 1e-5, 1, default_value=0.25) + "eta0", 1e-7, 1e-1, default_value=0.01) + power_t = UniformFloatHyperparameter("power_t", 1e-5, 1, + default_value=0.25) average = CategoricalHyperparameter( "average", ["False", "True"], default_value="False") cs.add_hyperparameters([loss, penalty, alpha, l1_ratio, fit_intercept, diff --git a/autosklearn/pipeline/components/regression/sgd.py b/autosklearn/pipeline/components/regression/sgd.py index 7008caadd8..871095e6be 100644 --- a/autosklearn/pipeline/components/regression/sgd.py +++ b/autosklearn/pipeline/components/regression/sgd.py @@ -29,16 +29,24 @@ def __init__(self, loss, penalty, alpha, fit_intercept, tol, self.scaler = None def fit(self, X, y): - self.iterative_fit(X, y, n_iter=1, refit=True) + self.iterative_fit(X, y, n_iter=2, refit=True) while not self.configuration_fully_fitted(): - self.iterative_fit(X, y, n_iter=1) + self.iterative_fit(X, y, n_iter=2) return self - def iterative_fit(self, X, y, n_iter=1, refit=False): + def iterative_fit(self, X, y, n_iter=2, refit=False): from sklearn.linear_model.stochastic_gradient import SGDRegressor import sklearn.preprocessing + # Need to fit at least two iterations, otherwise early stopping will not + # work because we cannot determine whether the algorithm actually + # converged. The only way of finding this out is if the sgd spends less + # iterations than max_iter. If max_iter == 1, it has to spend at least + # one iteration and will always spend at least one iteration, so we + # cannot know about convergence. + n_iter = max(n_iter, 2) + if refit: self.estimator = None self.scaler = None @@ -60,7 +68,8 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): penalty=self.penalty, alpha=self.alpha, fit_intercept=self.fit_intercept, - max_iter=1, + max_iter=n_iter, + tol=self.tol, learning_rate=self.learning_rate, l1_ratio=self.l1_ratio, epsilon=self.epsilon, @@ -68,18 +77,30 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): power_t=self.power_t, shuffle=True, average=self.average, - random_state=self.random_state) + random_state=self.random_state, + warm_start=True) self.scaler = sklearn.preprocessing.StandardScaler(copy=True) self.scaler.fit(y.reshape((-1, 1))) + Y_scaled = self.scaler.transform(y.reshape((-1, 1))).ravel() + self.estimator.fit(X, Y_scaled) else: self.estimator.max_iter += n_iter - - Y_scaled = self.scaler.transform(y.reshape((-1, 1))).ravel() - self.estimator.fit(X, Y_scaled) - - if self.estimator.max_iter >= 50 or \ - self.estimator.max_iter > self.estimator.n_iter_: + Y_scaled = self.scaler.transform(y.reshape((-1, 1))).ravel() + self.estimator._validate_params() + self.estimator._partial_fit( + X, Y_scaled, + alpha=self.estimator.alpha, + C=1.0, + loss=self.estimator.loss, + learning_rate=self.estimator.learning_rate, + max_iter=n_iter, + sample_weight=None, + coef_init=None, + intercept_init=None + ) + + if self.estimator._max_iter >= 1000 or n_iter > self.estimator.n_iter_: self.fully_fit_ = True return self @@ -122,7 +143,7 @@ def get_hyperparameter_search_space(dataset_properties=None): penalty = CategoricalHyperparameter( "penalty", ["l1", "l2", "elasticnet"], default_value="l2") alpha = UniformFloatHyperparameter( - "alpha", 10e-7, 1e-1, log=True, default_value=0.01) + "alpha", 1e-7, 1e-1, log=True, default_value=0.0001) l1_ratio = UniformFloatHyperparameter( "l1_ratio", 1e-9, 1., log=True, default_value=0.15) fit_intercept = UnParametrizedHyperparameter( @@ -130,14 +151,14 @@ def get_hyperparameter_search_space(dataset_properties=None): tol = UniformFloatHyperparameter( "tol", 1e-4, 1e-1, default_value=1e-3, log=True) epsilon = UniformFloatHyperparameter( - "epsilon", 1e-5, 1e-1, default_value=1e-4, log=True) + "epsilon", 1e-5, 1e-1, default_value=0.1, log=True) learning_rate = CategoricalHyperparameter( "learning_rate", ["optimal", "invscaling", "constant"], - default_value="optimal") + default_value="invscaling") eta0 = UniformFloatHyperparameter( - "eta0", 10 ** -7, 0.1, default_value=0.01) + "eta0", 1e-7, 1e-1, default_value=0.01) power_t = UniformFloatHyperparameter( - "power_t", 1e-5, 1, default_value=0.5) + "power_t", 1e-5, 1, default_value=0.25) average = CategoricalHyperparameter( "average", ["False", "True"], default_value="False") diff --git a/test/test_pipeline/components/classification/test_passive_aggressive.py b/test/test_pipeline/components/classification/test_passive_aggressive.py index 06c2a9c900..2923595174 100644 --- a/test/test_pipeline/components/classification/test_passive_aggressive.py +++ b/test/test_pipeline/components/classification/test_passive_aggressive.py @@ -11,15 +11,15 @@ class PassiveAggressiveComponentTest(BaseClassificationComponentTest): __test__ = True res = dict() - res["default_iris"] = 0.92 - res["default_iris_iterative"] = 0.9 - res["default_iris_proba"] = 0.25869970129843273 - res["default_iris_sparse"] = 0.46 + res["default_iris"] = 0.98 + res["default_iris_iterative"] = 0.95999999999999996 + res["default_iris_proba"] = 0.2185197378302077 + res["default_iris_sparse"] = 0.38 res["default_digits"] = 0.9174256223436551 res["default_digits_iterative"] = 0.9174256223436551 res["default_digits_binary"] = 0.99332119004250152 - res["default_digits_multilabel"] = 0.91507635802108533 - res["default_digits_multilabel_proba"] = 0.99942129629629628 + res["default_digits_multilabel"] = 0.94149891011963693 + res["default_digits_multilabel_proba"] = 1.0 sk_mod = sklearn.linear_model.PassiveAggressiveClassifier module = PassiveAggressive diff --git a/test/test_pipeline/components/classification/test_sgd.py b/test/test_pipeline/components/classification/test_sgd.py index 6ab2027233..c373743fee 100644 --- a/test/test_pipeline/components/classification/test_sgd.py +++ b/test/test_pipeline/components/classification/test_sgd.py @@ -1,7 +1,6 @@ import sklearn.linear_model from autosklearn.pipeline.components.classification.sgd import SGD - from .test_base import BaseClassificationComponentTest @@ -10,13 +9,13 @@ class SGDComponentTest(BaseClassificationComponentTest): __test__ = True res = dict() - res["default_iris"] = 0.92 - res["default_iris_iterative"] = 0.68 - res["default_iris_proba"] = 0.32146556612186317 - res["default_iris_sparse"] = 0.41999999999999998 - res["default_digits"] = 0.88767455980570731 - res["default_digits_iterative"] = 0.91317547055251969 - res["default_digits_binary"] = 0.9884638737097754 + res["default_iris"] = 0.97999999999999998 + res["default_iris_iterative"] = 0.9 + res["default_iris_proba"] = 0.29960543868641343 + res["default_iris_sparse"] = 0.54 + res["default_digits"] = 0.92167577413479052 + res["default_digits_iterative"] = 0.92167577413479052 + res["default_digits_binary"] = 0.99332119004250152 res["default_digits_multilabel"] = -1 res["default_digits_multilabel_proba"] = -1 diff --git a/test/test_pipeline/components/data_preprocessing/test_balancing.py b/test/test_pipeline/components/data_preprocessing/test_balancing.py index 979e4227dd..82d3ce7896 100644 --- a/test/test_pipeline/components/data_preprocessing/test_balancing.py +++ b/test/test_pipeline/components/data_preprocessing/test_balancing.py @@ -76,7 +76,7 @@ def test_weighting_effect(self): ('random_forest', RandomForest, 0.75, 0.821, 3), ('libsvm_svc', LibSVM_SVC, 0.769, 0.72, 3), ('liblinear_svc', LibLinear_SVC, 0.762, 0.735, 3), - ('sgd', SGD, 0.632, 0.708, 1) + ('sgd', SGD, 0.762, 0.708, 1) ]: for strategy, acc in [ ('none', acc_no_weighting), @@ -123,9 +123,9 @@ def test_weighting_effect(self): for name, pre, acc_no_weighting, acc_weighting in \ [('extra_trees_preproc_for_classification', - ExtraTreesPreprocessorClassification, 0.818, 0.706), + ExtraTreesPreprocessorClassification, 0.800, 0.750), ('liblinear_svc_preprocessor', LibLinear_Preprocessor, - 0.818, 0.735)]: + 0.750, 0.735)]: for strategy, acc in [('none', acc_no_weighting), ('weighting', acc_weighting)]: data_ = copy.copy(data) diff --git a/test/test_pipeline/components/regression/test_sgd.py b/test/test_pipeline/components/regression/test_sgd.py index d09b13b677..eb15b17ba6 100644 --- a/test/test_pipeline/components/regression/test_sgd.py +++ b/test/test_pipeline/components/regression/test_sgd.py @@ -8,14 +8,14 @@ class SGDComponentTest(BaseRegressionComponentTest): __test__ = True res = dict() - res["default_boston"] = -7.76508935746e+30 - res["default_boston_iterative"] = -7.76508935746e+30 - res["default_boston_sparse"] = -8.4314088748670574e+30 - res["default_boston_iterative_sparse"] = -8.4314088748670574e+30 - res["default_diabetes"] = 0.28217342878579466 - res["default_diabetes_iterative"] = 0.28217342878579466 - res["default_diabetes_sparse"] = 0.099141102939435899 - res["default_diabetes_iterative_sparse"] = 0.099141102939435899 + res["default_boston"] = -4.19960551863e+26 + res["default_boston_iterative"] = -4.19960551863e+26 + res["default_boston_sparse"] = -3.4078908871512885e+27 + res["default_boston_iterative_sparse"] = -3.4078908871512885e+27 + res["default_diabetes"] = 0.31775104100149354 + res["default_diabetes_iterative"] = 0.31775104100149354 + res["default_diabetes_sparse"] = 0.10849537848918589 + res["default_diabetes_iterative_sparse"] = 0.10849537848918589 sk_mod = sklearn.linear_model.SGDRegressor From b193a4984ea7319957aa4fc6ba4fa75802f8137e Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 17 Nov 2017 16:47:33 +0100 Subject: [PATCH 29/61] Allow adding additional run info to a pipeline --- autosklearn/evaluation/__init__.py | 9 ++- autosklearn/evaluation/abstract_evaluator.py | 14 +++- autosklearn/evaluation/train_evaluator.py | 82 ++++++++++++++++---- autosklearn/pipeline/base.py | 14 +++- test/test_evaluation/test_train_evaluator.py | 50 +++++++++++- 5 files changed, 145 insertions(+), 24 deletions(-) diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index a77e87e62f..b2f7539ce0 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -10,7 +10,8 @@ import pynisher from sklearn.model_selection import ShuffleSplit, StratifiedShuffleSplit, KFold, \ StratifiedKFold -from smac.tae.execute_ta_run import StatusType, BudgetExhaustedException +from smac.tae.execute_ta_run import StatusType, BudgetExhaustedException, \ + TAEAbortException from smac.tae.execute_func import AbstractTAFunc from ConfigSpace import Configuration @@ -213,6 +214,12 @@ def run(self, config, instance=None, additional_run_info = {'error': 'Memout (used more than %d MB).' % self.memory_limit} + elif obj.exit_status is TAEAbortException: + status = StatusType.ABORT + cost = WORST_POSSIBLE_RESULT + additional_run_info = {'error': 'Your configuration of ' + 'auto-sklearn does not work!'} + else: try: info = autosklearn.evaluation.util.get_last_result(queue) diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index 943190b7a6..23c41b0a3d 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -51,6 +51,9 @@ def predict_proba(self, X, batch_size=1000): def estimator_supports_iterative_fit(self): return False + def get_additional_run_info(self): + return None + class MyDummyRegressor(DummyRegressor): def __init__(self, configuration, random_state, init_params=None): @@ -79,6 +82,9 @@ def predict(self, X, batch_size=1000): def estimator_supports_iterative_fit(self): return False + def get_additional_run_info(self): + return None + class AbstractEvaluator(object): def __init__(self, datamanager, backend, queue, metric, @@ -191,7 +197,7 @@ def _loss(self, y_true, y_hat): return err def finish_up(self, loss, opt_pred, valid_pred, test_pred, - file_output=True, final_call=True): + additional_run_info=None, file_output=True, final_call=True): """This function does everything necessary after the fitting is done: * predicting @@ -211,15 +217,15 @@ def finish_up(self, loss, opt_pred, valid_pred, test_pred, if loss_ is not None: return self.duration, loss_, self.seed, additional_run_info_ - num_run = str(self.num_run).zfill(5) if isinstance(loss, dict): loss_ = loss loss = loss_[self.metric.name] else: loss_ = {} - additional_run_info = {metric_name: value for metric_name, value in - loss_.items()} + additional_run_info = {} if additional_run_info is None else additional_run_info + for metric_name, value in loss_.items(): + additional_run_info[metric_name] = value additional_run_info['duration'] = self.duration additional_run_info['num_run'] = self.num_run diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index ce20369523..3a6f58da3d 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -1,6 +1,7 @@ import json import numpy as np +from smac.tae.execute_ta_run import TAEAbortException import sklearn.model_selection from autosklearn.evaluation.abstract_evaluator import AbstractEvaluator @@ -83,14 +84,31 @@ def fit_predict_and_loss(self, iterative=False): Y_optimization_pred = [None] * self.cv_folds Y_valid_pred = [None] * self.cv_folds Y_test_pred = [None] * self.cv_folds - + additional_run_info = None y = _get_y_array(self.Y_train, self.task_type) + # TODO: mention that no additional run info is possible in this + # case! -> maybe remove full CV from the train evaluator anyway and + # make the user implement this! for i, (train_split, test_split) in enumerate(self.cv.split( self.X_train, y)): - opt_pred, valid_pred, test_pred = self._partial_fit_and_predict( - i, train_indices=train_split, test_indices=test_split) + opt_pred, valid_pred, test_pred, additional_run_info = ( + self._partial_fit_and_predict( + i, train_indices=train_split, test_indices=test_split + ) + ) + + if ( + additional_run_info is not None + and len(additional_run_info) > 0 + and i > 0 + ): + raise TAEAbortException( + 'Found additional run info "%s" in fold %d, ' + 'but cannot handle additional run info if fold >= 1.' % + (additional_run_info, i) + ) Y_optimization_pred[i] = opt_pred Y_valid_pred[i] = valid_pred @@ -133,8 +151,15 @@ def fit_predict_and_loss(self, iterative=False): # actually a new model self._added_empty_model = True - self.finish_up(loss, Y_optimization_pred, Y_valid_pred, Y_test_pred, - file_output=True, final_call=True) + self.finish_up( + loss, + Y_optimization_pred, + Y_valid_pred, + Y_test_pred, + additional_run_info=additional_run_info, + file_output=True, + final_call=True + ) def partial_fit_predict_and_loss(self, fold, iterative=False): if fold > self.cv_folds: @@ -157,9 +182,14 @@ def partial_fit_predict_and_loss(self, fold, iterative=False): fold, train_indices=train_split, test_indices=test_split, iterative=iterative) else: - opt_pred, valid_pred, test_pred = self._partial_fit_and_predict( - fold, train_indices=train_split, test_indices=test_split, - iterative=iterative) + opt_pred, valid_pred, test_pred, additional_run_info = ( + self._partial_fit_and_predict( + fold, + train_indices=train_split, + test_indices=test_split, + iterative=iterative, + ) + ) loss = self._loss(self.Y_targets[fold], opt_pred) if self.cv_folds > 1: @@ -201,14 +231,21 @@ def _partial_fit_and_predict(self, fold, train_indices, test_indices, self.model = model loss = self._loss(self.Y_train[test_indices], Y_optimization_pred) + additional_run_info = model.get_additional_run_info() if model.configuration_fully_fitted(): final_call = True else: final_call = False - self.finish_up(loss, Y_optimization_pred, Y_valid_pred, - Y_test_pred, file_output=file_output, - final_call=final_call) + self.finish_up( + loss, + Y_optimization_pred, + Y_valid_pred, + Y_test_pred, + additional_run_info=additional_run_info, + file_output=file_output, + final_call=final_call, + ) n_iter *= 2 return @@ -225,9 +262,16 @@ def _partial_fit_and_predict(self, fold, train_indices, test_indices, Y_optimization_pred, Y_valid_pred, Y_test_pred = self._predict( model=model, train_indices=train_indices, test_indices=test_indices) loss = self._loss(self.Y_train[test_indices], Y_optimization_pred) - self.finish_up(loss, Y_optimization_pred, Y_valid_pred, - Y_test_pred, file_output=file_output, - final_call=True) + additional_run_info = model.get_additional_run_info() + self.finish_up( + loss, + Y_optimization_pred, + Y_valid_pred, + Y_test_pred, + additional_run_info=additional_run_info, + file_output=file_output, + final_call=True + ) return else: @@ -240,8 +284,14 @@ def _partial_fit_and_predict(self, fold, train_indices, test_indices, train_indices, test_indices = self.indices[fold] self.Y_targets[fold] = self.Y_train[test_indices] - return self._predict(model=model, train_indices=train_indices, - test_indices=test_indices) + + opt_pred, valid_pred, test_pred = self._predict( + model=model, + train_indices=train_indices, + test_indices=test_indices, + ) + additional_run_info = model.get_additional_run_info() + return opt_pred, valid_pred, test_pred, additional_run_info def subsample_indices(self, train_indices): if self.subsample is not None: diff --git a/autosklearn/pipeline/base.py b/autosklearn/pipeline/base.py index d6ceeee975..4cd87d81f9 100644 --- a/autosklearn/pipeline/base.py +++ b/autosklearn/pipeline/base.py @@ -60,6 +60,8 @@ def __init__(self, config=None, pipeline=None, dataset_properties=None, self.random_state = check_random_state(random_state) super().__init__(steps=self.steps) + self._additional_run_info = {} + def fit(self, X, y, **fit_params): """Fit the selected algorithm to the training data. @@ -96,10 +98,10 @@ def fit_transformer(self, X, y, fit_params=None): fit_params = {} fit_params = {key.replace(":", "__"): value for key, value in fit_params.items()} - X, fit_params = self._fit(X, y, **fit_params) + Xt, fit_params = self._fit(X, y, **fit_params) if fit_params is None: fit_params = {} - return X, fit_params + return Xt, fit_params def fit_estimator(self, X, y, **fit_params): fit_params = {key.replace(":", "__"): value for key, value in @@ -378,3 +380,11 @@ def _get_pipeline(self): def _get_estimator_hyperparameter_name(self): raise NotImplementedError() + def get_additional_run_info(self): + """Allows retrieving additional run information from the pipeline. + + Can be overridden by subclasses to return additional information to + the optimization algorithm. + """ + return self._additional_run_info + diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 42bb820f45..506aaf6102 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -10,7 +10,7 @@ from ConfigSpace import Configuration import numpy as np from sklearn.model_selection import StratifiedKFold, ShuffleSplit -from smac.tae.execute_ta_run import StatusType +from smac.tae.execute_ta_run import StatusType, TAEAbortException from autosklearn.evaluation.util import get_last_result from autosklearn.evaluation.train_evaluator import TrainEvaluator, \ @@ -43,6 +43,7 @@ def test_holdout(self, pipeline_mock): pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock + pipeline_mock.get_additional_run_info.return_value = None output_dir = os.path.join(os.getcwd(), '.test_holdout') configuration = unittest.mock.Mock(spec=Configuration) @@ -100,6 +101,7 @@ def configuration_fully_fitted(self): pipeline_mock.configuration_fully_fitted.side_effect = SideEffect().configuration_fully_fitted pipeline_mock.fit_transformer.return_value = Xt_fixture, {} pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) + pipeline_mock.get_additional_run_info.return_value = None pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_iterative_holdout') @@ -174,6 +176,7 @@ def configuration_fully_fitted(self): pipeline_mock.fit_transformer.return_value = Xt_fixture, {} pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock + pipeline_mock.get_additional_run_info.return_value = None output_dir = os.path.join(os.getcwd(), '.test_iterative_holdout_interuption') configuration = unittest.mock.Mock(spec=Configuration) @@ -230,6 +233,7 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): pipeline_mock.fit_transformer.return_value = Xt_fixture, {} pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock + pipeline_mock.get_additional_run_info.return_value = None output_dir = os.path.join(os.getcwd(), '.test_iterative_holdout_not_iterative') configuration = unittest.mock.Mock(spec=Configuration) @@ -269,6 +273,7 @@ def test_cv(self, pipeline_mock): pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock + pipeline_mock.get_additional_run_info.return_value = None output_dir = os.path.join(os.getcwd(), '.test_cv') configuration = unittest.mock.Mock(spec=Configuration) @@ -311,6 +316,7 @@ def test_partial_cv(self, pipeline_mock): pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock + pipeline_mock.get_additional_run_info.return_value = None output_dir = os.path.join(os.getcwd(), '.test_partial_cv') D = get_binary_classification_datamanager() D.name = 'test' @@ -366,6 +372,7 @@ def configuration_fully_fitted(self): pipeline_mock.configuration_fully_fitted.side_effect = SideEffect().configuration_fully_fitted pipeline_mock.fit_transformer.return_value = Xt_fixture, {} pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) + pipeline_mock.get_additional_run_info.return_value = None pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_iterative_partial_cv') @@ -549,6 +556,47 @@ def test_predict_proba_binary_classification(self, mock, backend_mock): for i in range(7): self.assertEqual(0.9, Y_optimization_pred[i][1]) + @unittest.mock.patch.object(TrainEvaluator, 'file_output') + @unittest.mock.patch.object(TrainEvaluator, '_partial_fit_and_predict') + @unittest.mock.patch('autosklearn.util.backend.Backend') + @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') + def test_fit_predict_and_loss_additional_run_info( + self, mock, backend_mock, _partial_fit_and_predict_mock, + file_output_mock, + ): + D = get_binary_classification_datamanager() + mock.side_effect = lambda **kwargs: mock + _partial_fit_and_predict_mock.return_value = ( + [[0.1, 0.9]] * 7, [[0.1, 0.9]] * 7, [[0.1, 0.9]] * 7, {'a': 5} + ) + file_output_mock.return_value = None, None + + configuration = unittest.mock.Mock(spec=Configuration) + queue_ = multiprocessing.Queue() + kfold = ShuffleSplit(random_state=1, n_splits=1) + + evaluator = TrainEvaluator( + D, backend_mock, queue_, + configuration=configuration, + cv=kfold, output_y_hat_optimization=False, + metric=accuracy) + evaluator.Y_targets[0] = [1] * 7 + evaluator.fit_predict_and_loss(iterative=False) + + kfold = ShuffleSplit(random_state=1, n_splits=2) + evaluator = TrainEvaluator( + D, backend_mock, queue_, + configuration=configuration, + cv=kfold, output_y_hat_optimization=False, + metric=accuracy) + evaluator.Y_targets[0] = [1] * 7 + + self.assertRaises( + TAEAbortException, + evaluator.fit_predict_and_loss, + iterative=False + ) + def test_get_results(self): backend_mock = unittest.mock.Mock(spec=backend.Backend) backend_mock.get_model_dir.return_value = 'dutirapbdxvltcrpbdlcatepdeau' From 33a233b1d66ef37c5da7af8edaf5f82908396b7b Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 17 Nov 2017 16:48:00 +0100 Subject: [PATCH 30/61] Do not copy data on scaling --- .../components/data_preprocessing/rescaling/minmax.py | 2 +- .../components/data_preprocessing/rescaling/normalize.py | 2 +- .../data_preprocessing/rescaling/quantile_transformer.py | 3 +++ .../data_preprocessing/rescaling/robust_scaler.py | 6 +++++- .../components/data_preprocessing/rescaling/standardize.py | 2 +- 5 files changed, 11 insertions(+), 4 deletions(-) diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/minmax.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/minmax.py index 38beb19d55..e2cfba1e3e 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/minmax.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/minmax.py @@ -7,7 +7,7 @@ class MinMaxScalerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): def __init__(self, random_state): from sklearn.preprocessing import MinMaxScaler - self.preprocessor = MinMaxScaler() + self.preprocessor = MinMaxScaler(copy=False) @staticmethod def get_properties(dataset_properties=None): diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py index 2a9ae54e17..24562b9ff2 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py @@ -9,7 +9,7 @@ def __init__(self, random_state): # Use custom implementation because sklearn implementation cannot # handle float32 input matrix from autosklearn.pipeline.implementations.Normalizer import Normalizer - self.preprocessor = Normalizer() + self.preprocessor = Normalizer(copy=False) @staticmethod def get_properties(dataset_properties=None): diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py index 57ffd2eac1..1ed98016a7 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py @@ -12,9 +12,12 @@ class QuantileTransformerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): def __init__(self, n_quantiles, output_distribution, random_state): from sklearn.preprocessing import QuantileTransformer + self.n_quantiles = n_quantiles + self.output_distribution = output_distribution self.preprocessor = QuantileTransformer( n_quantiles=n_quantiles, output_distribution=output_distribution, + copy=False ) @staticmethod diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py index 4b36eee44b..75f1608788 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py @@ -11,7 +11,11 @@ class RobustScalerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): def __init__(self, q_min, q_max, random_state): from sklearn.preprocessing import RobustScaler - self.preprocessor = RobustScaler(quantile_range=(q_min, q_max)) + self.q_min = q_min + self.q_max = q_max + self.preprocessor = RobustScaler( + quantile_range=(self.q_min, self.q_max), copy=False, + ) @staticmethod def get_properties(dataset_properties=None): diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py index 2736f8e744..cb00f3f657 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py @@ -9,7 +9,7 @@ class StandardScalerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): def __init__(self, random_state): from sklearn.preprocessing import StandardScaler - self.preprocessor = StandardScaler() + self.preprocessor = StandardScaler(copy=False) @staticmethod def get_properties(dataset_properties=None): From 01ca5d5122ec64eb4afe15a3c9b175b04bb4c869 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 17 Nov 2017 16:48:27 +0100 Subject: [PATCH 31/61] Fix parametrization of extra trees for preprocessing --- .../extra_trees_preproc_for_classification.py | 9 +++------ .../extra_trees_preproc_for_regression.py | 2 +- 2 files changed, 4 insertions(+), 7 deletions(-) diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py index 6eb71eb87d..edcb45c940 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py @@ -57,11 +57,7 @@ def fit(self, X, Y, sample_weight=None): from sklearn.ensemble import ExtraTreesClassifier from sklearn.feature_selection import SelectFromModel - num_features = X.shape[1] - max_features = int( - float(self.max_features) * (np.log(num_features) + 1)) - # Use at most half of the features - max_features = max(1, min(int(X.shape[1] / 2), max_features)) + max_features = int(X.shape[1] ** float(self.max_features)) estimator = ExtraTreesClassifier( n_estimators=self.n_estimators, criterion=self.criterion, @@ -107,7 +103,8 @@ def get_hyperparameter_search_space(dataset_properties=None): n_estimators = Constant("n_estimators", 100) criterion = CategoricalHyperparameter( "criterion", ["gini", "entropy"], default_value="gini") - max_features = UniformFloatHyperparameter("max_features", 0.5, 5, default_value=1) + max_features = UniformFloatHyperparameter("max_features", 0, 1, + default_value=0.5) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py index 6e9dda6b25..d5f8a0b60c 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py @@ -99,7 +99,7 @@ def get_hyperparameter_search_space(dataset_properties=None): criterion = CategoricalHyperparameter("criterion", ["mse", 'friedman_mse', 'mae']) max_features = UniformFloatHyperparameter( - "max_features", 0.5, 5, default_value=1) + "max_features", 0.1, 1.0, default_value=1.0) max_depth = UnParametrizedHyperparameter(name="max_depth", value="None") max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None") From e92e45a59894ee9da96bdf044edb51e8a916185d Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 17 Nov 2017 16:50:27 +0100 Subject: [PATCH 32/61] OHE avoid keeping references --- .../pipeline/implementations/OneHotEncoder.py | 40 ++++++++++++------- 1 file changed, 26 insertions(+), 14 deletions(-) diff --git a/autosklearn/pipeline/implementations/OneHotEncoder.py b/autosklearn/pipeline/implementations/OneHotEncoder.py index 5bdd9fb512..d02c4a5b0b 100644 --- a/autosklearn/pipeline/implementations/OneHotEncoder.py +++ b/autosklearn/pipeline/implementations/OneHotEncoder.py @@ -6,7 +6,7 @@ from sklearn.utils import check_array -def _transform_selected(X, transform, selected="all", copy=True): +def _transform_selected(X, transform, selected="all", copy=False): """Apply a transform function to portion of selected features Parameters @@ -53,7 +53,19 @@ def _transform_selected(X, transform, selected="all", copy=True): X_not_sel = X[:, ind[not_sel]] if sparse.issparse(X_sel) or sparse.issparse(X_not_sel): - return sparse.hstack((X_sel, X_not_sel), format='csr') + # This is pretty memory-intense, making the memory usage for OpenML + # task 146810 go over 3GB + X_not_sel = sparse.coo_matrix(X_not_sel, dtype=np.float32) + del X + gc.collect() + X_tmp = sparse.hstack((X_sel, X_not_sel)) + del X_not_sel + del X_sel + gc.collect() + #X_tmp_csr = X_tmp.tocsr() + #print(X_tmp_csr.data.nbytes, X_tmp_csr.indices.nbytes, + # X_tmp_csr.indptr.nbytes) + return X_tmp.tocsr() else: return np.hstack((X_sel, X_not_sel)) @@ -166,7 +178,7 @@ def _fit_transform(self, X): X[~np.isfinite(X)] = 2 X = check_array(X, accept_sparse='csc', force_all_finite=False, - dtype=int) + dtype=np.int32) if X.min() < 0: raise ValueError("X needs to contain only non-negative integers.") @@ -242,20 +254,20 @@ def _fit_transform(self, X): column_indices.extend(column_indices_) data = np.ones(X.data.size) else: - column_indices = (X + indices[:-1]).ravel() + column_indices = (X + indices[:-1]).ravel().astype(np.int32) row_indices = np.repeat(np.arange(n_samples, dtype=np.int32), n_features) - data = np.ones(n_samples * n_features) + data = np.ones(n_samples * n_features, dtype=np.int32) out = sparse.coo_matrix((data, (row_indices, column_indices)), shape=(n_samples, indices[-1]), - dtype=self.dtype).tocsc() + dtype=np.int32).tocsc() mask = np.array(out.sum(axis=0)).ravel() != 0 active_features = np.where(mask)[0] out = out[:, active_features] self.active_features_ = active_features - return out.tocsr() if self.sparse else out.toarray() + return out.tocoo() if self.sparse else out.toarray() def fit_transform(self, X, y=None): """Fit OneHotEncoder to X, then transform X. @@ -264,7 +276,7 @@ def fit_transform(self, X, y=None): efficient. See fit for the parameters, transform for the return value. """ return _transform_selected(X, self._fit_transform, - self.categorical_features, copy=True) + self.categorical_features, copy=False) def _transform(self, X): """Asssumes X contains only categorical features.""" @@ -280,7 +292,7 @@ def _transform(self, X): X[~np.isfinite(X)] = 2 X = check_array(X, accept_sparse='csc', force_all_finite=False, - dtype=int) + dtype=np.int32) if X.min() < 0: raise ValueError("X needs to contain only non-negative integers.") n_samples, n_features = X.shape @@ -342,16 +354,16 @@ def _transform(self, X): column_indices.extend(column_indices_) data = np.ones(X.data.size) else: - column_indices = (X + indices[:-1]).ravel() + column_indices = (X + indices[:-1]).ravel().astype(np.int32) row_indices = np.repeat(np.arange(n_samples, dtype=np.int32), n_features) - data = np.ones(n_samples * n_features) + data = np.ones(n_samples * n_features, dtype=np.int32) out = sparse.coo_matrix((data, (row_indices, column_indices)), shape=(n_samples, indices[-1]), - dtype=self.dtype).tocsc() + dtype=np.int32).tocsc() out = out[:, self.active_features_] - return out.tocsr() if self.sparse else out.toarray() + return out.tocoo() if self.sparse else out.toarray() def transform(self, X): """Transform X using one-hot encoding. @@ -367,4 +379,4 @@ def transform(self, X): Transformed input. """ return _transform_selected(X, self._transform, - self.categorical_features, copy=True) + self.categorical_features, copy=False) From e41297066f71613bcda3504f6bb1f39ba6ee48cd Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 17 Nov 2017 16:50:45 +0100 Subject: [PATCH 33/61] FIX subclassing --- autosklearn/pipeline/components/base.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/autosklearn/pipeline/components/base.py b/autosklearn/pipeline/components/base.py index 612609ac59..f8fbc93d8f 100644 --- a/autosklearn/pipeline/components/base.py +++ b/autosklearn/pipeline/components/base.py @@ -4,6 +4,7 @@ import pkgutil import sys +from sklearn.base import BaseEstimator from sklearn.utils import check_random_state @@ -59,7 +60,7 @@ def add_component(self, obj): self.components[name] = classifier -class AutoSklearnComponent(object): +class AutoSklearnComponent(BaseEstimator): @staticmethod def get_properties(dataset_properties=None): """Get the properties of the underlying algorithm. From 1dd3a84c1c597addc465213062a06aa2b0d89945 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 17 Nov 2017 16:51:22 +0100 Subject: [PATCH 34/61] FIX worst possible result in SMBO to 1 --- autosklearn/smbo.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index ea5ddc94cc..61ca1450d6 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -1,4 +1,3 @@ -import glob import json import os import time @@ -9,16 +8,19 @@ import pynisher from smac.facade.smac_facade import SMAC +from smac.optimizer.objective import average_cost +from smac.runhistory.runhistory import RunHistory +from smac.runhistory.runhistory2epm import RunHistory2EPM4Cost from smac.scenario.scenario import Scenario from smac.tae.execute_ta_run import StatusType -from smac.runhistory.runhistory2epm import RunHistory2EPM4Cost + import autosklearn.metalearning from autosklearn.constants import * from autosklearn.metalearning.mismbo import suggest_via_metalearning from autosklearn.data.abstract_data_manager import AbstractDataManager from autosklearn.data.competition_data_manager import CompetitionDataManager -from autosklearn.evaluation import ExecuteTaFuncWithQueue +from autosklearn.evaluation import ExecuteTaFuncWithQueue, WORST_POSSIBLE_RESULT from autosklearn.util import get_logger from autosklearn.metalearning.metalearning.meta_base import MetaBase from autosklearn.metalearning.metafeatures.metafeatures import \ @@ -446,6 +448,7 @@ def run_smbo(self): 'run_obj': 'quality', 'shared-model': self.shared_mode, 'wallclock_limit': total_walltime_limit, + 'cost_for_crash': WORST_POSSIBLE_RESULT, } if self.smac_scenario_args is not None: for arg in [ @@ -456,6 +459,7 @@ def run_smbo(self): 'output-dir', 'run_obj', 'shared-model', + 'cost_for_crash', ]: if arg in self.smac_scenario_args: self.logger.warning('Cannot override scenario argument %s, ' @@ -475,12 +479,14 @@ def run_smbo(self): ) scenario_dict.update(self.smac_scenario_args) + runhistory = RunHistory(aggregate_func=average_cost) smac_args = { 'scenario_dict': scenario_dict, 'seed': seed, 'ta': ta, 'backend': self.backend, 'metalearning_configurations': metalearning_configurations, + 'runhistory': runhistory, } if self.get_smac_object_callback is not None: smac = self.get_smac_object_callback(**smac_args) From 6fc557edd7bc86e8979ebc1b01aec520822237b7 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 17 Nov 2017 16:52:32 +0100 Subject: [PATCH 35/61] FIX init params and balancing used together --- autosklearn/pipeline/base.py | 1 + autosklearn/pipeline/classification.py | 1 + 2 files changed, 2 insertions(+) diff --git a/autosklearn/pipeline/base.py b/autosklearn/pipeline/base.py index 4cd87d81f9..3bdc644bb4 100644 --- a/autosklearn/pipeline/base.py +++ b/autosklearn/pipeline/base.py @@ -22,6 +22,7 @@ def __init__(self, config=None, pipeline=None, dataset_properties=None, include=None, exclude=None, random_state=None, init_params=None): + self._init_params = init_params if init_params is not None else {} self.include_ = include if include is not None else {} self.exclude_ = exclude if exclude is not None else {} self.dataset_properties_ = dataset_properties if \ diff --git a/autosklearn/pipeline/classification.py b/autosklearn/pipeline/classification.py index b99ff2fbff..bb4f6669d3 100644 --- a/autosklearn/pipeline/classification.py +++ b/autosklearn/pipeline/classification.py @@ -93,6 +93,7 @@ def fit_transformer(self, X, y, fit_params=None): y, self.configuration['classifier:__choice__'], self.configuration['preprocessor:__choice__'], {}, {}) + _init_params.update(self._init_params) self.set_hyperparameters(configuration=self.configuration, init_params=_init_params) From f0e5a0cd328772a7b5fddcc0a5a353cfb7b775b3 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 17 Nov 2017 16:53:12 +0100 Subject: [PATCH 36/61] Properly propagate sparsity for regression --- autosklearn/util/pipeline.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/autosklearn/util/pipeline.py b/autosklearn/util/pipeline.py index dfb551afec..02ab3bd167 100755 --- a/autosklearn/util/pipeline.py +++ b/autosklearn/util/pipeline.py @@ -56,8 +56,10 @@ def _get_regression_configuration_space(info, include, exclude): if info['is_sparse'] == 1: sparse = True configuration_space = SimpleRegressionPipeline( - dataset_properties=info, include=include, exclude=exclude).\ - get_hyperparameter_search_space() + dataset_properties={'sparse': sparse}, + include=include, + exclude=exclude + ).get_hyperparameter_search_space() return configuration_space From 4388b98132e36d01ac4d1bd39bf24c923d51186c Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 21 Nov 2017 15:02:18 +0100 Subject: [PATCH 37/61] fix compability bugs --- autosklearn/automl.py | 2 +- autosklearn/smbo.py | 4 +++- autosklearn/util/backend.py | 13 ++++++++--- scripts/update_metadata_util.py | 6 +++++ test/test_automl/test_automl.py | 39 ++++++++++++++++++++++++++------- 5 files changed, 51 insertions(+), 13 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 052eb80f9b..09944bec66 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -438,7 +438,7 @@ def _fit(self, datamanager, metric): self.runhistory_, self.trajectory_ = \ _proc_smac.run_smbo() trajectory_filename = os.path.join( - self._backend.get_smac_output_directory(self._seed) + '_run1', + self._backend.get_smac_output_directory_for_run(self._seed), 'trajectory.json') saveable_trajectory = \ [list(entry[:2]) + [entry[2].get_dictionary()] + list(entry[3:]) diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index 61ca1450d6..40a752a3f3 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -167,6 +167,7 @@ def get_smac_object( ta, backend, metalearning_configurations, + runhistory, ): scenario_dict['input_psmac_dirs'] = backend.get_smac_output_glob() scenario = Scenario(scenario_dict) @@ -194,6 +195,7 @@ def get_smac_object( runhistory2epm=rh2EPM, tae_runner=ta, initial_configurations=initial_configurations, + runhistory=runhistory, ) @@ -444,7 +446,7 @@ def run_smbo(self): 'instances': instances, 'memory_limit': self.memory_limit, 'output-dir': - self.backend.get_smac_output_directory(self.seed), + self.backend.get_smac_output_directory(), 'run_obj': 'quality', 'shared-model': self.shared_mode, 'wallclock_limit': total_walltime_limit, diff --git a/autosklearn/util/backend.py b/autosklearn/util/backend.py index f012cb3941..cbb2c26da9 100644 --- a/autosklearn/util/backend.py +++ b/autosklearn/util/backend.py @@ -167,11 +167,18 @@ def load_start_time(self, seed): start_time = float(fh.read()) return start_time - def get_smac_output_directory(self, seed): - return os.path.join(self.temporary_directory, 'smac3-output_%d' % seed) + def get_smac_output_directory(self): + return os.path.join(self.temporary_directory, 'smac3-output') + + def get_smac_output_directory_for_run(self, seed): + return os.path.join( + self.temporary_directory, + 'smac3-output', + 'run_%d' % seed + ) def get_smac_output_glob(self): - return os.path.join(self.temporary_directory, 'smac3-output_*_run1') + return os.path.join(self.temporary_directory, 'smac3-output', 'run_1') def _get_targets_ensemble_filename(self): return os.path.join(self.internals_directory, diff --git a/scripts/update_metadata_util.py b/scripts/update_metadata_util.py index 115635b9ef..6b1019c579 100644 --- a/scripts/update_metadata_util.py +++ b/scripts/update_metadata_util.py @@ -1,3 +1,4 @@ +import numpy as np import openml @@ -46,4 +47,9 @@ def load_task(task_id): del dataset cat = ['categorical' if c else 'numerical' for c in cat] + unique = np.unique(y_train) + mapping = {unique_value: i for i, unique_value in enumerate(unique)} + y_train = np.array([mapping[value] for value in y_train]) + y_test = np.array([mapping[value] for value in y_test]) + return X_train, y_train, X_test, y_test, cat \ No newline at end of file diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py index 0eb0837a42..818e1d415d 100644 --- a/test/test_automl/test_automl.py +++ b/test/test_automl/test_automl.py @@ -8,6 +8,8 @@ import numpy as np import sklearn.datasets +from smac.scenario.scenario import Scenario +from smac.facade.roar_facade import ROAR from autosklearn.util.backend import Backend, BackendContext from autosklearn.automl import AutoML @@ -109,17 +111,35 @@ def test_fit(self): self._tearDown(output) def test_fit_roar(self): + def get_roar_object_callback( + scenario_dict, + seed, + ta, + **kwargs + ): + """Random online adaptive racing. + + http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf""" + scenario = Scenario(scenario_dict) + return ROAR( + scenario=scenario, + rng=seed, + tae_runner=ta, + ) + output = os.path.join(self.test_dir, '..', '.tmp_test_fit_roar') self._setUp(output) X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') backend_api = backend.create(output, output) - automl = autosklearn.automl.AutoML(backend_api, 20, 5, - initial_configurations_via_metalearning=0, - configuration_mode='ROAR') + automl = autosklearn.automl.AutoML( + backend=backend_api, + time_left_for_this_task=20, + per_run_time_limit=5, + initial_configurations_via_metalearning=0, + get_smac_object_callback=get_roar_object_callback, + ) automl.fit(X_train, Y_train, metric=accuracy) - # print(automl.show_models(), flush=True) - # print(automl.cv_results_, flush=True) score = automl.score(X_test, Y_test) self.assertGreaterEqual(score, 0.8) self.assertEqual(automl._task, MULTICLASS_CLASSIFICATION) @@ -169,11 +189,14 @@ def test_automl_outputs(self): data_manager_file = os.path.join(output, '.auto-sklearn', 'datamanager.pkl') - backend_api = backend.create(output, output) + backend_api = backend.create(output, output, + delete_tmp_folder_after_terminate=False) + print(backend_api.temporary_directory) auto = autosklearn.automl.AutoML( backend_api, 20, 5, - initial_configurations_via_metalearning=25, - seed=100) + initial_configurations_via_metalearning=0, + seed=100, + ) auto.fit_automl_dataset(dataset, accuracy) # pickled data manager (without one hot encoding!) From 44ae404d8792da0ee1c1c094483f7039948ac3ac Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 21 Nov 2017 15:57:22 +0100 Subject: [PATCH 38/61] reduce overhead of calls to fit --- .../components/classification/extra_trees.py | 8 +- .../classification/gradient_boosting.py | 8 +- .../classification/passive_aggressive.py | 11 +- .../classification/random_forest.py | 15 +- .../pipeline/components/classification/sgd.py | 11 +- .../components/regression/extra_trees.py | 8 +- .../regression/gradient_boosting.py | 8 +- .../components/regression/random_forest.py | 13 +- .../pipeline/components/regression/sgd.py | 1 + .../pipeline/implementations/OneHotEncoder.c | 13097 ++++++++++++++++ .../pipeline/implementations/OneHotEncoder.py | 54 +- test/test_evaluation/test_test_evaluator.py | 4 +- test/test_evaluation/test_train_evaluator.py | 8 +- .../classification/test_extra_trees.py | 10 +- .../classification/test_passive_aggressive.py | 10 +- .../classification/test_random_forest.py | 10 +- .../components/classification/test_sgd.py | 8 +- .../data_preprocessing/test_balancing.py | 10 +- .../test_extra_trees_classification.py | 2 +- .../components/regression/test_extra_trees.py | 8 +- .../regression/test_random_forests.py | 8 +- .../regression/test_ridge_regression.py | 2 +- test/test_pipeline/test_regression.py | 2 +- 23 files changed, 13208 insertions(+), 108 deletions(-) create mode 100644 autosklearn/pipeline/implementations/OneHotEncoder.c diff --git a/autosklearn/pipeline/components/classification/extra_trees.py b/autosklearn/pipeline/components/classification/extra_trees.py index 62df3eac38..6f7292f399 100644 --- a/autosklearn/pipeline/components/classification/extra_trees.py +++ b/autosklearn/pipeline/components/classification/extra_trees.py @@ -55,10 +55,12 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, self.estimator = None def fit(self, X, y, sample_weight=None): - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, + n_iter = 2 + self.iterative_fit(X, y, n_iter=n_iter, sample_weight=sample_weight, refit=True) while not self.configuration_fully_fitted(): - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight) + n_iter *= 2 + self.iterative_fit(X, y, n_iter=n_iter, sample_weight=sample_weight) return self def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): @@ -88,6 +90,8 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): else: self.estimator.n_estimators += n_iter + self.estimator.n_estimators = min(self.estimator.n_estimators, + self.n_estimators) self.estimator.fit(X, y, sample_weight=sample_weight) return self diff --git a/autosklearn/pipeline/components/classification/gradient_boosting.py b/autosklearn/pipeline/components/classification/gradient_boosting.py index d0b36fcacb..62428dd6d9 100644 --- a/autosklearn/pipeline/components/classification/gradient_boosting.py +++ b/autosklearn/pipeline/components/classification/gradient_boosting.py @@ -34,10 +34,12 @@ def __init__(self, loss, learning_rate, n_estimators, subsample, self.fully_fit_ = False def fit(self, X, y, sample_weight=None, refit=False): - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, + n_iter = 2 + self.iterative_fit(X, y, n_iter=n_iter, sample_weight=sample_weight, refit=True) while not self.configuration_fully_fitted(): - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight) + n_iter *= 2 + self.iterative_fit(X, y, n_iter=n_iter, sample_weight=sample_weight) return self def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): @@ -86,6 +88,8 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): else: self.estimator.n_estimators += n_iter + self.estimator.n_estimators = min(self.estimator.n_estimators, + self.n_estimators) self.estimator.fit(X, y, sample_weight=sample_weight) diff --git a/autosklearn/pipeline/components/classification/passive_aggressive.py b/autosklearn/pipeline/components/classification/passive_aggressive.py index 4fac16bcba..7e53be92ce 100644 --- a/autosklearn/pipeline/components/classification/passive_aggressive.py +++ b/autosklearn/pipeline/components/classification/passive_aggressive.py @@ -22,9 +22,11 @@ def __init__(self, C, fit_intercept, tol, loss, average, random_state=None): self.estimator = None def fit(self, X, y): - self.iterative_fit(X, y, n_iter=2, refit=True) + n_iter = 2 + self.iterative_fit(X, y, n_iter=n_iter, refit=True) while not self.configuration_fully_fitted(): - self.iterative_fit(X, y, n_iter=2) + n_iter *= 2 + self.iterative_fit(X, y, n_iter=n_iter) return self @@ -38,7 +40,6 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): # iterations than max_iter. If max_iter == 1, it has to spend at least # one iteration and will always spend at least one iteration, so we # cannot know about convergence. - n_iter = max(n_iter, 2) if refit: self.estimator = None @@ -73,6 +74,8 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): self.estimator.fit(X, y) else: self.estimator.max_iter += n_iter + self.estimator.max_iter = min(self.estimator.max_iter, + 1000) self.estimator._validate_params() lr = "pa1" if self.estimator.loss == "hinge" else "pa2" self.estimator._partial_fit( @@ -135,7 +138,7 @@ def get_hyperparameter_search_space(dataset_properties=None): "loss", ["hinge", "squared_hinge"], default_value="hinge" ) - tol = UniformFloatHyperparameter("tol", 1e-4, 1e-1, default_value=1e-3, + tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, log=True) average = CategoricalHyperparameter('average', [False, True]) diff --git a/autosklearn/pipeline/components/classification/random_forest.py b/autosklearn/pipeline/components/classification/random_forest.py index 96fdc1f790..7fdd425797 100644 --- a/autosklearn/pipeline/components/classification/random_forest.py +++ b/autosklearn/pipeline/components/classification/random_forest.py @@ -32,10 +32,12 @@ def __init__(self, n_estimators, criterion, max_features, self.estimator = None def fit(self, X, y, sample_weight=None, refit=False): - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, + n_iter = 2 + self.iterative_fit(X, y, n_iter=n_iter, sample_weight=sample_weight, refit=True) while not self.configuration_fully_fitted(): - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight) + n_iter *= 2 + self.iterative_fit(X, y, n_iter=n_iter, sample_weight=sample_weight) return self def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): @@ -82,10 +84,11 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): class_weight=self.class_weight, warm_start=True) - tmp = self.estimator - tmp.n_estimators += n_iter - tmp.fit(X, y, sample_weight=sample_weight) - self.estimator = tmp + self.estimator.n_estimators += n_iter + self.estimator.n_estimators = min(self.estimator.n_estimators, + self.n_estimators) + + self.estimator.fit(X, y, sample_weight=sample_weight) return self def configuration_fully_fitted(self): diff --git a/autosklearn/pipeline/components/classification/sgd.py b/autosklearn/pipeline/components/classification/sgd.py index 633b90729b..caa0babd53 100644 --- a/autosklearn/pipeline/components/classification/sgd.py +++ b/autosklearn/pipeline/components/classification/sgd.py @@ -30,10 +30,12 @@ def __init__(self, loss, penalty, alpha, fit_intercept, tol, self.estimator = None def fit(self, X, y, sample_weight=None): - self.iterative_fit(X, y, n_iter=2, sample_weight=sample_weight, + n_iter = 2 + self.iterative_fit(X, y, n_iter=n_iter, sample_weight=sample_weight, refit=True) while not self.configuration_fully_fitted(): - self.iterative_fit(X, y, n_iter=2, sample_weight=sample_weight) + n_iter *= 2 + self.iterative_fit(X, y, n_iter=n_iter, sample_weight=sample_weight) return self @@ -46,7 +48,6 @@ def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None): # iterations than max_iter. If max_iter == 1, it has to spend at least # one iteration and will always spend at least one iteration, so we # cannot know about convergence. - n_iter = max(n_iter, 2) if refit: self.estimator = None @@ -148,8 +149,8 @@ def get_hyperparameter_search_space(dataset_properties=None): l1_ratio = UniformFloatHyperparameter( "l1_ratio", 1e-9, 1, log=True, default_value=0.15) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") - tol = UniformFloatHyperparameter("tol", 1e-4, 1e-1, log=True, - default_value=1e-3) + tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, log=True, + default_value=1e-4) epsilon = UniformFloatHyperparameter( "epsilon", 1e-5, 1e-1, default_value=1e-4, log=True) learning_rate = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/components/regression/extra_trees.py b/autosklearn/pipeline/components/regression/extra_trees.py index 2b42d86ab1..734fc301c0 100644 --- a/autosklearn/pipeline/components/regression/extra_trees.py +++ b/autosklearn/pipeline/components/regression/extra_trees.py @@ -49,9 +49,11 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, self.estimator = None def fit(self, X, y, refit=False): - self.iterative_fit(X, y, n_iter=1, refit=refit) + n_iter = 2 + self.iterative_fit(X, y, n_iter=n_iter, refit=refit) while not self.configuration_fully_fitted(): - self.iterative_fit(X, y, n_iter=1) + n_iter *= 2 + self.iterative_fit(X, y, n_iter=n_iter) return self @@ -78,6 +80,8 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): warm_start=True) else: self.estimator.n_estimators += n_iter + self.estimator.n_estimators = min(self.estimator.n_estimators, + self.n_estimators) self.estimator.fit(X, y,) diff --git a/autosklearn/pipeline/components/regression/gradient_boosting.py b/autosklearn/pipeline/components/regression/gradient_boosting.py index 919b59dd29..5ce42d4962 100644 --- a/autosklearn/pipeline/components/regression/gradient_boosting.py +++ b/autosklearn/pipeline/components/regression/gradient_boosting.py @@ -34,10 +34,12 @@ def __init__(self, loss, learning_rate, n_estimators, subsample, self.estimator = None def fit(self, X, y, sample_weight=None, refit=False): - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, + n_iter = 2 + self.iterative_fit(X, y, n_iter=n_iter, sample_weight=sample_weight, refit=True) while not self.configuration_fully_fitted(): - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight) + n_iter *= 2 + self.iterative_fit(X, y, n_iter=n_iter, sample_weight=sample_weight) return self @@ -90,6 +92,8 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): ) else: self.estimator.n_estimators += n_iter + self.estimator.n_estimators = min(self.estimator.n_estimators, + self.n_estimators) self.estimator.fit(X, y, sample_weight=sample_weight) diff --git a/autosklearn/pipeline/components/regression/random_forest.py b/autosklearn/pipeline/components/regression/random_forest.py index 0721c82009..55d202c37d 100644 --- a/autosklearn/pipeline/components/regression/random_forest.py +++ b/autosklearn/pipeline/components/regression/random_forest.py @@ -30,9 +30,11 @@ def __init__(self, n_estimators, criterion, max_features, self.estimator = None def fit(self, X, y, sample_weight=None): - self.iterative_fit(X, y, n_iter=1, refit=True) + n_iter = 2 + self.iterative_fit(X, y, n_iter=n_iter, refit=True) while not self.configuration_fully_fitted(): - self.iterative_fit(X, y, n_iter=1) + n_iter *= 2 + self.iterative_fit(X, y, n_iter=n_iter) return self @@ -60,7 +62,7 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.min_impurity_decrease = float(self.min_impurity_decrease) self.estimator = RandomForestRegressor( - n_estimators=n_iter, + n_estimators=0, criterion=self.criterion, max_features=self.max_features, max_depth=self.max_depth, @@ -73,8 +75,9 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): random_state=self.random_state, n_jobs=self.n_jobs, warm_start=True) - else: - self.estimator.n_estimators += n_iter + self.estimator.n_estimators += n_iter + self.estimator.n_estimators = min(self.estimator.n_estimators, + self.n_estimators) self.estimator.fit(X, y) return self diff --git a/autosklearn/pipeline/components/regression/sgd.py b/autosklearn/pipeline/components/regression/sgd.py index 871095e6be..c76b8c947e 100644 --- a/autosklearn/pipeline/components/regression/sgd.py +++ b/autosklearn/pipeline/components/regression/sgd.py @@ -86,6 +86,7 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): self.estimator.fit(X, Y_scaled) else: self.estimator.max_iter += n_iter + self.estimator.max_iter = min(self.estimator.max_iter, 1000) Y_scaled = self.scaler.transform(y.reshape((-1, 1))).ravel() self.estimator._validate_params() self.estimator._partial_fit( diff --git a/autosklearn/pipeline/implementations/OneHotEncoder.c b/autosklearn/pipeline/implementations/OneHotEncoder.c new file mode 100644 index 0000000000..66fe45f695 --- /dev/null +++ b/autosklearn/pipeline/implementations/OneHotEncoder.c @@ -0,0 +1,13097 @@ +/* Generated by Cython 0.27.1 */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) + #error Cython requires Python 2.6+ or Python 3.3+. +#else +#define CYTHON_ABI "0_27_1" +#define CYTHON_FUTURE_DIVISION 0 +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x02070000 + #define HAVE_LONG_LONG + #endif +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT (0 && PY_VERSION_HEX >= 0x03050000) + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#if PY_VERSION_HEX < 0x030700A0 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject **args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_Current +#endif +#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef __Pyx_PyAsyncMethodsStruct + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int32 uint32_t; + #endif + #endif +#else + #include +#endif +#ifndef CYTHON_FALLTHROUGH + #ifdef __cplusplus + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #elif __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) || (defined(__GNUC__) && defined(__attribute__)) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + + +#define __PYX_ERR(f_index, lineno, Ln_error) \ +{ \ + __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ +} + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__autosklearn__pipeline__implementations__OneHotEncoder +#define __PYX_HAVE_API__autosklearn__pipeline__implementations__OneHotEncoder +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } + +static PyObject *__pyx_m = NULL; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_cython_runtime; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + + +static const char *__pyx_f[] = { + "autosklearn/pipeline/implementations/OneHotEncoder.py", +}; + +/*--- Type declarations ---*/ +struct __pyx_defaults; +typedef struct __pyx_defaults __pyx_defaults; +struct __pyx_defaults { + PyObject *__pyx_arg_dtype; +}; + +/* --- Runtime support code (head) --- */ +/* Refnanny.proto */ +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, int); + void (*DECREF)(void*, PyObject*, int); + void (*GOTREF)(void*, PyObject*, int); + void (*GIVEREF)(void*, PyObject*, int); + void* (*SetupContext)(const char*, int, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; +#ifdef WITH_THREAD + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + if (acquire_gil) {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ + PyGILState_Release(__pyx_gilstate_save);\ + } else {\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ + } +#else + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) +#endif + #define __Pyx_RefNannyFinishContext()\ + __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +/* GetModuleGlobalName.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* StrEquals.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals +#else +#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals +#endif + +/* PyCFunctionFastCall.proto */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); +#else +#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) +#endif + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs); +#else +#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) +#endif +#endif + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, long intval, int inplace); +#else +#define __Pyx_PyInt_EqObjC(op1, op2, intval, inplace)\ + PyObject_RichCompare(op1, op2, Py_EQ) + #endif + +/* PyObjectSetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +#define __Pyx_PyObject_DelAttrStr(o,n) __Pyx_PyObject_SetAttrStr(o,n,NULL) +static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_setattro)) + return tp->tp_setattro(obj, attr_name, value); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_setattr)) + return tp->tp_setattr(obj, PyString_AS_STRING(attr_name), value); +#endif + return PyObject_SetAttr(obj, attr_name, value); +} +#else +#define __Pyx_PyObject_DelAttrStr(o,n) PyObject_DelAttr(o,n) +#define __Pyx_PyObject_SetAttrStr(o,n,v) PyObject_SetAttr(o,n,v) +#endif + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace); +#else +#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace)\ + (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) +#endif + +/* PyObjectCallNoArg.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); +#else +#define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL) +#endif + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() PyErr_Occurred() +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* IterFinish.proto */ +static CYTHON_INLINE int __Pyx_IterFinish(void); + +/* UnpackItemEndCheck.proto */ +static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); + +/* ListAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_PyList_Append(L,x) PyList_Append(L,x) +#endif + +/* SliceObject.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice( + PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop, + PyObject** py_start, PyObject** py_stop, PyObject** py_slice, + int has_cstart, int has_cstop, int wraparound); + +/* PyObjectCallMethod1.proto */ +static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg); +static PyObject* __Pyx__PyObject_CallMethod1(PyObject* method, PyObject* arg); + +/* append.proto */ +static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x); + +/* PySequenceContains.proto */ +static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) { + int result = PySequence_Contains(seq, item); + return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); +} + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, long intval, int inplace); +#else +#define __Pyx_PyInt_SubtractObjC(op1, op2, intval, inplace)\ + (inplace ? PyNumber_InPlaceSubtract(op1, op2) : PyNumber_Subtract(op1, op2)) +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* ImportFrom.proto */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); + +/* FetchCommonType.proto */ +static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); + +/* CythonFunction.proto */ +#define __Pyx_CyFunction_USED 1 +#include +#define __Pyx_CYFUNCTION_STATICMETHOD 0x01 +#define __Pyx_CYFUNCTION_CLASSMETHOD 0x02 +#define __Pyx_CYFUNCTION_CCLASS 0x04 +#define __Pyx_CyFunction_GetClosure(f)\ + (((__pyx_CyFunctionObject *) (f))->func_closure) +#define __Pyx_CyFunction_GetClassObj(f)\ + (((__pyx_CyFunctionObject *) (f))->func_classobj) +#define __Pyx_CyFunction_Defaults(type, f)\ + ((type *)(((__pyx_CyFunctionObject *) (f))->defaults)) +#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\ + ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g) +typedef struct { + PyCFunctionObject func; +#if PY_VERSION_HEX < 0x030500A0 + PyObject *func_weakreflist; +#endif + PyObject *func_dict; + PyObject *func_name; + PyObject *func_qualname; + PyObject *func_doc; + PyObject *func_globals; + PyObject *func_code; + PyObject *func_closure; + PyObject *func_classobj; + void *defaults; + int defaults_pyobjects; + int flags; + PyObject *defaults_tuple; + PyObject *defaults_kwdict; + PyObject *(*defaults_getter)(PyObject *); + PyObject *func_annotations; +} __pyx_CyFunctionObject; +static PyTypeObject *__pyx_CyFunctionType = 0; +#define __Pyx_CyFunction_NewEx(ml, flags, qualname, self, module, globals, code)\ + __Pyx_CyFunction_New(__pyx_CyFunctionType, ml, flags, qualname, self, module, globals, code) +static PyObject *__Pyx_CyFunction_New(PyTypeObject *, PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *self, + PyObject *module, PyObject *globals, + PyObject* code); +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m, + size_t size, + int pyobjects); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m, + PyObject *tuple); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, + PyObject *dict); +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, + PyObject *dict); +static int __pyx_CyFunction_init(void); + +/* CalculateMetaclass.proto */ +static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases); + +/* Py3ClassCreate.proto */ +static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name, PyObject *qualname, + PyObject *mkw, PyObject *modname, PyObject *doc); +static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases, PyObject *dict, + PyObject *mkw, int calculate_metaclass, int allow_py2_metaclass); + +/* CLineInTraceback.proto */ +static int __Pyx_CLineForTraceback(int c_line); + +/* CodeObjectCache.proto */ +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) +#endif + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + + +/* Module declarations from 'autosklearn.pipeline.implementations.OneHotEncoder' */ +#define __Pyx_MODULE_NAME "autosklearn.pipeline.implementations.OneHotEncoder" +int __pyx_module_is_main_autosklearn__pipeline__implementations__OneHotEncoder = 0; + +/* Implementation of 'autosklearn.pipeline.implementations.OneHotEncoder' */ +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_enumerate; +static const char __pyx_k_X[] = "X"; +static const char __pyx_k_i[] = "i"; +static const char __pyx_k_y[] = "y"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k_all[] = "all"; +static const char __pyx_k_any[] = "any"; +static const char __pyx_k_csc[] = "csc"; +static const char __pyx_k_csr[] = "csr"; +static const char __pyx_k_doc[] = "__doc__"; +static const char __pyx_k_fit[] = "fit"; +static const char __pyx_k_ind[] = "ind"; +static const char __pyx_k_max[] = "max"; +static const char __pyx_k_min[] = "min"; +static const char __pyx_k_nbr[] = "nbr"; +static const char __pyx_k_out[] = "out"; +static const char __pyx_k_sel[] = "sel"; +static const char __pyx_k_sum[] = "sum"; +static const char __pyx_k_axis[] = "axis"; +static const char __pyx_k_copy[] = "copy"; +static const char __pyx_k_data[] = "data"; +static const char __pyx_k_init[] = "__init__"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mask[] = "mask"; +static const char __pyx_k_ones[] = "ones"; +static const char __pyx_k_self[] = "self"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_X_sel[] = "X_sel"; +static const char __pyx_k_array[] = "array"; +static const char __pyx_k_count[] = "count"; +static const char __pyx_k_dtype[] = "dtype"; +static const char __pyx_k_float[] = "float"; +static const char __pyx_k_int32[] = "int32"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_ravel[] = "ravel"; +static const char __pyx_k_scipy[] = "scipy"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_tocsc[] = "tocsc"; +static const char __pyx_k_tocsr[] = "tocsr"; +static const char __pyx_k_where[] = "where"; +static const char __pyx_k_zeros[] = "zeros"; +static const char __pyx_k_append[] = "append"; +static const char __pyx_k_arange[] = "arange"; +static const char __pyx_k_astype[] = "astype"; +static const char __pyx_k_column[] = "column"; +static const char __pyx_k_cumsum[] = "cumsum"; +static const char __pyx_k_extend[] = "extend"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_hstack[] = "hstack"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_indptr[] = "indptr"; +static const char __pyx_k_module[] = "__module__"; +static const char __pyx_k_nansum[] = "nansum"; +static const char __pyx_k_repeat[] = "repeat"; +static const char __pyx_k_sparse[] = "sparse"; +static const char __pyx_k_unique[] = "unique"; +static const char __pyx_k_asarray[] = "asarray"; +static const char __pyx_k_colsize[] = "colsize"; +static const char __pyx_k_flatten[] = "flatten"; +static const char __pyx_k_indices[] = "indices"; +static const char __pyx_k_not_sel[] = "not_sel"; +static const char __pyx_k_prepare[] = "__prepare__"; +static const char __pyx_k_toarray[] = "toarray"; +static const char __pyx_k_fraction[] = "fraction"; +static const char __pyx_k_isfinite[] = "isfinite"; +static const char __pyx_k_issparse[] = "issparse"; +static const char __pyx_k_n_values[] = "n_values_"; +static const char __pyx_k_qualname[] = "__qualname__"; +static const char __pyx_k_selected[] = "selected"; +static const char __pyx_k_X_not_sel[] = "X_not_sel"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_metaclass[] = "__metaclass__"; +static const char __pyx_k_n_samples[] = "n_samples"; +static const char __pyx_k_transform[] = "transform"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_coo_matrix[] = "coo_matrix"; +static const char __pyx_k_indptr_end[] = "indptr_end"; +static const char __pyx_k_n_features[] = "n_features"; +static const char __pyx_k_n_selected[] = "n_selected"; +static const char __pyx_k_n_values_2[] = "n_values"; +static const char __pyx_k_check_array[] = "check_array"; +static const char __pyx_k_logical_not[] = "logical_not"; +static const char __pyx_k_row_indices[] = "row_indices"; +static const char __pyx_k_transform_2[] = "_transform"; +static const char __pyx_k_indptr_start[] = "indptr_start"; +static const char __pyx_k_sklearn_base[] = "sklearn.base"; +static const char __pyx_k_unique_value[] = "unique_value"; +static const char __pyx_k_BaseEstimator[] = "BaseEstimator"; +static const char __pyx_k_OneHotEncoder[] = "OneHotEncoder"; +static const char __pyx_k_accept_sparse[] = "accept_sparse"; +static const char __pyx_k_fit_transform[] = "fit_transform"; +static const char __pyx_k_n_value_check[] = "n_value_check"; +static const char __pyx_k_sklearn_utils[] = "sklearn.utils"; +static const char __pyx_k_column_indices[] = "column_indices"; +static const char __pyx_k_n_values_check[] = "n_values_check"; +static const char __pyx_k_active_features[] = "active_features_"; +static const char __pyx_k_feature_indices[] = "feature_indices_"; +static const char __pyx_k_fit_transform_2[] = "_fit_transform"; +static const char __pyx_k_TransformerMixin[] = "TransformerMixin"; +static const char __pyx_k_column_indices_2[] = "column_indices_"; +static const char __pyx_k_force_all_finite[] = "force_all_finite"; +static const char __pyx_k_minimum_fraction[] = "minimum_fraction"; +static const char __pyx_k_OneHotEncoder_fit[] = "OneHotEncoder.fit"; +static const char __pyx_k_active_features_2[] = "active_features"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_transform_selected[] = "_transform_selected"; +static const char __pyx_k_OneHotEncoder___init[] = "OneHotEncoder.__init__"; +static const char __pyx_k_categorical_features[] = "categorical_features"; +static const char __pyx_k_OneHotEncoder_transform[] = "OneHotEncoder.transform"; +static const char __pyx_k_do_not_replace_by_other[] = "do_not_replace_by_other_"; +static const char __pyx_k_OneHotEncoder__transform[] = "OneHotEncoder._transform"; +static const char __pyx_k_do_not_replace_by_other_2[] = "do_not_replace_by_other"; +static const char __pyx_k_OneHotEncoder_fit_transform[] = "OneHotEncoder.fit_transform"; +static const char __pyx_k_OneHotEncoder__fit_transform[] = "OneHotEncoder._fit_transform"; +static const char __pyx_k_Encode_categorical_integer_featu[] = "Encode categorical integer features using a one-hot aka one-of-K scheme.\n\n The input to this transformer should be a matrix of integers, denoting\n the values taken on by categorical (discrete) features. The output will be\n a sparse matrix were each column corresponds to one possible value of one\n feature. It is assumed that input features take on values in the range\n [0, n_values).\n\n This encoding is needed for feeding categorical data to many scikit-learn\n estimators, notably linear models and SVMs with the standard kernels.\n\n Parameters\n ----------\n\n categorical_features: \"all\" or array of indices or mask\n Specify what features are treated as categorical.\n\n - 'all' (default): All features are treated as categorical.\n - array of indices: Array of categorical feature indices.\n - mask: Array of length n_features and with dtype=bool.\n\n Non-categorical features are always stacked to the right of the matrix.\n\n dtype : number type, default=np.float\n Desired dtype of output.\n\n sparse : boolean, default=True\n Will return sparse matrix if set True else will return an array.\n\n Attributes\n ----------\n `active_features_` : array\n Indices for active features, meaning values that actually occur\n in the training set. Only available when n_values is ``'auto'``.\n\n `feature_indices_` : array of shape (n_features,)\n Indices to feature ranges.\n Feature ``i`` in the original data is mapped to features\n from ``feature_indices_[i]`` to ``feature_indices_[i+1]``\n (and then potentially masked by `active_features_` afterwards)\n\n `n_values_` : array of shape (n_features,)\n Maximum number of values per feature.\n\n Examples\n --------\n Given a dataset with three features and two samples, we let the encoder\n find the maximum value per feature and transform the data to a binary\n one-hot ""encoding.\n\n >>> from sklearn.preprocessing import OneHotEncoder\n >>> enc = OneHotEncoder()\n >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]]) # doctest: +ELLIPSIS\n OneHotEncoder(categorical_features='all', dtype=<... 'float'>,\n sparse=True, minimum_fraction=None)\n >>> enc.n_values_\n array([2, 3, 4])\n >>> enc.feature_indices_\n array([0, 2, 5, 9])\n >>> enc.transform([[0, 1, 1]]).toarray()\n array([[ 1., 0., 0., 1., 0., 0., 1., 0., 0.]])\n\n See also\n --------\n sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of\n dictionary items (also handles string-valued features).\n sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot\n encoding of dictionary items or strings.\n "; +static const char __pyx_k_X_has_different_shape_than_durin[] = "X has different shape than during fitting. Expected %d, got %d."; +static const char __pyx_k_X_needs_to_contain_only_non_nega[] = "X needs to contain only non-negative integers."; +static const char __pyx_k_autosklearn_pipeline_implementat[] = "autosklearn/pipeline/implementations/OneHotEncoder.py"; +static const char __pyx_k_autosklearn_pipeline_implementat_2[] = "autosklearn.pipeline.implementations.OneHotEncoder"; +static PyObject *__pyx_n_s_BaseEstimator; +static PyObject *__pyx_kp_s_Encode_categorical_integer_featu; +static PyObject *__pyx_n_s_OneHotEncoder; +static PyObject *__pyx_n_s_OneHotEncoder___init; +static PyObject *__pyx_n_s_OneHotEncoder__fit_transform; +static PyObject *__pyx_n_s_OneHotEncoder__transform; +static PyObject *__pyx_n_s_OneHotEncoder_fit; +static PyObject *__pyx_n_s_OneHotEncoder_fit_transform; +static PyObject *__pyx_n_s_OneHotEncoder_transform; +static PyObject *__pyx_n_s_TransformerMixin; +static PyObject *__pyx_n_s_ValueError; +static PyObject *__pyx_n_s_X; +static PyObject *__pyx_kp_s_X_has_different_shape_than_durin; +static PyObject *__pyx_kp_s_X_needs_to_contain_only_non_nega; +static PyObject *__pyx_n_s_X_not_sel; +static PyObject *__pyx_n_s_X_sel; +static PyObject *__pyx_n_s_accept_sparse; +static PyObject *__pyx_n_s_active_features; +static PyObject *__pyx_n_s_active_features_2; +static PyObject *__pyx_n_s_all; +static PyObject *__pyx_n_s_any; +static PyObject *__pyx_n_s_append; +static PyObject *__pyx_n_s_arange; +static PyObject *__pyx_n_s_array; +static PyObject *__pyx_n_s_asarray; +static PyObject *__pyx_n_s_astype; +static PyObject *__pyx_kp_s_autosklearn_pipeline_implementat; +static PyObject *__pyx_n_s_autosklearn_pipeline_implementat_2; +static PyObject *__pyx_n_s_axis; +static PyObject *__pyx_n_s_categorical_features; +static PyObject *__pyx_n_s_check_array; +static PyObject *__pyx_n_s_cline_in_traceback; +static PyObject *__pyx_n_s_colsize; +static PyObject *__pyx_n_s_column; +static PyObject *__pyx_n_s_column_indices; +static PyObject *__pyx_n_s_column_indices_2; +static PyObject *__pyx_n_s_coo_matrix; +static PyObject *__pyx_n_s_copy; +static PyObject *__pyx_n_s_count; +static PyObject *__pyx_n_s_csc; +static PyObject *__pyx_n_s_csr; +static PyObject *__pyx_n_s_cumsum; +static PyObject *__pyx_n_s_data; +static PyObject *__pyx_n_s_do_not_replace_by_other; +static PyObject *__pyx_n_s_do_not_replace_by_other_2; +static PyObject *__pyx_n_s_doc; +static PyObject *__pyx_n_s_dtype; +static PyObject *__pyx_n_s_enumerate; +static PyObject *__pyx_n_s_extend; +static PyObject *__pyx_n_s_feature_indices; +static PyObject *__pyx_n_s_fit; +static PyObject *__pyx_n_s_fit_transform; +static PyObject *__pyx_n_s_fit_transform_2; +static PyObject *__pyx_n_s_flatten; +static PyObject *__pyx_n_s_float; +static PyObject *__pyx_n_s_force_all_finite; +static PyObject *__pyx_n_s_format; +static PyObject *__pyx_n_s_fraction; +static PyObject *__pyx_n_s_hstack; +static PyObject *__pyx_n_s_i; +static PyObject *__pyx_n_s_import; +static PyObject *__pyx_n_s_ind; +static PyObject *__pyx_n_s_indices; +static PyObject *__pyx_n_s_indptr; +static PyObject *__pyx_n_s_indptr_end; +static PyObject *__pyx_n_s_indptr_start; +static PyObject *__pyx_n_s_init; +static PyObject *__pyx_n_s_int32; +static PyObject *__pyx_n_s_isfinite; +static PyObject *__pyx_n_s_issparse; +static PyObject *__pyx_n_s_logical_not; +static PyObject *__pyx_n_s_main; +static PyObject *__pyx_n_s_mask; +static PyObject *__pyx_n_s_max; +static PyObject *__pyx_n_s_metaclass; +static PyObject *__pyx_n_s_min; +static PyObject *__pyx_n_s_minimum_fraction; +static PyObject *__pyx_n_s_module; +static PyObject *__pyx_n_s_n_features; +static PyObject *__pyx_n_s_n_samples; +static PyObject *__pyx_n_s_n_selected; +static PyObject *__pyx_n_s_n_value_check; +static PyObject *__pyx_n_s_n_values; +static PyObject *__pyx_n_s_n_values_2; +static PyObject *__pyx_n_s_n_values_check; +static PyObject *__pyx_n_s_nansum; +static PyObject *__pyx_n_s_nbr; +static PyObject *__pyx_n_s_not_sel; +static PyObject *__pyx_n_s_np; +static PyObject *__pyx_n_s_numpy; +static PyObject *__pyx_n_s_ones; +static PyObject *__pyx_n_s_out; +static PyObject *__pyx_n_s_prepare; +static PyObject *__pyx_n_s_qualname; +static PyObject *__pyx_n_s_range; +static PyObject *__pyx_n_s_ravel; +static PyObject *__pyx_n_s_repeat; +static PyObject *__pyx_n_s_row_indices; +static PyObject *__pyx_n_s_scipy; +static PyObject *__pyx_n_s_sel; +static PyObject *__pyx_n_s_selected; +static PyObject *__pyx_n_s_self; +static PyObject *__pyx_n_s_shape; +static PyObject *__pyx_n_s_size; +static PyObject *__pyx_n_s_sklearn_base; +static PyObject *__pyx_n_s_sklearn_utils; +static PyObject *__pyx_n_s_sparse; +static PyObject *__pyx_n_s_sum; +static PyObject *__pyx_n_s_test; +static PyObject *__pyx_n_s_toarray; +static PyObject *__pyx_n_s_tocsc; +static PyObject *__pyx_n_s_tocsr; +static PyObject *__pyx_n_s_transform; +static PyObject *__pyx_n_s_transform_2; +static PyObject *__pyx_n_s_transform_selected; +static PyObject *__pyx_n_s_unique; +static PyObject *__pyx_n_s_unique_value; +static PyObject *__pyx_n_s_where; +static PyObject *__pyx_n_s_y; +static PyObject *__pyx_n_s_zeros; +static PyObject *__pyx_pf_11autosklearn_8pipeline_15implementations_13OneHotEncoder__transform_selected(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_X, PyObject *__pyx_v_transform, PyObject *__pyx_v_selected, PyObject *__pyx_v_copy); /* proto */ +static PyObject *__pyx_pf_11autosklearn_8pipeline_15implementations_13OneHotEncoder_2__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */ +static PyObject *__pyx_pf_11autosklearn_8pipeline_15implementations_13OneHotEncoder_13OneHotEncoder___init__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_categorical_features, PyObject *__pyx_v_dtype, PyObject *__pyx_v_sparse, PyObject *__pyx_v_minimum_fraction); /* proto */ +static PyObject *__pyx_pf_11autosklearn_8pipeline_15implementations_13OneHotEncoder_13OneHotEncoder_2fit(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_X, CYTHON_UNUSED PyObject *__pyx_v_y); /* proto */ +static PyObject *__pyx_pf_11autosklearn_8pipeline_15implementations_13OneHotEncoder_13OneHotEncoder_4_fit_transform(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_X); /* proto */ +static PyObject *__pyx_pf_11autosklearn_8pipeline_15implementations_13OneHotEncoder_13OneHotEncoder_6fit_transform(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_X, CYTHON_UNUSED PyObject *__pyx_v_y); /* proto */ +static PyObject *__pyx_pf_11autosklearn_8pipeline_15implementations_13OneHotEncoder_13OneHotEncoder_8_transform(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_X); /* proto */ +static PyObject *__pyx_pf_11autosklearn_8pipeline_15implementations_13OneHotEncoder_13OneHotEncoder_10transform(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_X); /* proto */ +static PyObject *__pyx_int_0; +static PyObject *__pyx_int_1; +static PyObject *__pyx_int_2; +static PyObject *__pyx_int_3; +static PyObject *__pyx_int_neg_1; +static PyObject *__pyx_slice_; +static PyObject *__pyx_slice__2; +static PyObject *__pyx_slice__4; +static PyObject *__pyx_slice__5; +static PyObject *__pyx_slice__6; +static PyObject *__pyx_slice__7; +static PyObject *__pyx_slice__8; +static PyObject *__pyx_slice__9; +static PyObject *__pyx_tuple__3; +static PyObject *__pyx_slice__10; +static PyObject *__pyx_slice__12; +static PyObject *__pyx_slice__13; +static PyObject *__pyx_slice__14; +static PyObject *__pyx_slice__15; +static PyObject *__pyx_slice__16; +static PyObject *__pyx_slice__17; +static PyObject *__pyx_slice__18; +static PyObject *__pyx_tuple__11; +static PyObject *__pyx_tuple__19; +static PyObject *__pyx_tuple__21; +static PyObject *__pyx_tuple__22; +static PyObject *__pyx_tuple__24; +static PyObject *__pyx_tuple__26; +static PyObject *__pyx_tuple__27; +static PyObject *__pyx_tuple__29; +static PyObject *__pyx_tuple__31; +static PyObject *__pyx_tuple__32; +static PyObject *__pyx_tuple__34; +static PyObject *__pyx_codeobj__20; +static PyObject *__pyx_codeobj__23; +static PyObject *__pyx_codeobj__25; +static PyObject *__pyx_codeobj__28; +static PyObject *__pyx_codeobj__30; +static PyObject *__pyx_codeobj__33; +static PyObject *__pyx_codeobj__35; + +/* "autosklearn/pipeline/implementations/OneHotEncoder.py":7 + * + * + * def _transform_selected(X, transform, selected="all", copy=True): # <<<<<<<<<<<<<< + * """Apply a transform function to portion of selected features + * + */ + +/* Python wrapper */ +static PyObject *__pyx_pw_11autosklearn_8pipeline_15implementations_13OneHotEncoder_1_transform_selected(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static char __pyx_doc_11autosklearn_8pipeline_15implementations_13OneHotEncoder__transform_selected[] = "Apply a transform function to portion of selected features\n\n Parameters\n ----------\n X : array-like or sparse matrix, shape=(n_samples, n_features)\n Dense array or sparse matrix.\n\n transform : callable\n A callable transform(X) -> X_transformed\n\n copy : boolean, optional\n Copy X even if it could be avoided.\n\n selected: \"all\" or array of indices or mask\n Specify which features to apply the transform to.\n\n Returns\n -------\n X : array or sparse matrix, shape=(n_samples, n_features_new)\n "; 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"" : "s", num_found); +} + +/* RaiseDoubleKeywords */ +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +/* GetModuleGlobalName */ +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS + result = PyDict_GetItem(__pyx_d, name); + if (likely(result)) { + Py_INCREF(result); + } else { +#else + result = PyObject_GetItem(__pyx_d, name); + if (!result) { + PyErr_Clear(); +#endif + result = __Pyx_GetBuiltinName(name); + } + return result; +} + +/* PyObjectCall */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* BytesEquals */ + static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else + if (s1 == s2) { + return (equals == Py_EQ); + } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { + const char *ps1, *ps2; + Py_ssize_t length = PyBytes_GET_SIZE(s1); + if (length != PyBytes_GET_SIZE(s2)) + return (equals == Py_NE); + ps1 = PyBytes_AS_STRING(s1); + ps2 = PyBytes_AS_STRING(s2); + if (ps1[0] != ps2[0]) { + return (equals == Py_NE); + } else if (length == 1) { + return (equals == Py_EQ); + } else { + int result; +#if CYTHON_USE_UNICODE_INTERNALS + Py_hash_t hash1, hash2; + hash1 = ((PyBytesObject*)s1)->ob_shash; + hash2 = ((PyBytesObject*)s2)->ob_shash; + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + return (equals == Py_NE); + } +#endif + result = memcmp(ps1, ps2, (size_t)length); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ + static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else +#if PY_MAJOR_VERSION < 3 + PyObject* owned_ref = NULL; +#endif + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); +#if PY_MAJOR_VERSION < 3 + if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { + owned_ref = PyUnicode_FromObject(s2); + if (unlikely(!owned_ref)) + return -1; + s2 = owned_ref; + s2_is_unicode = 1; + } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { + owned_ref = PyUnicode_FromObject(s1); + if (unlikely(!owned_ref)) + return -1; + s1 = owned_ref; + s1_is_unicode = 1; + } else if (((!s2_is_unicode) & (!s1_is_unicode))) { + return __Pyx_PyBytes_Equals(s1, s2, equals); + } +#endif + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length; + int kind; + void *data1, *data2; + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + length = __Pyx_PyUnicode_GET_LENGTH(s1); + if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { + goto return_ne; + } +#if CYTHON_USE_UNICODE_INTERNALS + { + Py_hash_t hash1, hash2; + #if CYTHON_PEP393_ENABLED + hash1 = ((PyASCIIObject*)s1)->hash; + hash2 = ((PyASCIIObject*)s2)->hash; + #else + hash1 = ((PyUnicodeObject*)s1)->hash; + hash2 = ((PyUnicodeObject*)s2)->hash; + #endif + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + goto return_ne; + } + } +#endif + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ); +return_ne: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_NE); +#endif +} + +/* PyCFunctionFastCall */ + #if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { + PyCFunctionObject *func = (PyCFunctionObject*)func_obj; + PyCFunction meth = PyCFunction_GET_FUNCTION(func); + PyObject *self = PyCFunction_GET_SELF(func); + int flags = PyCFunction_GET_FLAGS(func); + assert(PyCFunction_Check(func)); + assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS))); + assert(nargs >= 0); + assert(nargs == 0 || args != NULL); + /* _PyCFunction_FastCallDict() must not be called with an exception set, + because it may clear it (directly or indirectly) and so the + caller loses its exception */ + assert(!PyErr_Occurred()); + if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { + return (*((__Pyx_PyCFunctionFastWithKeywords)meth)) (self, args, nargs, NULL); + } else { + return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs); + } +} +#endif + +/* PyFunctionFastCall */ + #if CYTHON_FAST_PYCALL +#include "frameobject.h" +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = f->f_localsplus; + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif +#endif + +/* PyObjectCallMethO */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallOneArg */ + #if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, &arg, 1); + } +#endif + if (likely(PyCFunction_Check(func))) { + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); +#if CYTHON_FAST_PYCCALL + } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { + return __Pyx_PyCFunction_FastCall(func, &arg, 1); +#endif + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_Pack(1, arg); + if (unlikely(!args)) return NULL; + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +#endif + +/* GetItemInt */ + static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (!j) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; + if (likely(m && m->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { + Py_ssize_t l = m->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return m->sq_item(o, i); + } + } +#else + if (is_list || PySequence_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + +/* PyIntBinop */ + #if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { + if (op1 == op2) { + Py_RETURN_TRUE; + } + #if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(op1))) { + const long b = intval; + long a = PyInt_AS_LONG(op1); + if (a == b) { + Py_RETURN_TRUE; + } else { + Py_RETURN_FALSE; + } + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a; + const digit* digits = ((PyLongObject*)op1)->ob_digit; + const Py_ssize_t size = Py_SIZE(op1); + if (likely(__Pyx_sst_abs(size) <= 1)) { + a = likely(size) ? digits[0] : 0; + if (size == -1) a = -a; + } else { + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + } + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + } + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + } + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + } + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + } + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + } + #if PyLong_SHIFT < 30 && PyLong_SHIFT != 15 + default: return PyLong_Type.tp_richcompare(op1, op2, Py_EQ); + #else + default: Py_RETURN_FALSE; + #endif + } + } + if (a == b) { + Py_RETURN_TRUE; + } else { + Py_RETURN_FALSE; + } + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = PyFloat_AS_DOUBLE(op1); + if ((double)a == (double)b) { + Py_RETURN_TRUE; + } else { + Py_RETURN_FALSE; + } + } + return PyObject_RichCompare(op1, op2, Py_EQ); +} +#endif + +/* PyIntBinop */ + #if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { + #if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(op1))) { + const long b = intval; + long x; + long a = PyInt_AS_LONG(op1); + x = (long)((unsigned long)a + b); + if (likely((x^a) >= 0 || (x^b) >= 0)) + return PyInt_FromLong(x); + return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a, x; +#ifdef HAVE_LONG_LONG + const PY_LONG_LONG llb = intval; + PY_LONG_LONG lla, llx; +#endif + const digit* digits = ((PyLongObject*)op1)->ob_digit; + const Py_ssize_t size = Py_SIZE(op1); + if (likely(__Pyx_sst_abs(size) <= 1)) { + a = likely(size) ? digits[0] : 0; + if (size == -1) a = -a; + } else { + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + default: return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + } + x = a + b; + return PyLong_FromLong(x); +#ifdef HAVE_LONG_LONG + long_long: + llx = lla + llb; + return PyLong_FromLongLong(llx); +#endif + + + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = PyFloat_AS_DOUBLE(op1); + double result; + PyFPE_START_PROTECT("add", return NULL) + result = ((double)a) + (double)b; + PyFPE_END_PROTECT(result) + return PyFloat_FromDouble(result); + } + return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); +} +#endif + +/* PyObjectCallNoArg */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, NULL, 0); + } +#endif +#ifdef __Pyx_CyFunction_USED + if (likely(PyCFunction_Check(func) || __Pyx_TypeCheck(func, __pyx_CyFunctionType))) { +#else + if (likely(PyCFunction_Check(func))) { +#endif + if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { + return __Pyx_PyObject_CallMethO(func, NULL); + } + } + return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); +} +#endif + +/* PyErrFetchRestore */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +} +#endif + +/* RaiseException */ + #if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + __Pyx_PyThreadState_declare + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* RaiseTooManyValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* IterFinish */ + static CYTHON_INLINE int __Pyx_IterFinish(void) { +#if CYTHON_FAST_THREAD_STATE + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* exc_type = tstate->curexc_type; + if (unlikely(exc_type)) { + if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) { + PyObject *exc_value, *exc_tb; + exc_value = tstate->curexc_value; + exc_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; + Py_DECREF(exc_type); + Py_XDECREF(exc_value); + Py_XDECREF(exc_tb); + return 0; + } else { + return -1; + } + } + return 0; +#else + if (unlikely(PyErr_Occurred())) { + if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { + PyErr_Clear(); + return 0; + } else { + return -1; + } + } + return 0; +#endif +} + +/* UnpackItemEndCheck */ + static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { + if (unlikely(retval)) { + Py_DECREF(retval); + __Pyx_RaiseTooManyValuesError(expected); + return -1; + } else { + return __Pyx_IterFinish(); + } + return 0; +} + +/* SliceObject */ + static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(PyObject* obj, + Py_ssize_t cstart, Py_ssize_t cstop, + PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice, + int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) { +#if CYTHON_USE_TYPE_SLOTS + PyMappingMethods* mp; +#if PY_MAJOR_VERSION < 3 + PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence; + if (likely(ms && ms->sq_slice)) { + if (!has_cstart) { + if (_py_start && (*_py_start != Py_None)) { + cstart = __Pyx_PyIndex_AsSsize_t(*_py_start); + if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; + } else + cstart = 0; + } + if (!has_cstop) { + if (_py_stop && (*_py_stop != Py_None)) { + cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop); + if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; + } else + cstop = PY_SSIZE_T_MAX; + } + if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) { + Py_ssize_t l = ms->sq_length(obj); + if (likely(l >= 0)) { + if (cstop < 0) { + cstop += l; + if (cstop < 0) cstop = 0; + } + if (cstart < 0) { + cstart += l; + if (cstart < 0) cstart = 0; + } + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + goto bad; + PyErr_Clear(); + } + } + return ms->sq_slice(obj, cstart, cstop); + } +#endif + mp = Py_TYPE(obj)->tp_as_mapping; + if (likely(mp && mp->mp_subscript)) +#endif + { + PyObject* result; + PyObject *py_slice, *py_start, *py_stop; + if (_py_slice) { + py_slice = *_py_slice; + } else { + PyObject* owned_start = NULL; + PyObject* owned_stop = NULL; + if (_py_start) { + py_start = *_py_start; + } else { + if (has_cstart) { + owned_start = py_start = PyInt_FromSsize_t(cstart); + if (unlikely(!py_start)) goto bad; + } else + py_start = Py_None; + } + if (_py_stop) { + py_stop = *_py_stop; + } else { + if (has_cstop) { + owned_stop = py_stop = PyInt_FromSsize_t(cstop); + if (unlikely(!py_stop)) { + Py_XDECREF(owned_start); + goto bad; + } + } else + py_stop = Py_None; + } + py_slice = PySlice_New(py_start, py_stop, Py_None); + Py_XDECREF(owned_start); + Py_XDECREF(owned_stop); + if (unlikely(!py_slice)) goto bad; + } +#if CYTHON_USE_TYPE_SLOTS + result = mp->mp_subscript(obj, py_slice); +#else + result = PyObject_GetItem(obj, py_slice); +#endif + if (!_py_slice) { + Py_DECREF(py_slice); + } + return result; + } + PyErr_Format(PyExc_TypeError, + "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name); +bad: + return NULL; +} + +/* PyObjectCallMethod1 */ + static PyObject* __Pyx__PyObject_CallMethod1(PyObject* method, PyObject* arg) { + PyObject *result = NULL; +#if CYTHON_UNPACK_METHODS + if (likely(PyMethod_Check(method))) { + PyObject *self = PyMethod_GET_SELF(method); + if (likely(self)) { + PyObject *args; + PyObject *function = PyMethod_GET_FUNCTION(method); + #if CYTHON_FAST_PYCALL + if (PyFunction_Check(function)) { + PyObject *args[2] = {self, arg}; + result = __Pyx_PyFunction_FastCall(function, args, 2); + goto done; + } + #endif + #if CYTHON_FAST_PYCCALL + if (__Pyx_PyFastCFunction_Check(function)) { + PyObject *args[2] = {self, arg}; + result = __Pyx_PyCFunction_FastCall(function, args, 2); + goto done; + } + #endif + args = PyTuple_New(2); + if (unlikely(!args)) goto done; + Py_INCREF(self); + PyTuple_SET_ITEM(args, 0, self); + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 1, arg); + Py_INCREF(function); + result = __Pyx_PyObject_Call(function, args, NULL); + Py_DECREF(args); + Py_DECREF(function); + return result; + } + } +#endif + result = __Pyx_PyObject_CallOneArg(method, arg); + goto done; +done: + return result; +} +static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) { + PyObject *method, *result = NULL; + method = __Pyx_PyObject_GetAttrStr(obj, method_name); + if (unlikely(!method)) goto done; + result = __Pyx__PyObject_CallMethod1(method, arg); +done: + Py_XDECREF(method); + return result; +} + +/* append */ + static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x) { + if (likely(PyList_CheckExact(L))) { + if (unlikely(__Pyx_PyList_Append(L, x) < 0)) return -1; + } else { + PyObject* retval = __Pyx_PyObject_CallMethod1(L, __pyx_n_s_append, x); + if (unlikely(!retval)) + return -1; + Py_DECREF(retval); + } + return 0; +} + +/* PyIntBinop */ + #if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { + #if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(op1))) { + const long b = intval; + long x; + long a = PyInt_AS_LONG(op1); + x = (long)((unsigned long)a - b); + if (likely((x^a) >= 0 || (x^~b) >= 0)) + return PyInt_FromLong(x); + return PyLong_Type.tp_as_number->nb_subtract(op1, op2); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a, x; +#ifdef HAVE_LONG_LONG + const PY_LONG_LONG llb = intval; + PY_LONG_LONG lla, llx; +#endif + const digit* digits = ((PyLongObject*)op1)->ob_digit; + const Py_ssize_t size = Py_SIZE(op1); + if (likely(__Pyx_sst_abs(size) <= 1)) { + a = likely(size) ? digits[0] : 0; + if (size == -1) a = -a; + } else { + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + default: return PyLong_Type.tp_as_number->nb_subtract(op1, op2); + } + } + x = a - b; + return PyLong_FromLong(x); +#ifdef HAVE_LONG_LONG + long_long: + llx = lla - llb; + return PyLong_FromLongLong(llx); +#endif + + + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = PyFloat_AS_DOUBLE(op1); + double result; + PyFPE_START_PROTECT("subtract", return NULL) + result = ((double)a) - (double)b; + PyFPE_END_PROTECT(result) + return PyFloat_FromDouble(result); + } + return (inplace ? PyNumber_InPlaceSubtract : PyNumber_Subtract)(op1, op2); +} +#endif + +/* Import */ + static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_MAJOR_VERSION < 3 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_MAJOR_VERSION < 3 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +/* ImportFrom */ + static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { + PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); + if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { + PyErr_Format(PyExc_ImportError, + #if PY_MAJOR_VERSION < 3 + "cannot import name %.230s", PyString_AS_STRING(name)); + #else + "cannot import name %S", name); + #endif + } + return value; +} + +/* FetchCommonType */ + static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { + PyObject* fake_module; + PyTypeObject* cached_type = NULL; + fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI); + if (!fake_module) return NULL; + Py_INCREF(fake_module); + cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name); + if (cached_type) { + if (!PyType_Check((PyObject*)cached_type)) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s is not a type object", + type->tp_name); + goto bad; + } + if (cached_type->tp_basicsize != type->tp_basicsize) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s has the wrong size, try recompiling", + type->tp_name); + goto bad; + } + } else { + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; + PyErr_Clear(); + if (PyType_Ready(type) < 0) goto bad; + if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0) + goto bad; + Py_INCREF(type); + cached_type = type; + } +done: + Py_DECREF(fake_module); + return cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} + +/* CythonFunction */ + static PyObject * +__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure) +{ + if (unlikely(op->func_doc == NULL)) { + if (op->func.m_ml->ml_doc) { +#if PY_MAJOR_VERSION >= 3 + op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc); +#else + op->func_doc = PyString_FromString(op->func.m_ml->ml_doc); +#endif + if (unlikely(op->func_doc == NULL)) + return NULL; + } else { + Py_INCREF(Py_None); + return Py_None; + } + } + Py_INCREF(op->func_doc); + return op->func_doc; +} +static int +__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value) +{ + PyObject *tmp = op->func_doc; + if (value == NULL) { + value = Py_None; + } + Py_INCREF(value); + op->func_doc = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op) +{ + if (unlikely(op->func_name == NULL)) { +#if PY_MAJOR_VERSION >= 3 + op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name); +#else + op->func_name = PyString_InternFromString(op->func.m_ml->ml_name); +#endif + if (unlikely(op->func_name == NULL)) + return NULL; + } + Py_INCREF(op->func_name); + return op->func_name; +} +static int +__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value) +{ + PyObject *tmp; +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) { +#else + if (unlikely(value == NULL || !PyString_Check(value))) { +#endif + PyErr_SetString(PyExc_TypeError, + "__name__ must be set to a string object"); + return -1; + } + tmp = op->func_name; + Py_INCREF(value); + op->func_name = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op) +{ + Py_INCREF(op->func_qualname); + return op->func_qualname; +} +static int +__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value) +{ + PyObject *tmp; +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) { +#else + if (unlikely(value == NULL || !PyString_Check(value))) { +#endif + PyErr_SetString(PyExc_TypeError, + "__qualname__ must be set to a string object"); + return -1; + } + tmp = op->func_qualname; + Py_INCREF(value); + op->func_qualname = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure) +{ + PyObject *self; + self = m->func_closure; + if (self == NULL) + self = Py_None; + Py_INCREF(self); + return self; +} +static PyObject * +__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op) +{ + if (unlikely(op->func_dict == NULL)) { + op->func_dict = PyDict_New(); + if (unlikely(op->func_dict == NULL)) + return NULL; + } + Py_INCREF(op->func_dict); + return op->func_dict; +} +static int +__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value) +{ + PyObject *tmp; + if (unlikely(value == NULL)) { + PyErr_SetString(PyExc_TypeError, + "function's dictionary may not be deleted"); + return -1; + } + if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "setting function's dictionary to a non-dict"); + return -1; + } + tmp = op->func_dict; + Py_INCREF(value); + op->func_dict = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op) +{ + Py_INCREF(op->func_globals); + return op->func_globals; +} +static PyObject * +__Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op) +{ + Py_INCREF(Py_None); + return Py_None; +} +static PyObject * +__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op) +{ + PyObject* result = (op->func_code) ? op->func_code : Py_None; + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { + int result = 0; + PyObject *res = op->defaults_getter((PyObject *) op); + if (unlikely(!res)) + return -1; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + op->defaults_tuple = PyTuple_GET_ITEM(res, 0); + Py_INCREF(op->defaults_tuple); + op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); + Py_INCREF(op->defaults_kwdict); + #else + op->defaults_tuple = PySequence_ITEM(res, 0); + if (unlikely(!op->defaults_tuple)) result = -1; + else { + op->defaults_kwdict = PySequence_ITEM(res, 1); + if (unlikely(!op->defaults_kwdict)) result = -1; + } + #endif + Py_DECREF(res); + return result; +} +static int +__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value) { + PyObject* tmp; + if (!value) { + value = Py_None; + } else if (value != Py_None && !PyTuple_Check(value)) { + PyErr_SetString(PyExc_TypeError, + "__defaults__ must be set to a tuple object"); + return -1; + } + Py_INCREF(value); + tmp = op->defaults_tuple; + op->defaults_tuple = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op) { + PyObject* result = op->defaults_tuple; + if (unlikely(!result)) { + if (op->defaults_getter) { + if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; + result = op->defaults_tuple; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value) { + PyObject* tmp; + if (!value) { + value = Py_None; + } else if (value != Py_None && !PyDict_Check(value)) { + PyErr_SetString(PyExc_TypeError, + "__kwdefaults__ must be set to a dict object"); + return -1; + } + Py_INCREF(value); + tmp = op->defaults_kwdict; + op->defaults_kwdict = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op) { + PyObject* result = op->defaults_kwdict; + if (unlikely(!result)) { + if (op->defaults_getter) { + if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; + result = op->defaults_kwdict; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value) { + PyObject* tmp; + if (!value || value == Py_None) { + value = NULL; + } else if (!PyDict_Check(value)) { + PyErr_SetString(PyExc_TypeError, + "__annotations__ must be set to a dict object"); + return -1; + } + Py_XINCREF(value); + tmp = op->func_annotations; + op->func_annotations = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op) { + PyObject* result = op->func_annotations; + if (unlikely(!result)) { + result = PyDict_New(); + if (unlikely(!result)) return NULL; + op->func_annotations = result; + } + Py_INCREF(result); + return result; +} +static PyGetSetDef __pyx_CyFunction_getsets[] = { + {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0}, + {(char *) "__self__", (getter)__Pyx_CyFunction_get_self, 0, 0, 0}, + {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, + {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, + {0, 0, 0, 0, 0} +}; +static PyMemberDef __pyx_CyFunction_members[] = { + {(char *) "__module__", T_OBJECT, offsetof(__pyx_CyFunctionObject, func.m_module), PY_WRITE_RESTRICTED, 0}, + {0, 0, 0, 0, 0} +}; +static PyObject * +__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args) +{ +#if PY_MAJOR_VERSION >= 3 + return PyUnicode_FromString(m->func.m_ml->ml_name); +#else + return PyString_FromString(m->func.m_ml->ml_name); +#endif +} +static PyMethodDef __pyx_CyFunction_methods[] = { + {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, + {0, 0, 0, 0} +}; +#if PY_VERSION_HEX < 0x030500A0 +#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) +#else +#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist) +#endif +static PyObject *__Pyx_CyFunction_New(PyTypeObject *type, PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { + __pyx_CyFunctionObject *op = PyObject_GC_New(__pyx_CyFunctionObject, type); + if (op == NULL) + return NULL; + op->flags = flags; + __Pyx_CyFunction_weakreflist(op) = NULL; + op->func.m_ml = ml; + op->func.m_self = (PyObject *) op; + Py_XINCREF(closure); + op->func_closure = closure; + Py_XINCREF(module); + op->func.m_module = module; + op->func_dict = NULL; + op->func_name = NULL; + Py_INCREF(qualname); + op->func_qualname = qualname; + op->func_doc = NULL; + op->func_classobj = NULL; + op->func_globals = globals; + Py_INCREF(op->func_globals); + Py_XINCREF(code); + op->func_code = code; + op->defaults_pyobjects = 0; + op->defaults = NULL; + op->defaults_tuple = NULL; + op->defaults_kwdict = NULL; + op->defaults_getter = NULL; + op->func_annotations = NULL; + PyObject_GC_Track(op); + return (PyObject *) op; +} +static int +__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m) +{ + Py_CLEAR(m->func_closure); + Py_CLEAR(m->func.m_module); + Py_CLEAR(m->func_dict); + Py_CLEAR(m->func_name); + Py_CLEAR(m->func_qualname); + Py_CLEAR(m->func_doc); + Py_CLEAR(m->func_globals); + Py_CLEAR(m->func_code); + Py_CLEAR(m->func_classobj); + Py_CLEAR(m->defaults_tuple); + Py_CLEAR(m->defaults_kwdict); + Py_CLEAR(m->func_annotations); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_XDECREF(pydefaults[i]); + PyObject_Free(m->defaults); + m->defaults = NULL; + } + return 0; +} +static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + if (__Pyx_CyFunction_weakreflist(m) != NULL) + PyObject_ClearWeakRefs((PyObject *) m); + __Pyx_CyFunction_clear(m); + PyObject_GC_Del(m); +} +static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + PyObject_GC_UnTrack(m); + __Pyx__CyFunction_dealloc(m); +} +static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) +{ + Py_VISIT(m->func_closure); + Py_VISIT(m->func.m_module); + Py_VISIT(m->func_dict); + Py_VISIT(m->func_name); + Py_VISIT(m->func_qualname); + Py_VISIT(m->func_doc); + Py_VISIT(m->func_globals); + Py_VISIT(m->func_code); + Py_VISIT(m->func_classobj); + Py_VISIT(m->defaults_tuple); + Py_VISIT(m->defaults_kwdict); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_VISIT(pydefaults[i]); + } + return 0; +} +static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type) +{ + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) { + Py_INCREF(func); + return func; + } + if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) { + if (type == NULL) + type = (PyObject *)(Py_TYPE(obj)); + return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type))); + } + if (obj == Py_None) + obj = NULL; + return __Pyx_PyMethod_New(func, obj, type); +} +static PyObject* +__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) +{ +#if PY_MAJOR_VERSION >= 3 + return PyUnicode_FromFormat("", + op->func_qualname, (void *)op); +#else + return PyString_FromFormat("", + PyString_AsString(op->func_qualname), (void *)op); +#endif +} +static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { + PyCFunctionObject* f = (PyCFunctionObject*)func; + PyCFunction meth = f->m_ml->ml_meth; + Py_ssize_t size; + switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { + case METH_VARARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) + return (*meth)(self, arg); + break; + case METH_VARARGS | METH_KEYWORDS: + return (*(PyCFunctionWithKeywords)meth)(self, arg, kw); + case METH_NOARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { + size = PyTuple_GET_SIZE(arg); + if (likely(size == 0)) + return (*meth)(self, NULL); + PyErr_Format(PyExc_TypeError, + "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); + return NULL; + } + break; + case METH_O: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { + size = PyTuple_GET_SIZE(arg); + if (likely(size == 1)) { + PyObject *result, *arg0; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + arg0 = PyTuple_GET_ITEM(arg, 0); + #else + arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL; + #endif + result = (*meth)(self, arg0); + #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(arg0); + #endif + return result; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); + return NULL; + } + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags in " + "__Pyx_CyFunction_Call. METH_OLDARGS is no " + "longer supported!"); + return NULL; + } + PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments", + f->m_ml->ml_name); + return NULL; +} +static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { + return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw); +} +static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) { + PyObject *result; + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + Py_ssize_t argc; + PyObject *new_args; + PyObject *self; + argc = PyTuple_GET_SIZE(args); + new_args = PyTuple_GetSlice(args, 1, argc); + if (unlikely(!new_args)) + return NULL; + self = PyTuple_GetItem(args, 0); + if (unlikely(!self)) { + Py_DECREF(new_args); + return NULL; + } + result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw); + Py_DECREF(new_args); + } else { + result = __Pyx_CyFunction_Call(func, args, kw); + } + return result; +} +static PyTypeObject __pyx_CyFunctionType_type = { + PyVarObject_HEAD_INIT(0, 0) + "cython_function_or_method", + sizeof(__pyx_CyFunctionObject), + 0, + (destructor) __Pyx_CyFunction_dealloc, + 0, + 0, + 0, +#if PY_MAJOR_VERSION < 3 + 0, +#else + 0, +#endif + (reprfunc) __Pyx_CyFunction_repr, + 0, + 0, + 0, + 0, + __Pyx_CyFunction_CallAsMethod, + 0, + 0, + 0, + 0, + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC, + 0, + (traverseproc) __Pyx_CyFunction_traverse, + (inquiry) __Pyx_CyFunction_clear, + 0, +#if PY_VERSION_HEX < 0x030500A0 + offsetof(__pyx_CyFunctionObject, func_weakreflist), +#else + offsetof(PyCFunctionObject, m_weakreflist), +#endif + 0, + 0, + __pyx_CyFunction_methods, + __pyx_CyFunction_members, + __pyx_CyFunction_getsets, + 0, + 0, + __Pyx_CyFunction_descr_get, + 0, + offsetof(__pyx_CyFunctionObject, func_dict), + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, +#if PY_VERSION_HEX >= 0x030400a1 + 0, +#endif +}; +static int __pyx_CyFunction_init(void) { + __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); + if (unlikely(__pyx_CyFunctionType == NULL)) { + return -1; + } + return 0; +} +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults = PyObject_Malloc(size); + if (unlikely(!m->defaults)) + return PyErr_NoMemory(); + memset(m->defaults, 0, size); + m->defaults_pyobjects = pyobjects; + return m->defaults; +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_tuple = tuple; + Py_INCREF(tuple); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_kwdict = dict; + Py_INCREF(dict); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->func_annotations = dict; + Py_INCREF(dict); +} + +/* CalculateMetaclass */ + static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases) { + Py_ssize_t i, nbases = PyTuple_GET_SIZE(bases); + for (i=0; i < nbases; i++) { + PyTypeObject *tmptype; + PyObject *tmp = PyTuple_GET_ITEM(bases, i); + tmptype = Py_TYPE(tmp); +#if PY_MAJOR_VERSION < 3 + if (tmptype == &PyClass_Type) + continue; +#endif + if (!metaclass) { + metaclass = tmptype; + continue; + } + if (PyType_IsSubtype(metaclass, tmptype)) + continue; + if (PyType_IsSubtype(tmptype, metaclass)) { + metaclass = tmptype; + continue; + } + PyErr_SetString(PyExc_TypeError, + "metaclass conflict: " + "the metaclass of a derived class " + "must be a (non-strict) subclass " + "of the metaclasses of all its bases"); + return NULL; + } + if (!metaclass) { +#if PY_MAJOR_VERSION < 3 + metaclass = &PyClass_Type; +#else + metaclass = &PyType_Type; +#endif + } + Py_INCREF((PyObject*) metaclass); + return (PyObject*) metaclass; +} + +/* Py3ClassCreate */ + static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name, + PyObject *qualname, PyObject *mkw, PyObject *modname, PyObject *doc) { + PyObject *ns; + if (metaclass) { + PyObject *prep = __Pyx_PyObject_GetAttrStr(metaclass, __pyx_n_s_prepare); + if (prep) { + PyObject *pargs = PyTuple_Pack(2, name, bases); + if (unlikely(!pargs)) { + Py_DECREF(prep); + return NULL; + } + ns = PyObject_Call(prep, pargs, mkw); + Py_DECREF(prep); + Py_DECREF(pargs); + } else { + if (unlikely(!PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + PyErr_Clear(); + ns = PyDict_New(); + } + } else { + ns = PyDict_New(); + } + if (unlikely(!ns)) + return NULL; + if (unlikely(PyObject_SetItem(ns, __pyx_n_s_module, modname) < 0)) goto bad; + if (unlikely(PyObject_SetItem(ns, __pyx_n_s_qualname, qualname) < 0)) goto bad; + if (unlikely(doc && PyObject_SetItem(ns, __pyx_n_s_doc, doc) < 0)) goto bad; + return ns; +bad: + Py_DECREF(ns); + return NULL; +} +static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases, + PyObject *dict, PyObject *mkw, + int calculate_metaclass, int allow_py2_metaclass) { + PyObject *result, *margs; + PyObject *owned_metaclass = NULL; + if (allow_py2_metaclass) { + owned_metaclass = PyObject_GetItem(dict, __pyx_n_s_metaclass); + if (owned_metaclass) { + metaclass = owned_metaclass; + } else if (likely(PyErr_ExceptionMatches(PyExc_KeyError))) { + PyErr_Clear(); + } else { + return NULL; + } + } + if (calculate_metaclass && (!metaclass || PyType_Check(metaclass))) { + metaclass = __Pyx_CalculateMetaclass((PyTypeObject*) metaclass, bases); + Py_XDECREF(owned_metaclass); + if (unlikely(!metaclass)) + return NULL; + owned_metaclass = metaclass; + } + margs = PyTuple_Pack(3, name, bases, dict); + if (unlikely(!margs)) { + result = NULL; + } else { + result = PyObject_Call(metaclass, margs, mkw); + Py_DECREF(margs); + } + Py_XDECREF(owned_metaclass); + return result; +} + +/* CLineInTraceback */ + static int __Pyx_CLineForTraceback(int c_line) { +#ifdef CYTHON_CLINE_IN_TRACEBACK + return ((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0; +#else + PyObject *use_cline; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + use_cline = PyDict_GetItem(*cython_runtime_dict, __pyx_n_s_cline_in_traceback); + } else +#endif + { + PyObject *ptype, *pvalue, *ptraceback; + PyObject *use_cline_obj; + PyErr_Fetch(&ptype, &pvalue, &ptraceback); + use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_DECREF(use_cline_obj); + } else { + use_cline = NULL; + } + PyErr_Restore(ptype, pvalue, ptraceback); + } + if (!use_cline) { + c_line = 0; + PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + } + else if (PyObject_Not(use_cline) != 0) { + c_line = 0; + } + return c_line; +#endif +} + +/* CodeObjectCache */ + static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +/* AddTraceback */ + #include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + if (c_line) { + c_line = __Pyx_CLineForTraceback(c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + __Pyx_PyThreadState_Current, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* FastTypeChecks */ + #if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = a->tp_base; + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +#if PY_MAJOR_VERSION == 2 +static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { + PyObject *exception, *value, *tb; + int res; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&exception, &value, &tb); + res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; + if (!res) { + res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } + return res; +} +#endif +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) { + if (likely(err == exc_type)) return 1; + if (likely(PyExceptionClass_Check(err))) { + return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type); + } + return PyErr_GivenExceptionMatches(err, exc_type); +} +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) { + if (likely(err == exc_type1 || err == exc_type2)) return 1; + if (likely(PyExceptionClass_Check(err))) { + return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2); + } + return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2)); +} +#endif + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + PyErr_Clear(); + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type %.200s). " + "The ability to return an instance of a strict subclass of int " + "is deprecated, and may be removed in a future version of Python.", + Py_TYPE(result)->tp_name)) { + Py_DECREF(result); + return NULL; + } + return result; + } +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + type_name, type_name, Py_TYPE(result)->tp_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x) || PyLong_Check(x))) +#else + if (likely(PyLong_Check(x))) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = m->nb_int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = m->nb_long(x); + } + #else + if (likely(m && m->nb_int)) { + name = "int"; + res = m->nb_int(x); + } + #endif +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Int(x); + } +#endif + if (likely(res)) { +#if PY_MAJOR_VERSION < 3 + if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { +#else + if (unlikely(!PyLong_CheckExact(res))) { +#endif + return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(x); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/autosklearn/pipeline/implementations/OneHotEncoder.py b/autosklearn/pipeline/implementations/OneHotEncoder.py index d02c4a5b0b..abd8e6a22e 100644 --- a/autosklearn/pipeline/implementations/OneHotEncoder.py +++ b/autosklearn/pipeline/implementations/OneHotEncoder.py @@ -1,12 +1,10 @@ import numpy as np from scipy import sparse - from sklearn.base import BaseEstimator, TransformerMixin - from sklearn.utils import check_array -def _transform_selected(X, transform, selected="all", copy=False): +def _transform_selected(X, transform, selected="all", copy=True): """Apply a transform function to portion of selected features Parameters @@ -55,17 +53,7 @@ def _transform_selected(X, transform, selected="all", copy=False): if sparse.issparse(X_sel) or sparse.issparse(X_not_sel): # This is pretty memory-intense, making the memory usage for OpenML # task 146810 go over 3GB - X_not_sel = sparse.coo_matrix(X_not_sel, dtype=np.float32) - del X - gc.collect() - X_tmp = sparse.hstack((X_sel, X_not_sel)) - del X_not_sel - del X_sel - gc.collect() - #X_tmp_csr = X_tmp.tocsr() - #print(X_tmp_csr.data.nbytes, X_tmp_csr.indices.nbytes, - # X_tmp_csr.indptr.nbytes) - return X_tmp.tocsr() + return sparse.hstack((X_sel, X_not_sel), format='csr') else: return np.hstack((X_sel, X_not_sel)) @@ -188,38 +176,26 @@ def _fit_transform(self, X): if self.minimum_fraction is not None: do_not_replace_by_other = list() for column in range(X.shape[1]): - do_not_replace_by_other.append(list()) + do_not_replace_by_other.append(set()) if sparse.issparse(X): indptr_start = X.indptr[column] indptr_end = X.indptr[column + 1] - unique = np.unique(X.data[indptr_start:indptr_end]) + unique, counts = np.unique( + X.data[indptr_start:indptr_end], return_counts=True, + ) colsize = indptr_end - indptr_start else: - unique = np.unique(X[:, column]) + unique, counts = np.unique( + X[:, column], return_counts=True, + ) colsize = X.shape[0] - for unique_value in unique: - if np.isfinite(unique_value): - if sparse.issparse(X): - indptr_start = X.indptr[column] - indptr_end = X.indptr[column + 1] - count = np.nansum(unique_value == - X.data[indptr_start:indptr_end]) - else: - count = np.nansum(unique_value == X[:, column]) - else: - if sparse.issparse(X): - indptr_start = X.indptr[column] - indptr_end = X.indptr[column + 1] - count = np.nansum(~np.isfinite( - X.data[indptr_start:indptr_end])) - else: - count = np.nansum(~np.isfinite(X[:, column])) + for unique_value, count in zip(unique, counts): fraction = float(count) / colsize if fraction >= self.minimum_fraction: - do_not_replace_by_other[-1].append(unique_value) + do_not_replace_by_other[-1].add(unique_value) for unique_value in unique: if unique_value not in do_not_replace_by_other[-1]: @@ -267,7 +243,7 @@ def _fit_transform(self, X): active_features = np.where(mask)[0] out = out[:, active_features] self.active_features_ = active_features - return out.tocoo() if self.sparse else out.toarray() + return out.tocsr() if self.sparse else out.toarray() def fit_transform(self, X, y=None): """Fit OneHotEncoder to X, then transform X. @@ -276,7 +252,7 @@ def fit_transform(self, X, y=None): efficient. See fit for the parameters, transform for the return value. """ return _transform_selected(X, self._fit_transform, - self.categorical_features, copy=False) + self.categorical_features, copy=True) def _transform(self, X): """Asssumes X contains only categorical features.""" @@ -363,7 +339,7 @@ def _transform(self, X): dtype=np.int32).tocsc() out = out[:, self.active_features_] - return out.tocoo() if self.sparse else out.toarray() + return out.tocsr() if self.sparse else out.toarray() def transform(self, X): """Transform X using one-hot encoding. @@ -379,4 +355,4 @@ def transform(self, X): Transformed input. """ return _transform_selected(X, self._transform, - self.categorical_features, copy=False) + self.categorical_features, copy=True) diff --git a/test/test_evaluation/test_test_evaluator.py b/test/test_evaluation/test_test_evaluator.py index c9b8bded16..f6a6a33be2 100644 --- a/test/test_evaluation/test_test_evaluator.py +++ b/test/test_evaluation/test_test_evaluator.py @@ -111,8 +111,8 @@ def test_eval_test_all_loss_functions(self): 'f1_macro': 0.0341005967604, 'f1_micro': 0.04, 'f1_weighted': 0.0396930946292, - 'log_loss': 1.1274919837, - 'pac_score': 0.185257565321, + 'log_loss': 1.1363453286141156, + 'pac_score': 0.19726638744002467, 'precision_macro': 0.037037037037, 'precision_micro': 0.04, 'precision_weighted': 0.0355555555556, diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 506aaf6102..972c3a5a23 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -682,8 +682,8 @@ def test_eval_holdout_all_loss_functions(self): 'f1_macro': 0.0636363636364, 'f1_micro': 0.0606060606061, 'f1_weighted': 0.0606060606061, - 'log_loss': 1.1322787511303569, - 'pac_score': 0.18611197712364891, + 'log_loss': 1.1506551065849084, + 'pac_score': 0.21007372721643125, 'precision_macro': 0.0636363636364, 'precision_micro': 0.0606060606061, 'precision_weighted': 0.0606060606061, @@ -763,8 +763,8 @@ def test_eval_cv_all_loss_functions(self): 'f1_macro': 0.063508064516129004, 'f1_micro': 0.06, 'f1_weighted': 0.06, - 'log_loss': 1.4434709423780441, - 'pac_score': 0.35696863167504633, + 'log_loss': 1.1354268574714661, + 'pac_score': 0.19027640525195577, 'precision_macro': 0.063508064516129004, 'precision_micro': 0.06, 'precision_weighted': 0.06, diff --git a/test/test_pipeline/components/classification/test_extra_trees.py b/test/test_pipeline/components/classification/test_extra_trees.py index a07b5ef7fd..b4d8089a49 100644 --- a/test/test_pipeline/components/classification/test_extra_trees.py +++ b/test/test_pipeline/components/classification/test_extra_trees.py @@ -13,13 +13,13 @@ class ExtraTreesComponentTest(BaseClassificationComponentTest): res = dict() res["default_iris"] = 0.95999999999999996 res["default_iris_iterative"] = 0.93999999999999995 - res["default_iris_proba"] = 0.1086791056721286 - res["default_iris_sparse"] = 0.71999999999999997 - res["default_digits"] = 0.91499696417729204 + res["default_iris_proba"] = 0.10751929472112302 + res["default_iris_sparse"] = 0.74 + res["default_digits"] = 0.91135397692774744 res["default_digits_iterative"] = 0.85428051001821492 res["default_digits_binary"] = 0.99453551912568305 - res["default_digits_multilabel"] = 0.99838668373879624 - res["default_digits_multilabel_proba"] = 0.99628313054140716 + res["default_digits_multilabel"] = 0.9990291262135923 + res["default_digits_multilabel_proba"] = 0.99628951691504752 sk_mod = sklearn.ensemble.ExtraTreesClassifier module = ExtraTreesClassifier \ No newline at end of file diff --git a/test/test_pipeline/components/classification/test_passive_aggressive.py b/test/test_pipeline/components/classification/test_passive_aggressive.py index 2923595174..9d34390466 100644 --- a/test/test_pipeline/components/classification/test_passive_aggressive.py +++ b/test/test_pipeline/components/classification/test_passive_aggressive.py @@ -11,14 +11,14 @@ class PassiveAggressiveComponentTest(BaseClassificationComponentTest): __test__ = True res = dict() - res["default_iris"] = 0.98 + res["default_iris"] = 0.66000000000000003 res["default_iris_iterative"] = 0.95999999999999996 - res["default_iris_proba"] = 0.2185197378302077 + res["default_iris_proba"] = 0.52834934001514167 res["default_iris_sparse"] = 0.38 - res["default_digits"] = 0.9174256223436551 + res["default_digits"] = 0.91803278688524592 res["default_digits_iterative"] = 0.9174256223436551 - res["default_digits_binary"] = 0.99332119004250152 - res["default_digits_multilabel"] = 0.94149891011963693 + res["default_digits_binary"] = 0.99089253187613846 + res["default_digits_multilabel"] = 0.93818765184149111 res["default_digits_multilabel_proba"] = 1.0 sk_mod = sklearn.linear_model.PassiveAggressiveClassifier diff --git a/test/test_pipeline/components/classification/test_random_forest.py b/test/test_pipeline/components/classification/test_random_forest.py index 47500d7837..398ee4f318 100644 --- a/test/test_pipeline/components/classification/test_random_forest.py +++ b/test/test_pipeline/components/classification/test_random_forest.py @@ -13,13 +13,13 @@ class RandomForestComponentTest(BaseClassificationComponentTest): res = dict() res["default_iris"] = 0.95999999999999996 res["default_iris_iterative"] = 0.95999999999999996 - res["default_iris_proba"] = 0.12735301219200268 + res["default_iris_proba"] = 0.12694770574921302 res["default_iris_sparse"] = 0.85999999999999999 - res["default_digits"] = 0.88281724347298118 + res["default_digits"] = 0.90771098967820274 res["default_digits_iterative"] = 0.77231329690346084 - res["default_digits_binary"] = 0.98907103825136611 - res["default_digits_multilabel"] = 0.99232171389942414 - res["default_digits_multilabel_proba"] = 0.99514488225492048 + res["default_digits_binary"] = 0.98967820279295693 + res["default_digits_multilabel"] = 0.99421420083184786 + res["default_digits_multilabel_proba"] = 0.9942895994466453 sk_mod = sklearn.ensemble.RandomForestClassifier module = RandomForest diff --git a/test/test_pipeline/components/classification/test_sgd.py b/test/test_pipeline/components/classification/test_sgd.py index c373743fee..f4d661a1d9 100644 --- a/test/test_pipeline/components/classification/test_sgd.py +++ b/test/test_pipeline/components/classification/test_sgd.py @@ -9,13 +9,13 @@ class SGDComponentTest(BaseClassificationComponentTest): __test__ = True res = dict() - res["default_iris"] = 0.97999999999999998 + res["default_iris"] = 0.84 res["default_iris_iterative"] = 0.9 - res["default_iris_proba"] = 0.29960543868641343 + res["default_iris_proba"] = 0.5028042799123823 res["default_iris_sparse"] = 0.54 - res["default_digits"] = 0.92167577413479052 + res["default_digits"] = 0.92349726775956287 res["default_digits_iterative"] = 0.92167577413479052 - res["default_digits_binary"] = 0.99332119004250152 + res["default_digits_binary"] = 0.99392835458409234 res["default_digits_multilabel"] = -1 res["default_digits_multilabel_proba"] = -1 diff --git a/test/test_pipeline/components/data_preprocessing/test_balancing.py b/test/test_pipeline/components/data_preprocessing/test_balancing.py index 82d3ce7896..1301582905 100644 --- a/test/test_pipeline/components/data_preprocessing/test_balancing.py +++ b/test/test_pipeline/components/data_preprocessing/test_balancing.py @@ -70,13 +70,13 @@ def test_weighting_effect(self): for name, clf, acc_no_weighting, acc_weighting, places in \ [('adaboost', AdaboostClassifier, 0.810, 0.735, 3), ('decision_tree', DecisionTree, 0.780, 0.643, 3), - ('extra_trees', ExtraTreesClassifier, 0.75, 0.800, 3), + ('extra_trees', ExtraTreesClassifier, 0.821, 0.842, 3), ('gradient_boosting', GradientBoostingClassifier, 0.737, 0.684, 3), - ('random_forest', RandomForest, 0.75, 0.821, 3), + ('random_forest', RandomForest, 0.78, 0.778, 3), ('libsvm_svc', LibSVM_SVC, 0.769, 0.72, 3), ('liblinear_svc', LibLinear_SVC, 0.762, 0.735, 3), - ('sgd', SGD, 0.762, 0.708, 1) + ('sgd', SGD, 0.72, 0.6, 2) ]: for strategy, acc in [ ('none', acc_no_weighting), @@ -123,9 +123,9 @@ def test_weighting_effect(self): for name, pre, acc_no_weighting, acc_weighting in \ [('extra_trees_preproc_for_classification', - ExtraTreesPreprocessorClassification, 0.800, 0.750), + ExtraTreesPreprocessorClassification, 0.800, 0.621), ('liblinear_svc_preprocessor', LibLinear_Preprocessor, - 0.750, 0.735)]: + 0.780, 0.61)]: for strategy, acc in [('none', acc_no_weighting), ('weighting', acc_weighting)]: data_ = copy.copy(data) diff --git a/test/test_pipeline/components/feature_preprocessing/test_extra_trees_classification.py b/test/test_pipeline/components/feature_preprocessing/test_extra_trees_classification.py index 35f135e6f7..b1c6f39798 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_extra_trees_classification.py +++ b/test/test_pipeline/components/feature_preprocessing/test_extra_trees_classification.py @@ -56,7 +56,7 @@ def test_default_configuration_classify_sparse(self): predictor = classifier.fit(X_train_trans, Y_train) predictions = predictor.predict(X_test_trans) accuracy = sklearn.metrics.accuracy_score(predictions, Y_test) - self.assertAlmostEqual(accuracy, 0.45051608986035213, places=2) + self.assertAlmostEqual(accuracy, 0.43715846994535518, places=2) def test_preprocessing_dtype(self): super(ExtreTreesClassificationComponentTest, self).\ diff --git a/test/test_pipeline/components/regression/test_extra_trees.py b/test/test_pipeline/components/regression/test_extra_trees.py index 6b29316377..2bc13aec78 100644 --- a/test/test_pipeline/components/regression/test_extra_trees.py +++ b/test/test_pipeline/components/regression/test_extra_trees.py @@ -10,13 +10,13 @@ class ExtraTreesComponentTest(BaseRegressionComponentTest): __test__ = True res = dict() - res["default_boston"] = 0.84600084385083418 + res["default_boston"] = 0.85057762155064132 res["default_boston_iterative"] = 0.84600084385083418 - res["default_boston_sparse"] = 0.4241336980285072 + res["default_boston_sparse"] = 0.40009651469914886 res["default_boston_iterative_sparse"] = 0.4241336980285072 - res["default_diabetes"] = 0.38869684680884398 + res["default_diabetes"] = 0.38997024548179593 res["default_diabetes_iterative"] = 0.38869684680884398 - res["default_diabetes_sparse"] = 0.25345808769763623 + res["default_diabetes_sparse"] = 0.25171463472003841 res["default_diabetes_iterative_sparse"] = 0.25345808769763623 sk_mod = sklearn.ensemble.ExtraTreesRegressor diff --git a/test/test_pipeline/components/regression/test_random_forests.py b/test/test_pipeline/components/regression/test_random_forests.py index 24be9a2d44..abdb1544db 100644 --- a/test/test_pipeline/components/regression/test_random_forests.py +++ b/test/test_pipeline/components/regression/test_random_forests.py @@ -9,13 +9,13 @@ class RandomForestComponentTest(BaseRegressionComponentTest): __test__ = True res = dict() - res["default_boston"] = 0.83555736176073558 + res["default_boston"] = 0.84268339869543274 res["default_boston_iterative"] = 0.83555736176073558 - res["default_boston_sparse"] = 0.40733598090972378 + res["default_boston_sparse"] = 0.43023364434667044 res["default_boston_iterative_sparse"] = 0.40733598090972378 - res["default_diabetes"] = 0.34470637712379781 + res["default_diabetes"] = 0.34759241745667557 res["default_diabetes_iterative"] = 0.34470637712379781 - res["default_diabetes_sparse"] = 0.22910562828064962 + res["default_diabetes_sparse"] = 0.24593112889855551 res["default_diabetes_iterative_sparse"] = 0.22910562828064962 sk_mod = sklearn.ensemble.RandomForestRegressor diff --git a/test/test_pipeline/components/regression/test_ridge_regression.py b/test/test_pipeline/components/regression/test_ridge_regression.py index 42f734aee8..f8b0d5a06a 100644 --- a/test/test_pipeline/components/regression/test_ridge_regression.py +++ b/test/test_pipeline/components/regression/test_ridge_regression.py @@ -9,7 +9,7 @@ class RidgeComponentTest(BaseRegressionComponentTest): __test__ = True res = dict() - res["default_boston"] = 0.70337994943813897 + res["default_boston"] = 0.70337988575176991 res["default_boston_iterative"] = None res["default_boston_sparse"] = 0.1086535440527433 res["default_boston_iterative_sparse"] = None diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 7fe0fe9bbc..200299719a 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -193,7 +193,7 @@ def test_default_configuration(self): predictions = auto.predict(copy.deepcopy(X_test)) # The lower the worse r2_score = sklearn.metrics.r2_score(Y_test, predictions) - self.assertAlmostEqual(0.343, r2_score, places=3) + self.assertAlmostEqual(0.347, r2_score, places=3) model_score = auto.score(copy.deepcopy(X_test), Y_test) self.assertAlmostEqual(model_score, r2_score, places=5) From 6251434bd1d71c960b1ac340f4645d9e8439ca86 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 21 Nov 2017 16:16:51 +0100 Subject: [PATCH 39/61] ADD fit_transform to preprocessors --- .../imputation/imputation.py | 2 +- .../one_hot_encoding/one_hot_encoding.py | 10 +- .../rescaling/abstract_rescaling.py | 2 + .../variance_threshold/variance_threshold.py | 1 + .../feature_preprocessing/fast_ica.py | 11 +- .../random_trees_embedding.py | 9 +- .../pipeline/implementations/OneHotEncoder.c | 13097 ---------------- 7 files changed, 29 insertions(+), 13103 deletions(-) delete mode 100644 autosklearn/pipeline/implementations/OneHotEncoder.c diff --git a/autosklearn/pipeline/components/data_preprocessing/imputation/imputation.py b/autosklearn/pipeline/components/data_preprocessing/imputation/imputation.py index f50aa22644..a817d205b6 100644 --- a/autosklearn/pipeline/components/data_preprocessing/imputation/imputation.py +++ b/autosklearn/pipeline/components/data_preprocessing/imputation/imputation.py @@ -7,10 +7,10 @@ class Imputation(AutoSklearnPreprocessingAlgorithm): def __init__(self, strategy='median', random_state=None): - # TODO pay attention to the cases when a copy is made (CSR matrices) self.strategy = strategy def fit(self, X, y=None): + # Imputation does not support fit_transform (as of 0.19.1)! import sklearn.preprocessing self.preprocessor = sklearn.preprocessing.Imputer( diff --git a/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py b/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py index cf85507422..900558ff06 100644 --- a/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py @@ -19,7 +19,7 @@ def __init__(self, use_minimum_fraction=True, minimum_fraction=0.01, self.minimum_fraction = minimum_fraction self.categorical_features = categorical_features - def fit(self, X, y=None): + def _fit(self, X, y=None): if self.use_minimum_fraction is None or \ self.use_minimum_fraction is False or \ (isinstance(self.use_minimum_fraction, str) and @@ -37,9 +37,15 @@ def fit(self, X, y=None): .OneHotEncoder(minimum_fraction=self.minimum_fraction, categorical_features=categorical_features) - self.preprocessor = self.preprocessor.fit(X) + return self.preprocessor.fit_transform(X) + + def fit(self, X, y=None): + self._fit(X, y) return self + def fit_transform(self, X, y=None): + return self._fit(X, y) + def transform(self, X): import scipy.sparse diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py index 6e53a02e58..cea515a4ee 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py @@ -2,6 +2,8 @@ class Rescaling(object): + # Rescaling does not support fit_transform (as of 0.19.1)! + def fit(self, X, y=None): self.preprocessor.fit(X) return self diff --git a/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py b/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py index c242ef4778..d20bc42e1f 100644 --- a/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py +++ b/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py @@ -9,6 +9,7 @@ class VarianceThreshold(AutoSklearnPreprocessingAlgorithm): def __init__(self, random_state=None): + # VarianceThreshold does not support fit_transform (as of 0.19.1)! pass def fit(self, X, y=None): diff --git a/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py b/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py index 96ed15b92c..f209629f8b 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py +++ b/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py @@ -19,7 +19,7 @@ def __init__(self, algorithm, whiten, fun, n_components=None, self.fun = fun self.random_state = random_state - def fit(self, X, Y=None): + def _fit(self, X, Y=None): import sklearn.decomposition self.preprocessor = sklearn.decomposition.FastICA( @@ -30,13 +30,20 @@ def fit(self, X, Y=None): with warnings.catch_warnings(): warnings.filterwarnings("error", message='array must not contain infs or NaNs') try: - self.preprocessor.fit(X) + return self.preprocessor.fit_transform(X) except ValueError as e: if 'array must not contain infs or NaNs' in e.args[0]: raise ValueError("Bug in scikit-learn: https://github.com/scikit-learn/scikit-learn/pull/2738") return self + def fit(self, X, y): + self._fit(X) + return self + + def fit_transform(self, X, y=None): + return self._fit(X) + def transform(self, X): if self.preprocessor is None: raise NotImplementedError() diff --git a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py index cd66033dfa..235be68753 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py +++ b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py @@ -22,7 +22,7 @@ def __init__(self, n_estimators, max_depth, min_samples_split, self.n_jobs = n_jobs self.random_state = random_state - def fit(self, X, Y=None): + def _fit(self, X, Y=None): import sklearn.ensemble if self.max_depth == "None" or self.max_depth is None: @@ -49,6 +49,13 @@ def fit(self, X, Y=None): self.preprocessor.fit(X, Y) return self + def fit(self, X, y): + self._fit(X) + return self + + def fit_transform(self, X, y=None): + return self._fit(X) + def transform(self, X): if self.preprocessor is None: raise NotImplementedError() diff --git a/autosklearn/pipeline/implementations/OneHotEncoder.c b/autosklearn/pipeline/implementations/OneHotEncoder.c deleted file mode 100644 index 66fe45f695..0000000000 --- a/autosklearn/pipeline/implementations/OneHotEncoder.c +++ /dev/null @@ -1,13097 +0,0 @@ -/* Generated by Cython 0.27.1 */ - -#define PY_SSIZE_T_CLEAN -#include "Python.h" -#ifndef Py_PYTHON_H - #error Python headers needed to compile C extensions, please install development version of Python. -#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) - #error Cython requires Python 2.6+ or Python 3.3+. -#else -#define CYTHON_ABI "0_27_1" -#define CYTHON_FUTURE_DIVISION 0 -#include -#ifndef offsetof - 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- Py_INCREF(k[i+1]); - i += 2; - } - nk = i / 2; - } - else { - kwtuple = NULL; - k = NULL; - } - closure = PyFunction_GET_CLOSURE(func); -#if PY_MAJOR_VERSION >= 3 - kwdefs = PyFunction_GET_KW_DEFAULTS(func); -#endif - if (argdefs != NULL) { - d = &PyTuple_GET_ITEM(argdefs, 0); - nd = Py_SIZE(argdefs); - } - else { - d = NULL; - nd = 0; - } -#if PY_MAJOR_VERSION >= 3 - result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, - args, nargs, - k, (int)nk, - d, (int)nd, kwdefs, closure); -#else - result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, - args, nargs, - k, (int)nk, - d, (int)nd, closure); -#endif - Py_XDECREF(kwtuple); -done: - Py_LeaveRecursiveCall(); - return result; -} -#endif -#endif - -/* PyObjectCallMethO */ - #if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { - PyObject *self, *result; - PyCFunction cfunc; - cfunc = PyCFunction_GET_FUNCTION(func); - self = PyCFunction_GET_SELF(func); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = cfunc(self, arg); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectCallOneArg */ - #if CYTHON_COMPILING_IN_CPYTHON -static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_New(1); - if (unlikely(!args)) return NULL; - Py_INCREF(arg); - PyTuple_SET_ITEM(args, 0, arg); - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { -#if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCall(func, &arg, 1); - } -#endif - if (likely(PyCFunction_Check(func))) { - if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { - return __Pyx_PyObject_CallMethO(func, arg); -#if CYTHON_FAST_PYCCALL - } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { - return __Pyx_PyCFunction_FastCall(func, &arg, 1); -#endif - } - } - return __Pyx__PyObject_CallOneArg(func, arg); -} -#else -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_Pack(1, arg); - if (unlikely(!args)) return NULL; - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -#endif - -/* GetItemInt */ - static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { - PyObject *r; - if (!j) return NULL; - r = PyObject_GetItem(o, j); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyList_GET_SIZE(o); - } - if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyList_GET_SIZE(o)))) { - PyObject *r = PyList_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyTuple_GET_SIZE(o); - } - if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); - if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) { - PyObject *r = PyList_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } - else if (PyTuple_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } else { - PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; - if (likely(m && m->sq_item)) { - if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { - Py_ssize_t l = m->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - return NULL; - PyErr_Clear(); - } - } - return m->sq_item(o, i); - } - } -#else - if (is_list || PySequence_Check(o)) { - return PySequence_GetItem(o, i); - } -#endif - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -} - -/* PyIntBinop */ - #if !CYTHON_COMPILING_IN_PYPY -static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { - if (op1 == op2) { - Py_RETURN_TRUE; - } - #if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(op1))) { - const long b = intval; - long a = PyInt_AS_LONG(op1); - if (a == b) { - Py_RETURN_TRUE; - } else { - Py_RETURN_FALSE; - } - } - #endif - #if CYTHON_USE_PYLONG_INTERNALS - if (likely(PyLong_CheckExact(op1))) { - const long b = intval; - long a; - const digit* digits = ((PyLongObject*)op1)->ob_digit; - const Py_ssize_t size = Py_SIZE(op1); - if (likely(__Pyx_sst_abs(size) <= 1)) { - a = likely(size) ? digits[0] : 0; - if (size == -1) a = -a; - } else { - switch (size) { - case -2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; - } - case 2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; - } - case -3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; - } - case 3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; - } - case -4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; - } - case 4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; - } - #if PyLong_SHIFT < 30 && PyLong_SHIFT != 15 - default: return PyLong_Type.tp_richcompare(op1, op2, Py_EQ); - #else - default: Py_RETURN_FALSE; - #endif - } - } - if (a == b) { - Py_RETURN_TRUE; - } else { - Py_RETURN_FALSE; - } - } - #endif - if (PyFloat_CheckExact(op1)) { - const long b = intval; - double a = PyFloat_AS_DOUBLE(op1); - if ((double)a == (double)b) { - Py_RETURN_TRUE; - } else { - Py_RETURN_FALSE; - } - } - return PyObject_RichCompare(op1, op2, Py_EQ); -} -#endif - -/* PyIntBinop */ - #if !CYTHON_COMPILING_IN_PYPY -static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { - #if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(op1))) { - const long b = intval; - long x; - long a = PyInt_AS_LONG(op1); - x = (long)((unsigned long)a + b); - if (likely((x^a) >= 0 || (x^b) >= 0)) - return PyInt_FromLong(x); - return PyLong_Type.tp_as_number->nb_add(op1, op2); - } - #endif - #if CYTHON_USE_PYLONG_INTERNALS - if (likely(PyLong_CheckExact(op1))) { - const long b = intval; - long a, x; -#ifdef HAVE_LONG_LONG - const PY_LONG_LONG llb = intval; - PY_LONG_LONG lla, llx; -#endif - const digit* digits = ((PyLongObject*)op1)->ob_digit; - const Py_ssize_t size = Py_SIZE(op1); - if (likely(__Pyx_sst_abs(size) <= 1)) { - a = likely(size) ? digits[0] : 0; - if (size == -1) a = -a; - } else { - switch (size) { - case -2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case 2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case -3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case 3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case -4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case 4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - default: return PyLong_Type.tp_as_number->nb_add(op1, op2); - } - } - x = a + b; - return PyLong_FromLong(x); -#ifdef HAVE_LONG_LONG - long_long: - llx = lla + llb; - return PyLong_FromLongLong(llx); -#endif - - - } - #endif - if (PyFloat_CheckExact(op1)) { - const long b = intval; - double a = PyFloat_AS_DOUBLE(op1); - double result; - PyFPE_START_PROTECT("add", return NULL) - result = ((double)a) + (double)b; - PyFPE_END_PROTECT(result) - return PyFloat_FromDouble(result); - } - return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); -} -#endif - -/* PyObjectCallNoArg */ - #if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { -#if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCall(func, NULL, 0); - } -#endif -#ifdef __Pyx_CyFunction_USED - if (likely(PyCFunction_Check(func) || __Pyx_TypeCheck(func, __pyx_CyFunctionType))) { -#else - if (likely(PyCFunction_Check(func))) { -#endif - if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { - return __Pyx_PyObject_CallMethO(func, NULL); - } - } - return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); -} -#endif - -/* PyErrFetchRestore */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - tmp_type = tstate->curexc_type; - tmp_value = tstate->curexc_value; - tmp_tb = tstate->curexc_traceback; - tstate->curexc_type = type; - tstate->curexc_value = value; - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - *type = tstate->curexc_type; - *value = tstate->curexc_value; - *tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -} -#endif - -/* RaiseException */ - #if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - __Pyx_PyThreadState_declare - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_PyThreadState_assign - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } - if (cause) { - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { -#if CYTHON_COMPILING_IN_PYPY - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#else - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -/* RaiseTooManyValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -/* RaiseNeedMoreValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -/* IterFinish */ - static CYTHON_INLINE int __Pyx_IterFinish(void) { -#if CYTHON_FAST_THREAD_STATE - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject* exc_type = tstate->curexc_type; - if (unlikely(exc_type)) { - if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) { - PyObject *exc_value, *exc_tb; - exc_value = tstate->curexc_value; - exc_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; - Py_DECREF(exc_type); - Py_XDECREF(exc_value); - Py_XDECREF(exc_tb); - return 0; - } else { - return -1; - } - } - return 0; -#else - if (unlikely(PyErr_Occurred())) { - if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { - PyErr_Clear(); - return 0; - } else { - return -1; - } - } - return 0; -#endif -} - -/* UnpackItemEndCheck */ - static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { - if (unlikely(retval)) { - Py_DECREF(retval); - __Pyx_RaiseTooManyValuesError(expected); - return -1; - } else { - return __Pyx_IterFinish(); - } - return 0; -} - -/* SliceObject */ - static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(PyObject* obj, - Py_ssize_t cstart, Py_ssize_t cstop, - PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice, - int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) { -#if CYTHON_USE_TYPE_SLOTS - PyMappingMethods* mp; -#if PY_MAJOR_VERSION < 3 - PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence; - if (likely(ms && ms->sq_slice)) { - if (!has_cstart) { - if (_py_start && (*_py_start != Py_None)) { - cstart = __Pyx_PyIndex_AsSsize_t(*_py_start); - if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; - } else - cstart = 0; - } - if (!has_cstop) { - if (_py_stop && (*_py_stop != Py_None)) { - cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop); - if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; - } else - cstop = PY_SSIZE_T_MAX; - } - if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) { - Py_ssize_t l = ms->sq_length(obj); - if (likely(l >= 0)) { - if (cstop < 0) { - cstop += l; - if (cstop < 0) cstop = 0; - } - if (cstart < 0) { - cstart += l; - if (cstart < 0) cstart = 0; - } - } else { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - goto bad; - PyErr_Clear(); - } - } - return ms->sq_slice(obj, cstart, cstop); - } -#endif - mp = Py_TYPE(obj)->tp_as_mapping; - if (likely(mp && mp->mp_subscript)) -#endif - { - PyObject* result; - PyObject *py_slice, *py_start, *py_stop; - if (_py_slice) { - py_slice = *_py_slice; - } else { - PyObject* owned_start = NULL; - PyObject* owned_stop = NULL; - if (_py_start) { - py_start = *_py_start; - } else { - if (has_cstart) { - owned_start = py_start = PyInt_FromSsize_t(cstart); - if (unlikely(!py_start)) goto bad; - } else - py_start = Py_None; - } - if (_py_stop) { - py_stop = *_py_stop; - } else { - if (has_cstop) { - owned_stop = py_stop = PyInt_FromSsize_t(cstop); - if (unlikely(!py_stop)) { - Py_XDECREF(owned_start); - goto bad; - } - } else - py_stop = Py_None; - } - py_slice = PySlice_New(py_start, py_stop, Py_None); - Py_XDECREF(owned_start); - Py_XDECREF(owned_stop); - if (unlikely(!py_slice)) goto bad; - } -#if CYTHON_USE_TYPE_SLOTS - result = mp->mp_subscript(obj, py_slice); -#else - result = PyObject_GetItem(obj, py_slice); -#endif - if (!_py_slice) { - Py_DECREF(py_slice); - } - return result; - } - PyErr_Format(PyExc_TypeError, - "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name); -bad: - return NULL; -} - -/* PyObjectCallMethod1 */ - static PyObject* __Pyx__PyObject_CallMethod1(PyObject* method, PyObject* arg) { - PyObject *result = NULL; -#if CYTHON_UNPACK_METHODS - if (likely(PyMethod_Check(method))) { - PyObject *self = PyMethod_GET_SELF(method); - if (likely(self)) { - PyObject *args; - PyObject *function = PyMethod_GET_FUNCTION(method); - #if CYTHON_FAST_PYCALL - if (PyFunction_Check(function)) { - PyObject *args[2] = {self, arg}; - result = __Pyx_PyFunction_FastCall(function, args, 2); - goto done; - } - #endif - #if CYTHON_FAST_PYCCALL - if (__Pyx_PyFastCFunction_Check(function)) { - PyObject *args[2] = {self, arg}; - result = __Pyx_PyCFunction_FastCall(function, args, 2); - goto done; - } - #endif - args = PyTuple_New(2); - if (unlikely(!args)) goto done; - Py_INCREF(self); - PyTuple_SET_ITEM(args, 0, self); - Py_INCREF(arg); - PyTuple_SET_ITEM(args, 1, arg); - Py_INCREF(function); - result = __Pyx_PyObject_Call(function, args, NULL); - Py_DECREF(args); - Py_DECREF(function); - return result; - } - } -#endif - result = __Pyx_PyObject_CallOneArg(method, arg); - goto done; -done: - return result; -} -static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) { - PyObject *method, *result = NULL; - method = __Pyx_PyObject_GetAttrStr(obj, method_name); - if (unlikely(!method)) goto done; - result = __Pyx__PyObject_CallMethod1(method, arg); -done: - Py_XDECREF(method); - return result; -} - -/* append */ - static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x) { - if (likely(PyList_CheckExact(L))) { - if (unlikely(__Pyx_PyList_Append(L, x) < 0)) return -1; - } else { - PyObject* retval = __Pyx_PyObject_CallMethod1(L, __pyx_n_s_append, x); - if (unlikely(!retval)) - return -1; - Py_DECREF(retval); - } - return 0; -} - -/* PyIntBinop */ - #if !CYTHON_COMPILING_IN_PYPY -static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { - #if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(op1))) { - const long b = intval; - long x; - long a = PyInt_AS_LONG(op1); - x = (long)((unsigned long)a - b); - if (likely((x^a) >= 0 || (x^~b) >= 0)) - return PyInt_FromLong(x); - return PyLong_Type.tp_as_number->nb_subtract(op1, op2); - } - #endif - #if CYTHON_USE_PYLONG_INTERNALS - if (likely(PyLong_CheckExact(op1))) { - const long b = intval; - long a, x; -#ifdef HAVE_LONG_LONG - const PY_LONG_LONG llb = intval; - PY_LONG_LONG lla, llx; -#endif - const digit* digits = ((PyLongObject*)op1)->ob_digit; - const Py_ssize_t size = Py_SIZE(op1); - if (likely(__Pyx_sst_abs(size) <= 1)) { - a = likely(size) ? digits[0] : 0; - if (size == -1) a = -a; - } else { - switch (size) { - case -2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case 2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case -3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case 3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case -4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case 4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - default: return PyLong_Type.tp_as_number->nb_subtract(op1, op2); - } - } - x = a - b; - return PyLong_FromLong(x); -#ifdef HAVE_LONG_LONG - long_long: - llx = lla - llb; - return PyLong_FromLongLong(llx); -#endif - - - } - #endif - if (PyFloat_CheckExact(op1)) { - const long b = intval; - double a = PyFloat_AS_DOUBLE(op1); - double result; - PyFPE_START_PROTECT("subtract", return NULL) - result = ((double)a) - (double)b; - PyFPE_END_PROTECT(result) - return PyFloat_FromDouble(result); - } - return (inplace ? PyNumber_InPlaceSubtract : PyNumber_Subtract)(op1, op2); -} -#endif - -/* Import */ - static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_MAJOR_VERSION < 3 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if (strchr(__Pyx_MODULE_NAME, '.')) { - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_MAJOR_VERSION < 3 - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } -bad: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -/* ImportFrom */ - static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { - PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); - if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { - PyErr_Format(PyExc_ImportError, - #if PY_MAJOR_VERSION < 3 - "cannot import name %.230s", PyString_AS_STRING(name)); - #else - "cannot import name %S", name); - #endif - } - return value; -} - -/* FetchCommonType */ - static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { - PyObject* fake_module; - PyTypeObject* cached_type = NULL; - fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI); - if (!fake_module) return NULL; - Py_INCREF(fake_module); - cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name); - if (cached_type) { - if (!PyType_Check((PyObject*)cached_type)) { - PyErr_Format(PyExc_TypeError, - "Shared Cython type %.200s is not a type object", - type->tp_name); - goto bad; - } - if (cached_type->tp_basicsize != type->tp_basicsize) { - PyErr_Format(PyExc_TypeError, - "Shared Cython type %.200s has the wrong size, try recompiling", - type->tp_name); - goto bad; - } - } else { - if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; - PyErr_Clear(); - if (PyType_Ready(type) < 0) goto bad; - if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0) - goto bad; - Py_INCREF(type); - cached_type = type; - } -done: - Py_DECREF(fake_module); - return cached_type; -bad: - Py_XDECREF(cached_type); - cached_type = NULL; - goto done; -} - -/* CythonFunction */ - static PyObject * -__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure) -{ - if (unlikely(op->func_doc == NULL)) { - if (op->func.m_ml->ml_doc) { -#if PY_MAJOR_VERSION >= 3 - op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc); -#else - op->func_doc = PyString_FromString(op->func.m_ml->ml_doc); -#endif - if (unlikely(op->func_doc == NULL)) - return NULL; - } else { - Py_INCREF(Py_None); - return Py_None; - } - } - Py_INCREF(op->func_doc); - return op->func_doc; -} -static int -__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value) -{ - PyObject *tmp = op->func_doc; - if (value == NULL) { - value = Py_None; - } - Py_INCREF(value); - op->func_doc = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op) -{ - if (unlikely(op->func_name == NULL)) { -#if PY_MAJOR_VERSION >= 3 - op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name); -#else - op->func_name = PyString_InternFromString(op->func.m_ml->ml_name); -#endif - if (unlikely(op->func_name == NULL)) - return NULL; - } - Py_INCREF(op->func_name); - return op->func_name; -} -static int -__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value) -{ - PyObject *tmp; -#if PY_MAJOR_VERSION >= 3 - if (unlikely(value == NULL || !PyUnicode_Check(value))) { -#else - if (unlikely(value == NULL || !PyString_Check(value))) { -#endif - PyErr_SetString(PyExc_TypeError, - "__name__ must be set to a string object"); - return -1; - } - tmp = op->func_name; - Py_INCREF(value); - op->func_name = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op) -{ - Py_INCREF(op->func_qualname); - return op->func_qualname; -} -static int -__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value) -{ - PyObject *tmp; -#if PY_MAJOR_VERSION >= 3 - if (unlikely(value == NULL || !PyUnicode_Check(value))) { -#else - if (unlikely(value == NULL || !PyString_Check(value))) { -#endif - PyErr_SetString(PyExc_TypeError, - "__qualname__ must be set to a string object"); - return -1; - } - tmp = op->func_qualname; - Py_INCREF(value); - op->func_qualname = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure) -{ - PyObject *self; - self = m->func_closure; - if (self == NULL) - self = Py_None; - Py_INCREF(self); - return self; -} -static PyObject * -__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op) -{ - if (unlikely(op->func_dict == NULL)) { - op->func_dict = PyDict_New(); - if (unlikely(op->func_dict == NULL)) - return NULL; - } - Py_INCREF(op->func_dict); - return op->func_dict; -} -static int -__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value) -{ - PyObject *tmp; - if (unlikely(value == NULL)) { - PyErr_SetString(PyExc_TypeError, - "function's dictionary may not be deleted"); - return -1; - } - if (unlikely(!PyDict_Check(value))) { - PyErr_SetString(PyExc_TypeError, - "setting function's dictionary to a non-dict"); - return -1; - } - tmp = op->func_dict; - Py_INCREF(value); - op->func_dict = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op) -{ - Py_INCREF(op->func_globals); - return op->func_globals; -} -static PyObject * -__Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op) -{ - Py_INCREF(Py_None); - return Py_None; -} -static PyObject * -__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op) -{ - PyObject* result = (op->func_code) ? op->func_code : Py_None; - Py_INCREF(result); - return result; -} -static int -__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { - int result = 0; - PyObject *res = op->defaults_getter((PyObject *) op); - if (unlikely(!res)) - return -1; - #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - op->defaults_tuple = PyTuple_GET_ITEM(res, 0); - Py_INCREF(op->defaults_tuple); - op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); - Py_INCREF(op->defaults_kwdict); - #else - op->defaults_tuple = PySequence_ITEM(res, 0); - if (unlikely(!op->defaults_tuple)) result = -1; - else { - op->defaults_kwdict = PySequence_ITEM(res, 1); - if (unlikely(!op->defaults_kwdict)) result = -1; - } - #endif - Py_DECREF(res); - return result; -} -static int -__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value) { - PyObject* tmp; - if (!value) { - value = Py_None; - } else if (value != Py_None && !PyTuple_Check(value)) { - PyErr_SetString(PyExc_TypeError, - "__defaults__ must be set to a tuple object"); - return -1; - } - Py_INCREF(value); - tmp = op->defaults_tuple; - op->defaults_tuple = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op) { - PyObject* result = op->defaults_tuple; - if (unlikely(!result)) { - if (op->defaults_getter) { - if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; - result = op->defaults_tuple; - } else { - result = Py_None; - } - } - Py_INCREF(result); - return result; -} -static int -__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value) { - PyObject* tmp; - if (!value) { - value = Py_None; - } else if (value != Py_None && !PyDict_Check(value)) { - PyErr_SetString(PyExc_TypeError, - "__kwdefaults__ must be set to a dict object"); - return -1; - } - Py_INCREF(value); - tmp = op->defaults_kwdict; - op->defaults_kwdict = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op) { - PyObject* result = op->defaults_kwdict; - if (unlikely(!result)) { - if (op->defaults_getter) { - if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; - result = op->defaults_kwdict; - } else { - result = Py_None; - } - } - Py_INCREF(result); - return result; -} -static int -__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value) { - PyObject* tmp; - if (!value || value == Py_None) { - value = NULL; - } else if (!PyDict_Check(value)) { - PyErr_SetString(PyExc_TypeError, - "__annotations__ must be set to a dict object"); - return -1; - } - Py_XINCREF(value); - tmp = op->func_annotations; - op->func_annotations = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op) { - PyObject* result = op->func_annotations; - if (unlikely(!result)) { - result = PyDict_New(); - if (unlikely(!result)) return NULL; - op->func_annotations = result; - } - Py_INCREF(result); - return result; -} -static PyGetSetDef __pyx_CyFunction_getsets[] = { - {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, - {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, - {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, - {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, - {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0}, - {(char *) "__self__", (getter)__Pyx_CyFunction_get_self, 0, 0, 0}, - {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, - {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, - {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, - {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, - {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, - {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, - {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, - {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, - {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, - {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, - {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, - {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, - {0, 0, 0, 0, 0} -}; -static PyMemberDef __pyx_CyFunction_members[] = { - {(char *) "__module__", T_OBJECT, offsetof(__pyx_CyFunctionObject, func.m_module), PY_WRITE_RESTRICTED, 0}, - {0, 0, 0, 0, 0} -}; -static PyObject * -__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args) -{ -#if PY_MAJOR_VERSION >= 3 - return PyUnicode_FromString(m->func.m_ml->ml_name); -#else - return PyString_FromString(m->func.m_ml->ml_name); -#endif -} -static PyMethodDef __pyx_CyFunction_methods[] = { - {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, - {0, 0, 0, 0} -}; -#if PY_VERSION_HEX < 0x030500A0 -#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) -#else -#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist) -#endif -static PyObject *__Pyx_CyFunction_New(PyTypeObject *type, PyMethodDef *ml, int flags, PyObject* qualname, - PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { - __pyx_CyFunctionObject *op = PyObject_GC_New(__pyx_CyFunctionObject, type); 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- Py_CLEAR(m->func_code); - Py_CLEAR(m->func_classobj); - Py_CLEAR(m->defaults_tuple); - Py_CLEAR(m->defaults_kwdict); - Py_CLEAR(m->func_annotations); - if (m->defaults) { - PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); - int i; - for (i = 0; i < m->defaults_pyobjects; i++) - Py_XDECREF(pydefaults[i]); - PyObject_Free(m->defaults); - m->defaults = NULL; - } - return 0; -} -static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m) -{ - if (__Pyx_CyFunction_weakreflist(m) != NULL) - PyObject_ClearWeakRefs((PyObject *) m); - __Pyx_CyFunction_clear(m); - PyObject_GC_Del(m); -} -static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) -{ - PyObject_GC_UnTrack(m); - __Pyx__CyFunction_dealloc(m); -} -static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) -{ - Py_VISIT(m->func_closure); - Py_VISIT(m->func.m_module); - Py_VISIT(m->func_dict); - Py_VISIT(m->func_name); - Py_VISIT(m->func_qualname); - Py_VISIT(m->func_doc); - Py_VISIT(m->func_globals); - Py_VISIT(m->func_code); - Py_VISIT(m->func_classobj); - Py_VISIT(m->defaults_tuple); - Py_VISIT(m->defaults_kwdict); - if (m->defaults) { - PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); - int i; - for (i = 0; i < m->defaults_pyobjects; i++) - Py_VISIT(pydefaults[i]); - } - return 0; -} -static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type) -{ - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) { - Py_INCREF(func); - return func; - } - if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) { - if (type == NULL) - type = (PyObject *)(Py_TYPE(obj)); - return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type))); - } - if (obj == Py_None) - obj = NULL; - return __Pyx_PyMethod_New(func, obj, type); -} -static PyObject* -__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) -{ -#if PY_MAJOR_VERSION >= 3 - return PyUnicode_FromFormat("", - op->func_qualname, (void *)op); -#else - return PyString_FromFormat("", - PyString_AsString(op->func_qualname), (void *)op); 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METH_OLDARGS is no " - "longer supported!"); - return NULL; - } - PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments", - f->m_ml->ml_name); - return NULL; -} -static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { - return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw); -} -static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) { - PyObject *result; - __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; - if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { - Py_ssize_t argc; - PyObject *new_args; - PyObject *self; - argc = PyTuple_GET_SIZE(args); - new_args = PyTuple_GetSlice(args, 1, argc); - if (unlikely(!new_args)) - return NULL; - self = PyTuple_GetItem(args, 0); - if (unlikely(!self)) { - Py_DECREF(new_args); - return NULL; - } - result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw); - Py_DECREF(new_args); - } else { - result = __Pyx_CyFunction_Call(func, args, kw); - } - return result; -} -static PyTypeObject __pyx_CyFunctionType_type = { - PyVarObject_HEAD_INIT(0, 0) - "cython_function_or_method", - sizeof(__pyx_CyFunctionObject), - 0, - (destructor) __Pyx_CyFunction_dealloc, - 0, - 0, - 0, -#if PY_MAJOR_VERSION < 3 - 0, -#else - 0, -#endif - (reprfunc) __Pyx_CyFunction_repr, - 0, - 0, - 0, - 0, - __Pyx_CyFunction_CallAsMethod, - 0, - 0, - 0, - 0, - Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC, - 0, - (traverseproc) __Pyx_CyFunction_traverse, - (inquiry) __Pyx_CyFunction_clear, - 0, -#if PY_VERSION_HEX < 0x030500A0 - offsetof(__pyx_CyFunctionObject, func_weakreflist), -#else - offsetof(PyCFunctionObject, m_weakreflist), -#endif - 0, - 0, - __pyx_CyFunction_methods, - __pyx_CyFunction_members, - __pyx_CyFunction_getsets, - 0, - 0, - __Pyx_CyFunction_descr_get, - 0, - offsetof(__pyx_CyFunctionObject, func_dict), - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, -#if PY_VERSION_HEX >= 0x030400a1 - 0, -#endif -}; -static int __pyx_CyFunction_init(void) { - __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); - if (unlikely(__pyx_CyFunctionType == NULL)) { - return -1; - } - return 0; -} -static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->defaults = PyObject_Malloc(size); - if (unlikely(!m->defaults)) - return PyErr_NoMemory(); - memset(m->defaults, 0, size); - m->defaults_pyobjects = pyobjects; - return m->defaults; -} -static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->defaults_tuple = tuple; - Py_INCREF(tuple); -} -static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->defaults_kwdict = dict; - Py_INCREF(dict); -} -static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->func_annotations = dict; - Py_INCREF(dict); -} - -/* CalculateMetaclass */ - static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases) { - Py_ssize_t i, nbases = PyTuple_GET_SIZE(bases); - for (i=0; i < nbases; i++) { - PyTypeObject *tmptype; - PyObject *tmp = PyTuple_GET_ITEM(bases, i); - tmptype = Py_TYPE(tmp); -#if PY_MAJOR_VERSION < 3 - if (tmptype == &PyClass_Type) - continue; -#endif - if (!metaclass) { - metaclass = tmptype; - continue; - } - if (PyType_IsSubtype(metaclass, tmptype)) - continue; - if (PyType_IsSubtype(tmptype, metaclass)) { - metaclass = tmptype; - continue; - } - PyErr_SetString(PyExc_TypeError, - "metaclass conflict: " - "the metaclass of a derived class " - "must be a (non-strict) subclass " - "of the metaclasses of all its bases"); - return NULL; - } - if (!metaclass) { -#if PY_MAJOR_VERSION < 3 - metaclass = &PyClass_Type; -#else - metaclass = &PyType_Type; -#endif - } - Py_INCREF((PyObject*) metaclass); - return (PyObject*) metaclass; 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-} -static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases, - PyObject *dict, PyObject *mkw, - int calculate_metaclass, int allow_py2_metaclass) { - PyObject *result, *margs; - PyObject *owned_metaclass = NULL; - if (allow_py2_metaclass) { - owned_metaclass = PyObject_GetItem(dict, __pyx_n_s_metaclass); - if (owned_metaclass) { - metaclass = owned_metaclass; - } else if (likely(PyErr_ExceptionMatches(PyExc_KeyError))) { - PyErr_Clear(); - } else { - return NULL; - } - } - if (calculate_metaclass && (!metaclass || PyType_Check(metaclass))) { - metaclass = __Pyx_CalculateMetaclass((PyTypeObject*) metaclass, bases); - Py_XDECREF(owned_metaclass); - if (unlikely(!metaclass)) - return NULL; - owned_metaclass = metaclass; - } - margs = PyTuple_Pack(3, name, bases, dict); - if (unlikely(!margs)) { - result = NULL; - } else { - result = PyObject_Call(metaclass, margs, mkw); - Py_DECREF(margs); - } - Py_XDECREF(owned_metaclass); - return result; -} - -/* CLineInTraceback */ - static int __Pyx_CLineForTraceback(int c_line) { -#ifdef CYTHON_CLINE_IN_TRACEBACK - return ((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0; 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Py_False : Py_True; - Py_DECREF(use_cline_obj); - } else { - use_cline = NULL; - } - PyErr_Restore(ptype, pvalue, ptraceback); - } - if (!use_cline) { - c_line = 0; - PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); - } - else if (PyObject_Not(use_cline) != 0) { - c_line = 0; - } - return c_line; -#endif -} - -/* CodeObjectCache */ - static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { - int start = 0, mid = 0, end = count - 1; - if (end >= 0 && code_line > entries[end].code_line) { - return count; - } - while (start < end) { - mid = start + (end - start) / 2; - if (code_line < entries[mid].code_line) { - end = mid; - } else if (code_line > entries[mid].code_line) { - start = mid + 1; - } else { - return mid; - } - } - if (code_line <= entries[mid].code_line) { - return mid; - } else { - return mid + 1; - } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; 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- } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -/* AddTraceback */ - #include "compile.h" -#include "frameobject.h" -#include "traceback.h" -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; - #if PY_MAJOR_VERSION < 3 - py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif - if (!py_srcfile) goto bad; - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); - #endif - } - if (!py_funcname) goto bad; - py_code = __Pyx_PyCode_New( - 0, - 0, - 0, - 0, - 0, - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); - return py_code; -bad: - Py_XDECREF(py_srcfile); - Py_XDECREF(py_funcname); - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyFrameObject *py_frame = 0; - if (c_line) { - c_line = __Pyx_CLineForTraceback(c_line); - } - py_code = __pyx_find_code_object(c_line ? -c_line : py_line); - if (!py_code) { - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) goto bad; - __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); - } - py_frame = PyFrame_New( - __Pyx_PyThreadState_Current, /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - __pyx_d, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - __Pyx_PyFrame_SetLineNumber(py_frame, py_line); - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -/* CIntFromPyVerify */ - #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -/* CIntFromPy */ - static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) - case -2: - if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } -#endif - if (sizeof(long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - long val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -/* CIntFromPy */ - static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) - case -2: - if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -/* FastTypeChecks */ - #if CYTHON_COMPILING_IN_CPYTHON -static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { - while (a) { - a = a->tp_base; - if (a == b) - return 1; - } - return b == &PyBaseObject_Type; -} -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { - PyObject *mro; - if (a == b) return 1; - mro = a->tp_mro; - if (likely(mro)) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(mro); - for (i = 0; i < n; i++) { - if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) - return 1; - } - return 0; - } - return __Pyx_InBases(a, b); -} -#if PY_MAJOR_VERSION == 2 -static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { - PyObject *exception, *value, *tb; - int res; - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - __Pyx_ErrFetch(&exception, &value, &tb); - res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - if (!res) { - res = PyObject_IsSubclass(err, exc_type2); - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - } - __Pyx_ErrRestore(exception, value, tb); - return res; -} -#else -static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { - int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; - if (!res) { - res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); - } - return res; -} -#endif -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) { - if (likely(err == exc_type)) return 1; - if (likely(PyExceptionClass_Check(err))) { - return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type); - } - return PyErr_GivenExceptionMatches(err, exc_type); -} -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) { - if (likely(err == exc_type1 || err == exc_type2)) return 1; - if (likely(PyExceptionClass_Check(err))) { - return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2); - } - return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2)); -} -#endif - -/* CheckBinaryVersion */ - static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -/* InitStrings */ - static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION < 3 - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); 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} - break; - case -4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - } - } - #endif - return PyLong_AsSsize_t(b); - } - x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { - return PyInt_FromSize_t(ival); -} - - -#endif /* Py_PYTHON_H */ From a6036a8517b04e312de7123ba4b28313d79fe38c Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 21 Nov 2017 19:56:56 +0100 Subject: [PATCH 40/61] FIX unit tests --- autosklearn/smbo.py | 3 + scripts/02_retrieve_metadata.py | 2 +- ...un_auto-sklearn_for_metadata_generation.py | 4 +- test/test_automl/test_automl.py | 4 +- test/test_automl/test_smbo.py | 57 ------------------- test/test_evaluation/test_test_evaluator.py | 4 +- test/test_evaluation/test_train_evaluator.py | 10 ++-- test/test_scripts/test_metadata_generation.py | 5 +- 8 files changed, 18 insertions(+), 71 deletions(-) delete mode 100644 test/test_automl/test_smbo.py diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index 40a752a3f3..53d4f05750 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -168,6 +168,7 @@ def get_smac_object( backend, metalearning_configurations, runhistory, + run_id, ): scenario_dict['input_psmac_dirs'] = backend.get_smac_output_glob() scenario = Scenario(scenario_dict) @@ -196,6 +197,7 @@ def get_smac_object( tae_runner=ta, initial_configurations=initial_configurations, runhistory=runhistory, + run_id=run_id, ) @@ -489,6 +491,7 @@ def run_smbo(self): 'backend': self.backend, 'metalearning_configurations': metalearning_configurations, 'runhistory': runhistory, + 'run_id': seed, } if self.get_smac_object_callback is not None: smac = self.get_smac_object_callback(**smac_args) diff --git a/scripts/02_retrieve_metadata.py b/scripts/02_retrieve_metadata.py index 6687fd444e..dfc3ce23c7 100644 --- a/scripts/02_retrieve_metadata.py +++ b/scripts/02_retrieve_metadata.py @@ -37,7 +37,7 @@ def retrieve_matadata(validation_directory, metric, configuration_space, continue print("Going through directory %s" % ped) - validation_trajectory_file = os.path.join(ped, 'smac3-output_1_run1', + validation_trajectory_file = os.path.join(ped, 'smac3-output', 'run_1', 'validation_trajectory.json') with open(validation_trajectory_file) as fh: validation_trajectory = json.load(fh) diff --git a/scripts/run_auto-sklearn_for_metadata_generation.py b/scripts/run_auto-sklearn_for_metadata_generation.py index 89005c1069..87f77ffed5 100644 --- a/scripts/run_auto-sklearn_for_metadata_generation.py +++ b/scripts/run_auto-sklearn_for_metadata_generation.py @@ -125,7 +125,9 @@ validated_trajectory = [entry[:2] + [entry[2].get_dictionary()] + entry[3:] for entry in validated_trajectory] -validated_trajectory_file = os.path.join(tmp_dir, 'smac3-output_%d_run1' % seed, +validated_trajectory_file = os.path.join(tmp_dir, + 'smac3-output', + 'run_%d' % seed, 'validation_trajectory.json') with open(validated_trajectory_file, 'w') as fh: json.dump(validated_trajectory, fh) diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py index 818e1d415d..5b0ddb0465 100644 --- a/test/test_automl/test_automl.py +++ b/test/test_automl/test_automl.py @@ -189,9 +189,7 @@ def test_automl_outputs(self): data_manager_file = os.path.join(output, '.auto-sklearn', 'datamanager.pkl') - backend_api = backend.create(output, output, - delete_tmp_folder_after_terminate=False) - print(backend_api.temporary_directory) + backend_api = backend.create(output, output) auto = autosklearn.automl.AutoML( backend_api, 20, 5, initial_configurations_via_metalearning=0, diff --git a/test/test_automl/test_smbo.py b/test/test_automl/test_smbo.py deleted file mode 100644 index 7094e9c51a..0000000000 --- a/test/test_automl/test_smbo.py +++ /dev/null @@ -1,57 +0,0 @@ -import unittest - -from autosklearn.smbo import AutoMLSMBO -from autosklearn.metrics import accuracy -from smac.facade.smac_facade import SMAC -from smac.scenario.scenario import Scenario -from smac.tae.execute_ta_run import StatusType -from ConfigSpace import ConfigurationSpace, UniformFloatHyperparameter, Configuration - - -class TestSMBO(unittest.TestCase): - - def test_choose_next(self): - configspace = ConfigurationSpace() - configspace.add_hyperparameter(UniformFloatHyperparameter('a', 0, 1)) - configspace.add_hyperparameter(UniformFloatHyperparameter('b', 0, 1)) - - dataset_name = 'foo' - func_eval_time_limit = 15 - total_walltime_limit = 15 - memory_limit = 3072 - - auto = AutoMLSMBO( - config_space=None, - dataset_name=dataset_name, - backend=None, - func_eval_time_limit=func_eval_time_limit, - total_walltime_limit=total_walltime_limit, - memory_limit=memory_limit, - watcher=None, - metric=accuracy - ) - auto.config_space = configspace - scenario = Scenario({ - 'cs': configspace, - 'cutoff_time': func_eval_time_limit, - 'wallclock_limit': total_walltime_limit, - 'memory_limit': memory_limit, - 'run_obj': 'quality', - }) - smac = SMAC(scenario) - - self.assertRaisesRegex( - ValueError, - 'Cannot use SMBO algorithm on empty runhistory', - auto.choose_next, - smac - ) - - config = Configuration(configspace, values={'a': 0.1, 'b': 0.2}) - # TODO make sure the incumbent is always set? - smac.solver.incumbent = config - runhistory = smac.solver.runhistory - runhistory.add(config=config, cost=0.5, time=0.5, - status=StatusType.SUCCESS) - - auto.choose_next(smac) diff --git a/test/test_evaluation/test_test_evaluator.py b/test/test_evaluation/test_test_evaluator.py index f6a6a33be2..409888ba96 100644 --- a/test/test_evaluation/test_test_evaluator.py +++ b/test/test_evaluation/test_test_evaluator.py @@ -111,8 +111,8 @@ def test_eval_test_all_loss_functions(self): 'f1_macro': 0.0341005967604, 'f1_micro': 0.04, 'f1_weighted': 0.0396930946292, - 'log_loss': 1.1363453286141156, - 'pac_score': 0.19726638744002467, + 'log_loss': 1.1352229526638984, + 'pac_score': 0.19574985585209126, 'precision_macro': 0.037037037037, 'precision_micro': 0.04, 'precision_weighted': 0.0355555555556, diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 972c3a5a23..39e913fea2 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -682,8 +682,8 @@ def test_eval_holdout_all_loss_functions(self): 'f1_macro': 0.0636363636364, 'f1_micro': 0.0606060606061, 'f1_weighted': 0.0606060606061, - 'log_loss': 1.1506551065849084, - 'pac_score': 0.21007372721643125, + 'log_loss': 1.153175128398068, + 'pac_score': 0.21332550117206728, 'precision_macro': 0.0636363636364, 'precision_micro': 0.0606060606061, 'precision_weighted': 0.0606060606061, @@ -763,8 +763,8 @@ def test_eval_cv_all_loss_functions(self): 'f1_macro': 0.063508064516129004, 'f1_micro': 0.06, 'f1_weighted': 0.06, - 'log_loss': 1.1354268574714661, - 'pac_score': 0.19027640525195577, + 'log_loss': 1.1299444831535221, + 'pac_score': 0.18306366567302557, 'precision_macro': 0.063508064516129004, 'precision_micro': 0.06, 'precision_weighted': 0.06, @@ -799,7 +799,7 @@ def test_eval_partial_cv(self): results = [0.090909090909090939, 0.047619047619047672, 0.052631578947368474, - 0.10526315789473684, + 0.15789473684210531, 0.0] for fold in range(5): instance = json.dumps({'task_id': 'data', 'fold': fold}) diff --git a/test/test_scripts/test_metadata_generation.py b/test/test_scripts/test_metadata_generation.py index 4ace6ce776..9409db9974 100644 --- a/test/test_scripts/test_metadata_generation.py +++ b/test/test_scripts/test_metadata_generation.py @@ -76,11 +76,12 @@ def test_metadata_generation(self): smac_output = fh.read() self.assertEqual(rval.returncode, 0, msg=str(rval) + '\n' + smac_output) expected_validation_output = os.path.join(expected_output_directory, - 'smac3-output_1_run1', + 'smac3-output', + 'run_1', 'validation_trajectory.json') self.assertTrue(os.path.exists(expected_validation_output)) trajectory = os.path.join(expected_output_directory, - 'smac3-output_1_run1', 'trajectory.json') + 'smac3-output', 'run_1', 'trajectory.json') with open(expected_validation_output) as fh_validation: with open(trajectory) as fh_trajectory: From c1119d49f9ca21b2517469234d9ebf7305560686 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 17 Nov 2017 16:54:58 +0100 Subject: [PATCH 41/61] ADD new option to not have one hot encoding --- autosklearn/pipeline/classification.py | 6 +- .../one_hot_encoding/__init__.py | 97 +++++++++++++++++++ .../one_hot_encoding/no_encoding.py | 41 ++++++++ .../one_hot_encoding/one_hot_encoding.py | 24 ++--- test/test_pipeline/test_classification.py | 28 ++++-- 5 files changed, 174 insertions(+), 22 deletions(-) create mode 100644 autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/no_encoding.py diff --git a/autosklearn/pipeline/classification.py b/autosklearn/pipeline/classification.py index bb4f6669d3..489ac75aa2 100644 --- a/autosklearn/pipeline/classification.py +++ b/autosklearn/pipeline/classification.py @@ -16,8 +16,8 @@ Balancing from autosklearn.pipeline.components.data_preprocessing.imputation.imputation \ import Imputation -from autosklearn.pipeline.components.data_preprocessing.one_hot_encoding\ - .one_hot_encoding import OneHotEncoder +from autosklearn.pipeline.components.data_preprocessing.one_hot_encoding \ + import OHEChoice from autosklearn.pipeline.components import feature_preprocessing as \ feature_preprocessing_components from autosklearn.pipeline.components.data_preprocessing.variance_threshold.variance_threshold \ @@ -287,7 +287,7 @@ def _get_pipeline(self): # Add the always active preprocessing components steps.extend( - [["one_hot_encoding", OneHotEncoder()], + [["categorical_encoding", OHEChoice(default_dataset_properties)], ["imputation", Imputation()], ["variance_threshold", VarianceThreshold()], ["rescaling", diff --git a/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/__init__.py b/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/__init__.py index e69de29bb2..70f79ae5e3 100644 --- a/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/__init__.py @@ -0,0 +1,97 @@ +from collections import OrderedDict +import os +from ...base import AutoSklearnPreprocessingAlgorithm, find_components, \ + ThirdPartyComponents, AutoSklearnChoice +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import CategoricalHyperparameter + +ohe_directory = os.path.split(__file__)[0] +_ohes = find_components(__package__, + ohe_directory, + AutoSklearnPreprocessingAlgorithm) +_addons = ThirdPartyComponents(AutoSklearnPreprocessingAlgorithm) + + +def add_ohe(ohe): + _addons.add_component(ohe) + + +class OHEChoice(AutoSklearnChoice): + def get_components(self): + components = OrderedDict() + components.update(_ohes) + components.update(_addons.components) + return components + + def get_hyperparameter_search_space(self, dataset_properties=None, + default=None, + include=None, + exclude=None): + cs = ConfigurationSpace() + + if dataset_properties is None: + dataset_properties = {} + + # Compile a list of legal preprocessors for this problem + available_preprocessors = self.get_available_components( + dataset_properties=dataset_properties, + include=include, exclude=exclude) + + if len(available_preprocessors) == 0: + raise ValueError( + "No ohe hot encoders found, please add any one hot encoder " + "component.") + + if default is None: + defaults = ['one_hot_encoding', 'none'] + for default_ in defaults: + if default_ in available_preprocessors: + default = default_ + break + + preprocessor = CategoricalHyperparameter('__choice__', + list( + available_preprocessors.keys()), + default_value=default) + cs.add_hyperparameter(preprocessor) + for name in available_preprocessors: + preprocessor_configuration_space = available_preprocessors[name]. \ + get_hyperparameter_search_space(dataset_properties) + parent_hyperparameter = {'parent': preprocessor, 'value': name} + cs.add_configuration_space(name, preprocessor_configuration_space, + parent_hyperparameter=parent_hyperparameter) + + self.configuration_space_ = cs + self.dataset_properties_ = dataset_properties + return cs + + def set_hyperparameters(self, configuration, init_params=None): + new_params = {} + + params = configuration.get_dictionary() + choice = params['__choice__'] + del params['__choice__'] + + for param, value in params.items(): + param = param.replace(choice, '').replace(':', '') + new_params[param] = value + + if init_params is not None: + for param, value in init_params.items(): + # These next two lines are different than in the base class - + # they allow removing the categorical feature indicator array + # in order to not pass it to the no encoding + if choice not in param: + continue + param = param.replace(choice, '').replace(':', '') + new_params[param] = value + + new_params['random_state'] = self.random_state + + self.new_params = new_params + self.choice = self.get_components()[choice](**new_params) + + return self + + def transform(self, X): + return self.choice.transform(X) \ No newline at end of file diff --git a/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/no_encoding.py b/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/no_encoding.py new file mode 100644 index 0000000000..68ecf074cd --- /dev/null +++ b/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/no_encoding.py @@ -0,0 +1,41 @@ +import numpy as np + +import autosklearn.pipeline.implementations.OneHotEncoder + +from ConfigSpace.configuration_space import ConfigurationSpace +from ConfigSpace.hyperparameters import CategoricalHyperparameter, \ + UniformFloatHyperparameter +from ConfigSpace.conditions import EqualsCondition + +from autosklearn.pipeline.components.base import \ + AutoSklearnPreprocessingAlgorithm +from autosklearn.pipeline.constants import * + + +class NoEncoding(AutoSklearnPreprocessingAlgorithm): + def __init__(self, random_state=None): + pass + + def fit(self, X, y=None): + return self + + def transform(self, X): + return X + + @staticmethod + def get_properties(dataset_properties=None): + return {'shortname': 'no encoding', + 'name': 'No categorical variable encoding', + 'handles_regression': True, + 'handles_classification': True, + 'handles_multiclass': True, + 'handles_multilabel': True, + 'handles_sparse': True, + 'handles_dense': True, + 'input': (DENSE, SPARSE, UNSIGNED_DATA), + 'output': (INPUT,)} + + @staticmethod + def get_hyperparameter_search_space(dataset_properties=None): + cs = ConfigurationSpace() + return cs diff --git a/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py b/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py index 900558ff06..ea19834973 100644 --- a/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py @@ -1,4 +1,5 @@ import numpy as np +import scipy.sparse import autosklearn.pipeline.implementations.OneHotEncoder @@ -35,7 +36,8 @@ def _fit(self, X, y=None): self.preprocessor = autosklearn.pipeline.implementations.OneHotEncoder\ .OneHotEncoder(minimum_fraction=self.minimum_fraction, - categorical_features=categorical_features) + categorical_features=categorical_features, + sparse=True) return self.preprocessor.fit_transform(X) @@ -44,11 +46,16 @@ def fit(self, X, y=None): return self def fit_transform(self, X, y=None): - return self._fit(X, y) + is_sparse = scipy.sparse.issparse(X) + X = self._fit(X) + if is_sparse: + return X + elif isinstance(X, np.ndarray): + return X + else: + return X.toarray() def transform(self, X): - import scipy.sparse - is_sparse = scipy.sparse.issparse(X) if self.preprocessor is None: raise NotImplementedError() @@ -64,22 +71,15 @@ def transform(self, X): def get_properties(dataset_properties=None): return {'shortname': '1Hot', 'name': 'One Hot Encoder', - 'handles_missing_values': True, - 'handles_nominal_values': True, - 'handles_numerical_features': True, - 'prefers_data_scaled': False, - 'prefers_data_normalized': False, 'handles_regression': True, 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, - 'is_deterministic': True, # TODO find out of this is right! 'handles_sparse': True, 'handles_dense': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), - 'output': (INPUT,), - 'preferred_dtype': None} + 'output': (INPUT,),} @staticmethod def get_hyperparameter_search_space(dataset_properties=None): diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 705566fdc0..b5bfbab1f1 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -202,8 +202,13 @@ def test_configurations_sparse(self): self._test_configurations(configurations_space=cs, make_sparse=True) def test_configurations_categorical_data(self): - cs = SimpleClassificationPipeline(dataset_properties={'sparse': True}).\ - get_hyperparameter_search_space() + cs = SimpleClassificationPipeline( + dataset_properties={'sparse': False}, + include={ + 'preprocessor': ['no_preprocessing'], + 'classifier': ['sgd', 'gradient_boosting'] + } + ).get_hyperparameter_search_space() categorical = [True, True, True, False, False, True, True, True, False, True, True, True, True, True, True, True, @@ -220,7 +225,10 @@ def test_configurations_categorical_data(self): data = {'X_train': X_train, 'Y_train': Y_train, 'X_test': X_test, 'Y_test': Y_test} - init_params = {'one_hot_encoding:categorical_features': categorical} + init_params = { + 'categorical_encoding:one_hot_encoding:categorical_features': + categorical + } self._test_configurations(configurations_space=cs, make_sparse=True, data=data, init_params=init_params) @@ -246,6 +254,8 @@ def _test_configurations(self, configurations_space, make_sparse=False, limit = 3072 * 1024 * 1024 resource.setrlimit(resource.RLIMIT_AS, (limit, limit)) + print(configurations_space) + for i in range(10): config = configurations_space.sample_configuration() config._populate_values() @@ -264,7 +274,9 @@ def _test_configurations(self, configurations_space, make_sparse=False, 'preprocessor:polynomial:degree': 2, 'classifier:lda:n_components': 10, 'preprocessor:nystroem_sampler:n_components': 50, - 'preprocessor:feature_agglomeration:n_clusters': 2} + 'preprocessor:feature_agglomeration:n_clusters': 2, + 'classifier:gradient_boosting:max_depth': 2, + 'classifier:gradient_boosting:n_estimators': 50} for restrict_parameter in restrictions: restrict_to = restrictions[restrict_parameter] @@ -272,6 +284,7 @@ def _test_configurations(self, configurations_space, make_sparse=False, config[restrict_parameter] is not None: config._values[restrict_parameter] = restrict_to + print(config) if data is None: X_train, Y_train, X_test, Y_test = get_dataset( @@ -285,11 +298,12 @@ def _test_configurations(self, configurations_space, make_sparse=False, init_params_ = copy.deepcopy(init_params) cls = SimpleClassificationPipeline(random_state=1, dataset_properties=dataset_properties, - init_params=init_params_) - cls.set_hyperparameters(config) + init_params=init_params_,) + cls.set_hyperparameters(config, init_params=init_params_) try: cls.fit(X_train, Y_train, ) - predictions = cls.predict(X_test) + predictions = cls.predict(X_test.copy()) + predictions = cls.predict_proba(X_test) except MemoryError as e: continue except ValueError as e: From 57b14db8d7ac345c3aef928033c133a5e04752f1 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 22 Nov 2017 10:35:33 +0100 Subject: [PATCH 42/61] FIX bugs --- autosklearn/evaluation/abstract_evaluator.py | 6 ++- .../one_hot_encoding/__init__.py | 2 +- autosklearn/pipeline/regression.py | 4 +- ...un_auto-sklearn_for_metadata_generation.py | 25 +++++++----- .../test_meta_base_data/configurations.csv | 40 +++++++++---------- test/test_pipeline/test_classification.py | 31 +++++++++----- test/test_pipeline/test_regression.py | 2 +- 7 files changed, 63 insertions(+), 47 deletions(-) diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index 23c41b0a3d..92fd39dcae 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -143,8 +143,10 @@ def __init__(self, datamanager, backend, queue, metric, else: raise ValueError(feat) if np.sum(categorical_mask) > 0: - self._init_params = {'one_hot_encoding:categorical_features': - categorical_mask} + self._init_params = { + 'categorical_encoding:one_hot_encoding:categorical_features': + categorical_mask + } else: self._init_params = {} diff --git a/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/__init__.py b/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/__init__.py index 70f79ae5e3..dbe550ad00 100644 --- a/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/__init__.py +++ b/autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/__init__.py @@ -43,7 +43,7 @@ def get_hyperparameter_search_space(self, dataset_properties=None, "component.") if default is None: - defaults = ['one_hot_encoding', 'none'] + defaults = ['one_hot_encoding', 'no_encoding'] for default_ in defaults: if default_ in available_preprocessors: default = default_ diff --git a/autosklearn/pipeline/regression.py b/autosklearn/pipeline/regression.py index 628e4c1ce9..cc68d66509 100644 --- a/autosklearn/pipeline/regression.py +++ b/autosklearn/pipeline/regression.py @@ -14,7 +14,7 @@ from autosklearn.pipeline.components.data_preprocessing.imputation.imputation \ import Imputation from autosklearn.pipeline.components.data_preprocessing.one_hot_encoding \ - .one_hot_encoding import OneHotEncoder + import OHEChoice from autosklearn.pipeline.components import feature_preprocessing as \ feature_preprocessing_components from autosklearn.pipeline.components.data_preprocessing.variance_threshold.variance_threshold \ @@ -259,7 +259,7 @@ def _get_pipeline(self, init_params=None): categorical_features = None steps.extend( - [["one_hot_encoding", OneHotEncoder(categorical_features=categorical_features)], + [["categorical_encoding", OHEChoice(default_dataset_properties)], ["imputation", Imputation()], ["variance_threshold", VarianceThreshold()], ["rescaling", rescaling_components.RescalingChoice( diff --git a/scripts/run_auto-sklearn_for_metadata_generation.py b/scripts/run_auto-sklearn_for_metadata_generation.py index 87f77ffed5..1bddbf98c8 100644 --- a/scripts/run_auto-sklearn_for_metadata_generation.py +++ b/scripts/run_auto-sklearn_for_metadata_generation.py @@ -44,17 +44,20 @@ pass tmp_dir = os.path.join(configuration_output_dir, str(task_id)) -automl_arguments = {'time_left_for_this_task': time_limit, - 'per_run_time_limit': per_run_time_limit, - 'initial_configurations_via_metalearning': 0, - 'ensemble_size': 0, - 'ensemble_nbest': 0, - 'seed': seed, - 'ml_memory_limit': 3072, - 'resampling_strategy': 'partial-cv', - 'resampling_strategy_arguments': {'folds': 10}, - 'delete_tmp_folder_after_terminate': False, - 'tmp_folder': tmp_dir} +automl_arguments = { + 'time_left_for_this_task': time_limit, + 'per_run_time_limit': per_run_time_limit, + 'initial_configurations_via_metalearning': 0, + 'ensemble_size': 0, + 'ensemble_nbest': 0, + 'seed': seed, + 'ml_memory_limit': 3072, + 'resampling_strategy': 'partial-cv', + 'resampling_strategy_arguments': {'folds': 10}, + 'delete_tmp_folder_after_terminate': False, + 'tmp_folder': tmp_dir, + 'disable_evaluator_output': True, +} X_train, y_train, X_test, y_test, cat = load_task(task_id) diff --git a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv index 46e1d06d89..8f3ddbc0f6 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv +++ b/test/test_metalearning/pyMetaLearn/test_meta_base_data/configurations.csv @@ -1,20 +1,20 @@ -idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:decision_tree:splitter,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:n_iter,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:n_iter,classifier:sgd:penalty,classifier:sgd:power_t,imputation:strategy,one_hot_encoding:minimum_fraction,one_hot_encoding:use_minimum_fraction,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:gem:N,preprocessor:gem:precond,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_samp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+20,weighting,no_encoding,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30848.1086905961,False,True,1,squared_hinge,ovr,l2,0.0554582945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,105,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +21,none,no_encoding,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,9,19,0,0,100,,,,,,,,,,,,mean,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +23,none,no_encoding,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5,None,0,11,20,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +24,none,no_encoding,adaboost,SAMME,0.723185509,5,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0,5,8,0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index b5bfbab1f1..d881a83171 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -234,18 +234,29 @@ def test_configurations_categorical_data(self): data=data, init_params=init_params) @unittest.mock.patch('autosklearn.pipeline.components.data_preprocessing' - '.one_hot_encoding.one_hot_encoding.OneHotEncoder' - '.set_hyperparameters') + '.one_hot_encoding.OHEChoice.set_hyperparameters') def test_categorical_passed_to_one_hot_encoder(self, ohe_mock): - cls = SimpleClassificationPipeline(init_params={ - 'one_hot_encoding:categorical_features': [True, False]}) - self.assertEqual(ohe_mock.call_args[1]['init_params'], - {'categorical_features': [True, False]}) + cls = SimpleClassificationPipeline( + init_params={ + 'categorical_encoding:one_hot_encoding:categorical_features': + [True, False] + } + ) + self.assertEqual( + ohe_mock.call_args[1]['init_params'], + {'one_hot_encoding:categorical_features': [True, False]} + ) default = cls.get_hyperparameter_search_space().get_default_configuration() cls.set_hyperparameters(configuration=default, - init_params={'one_hot_encoding:categorical_features': [True, True, False]}) - self.assertEqual(ohe_mock.call_args[1]['init_params'], - {'categorical_features': [True, True, False]}) + init_params={ + 'categorical_encoding:one_hot_encoding:categorical_features': + [True, True, False] + } + ) + self.assertEqual( + ohe_mock.call_args[1]['init_params'], + {'one_hot_encoding:categorical_features': [True, True, False]} + ) def _test_configurations(self, configurations_space, make_sparse=False, data=None, init_params=None, @@ -366,7 +377,7 @@ def test_get_hyperparameter_search_space(self): 'preprocessor:__choice__').choices), 13) hyperparameters = cs.get_hyperparameters() - self.assertEqual(154, len(hyperparameters)) + self.assertEqual(155, len(hyperparameters)) #for hp in sorted([str(h) for h in hyperparameters]): # print hp diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 200299719a..a505eb5b53 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -218,7 +218,7 @@ def test_get_hyperparameter_search_space(self): self.assertIsInstance(cs, ConfigurationSpace) conditions = cs.get_conditions() hyperparameters = cs.get_hyperparameters() - self.assertEqual(140, len(hyperparameters)) + self.assertEqual(141, len(hyperparameters)) self.assertEqual(len(hyperparameters) - 5, len(conditions)) def test_get_hyperparameter_search_space_include_exclude_models(self): From 2a6b5614f6ea0fca08b24269f1a4ee306a1875c7 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 22 Nov 2017 11:06:45 +0100 Subject: [PATCH 43/61] fix unittest/change resolution of equals comparison --- .../test_pipeline/components/regression/test_ridge_regression.py | 1 + 1 file changed, 1 insertion(+) diff --git a/test/test_pipeline/components/regression/test_ridge_regression.py b/test/test_pipeline/components/regression/test_ridge_regression.py index f8b0d5a06a..483e944f41 100644 --- a/test/test_pipeline/components/regression/test_ridge_regression.py +++ b/test/test_pipeline/components/regression/test_ridge_regression.py @@ -10,6 +10,7 @@ class RidgeComponentTest(BaseRegressionComponentTest): res = dict() res["default_boston"] = 0.70337988575176991 + res["default_boston_places"] = 4 res["default_boston_iterative"] = None res["default_boston_sparse"] = 0.1086535440527433 res["default_boston_iterative_sparse"] = None From a194e0b054acbebf68338356e386e6c2c9bb3603 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 22 Nov 2017 11:09:08 +0100 Subject: [PATCH 44/61] CI: Use latest miniconda again --- .travis.yml | 16 ++++------------ 1 file changed, 4 insertions(+), 12 deletions(-) diff --git a/.travis.yml b/.travis.yml index 85e14687ae..f8271cab72 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,6 +6,7 @@ env: global: - OPENBLAS_NUM_THREADS=1 - OMP_NUM_THREADS=1 + - MKL_NUM_THREADS=1 matrix: allow_failures: @@ -13,11 +14,9 @@ matrix: include: - os: linux - env: DISTRIB="conda" PYTHON_VERSION="3.4" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-4.3.21-Linux-x86_64.sh" + env: DISTRIB="conda" PYTHON_VERSION="3.5" COVERAGE="true" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh" - os: linux - env: DISTRIB="conda" PYTHON_VERSION="3.5" COVERAGE="true" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-4.3.21-Linux-x86_64.sh" - - os: linux - env: DISTRIB="conda" PYTHON_VERSION="3.6" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-4.3.21-Linux-x86_64.sh" + env: DISTRIB="conda" PYTHON_VERSION="3.6" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh" # Temporarily disabling OSX builds because thy take too long # Set language to generic to not break travis-ci @@ -27,10 +26,6 @@ matrix: #- os: osx # sudo: required # language: generic - # env: DISTRIB="conda" PYTHON_VERSION="3.4" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh" - #- os: osx - # sudo: required - # language: generic # env: DISTRIB="conda" PYTHON_VERSION="3.5" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh" cache: @@ -55,11 +50,8 @@ before_install: - bash miniconda.sh -b -p $HOME/miniconda - export PATH="$HOME/miniconda/bin:$PATH" - if [[ `which conda` ]]; then echo 'Conda installation successful'; else exit 1; fi - - conda create -n testenv --yes python=$PYTHON_VERSION pip wheel nose + - conda create -n testenv --yes python=$PYTHON_VERSION pip wheel nose gxx_linux-64 gcc_linux-64 swig - source activate testenv - - conda install --yes gcc swig - - echo "Using GCC at "`which gcc` - - export CC=`which gcc` install: # Install general requirements the way setup.py suggests From 2d16542d6ef65de055437e11e2b71ab434f5563d Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 22 Nov 2017 17:48:15 +0100 Subject: [PATCH 45/61] Save origin of configuration in additional run data --- autosklearn/evaluation/__init__.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index b2f7539ce0..b45a0b9162 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -241,6 +241,12 @@ def run(self, config, instance=None, status = StatusType.CRASHED cost = WORST_POSSIBLE_RESULT + if isinstance(config, int): + origin = 'DUMMY' + else: + origin = getattr(config, 'origin', 'UNKNOWN') + additional_run_info['configuration_origin'] = origin + runtime = float(obj.wall_clock_time) self.num_run += 1 return status, cost, runtime, additional_run_info From 33529323a5f3e89a8c6208e90181dd5e2a87cdaf Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 23 Nov 2017 09:11:08 +0100 Subject: [PATCH 46/61] FIX bug, add init_params to evaluation --- autosklearn/evaluation/__init__.py | 12 +++++-- autosklearn/evaluation/abstract_evaluator.py | 5 ++- autosklearn/evaluation/test_evaluator.py | 12 ++++--- autosklearn/evaluation/train_evaluator.py | 33 +++++++++++++------- test/test_evaluation/test_evaluation.py | 6 ++-- 5 files changed, 47 insertions(+), 21 deletions(-) diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index b45a0b9162..d3b3990875 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -51,7 +51,7 @@ def __init__(self, backend, autosklearn_seed, resampling_strategy, metric, logger, initial_num_run=1, stats=None, runhistory=None, run_obj='quality', par_factor=1, all_scoring_functions=False, output_y_hat_optimization=True, include=None, exclude=None, - memory_limit=None, disable_file_output=False, + memory_limit=None, disable_file_output=False, init_params=None, **resampling_strategy_args): if resampling_strategy == 'holdout': @@ -95,6 +95,7 @@ def __init__(self, backend, autosklearn_seed, resampling_strategy, metric, self.include = include self.exclude = exclude self.disable_file_output = disable_file_output + self.init_params = init_params self.logger = logger if memory_limit is not None: @@ -160,6 +161,9 @@ def run(self, config, instance=None, if not (instance_specific is None or instance_specific == '0'): raise ValueError(instance_specific) + init_params = {'instance': instance} + if self.init_params is not None: + init_params.update(self.init_params) arguments = dict(logger=logging.getLogger("pynisher"), wall_time_in_s=cutoff, @@ -176,7 +180,8 @@ def run(self, config, instance=None, include=self.include, exclude=self.exclude, disable_file_output=self.disable_file_output, - instance=instance) + instance=instance, + init_params=init_params) if self.resampling_strategy != 'test': cv = self.get_splitter(D) @@ -241,6 +246,9 @@ def run(self, config, instance=None, status = StatusType.CRASHED cost = WORST_POSSIBLE_RESULT + if not isinstance(additional_run_info, dict): + additional_run_info = {'message': additional_run_info} + if isinstance(config, int): origin = 'DUMMY' else: diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index 92fd39dcae..fb3a485466 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -96,7 +96,8 @@ def __init__(self, datamanager, backend, queue, metric, subsample=None, include=None, exclude=None, - disable_file_output=False): + disable_file_output=False, + init_params=None): self.starttime = time.time() @@ -149,6 +150,8 @@ def __init__(self, datamanager, backend, queue, metric, } else: self._init_params = {} + if init_params is not None: + self._init_params.update(init_params) if num_run is None: num_run = 0 diff --git a/autosklearn/evaluation/test_evaluator.py b/autosklearn/evaluation/test_evaluator.py index 3fdccd638d..574784fbd5 100644 --- a/autosklearn/evaluation/test_evaluator.py +++ b/autosklearn/evaluation/test_evaluator.py @@ -19,7 +19,8 @@ def __init__(self, datamanager, backend, queue, metric, seed=1, include=None, exclude=None, - disable_file_output=False): + disable_file_output=False, + init_params=None): super(TestEvaluator, self).__init__( datamanager=datamanager, backend=backend, @@ -33,7 +34,9 @@ def __init__(self, datamanager, backend, queue, metric, subsample=None, include=include, exclude=exclude, - disable_file_output= disable_file_output) + disable_file_output= disable_file_output, + init_params=init_params + ) self.configuration = configuration self.X_train = datamanager.data['X_train'] @@ -82,13 +85,14 @@ def predict_and_loss(self, train=False): # Has a stupid name so nosetests doesn't regard it as a test def eval_t(queue, config, datamanager, backend, metric, seed, num_run, instance, all_scoring_functions, output_y_hat_optimization, include, - exclude, disable_file_output): + exclude, disable_file_output, init_params=None): evaluator = TestEvaluator(datamanager=datamanager, configuration=config, backend=backend, metric=metric, seed=seed, queue=queue, all_scoring_functions=all_scoring_functions, include=include, exclude=exclude, - disable_file_output=disable_file_output) + disable_file_output=disable_file_output, + init_params=init_params) evaluator.fit_predict_and_loss() diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index 3a6f58da3d..67b3c88d74 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -33,7 +33,8 @@ def __init__(self, datamanager, backend, queue, metric, keep_models=False, include=None, exclude=None, - disable_file_output=False): + disable_file_output=False, + init_params=None): super().__init__( datamanager=datamanager, backend=backend, @@ -47,7 +48,9 @@ def __init__(self, datamanager, backend, queue, metric, subsample=subsample, include=include, exclude=exclude, - disable_file_output=disable_file_output) + disable_file_output=disable_file_output, + init_params=init_params, + ) self.cv = cv self.cv_folds = cv.n_splits @@ -342,7 +345,8 @@ def _predict(self, model, test_indices, train_indices): # create closure for evaluating an algorithm def eval_holdout(queue, config, datamanager, backend, cv, metric, seed, num_run, instance, all_scoring_functions, output_y_hat_optimization, - include, exclude, disable_file_output, iterative=False): + include, exclude, disable_file_output, init_params=None, + iterative=False): instance = json.loads(instance) if instance is not None else {} subsample = instance.get('subsample') evaluator = TrainEvaluator(datamanager=datamanager, @@ -358,14 +362,15 @@ def eval_holdout(queue, config, datamanager, backend, cv, metric, seed, num_run, output_y_hat_optimization=output_y_hat_optimization, include=include, exclude=exclude, - disable_file_output=disable_file_output) + disable_file_output=disable_file_output, + init_params=init_params) evaluator.fit_predict_and_loss(iterative=iterative) def eval_iterative_holdout(queue, config, datamanager, backend, cv, metric, seed, num_run, instance, all_scoring_functions, output_y_hat_optimization, include, exclude, - disable_file_output): + disable_file_output, init_params=None): return eval_holdout(queue=queue, config=config, datamanager=datamanager, @@ -380,13 +385,14 @@ def eval_iterative_holdout(queue, config, datamanager, backend, cv, metric, exclude=exclude, instance=instance, disable_file_output=disable_file_output, - iterative=True) + iterative=True, + init_params=init_params) def eval_partial_cv(queue, config, datamanager, backend, cv, metric, seed, num_run, instance, all_scoring_functions, output_y_hat_optimization, include, exclude, - disable_file_output, iterative=False): + disable_file_output, init_params=None, iterative=False): instance = json.loads(instance) if instance is not None else {} subsample = instance.get('subsample') fold = instance['fold'] @@ -404,7 +410,8 @@ def eval_partial_cv(queue, config, datamanager, backend, cv, metric, seed, output_y_hat_optimization=False, include=include, exclude=exclude, - disable_file_output=disable_file_output) + disable_file_output=disable_file_output, + init_params=init_params) evaluator.partial_fit_predict_and_loss(fold=fold, iterative=iterative) @@ -412,7 +419,7 @@ def eval_partial_cv(queue, config, datamanager, backend, cv, metric, seed, def eval_partial_cv_iterative(queue, config, datamanager, backend, cv, metric, seed, num_run, instance, all_scoring_functions, output_y_hat_optimization, include, exclude, - disable_file_output): + disable_file_output, init_params=None): return eval_partial_cv(queue=queue, config=config, datamanager=datamanager, @@ -427,13 +434,14 @@ def eval_partial_cv_iterative(queue, config, datamanager, backend, cv, metric, include=include, exclude=exclude, disable_file_output=disable_file_output, - iterative=True) + iterative=True, + init_params=init_params) # create closure for evaluating an algorithm def eval_cv(queue, config, datamanager, backend, cv, metric, seed, num_run, instance, all_scoring_functions, output_y_hat_optimization, - include, exclude, disable_file_output): + include, exclude, disable_file_output, init_params=None): instance = json.loads(instance) if instance is not None else {} subsample = instance.get('subsample') evaluator = TrainEvaluator(datamanager=datamanager, @@ -449,6 +457,7 @@ def eval_cv(queue, config, datamanager, backend, cv, metric, seed, num_run, output_y_hat_optimization=output_y_hat_optimization, include=include, exclude=exclude, - disable_file_output=disable_file_output) + disable_file_output=disable_file_output, + init_params=init_params) evaluator.fit_predict_and_loss() diff --git a/test/test_evaluation/test_evaluation.py b/test/test_evaluation/test_evaluation.py index 9aa7345094..4c10a88949 100644 --- a/test/test_evaluation/test_evaluation.py +++ b/test/test_evaluation/test_evaluation.py @@ -144,7 +144,8 @@ def test_eval_with_limits_holdout_fail_silent(self, pynisher_mock): self.assertEqual(info[0], StatusType.CRASHED) self.assertEqual(info[1], 1.0) self.assertIsInstance(info[2], float) - self.assertEqual(info[3], {'error': "Result queue is empty"}) + self.assertEqual(info[3], {'configuration_origin': 'UNKNOWN', + 'error': "Result queue is empty"}) @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout') def test_eval_with_limits_holdout_fail_memory_error(self, pynisher_mock): @@ -242,7 +243,8 @@ def side_effect(*args, **kwargs): instance = "{'subsample': 30}" info = ta.start(None, cutoff=30, instance=instance) self.assertEqual(info[0], StatusType.SUCCESS) - self.assertEqual(info[-1], instance) + self.assertEqual(info[-1], {'message': "{'subsample': 30}", + 'configuration_origin': 'UNKNOWN'}) @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout') def test_exception_in_target_function(self, eval_holdout_mock): From 7ffde8f8c6e951c58ce2ecd43c3c29beeb9cac8c Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 30 Nov 2017 15:07:31 +0100 Subject: [PATCH 47/61] initialize splitter in child process --- autosklearn/evaluation/__init__.py | 51 +-- autosklearn/evaluation/train_evaluator.py | 353 ++++++++++++------ autosklearn/evaluation/util.py | 11 + test/test_evaluation/test_evaluation.py | 54 --- test/test_evaluation/test_train_evaluator.py | 354 ++++++++++++++----- 5 files changed, 523 insertions(+), 300 deletions(-) diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index d3b3990875..057fb37cb4 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -8,8 +8,6 @@ import traceback import pynisher -from sklearn.model_selection import ShuffleSplit, StratifiedShuffleSplit, KFold, \ - StratifiedKFold from smac.tae.execute_ta_run import StatusType, BudgetExhaustedException, \ TAEAbortException from smac.tae.execute_func import AbstractTAFunc @@ -184,8 +182,8 @@ def run(self, config, instance=None, init_params=init_params) if self.resampling_strategy != 'test': - cv = self.get_splitter(D) - obj_kwargs['cv'] = cv + obj_kwargs['resampling_strategy'] = self.resampling_strategy + obj_kwargs['resampling_strategy_args'] = self.resampling_strategy_args #if instance is not None: # obj_kwargs['instance'] = instance @@ -257,47 +255,10 @@ def run(self, config, instance=None, runtime = float(obj.wall_clock_time) self.num_run += 1 + + autosklearn.evaluation.util.empty_queue(queue) + return status, cost, runtime, additional_run_info - def get_splitter(self, D): - y = D.data['Y_train'].ravel() - train_size = 0.67 - if self.resampling_strategy_args: - train_size = self.resampling_strategy_args.get('train_size', train_size) - test_size = 1 - train_size - if D.info['task'] in CLASSIFICATION_TASKS and \ - D.info['task'] != MULTILABEL_CLASSIFICATION: - - if self.resampling_strategy in ['holdout', - 'holdout-iterative-fit']: - try: - cv = StratifiedShuffleSplit(n_splits=1, train_size=train_size, - test_size=test_size, random_state=1) - test_cv = copy.deepcopy(cv) - next(test_cv.split(y, y)) - except ValueError as e: - if 'The least populated class in y has only' in e.args[0]: - cv = ShuffleSplit(n_splits=1, train_size=train_size, - test_size=test_size, random_state=1) - else: - raise - - elif self.resampling_strategy in ['cv', 'partial-cv', - 'partial-cv-iterative-fit']: - cv = StratifiedKFold(n_splits=self.resampling_strategy_args['folds'], - shuffle=True, random_state=1) - else: - raise ValueError(self.resampling_strategy) - else: - if self.resampling_strategy in ['holdout', - 'holdout-iterative-fit']: - cv = ShuffleSplit(n_splits=1, train_size=train_size, - test_size=test_size, random_state=1) - elif self.resampling_strategy in ['cv', 'partial-cv', - 'partial-cv-iterative-fit']: - cv = KFold(n_splits=self.resampling_strategy_args['folds'], - shuffle=True, random_state=1) - else: - raise ValueError(self.resampling_strategy) - return cv + diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index 67b3c88d74..a69730c42c 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -1,8 +1,10 @@ +import copy import json import numpy as np from smac.tae.execute_ta_run import TAEAbortException -import sklearn.model_selection +from sklearn.model_selection import ShuffleSplit, StratifiedShuffleSplit, KFold, \ + StratifiedKFold, train_test_split from autosklearn.evaluation.abstract_evaluator import AbstractEvaluator from autosklearn.constants import * @@ -27,7 +29,8 @@ def __init__(self, datamanager, backend, queue, metric, all_scoring_functions=False, seed=1, output_y_hat_optimization=True, - cv=None, + resampling_strategy=None, + resampling_strategy_args=None, num_run=None, subsample=None, keep_models=False, @@ -52,8 +55,10 @@ def __init__(self, datamanager, backend, queue, metric, init_params=init_params, ) - self.cv = cv - self.cv_folds = cv.n_splits + self.resampling_strategy = resampling_strategy + self.resampling_strategy_args = resampling_strategy_args + self.cv = self.get_splitter(datamanager) + self.cv_folds = self.cv.n_splits self.X_train = self.datamanager.data['X_train'] self.Y_train = self.datamanager.data['Y_train'] self.Y_optimization = None @@ -101,6 +106,7 @@ def fit_predict_and_loss(self, iterative=False): i, train_indices=train_split, test_indices=test_split ) ) + assert len(opt_pred) == len(test_split), (len(opt_pred), len(test_split)) if ( additional_run_info is not None @@ -309,9 +315,12 @@ def subsample_indices(self, train_indices): if len(train_indices) > self.subsample: indices = np.arange(len(train_indices)) - cv_indices_train, _ = sklearn.model_selection.train_test_split( - indices, stratify=stratify, - train_size=self.subsample, random_state=1) + cv_indices_train, _ = train_test_split( + indices, + stratify=stratify, + train_size=self.subsample, + random_state=1 + ) train_indices = train_indices[cv_indices_train] return train_indices @@ -341,123 +350,257 @@ def _predict(self, model, test_indices, train_indices): return opt_pred, valid_pred, test_pred + def get_splitter(self, D): + y = D.data['Y_train'].ravel() + train_size = 0.67 + if self.resampling_strategy_args: + train_size = self.resampling_strategy_args.get('train_size', + train_size) + test_size = 1 - train_size + if D.info['task'] in CLASSIFICATION_TASKS and \ + D.info['task'] != MULTILABEL_CLASSIFICATION: + + if self.resampling_strategy in ['holdout', + 'holdout-iterative-fit']: + try: + cv = StratifiedShuffleSplit(n_splits=1, + train_size=train_size, + test_size=test_size, + random_state=1) + test_cv = copy.deepcopy(cv) + next(test_cv.split(y, y)) + except ValueError as e: + if 'The least populated class in y has only' in e.args[0]: + cv = ShuffleSplit(n_splits=1, train_size=train_size, + test_size=test_size, random_state=1) + else: + raise + + elif self.resampling_strategy in ['cv', 'partial-cv', + 'partial-cv-iterative-fit']: + cv = StratifiedKFold( + n_splits=self.resampling_strategy_args['folds'], + shuffle=True, random_state=1) + else: + raise ValueError(self.resampling_strategy) + else: + if self.resampling_strategy in ['holdout', + 'holdout-iterative-fit']: + cv = ShuffleSplit(n_splits=1, train_size=train_size, + test_size=test_size, random_state=1) + elif self.resampling_strategy in ['cv', 'partial-cv', + 'partial-cv-iterative-fit']: + cv = KFold(n_splits=self.resampling_strategy_args['folds'], + shuffle=True, random_state=1) + else: + raise ValueError(self.resampling_strategy) + return cv + # create closure for evaluating an algorithm -def eval_holdout(queue, config, datamanager, backend, cv, metric, seed, num_run, - instance, all_scoring_functions, output_y_hat_optimization, - include, exclude, disable_file_output, init_params=None, - iterative=False): +def eval_holdout( + queue, + config, + datamanager, + backend, + resampling_strategy, + resampling_strategy_args, + metric, + seed, + num_run, + instance, + all_scoring_functions, + output_y_hat_optimization, + include, + exclude, + disable_file_output, + init_params=None, + iterative=False, +): instance = json.loads(instance) if instance is not None else {} subsample = instance.get('subsample') - evaluator = TrainEvaluator(datamanager=datamanager, - backend=backend, - queue=queue, - cv=cv, - metric=metric, - configuration=config, - seed=seed, - num_run=num_run, - subsample=subsample, - all_scoring_functions=all_scoring_functions, - output_y_hat_optimization=output_y_hat_optimization, - include=include, - exclude=exclude, - disable_file_output=disable_file_output, - init_params=init_params) + evaluator = TrainEvaluator( + datamanager=datamanager, + backend=backend, + queue=queue, + resampling_strategy=resampling_strategy, + resampling_strategy_args=resampling_strategy_args, + metric=metric, + configuration=config, + seed=seed, + num_run=num_run, + subsample=subsample, + all_scoring_functions=all_scoring_functions, + output_y_hat_optimization=output_y_hat_optimization, + include=include, + exclude=exclude, + disable_file_output=disable_file_output, + init_params=init_params + ) evaluator.fit_predict_and_loss(iterative=iterative) -def eval_iterative_holdout(queue, config, datamanager, backend, cv, metric, - seed, num_run, instance, all_scoring_functions, - output_y_hat_optimization, include, exclude, - disable_file_output, init_params=None): - return eval_holdout(queue=queue, - config=config, - datamanager=datamanager, - backend=backend, - metric=metric, - cv=cv, - seed=seed, - num_run=num_run, - all_scoring_functions=all_scoring_functions, - output_y_hat_optimization=output_y_hat_optimization, - include=include, - exclude=exclude, - instance=instance, - disable_file_output=disable_file_output, - iterative=True, - init_params=init_params) - - -def eval_partial_cv(queue, config, datamanager, backend, cv, metric, seed, - num_run, instance, all_scoring_functions, - output_y_hat_optimization, include, exclude, - disable_file_output, init_params=None, iterative=False): +def eval_iterative_holdout( + queue, + config, + datamanager, + backend, + resampling_strategy, + resampling_strategy_args, + metric, + seed, + num_run, + instance, + all_scoring_functions, + output_y_hat_optimization, + include, + exclude, + disable_file_output, + init_params=None, +): + return eval_holdout( + queue=queue, + config=config, + datamanager=datamanager, + backend=backend, + metric=metric, + resampling_strategy=resampling_strategy, + resampling_strategy_args=resampling_strategy_args, + seed=seed, + num_run=num_run, + all_scoring_functions=all_scoring_functions, + output_y_hat_optimization=output_y_hat_optimization, + include=include, + exclude=exclude, + instance=instance, + disable_file_output=disable_file_output, + iterative=True, + init_params=init_params + ) + + +def eval_partial_cv( + queue, + config, + datamanager, + backend, + resampling_strategy, + resampling_strategy_args, + metric, + seed, + num_run, + instance, + all_scoring_functions, + output_y_hat_optimization, + include, + exclude, + disable_file_output, + init_params=None, + iterative=False +): instance = json.loads(instance) if instance is not None else {} subsample = instance.get('subsample') fold = instance['fold'] - evaluator = TrainEvaluator(datamanager=datamanager, - backend=backend, - queue=queue, - metric=metric, - configuration=config, - cv=cv, - seed=seed, - num_run=num_run, - subsample=subsample, - all_scoring_functions=all_scoring_functions, - output_y_hat_optimization=False, - include=include, - exclude=exclude, - disable_file_output=disable_file_output, - init_params=init_params) + evaluator = TrainEvaluator( + datamanager=datamanager, + backend=backend, + queue=queue, + metric=metric, + configuration=config, + resampling_strategy=resampling_strategy, + resampling_strategy_args=resampling_strategy_args, + seed=seed, + num_run=num_run, + subsample=subsample, + all_scoring_functions=all_scoring_functions, + output_y_hat_optimization=False, + include=include, + exclude=exclude, + disable_file_output=disable_file_output, + init_params=init_params, + ) evaluator.partial_fit_predict_and_loss(fold=fold, iterative=iterative) -def eval_partial_cv_iterative(queue, config, datamanager, backend, cv, metric, - seed, num_run, instance, all_scoring_functions, - output_y_hat_optimization, include, exclude, - disable_file_output, init_params=None): - return eval_partial_cv(queue=queue, - config=config, - datamanager=datamanager, - backend=backend, - metric=metric, - cv=cv, - seed=seed, - num_run=num_run, - instance=instance, - all_scoring_functions=all_scoring_functions, - output_y_hat_optimization=output_y_hat_optimization, - include=include, - exclude=exclude, - disable_file_output=disable_file_output, - iterative=True, - init_params=init_params) +def eval_partial_cv_iterative( + queue, + config, + datamanager, + backend, + resampling_strategy, + resampling_strategy_args, + metric, + seed, + num_run, + instance, + all_scoring_functions, + output_y_hat_optimization, + include, + exclude, + disable_file_output, + init_params=None, +): + return eval_partial_cv( + queue=queue, + config=config, + datamanager=datamanager, + backend=backend, + metric=metric, + resampling_strategy=resampling_strategy, + resampling_strategy_args=resampling_strategy_args, + seed=seed, + num_run=num_run, + instance=instance, + all_scoring_functions=all_scoring_functions, + output_y_hat_optimization=output_y_hat_optimization, + include=include, + exclude=exclude, + disable_file_output=disable_file_output, + iterative=True, + init_params=init_params, + ) # create closure for evaluating an algorithm -def eval_cv(queue, config, datamanager, backend, cv, metric, seed, num_run, - instance, all_scoring_functions, output_y_hat_optimization, - include, exclude, disable_file_output, init_params=None): +def eval_cv( + queue, + config, + datamanager, + backend, + resampling_strategy, + resampling_strategy_args, + metric, + seed, + num_run, + instance, + all_scoring_functions, + output_y_hat_optimization, + include, + exclude, + disable_file_output, + init_params=None, +): instance = json.loads(instance) if instance is not None else {} subsample = instance.get('subsample') - evaluator = TrainEvaluator(datamanager=datamanager, - backend=backend, - queue=queue, - metric=metric, - configuration=config, - seed=seed, - num_run=num_run, - cv=cv, - subsample=subsample, - all_scoring_functions=all_scoring_functions, - output_y_hat_optimization=output_y_hat_optimization, - include=include, - exclude=exclude, - disable_file_output=disable_file_output, - init_params=init_params) + evaluator = TrainEvaluator( + datamanager=datamanager, + backend=backend, + queue=queue, + metric=metric, + configuration=config, + seed=seed, + num_run=num_run, + resampling_strategy=resampling_strategy, + resampling_strategy_args=resampling_strategy_args, + subsample=subsample, + all_scoring_functions=all_scoring_functions, + output_y_hat_optimization=output_y_hat_optimization, + include=include, + exclude=exclude, + disable_file_output=disable_file_output, + init_params=init_params, + ) evaluator.fit_predict_and_loss() diff --git a/autosklearn/evaluation/util.py b/autosklearn/evaluation/util.py index eb897a175a..a334b9a8a2 100644 --- a/autosklearn/evaluation/util.py +++ b/autosklearn/evaluation/util.py @@ -30,3 +30,14 @@ def get_last_result(queue_): raise queue.Empty else: return stack.pop() + + +def empty_queue(queue_): + while True: + try: + rval = queue_.get(block=False) + except queue.Empty: + break + + queue_.close() + diff --git a/test/test_evaluation/test_evaluation.py b/test/test_evaluation/test_evaluation.py index 4c10a88949..d84c24370d 100644 --- a/test/test_evaluation/test_evaluation.py +++ b/test/test_evaluation/test_evaluation.py @@ -15,12 +15,9 @@ from smac.tae.execute_ta_run import StatusType, FirstRunCrashedException, \ BudgetExhaustedException from smac.stats.stats import Stats -import sklearn.model_selection from evaluation_util import get_multiclass_classification_datamanager -from autosklearn.constants import * from autosklearn.evaluation import ExecuteTaFuncWithQueue -from autosklearn.data.abstract_data_manager import AbstractDataManager from autosklearn.metrics import accuracy @@ -262,54 +259,3 @@ def test_exception_in_target_function(self, eval_holdout_mock): self.assertIsInstance(info[2], float) self.assertEqual(info[3]['error'], 'ValueError()') self.assertIn('traceback', info[3]) - - ############################################################################ - # Test obtaining a splitter object from scikit-learn - def test_get_splitter(self): - ta_args = dict(backend=BackendMock(), autosklearn_seed=1, - logger=self.logger, stats=self.stats, memory_limit=3072, - metric=accuracy) - D = unittest.mock.Mock(spec=AbstractDataManager) - D.data = dict(Y_train=np.array([0, 0, 0, 1, 1, 1])) - D.info = dict(task=BINARY_CLASSIFICATION) - - # holdout, binary classification - ta = ExecuteTaFuncWithQueue(resampling_strategy='holdout', **ta_args) - cv = ta.get_splitter(D) - self.assertIsInstance(cv, - sklearn.model_selection.StratifiedShuffleSplit) - - # holdout, binary classification, fallback to shuffle split - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1, 2]) - ta = ExecuteTaFuncWithQueue(resampling_strategy='holdout', **ta_args) - cv = ta.get_splitter(D) - self.assertIsInstance(cv, sklearn.model_selection._split.ShuffleSplit) - - # cv, binary classification - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) - ta = ExecuteTaFuncWithQueue(resampling_strategy='cv', folds=5, - **ta_args) - cv = ta.get_splitter(D) - self.assertIsInstance(cv, sklearn.model_selection._split.StratifiedKFold) - - # cv, binary classification, no fallback anticipated - D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1, 2]) - ta = ExecuteTaFuncWithQueue(resampling_strategy='cv', folds=5, - **ta_args) - cv = ta.get_splitter(D) - self.assertIsInstance(cv, sklearn.model_selection._split.StratifiedKFold) - - # regression, shuffle split - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION - ta = ExecuteTaFuncWithQueue(resampling_strategy='holdout', **ta_args) - cv = ta.get_splitter(D) - self.assertIsInstance(cv, sklearn.model_selection._split.ShuffleSplit) - - # regression cv, KFold - D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) - D.info['task'] = REGRESSION - ta = ExecuteTaFuncWithQueue(resampling_strategy='cv', folds=5, - **ta_args) - cv = ta.get_splitter(D) - self.assertIsInstance(cv, sklearn.model_selection._split.KFold) diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 39e913fea2..90b39bb824 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -10,8 +10,11 @@ from ConfigSpace import Configuration import numpy as np from sklearn.model_selection import StratifiedKFold, ShuffleSplit +import sklearn.model_selection from smac.tae.execute_ta_run import StatusType, TAEAbortException +from autosklearn.data.abstract_data_manager import AbstractDataManager +from autosklearn.evaluation import ExecuteTaFuncWithQueue from autosklearn.evaluation.util import get_last_result from autosklearn.evaluation.train_evaluator import TrainEvaluator, \ eval_holdout, eval_iterative_holdout, eval_cv, eval_partial_cv @@ -27,6 +30,11 @@ get_multiclass_classification_datamanager +class BackendMock(object): + def load_datamanager(self): + return get_multiclass_classification_datamanager() + + class Dummy(object): def __init__(self): self.name = 'dummy' @@ -39,7 +47,6 @@ class TestTrainEvaluator(BaseEvaluatorTest, unittest.TestCase): def test_holdout(self, pipeline_mock): D = get_binary_classification_datamanager() D.name = 'test' - kfold = ShuffleSplit(random_state=1, n_splits=1) pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock @@ -52,7 +59,8 @@ def test_holdout(self, pipeline_mock): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, - cv=kfold, + resampling_strategy='holdout', + resampling_strategy_args={'train_size': 0.66}, all_scoring_functions=False, output_y_hat_optimization=True, metric=accuracy, @@ -68,12 +76,12 @@ def test_holdout(self, pipeline_mock): self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(evaluator.file_output.call_count, 1) - self.assertEqual(result, 0.85714285714285721) + self.assertEqual(result, 0.45833333333333337) self.assertEqual(pipeline_mock.fit.call_count, 1) # three calls because of the holdout, the validation and the test set self.assertEqual(pipeline_mock.predict_proba.call_count, 3) self.assertEqual(evaluator.file_output.call_count, 1) - self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 7) + self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 24) self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], D.data['Y_valid'].shape[0]) self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], D.data['Y_test'].shape[0]) self.assertEqual(evaluator.model.fit.call_count, 1) @@ -111,7 +119,7 @@ def configuration_fully_fitted(self): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, - cv=kfold, + resampling_strategy='holdout', all_scoring_functions=False, output_y_hat_optimization=True, metric=accuracy) @@ -144,7 +152,8 @@ def side_effect(self, *args): # fifteen calls because of the holdout, the validation and the test set # and a total of five calls because of five iterations of fitting self.assertEqual(evaluator.model.predict_proba.call_count, 15) - self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 7) + # 1/3 of 69 + self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 23) self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], D.data['Y_valid'].shape[0]) self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], D.data['Y_test'].shape[0]) self.assertEqual(evaluator.file_output.call_count, 5) @@ -155,7 +164,6 @@ def test_iterative_holdout_interuption(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() D.name = 'test' - kfold = ShuffleSplit(random_state=1, n_splits=1) class SideEffect(object): def __init__(self): @@ -185,7 +193,7 @@ def configuration_fully_fitted(self): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, - cv=kfold, + resampling_strategy='holdout-iterative-fit', all_scoring_functions=False, output_y_hat_optimization=True, metric=accuracy) @@ -215,7 +223,7 @@ def side_effect(self, *args): # fifteen calls because of the holdout, the validation and the test set # and a total of five calls because of five iterations of fitting self.assertEqual(evaluator.model.predict_proba.call_count, 6) - self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 7) + self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 23) self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], D.data['Y_valid'].shape[0]) self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], D.data['Y_test'].shape[0]) self.assertEqual(evaluator.file_output.call_count, 2) @@ -242,7 +250,7 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, - cv=kfold, + resampling_strategy='holdout-iterative-fit', all_scoring_functions=False, output_y_hat_optimization=True, metric=accuracy) @@ -253,14 +261,14 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): self.assertEqual(evaluator.file_output.call_count, 1) rval = evaluator.queue.get(timeout=1) - self.assertAlmostEqual(rval['loss'], 0.85714285714285721) + self.assertAlmostEqual(rval['loss'], 0.47826086956521741) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 0) # fifteen calls because of the holdout, the validation and the test set # and a total of five calls because of five iterations of fitting self.assertEqual(evaluator.model.predict_proba.call_count, 3) - self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 7) + self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 23) self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], D.data['Y_valid'].shape[0]) self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], D.data['Y_test'].shape[0]) self.assertEqual(evaluator.file_output.call_count, 1) @@ -269,7 +277,6 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_cv(self, pipeline_mock): D = get_binary_classification_datamanager() - kfold = StratifiedKFold(random_state=1, n_splits=5, shuffle=True) pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock @@ -282,7 +289,8 @@ def test_cv(self, pipeline_mock): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, - cv=kfold, + resampling_strategy='cv', + resampling_strategy_args={'folds': 5}, all_scoring_functions=False, output_y_hat_optimization=True, metric=accuracy) @@ -312,7 +320,6 @@ def test_cv(self, pipeline_mock): @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_partial_cv(self, pipeline_mock): D = get_binary_classification_datamanager() - kfold = StratifiedKFold(random_state=1, n_splits=5, shuffle=True) pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock @@ -327,7 +334,8 @@ def test_partial_cv(self, pipeline_mock): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, - cv=kfold, + resampling_strategy='partial-cv', + resampling_strategy_args={'folds': 5}, all_scoring_functions=False, output_y_hat_optimization=True, metric=accuracy) @@ -382,7 +390,8 @@ def configuration_fully_fitted(self): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, - cv=kfold, + resampling_strategy='partial-cv-iterative-fit', + resampling_strategy_args={'folds': 5}, all_scoring_functions=False, output_y_hat_optimization=True, metric=accuracy) @@ -431,7 +440,8 @@ def test_file_output(self, makedirs_mock, backend_mock): kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=1) evaluator = TrainEvaluator(D, backend_mock, queue=queue_, configuration=configuration, - cv=kfold, + resampling_strategy='cv', + resampling_strategy_args={'folds': 5}, all_scoring_functions=True, output_y_hat_optimization=True, metric=accuracy) @@ -469,10 +479,11 @@ def test_subsample_indices_classification(self, mock, backend_mock): configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - kfold = ShuffleSplit(random_state=1, n_splits=1) evaluator = TrainEvaluator(D, backend_mock, queue_, configuration=configuration, - cv=kfold, subsample=10, + resampling_strategy='cv', + resampling_strategy_args={'folds': 10}, + subsample=10, metric=accuracy) train_indices = np.arange(69, dtype=int) train_indices1 = evaluator.subsample_indices(train_indices) @@ -513,10 +524,11 @@ def test_subsample_indices_regression(self, mock, backend_mock): configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - kfold = ShuffleSplit(random_state=1, n_splits=1) evaluator = TrainEvaluator(D, backend_mock, queue_, configuration=configuration, - cv=kfold, subsample=30, + resampling_strategy='cv', + resampling_strategy_args={'folds': 10}, + subsample=30, metric=accuracy) train_indices = np.arange(69, dtype=int) train_indices3 = evaluator.subsample_indices(train_indices) @@ -539,16 +551,19 @@ def test_subsample_indices_regression(self, mock, backend_mock): @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_predict_proba_binary_classification(self, mock, backend_mock): D = get_binary_classification_datamanager() - mock.predict_proba.side_effect = lambda y, batch_size: np.array([[0.1, 0.9]] * 7) + mock.predict_proba.side_effect = lambda y, batch_size: np.array( + [[0.1, 0.9]] * y.shape[0] + ) mock.side_effect = lambda **kwargs: mock configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - kfold = ShuffleSplit(random_state=1, n_splits=1) evaluator = TrainEvaluator(D, backend_mock, queue_, configuration=configuration, - cv=kfold, output_y_hat_optimization=False, + resampling_strategy='cv', + resampling_strategy_args={'folds': 10}, + output_y_hat_optimization=False, metric=accuracy) evaluator.fit_predict_and_loss() Y_optimization_pred = backend_mock.save_predictions_as_npy.call_args_list[0][0][0] @@ -567,29 +582,53 @@ def test_fit_predict_and_loss_additional_run_info( D = get_binary_classification_datamanager() mock.side_effect = lambda **kwargs: mock _partial_fit_and_predict_mock.return_value = ( - [[0.1, 0.9]] * 7, [[0.1, 0.9]] * 7, [[0.1, 0.9]] * 7, {'a': 5} + [[0.1, 0.9]] * 23, [[0.1, 0.9]] * 7, [[0.1, 0.9]] * 7, {'a': 5} ) file_output_mock.return_value = None, None configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - kfold = ShuffleSplit(random_state=1, n_splits=1) evaluator = TrainEvaluator( D, backend_mock, queue_, configuration=configuration, - cv=kfold, output_y_hat_optimization=False, - metric=accuracy) - evaluator.Y_targets[0] = [1] * 7 + resampling_strategy='holdout', + output_y_hat_optimization=False, + metric=accuracy, + ) + evaluator.Y_targets[0] = [1] * 23 evaluator.fit_predict_and_loss(iterative=False) - kfold = ShuffleSplit(random_state=1, n_splits=2) + class SideEffect(object): + def __init__(self): + self.n_call = 0 + def __call__(self, *args, **kwargs): + if self.n_call == 0: + self.n_call += 1 + return ( + [[0.1, 0.9]] * 35, + [[0.1, 0.9]] * 7, + [[0.1, 0.9]] * 7, + {'a': 5} + ) + else: + return ( + [[0.1, 0.9]] * 34, + [[0.1, 0.9]] * 7, + [[0.1, 0.9]] * 7, + {'a': 5} + ) + _partial_fit_and_predict_mock.side_effect = SideEffect() evaluator = TrainEvaluator( D, backend_mock, queue_, configuration=configuration, - cv=kfold, output_y_hat_optimization=False, - metric=accuracy) - evaluator.Y_targets[0] = [1] * 7 + resampling_strategy='cv', + resampling_strategy_args={'folds': 2}, + output_y_hat_optimization=False, + metric=accuracy, + ) + evaluator.Y_targets[0] = [1] * 35 + evaluator.Y_targets[1] = [1] * 34 self.assertRaises( TAEAbortException, @@ -622,14 +661,14 @@ def test_datasets(self): y = D.data['Y_train'] if len(y.shape) == 2 and y.shape[1] == 1: D_.data['Y_train'] = y.flatten() - kfold = ShuffleSplit(n_splits=5, random_state=1) queue_ = multiprocessing.Queue() metric_lookup = {MULTILABEL_CLASSIFICATION: f1_macro, BINARY_CLASSIFICATION: accuracy, MULTICLASS_CLASSIFICATION: accuracy, REGRESSION: r2} evaluator = TrainEvaluator(D_, backend_mock, queue_, - cv=kfold, + resampling_strategy='cv', + resampling_strategy_args={'folds': 2}, output_y_hat_optimization=False, metric=metric_lookup[D.info['task']]) @@ -637,6 +676,70 @@ def test_datasets(self): rval = evaluator.queue.get(timeout=1) self.assertTrue(np.isfinite(rval['loss'])) + ############################################################################ + # Test obtaining a splitter object from scikit-learn + @unittest.mock.patch.object(TrainEvaluator, "__init__") + def test_get_splitter(self, te_mock): + te_mock.return_value = None + D = unittest.mock.Mock(spec=AbstractDataManager) + D.data = dict(Y_train=np.array([0, 0, 0, 1, 1, 1])) + D.info = dict(task=BINARY_CLASSIFICATION) + D.feat_type = [] + + # holdout, binary classification + evaluator = TrainEvaluator() + evaluator.resampling_strategy = 'holdout' + evaluator.resampling_strategy_args = None + cv = evaluator.get_splitter(D) + self.assertIsInstance(cv, + sklearn.model_selection.StratifiedShuffleSplit) + + # holdout, binary classification, fallback to shuffle split + D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1, 2]) + evaluator = TrainEvaluator() + evaluator.resampling_strategy = 'holdout' + evaluator.resampling_strategy_args = None + cv = evaluator.get_splitter(D) + self.assertIsInstance(cv, + sklearn.model_selection._split.ShuffleSplit) + + # cv, binary classification + D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) + evaluator = TrainEvaluator() + evaluator.resampling_strategy = 'cv' + evaluator.resampling_strategy_args = {'folds': 5} + cv = evaluator.get_splitter(D) + self.assertIsInstance(cv, + sklearn.model_selection._split.StratifiedKFold) + + # cv, binary classification, no fallback anticipated + D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1, 2]) + evaluator = TrainEvaluator() + evaluator.resampling_strategy = 'cv' + evaluator.resampling_strategy_args = {'folds': 5} + cv = evaluator.get_splitter(D) + self.assertIsInstance(cv, + sklearn.model_selection._split.StratifiedKFold) + + # regression, shuffle split + D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info['task'] = REGRESSION + evaluator = TrainEvaluator() + evaluator.resampling_strategy = 'holdout' + evaluator.resampling_strategy_args = None + cv = evaluator.get_splitter(D) + self.assertIsInstance(cv, + sklearn.model_selection._split.ShuffleSplit) + + # regression cv, KFold + D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) + D.info['task'] = REGRESSION + evaluator = TrainEvaluator() + evaluator.resampling_strategy = 'cv' + evaluator.resampling_strategy_args = {'folds': 5} + cv = evaluator.get_splitter(D) + self.assertIsInstance(cv, sklearn.model_selection._split.KFold) + class FunctionsTest(unittest.TestCase): def setUp(self): @@ -655,41 +758,61 @@ def setUp(self): self.dataset_name = json.dumps({'task_id': 'test'}) def test_eval_holdout(self): - kfold = ShuffleSplit(random_state=1, n_splits=1, test_size=0.33) - eval_holdout(queue=self.queue, config=self.configuration, - datamanager=self.data, backend=self.backend, cv=kfold, - seed=1, num_run=1, all_scoring_functions=False, - output_y_hat_optimization=True, include=None, exclude=None, - disable_file_output=False, instance=self.dataset_name, - metric=accuracy) + eval_holdout( + queue=self.queue, + config=self.configuration, + datamanager=self.data, + backend=self.backend, + resampling_strategy='holdout', + resampling_strategy_args=None, + seed=1, + num_run=1, + all_scoring_functions=False, + output_y_hat_optimization=True, + include=None, + exclude=None, + disable_file_output=False, + instance=self.dataset_name, + metric=accuracy, + ) info = get_last_result(self.queue) - self.assertAlmostEqual(info['loss'], 0.060606060606060552, places=3) + self.assertAlmostEqual(info['loss'], 0.030303030303030276, places=3) self.assertEqual(info['status'], StatusType.SUCCESS) self.assertNotIn('bac_metric', info['additional_run_info']) def test_eval_holdout_all_loss_functions(self): - kfold = ShuffleSplit(random_state=1, n_splits=1, test_size=0.33) - eval_holdout(queue=self.queue, config=self.configuration, - datamanager=self.data, backend=self.backend, cv=kfold, - seed=1, num_run=1, all_scoring_functions=True, - output_y_hat_optimization=True, include=None, exclude=None, - disable_file_output=False, instance=self.dataset_name, - metric=accuracy) + eval_holdout( + queue=self.queue, + config=self.configuration, + datamanager=self.data, + backend=self.backend, + resampling_strategy='holdout', + resampling_strategy_args=None, + seed=1, + num_run=1, + all_scoring_functions=True, + output_y_hat_optimization=True, + include=None, + exclude=None, + disable_file_output=False, + instance=self.dataset_name, + metric=accuracy, + ) rval = get_last_result(self.queue) - fixture = {'accuracy': 0.0606060606061, - 'balanced_accuracy': 0.0636363636364, - 'f1_macro': 0.0636363636364, - 'f1_micro': 0.0606060606061, - 'f1_weighted': 0.0606060606061, - 'log_loss': 1.153175128398068, - 'pac_score': 0.21332550117206728, - 'precision_macro': 0.0636363636364, - 'precision_micro': 0.0606060606061, - 'precision_weighted': 0.0606060606061, - 'recall_macro': 0.0636363636364, - 'recall_micro': 0.0606060606061, - 'recall_weighted': 0.0606060606061, + fixture = {'accuracy': 0.030303030303030276, + 'balanced_accuracy': 0.033333333333333326, + 'f1_macro': 0.032036613272311221, + 'f1_micro': 0.030303030303030276, + 'f1_weighted': 0.030441716940572849, + 'log_loss': 1.0634089940876672, + 'pac_score': 0.092288218582651682, + 'precision_macro': 0.02777777777777779, + 'precision_micro': 0.030303030303030276, + 'precision_weighted': 0.027777777777777901, + 'recall_macro': 0.033333333333333326, + 'recall_micro': 0.030303030303030276, + 'recall_weighted': 0.030303030303030276, 'num_run': 1} additional_run_info = rval['additional_run_info'] @@ -700,7 +823,7 @@ def test_eval_holdout_all_loss_functions(self): self.assertEqual(len(additional_run_info), len(fixture) + 1, msg=sorted(additional_run_info.items())) - self.assertAlmostEqual(rval['loss'], 0.060606060606060552, places=3) + self.assertAlmostEqual(rval['loss'], 0.030303030303030276, places=3) self.assertEqual(rval['status'], StatusType.SUCCESS) # def test_eval_holdout_on_subset(self): @@ -713,16 +836,25 @@ def test_eval_holdout_all_loss_functions(self): # self.assertEqual(info[2], 1) def test_eval_holdout_iterative_fit_no_timeout(self): - kfold = ShuffleSplit(random_state=1, n_splits=1, test_size=0.33) - eval_iterative_holdout(queue=self.queue, config=self.configuration, - datamanager=self.data, backend=self.backend, - cv=kfold, seed=1, num_run=1, - all_scoring_functions=False, - output_y_hat_optimization=True, include=None, - exclude=None, disable_file_output=False, - instance=self.dataset_name, metric=accuracy) + eval_iterative_holdout( + queue=self.queue, + config=self.configuration, + datamanager=self.data, + backend=self.backend, + resampling_strategy='holdout', + resampling_strategy_args=None, + seed=1, + num_run=1, + all_scoring_functions=False, + output_y_hat_optimization=True, + include=None, + exclude=None, + disable_file_output=False, + instance=self.dataset_name, + metric=accuracy, + ) rval = get_last_result(self.queue) - self.assertAlmostEqual(rval['loss'], 0.060606060606060552) + self.assertAlmostEqual(rval['loss'], 0.030303030303030276) self.assertEqual(rval['status'], StatusType.SUCCESS) # def test_eval_holdout_iterative_fit_on_subset_no_timeout(self): @@ -736,26 +868,46 @@ def test_eval_holdout_iterative_fit_no_timeout(self): # self.assertEqual(info[2], 1) def test_eval_cv(self): - cv = StratifiedKFold(shuffle=True, random_state=1) - eval_cv(queue=self.queue, config=self.configuration, - datamanager=self.data, backend=self.backend, seed=1, num_run=1, - cv=cv, all_scoring_functions=False, - output_y_hat_optimization=True, include=None, exclude=None, - disable_file_output=False, instance=self.dataset_name, - metric=accuracy) + eval_cv( + queue=self.queue, + config=self.configuration, + datamanager=self.data, + backend=self.backend, + seed=1, + num_run=1, + resampling_strategy='cv', + resampling_strategy_args={'folds': 3}, + all_scoring_functions=False, + output_y_hat_optimization=True, + include=None, + exclude=None, + disable_file_output=False, + instance=self.dataset_name, + metric=accuracy, + ) rval = get_last_result(self.queue) self.assertAlmostEqual(rval['loss'], 0.06) self.assertEqual(rval['status'], StatusType.SUCCESS) self.assertNotIn('bac_metric', rval['additional_run_info']) def test_eval_cv_all_loss_functions(self): - cv = StratifiedKFold(shuffle=True, random_state=1) - eval_cv(queue=self.queue, config=self.configuration, - datamanager=self.data, backend=self.backend, seed=1, num_run=1, - cv=cv, all_scoring_functions=True, - output_y_hat_optimization=True, include=None, exclude=None, - disable_file_output=False, instance=self.dataset_name, - metric=accuracy) + eval_cv( + queue=self.queue, + config=self.configuration, + datamanager=self.data, + backend=self.backend, + seed=1, + num_run=1, + resampling_strategy='cv', + resampling_strategy_args={'folds': 3}, + all_scoring_functions=True, + output_y_hat_optimization=True, + include=None, + exclude=None, + disable_file_output=False, + instance=self.dataset_name, + metric=accuracy, + ) rval = get_last_result(self.queue) fixture = {'accuracy': 0.06, @@ -795,7 +947,6 @@ def test_eval_cv_all_loss_functions(self): # self.assertEqual(info[2], 1) def test_eval_partial_cv(self): - cv = StratifiedKFold(shuffle=True, random_state=1, n_splits=5) results = [0.090909090909090939, 0.047619047619047672, 0.052631578947368474, @@ -803,13 +954,23 @@ def test_eval_partial_cv(self): 0.0] for fold in range(5): instance = json.dumps({'task_id': 'data', 'fold': fold}) - eval_partial_cv(queue=self.queue, config=self.configuration, - datamanager=self.data, backend=self.backend, seed=1, - num_run=1, instance=instance, cv=cv, - all_scoring_functions=False, - output_y_hat_optimization=True, include=None, - exclude=None, disable_file_output=False, - metric=accuracy) + eval_partial_cv( + queue=self.queue, + config=self.configuration, + datamanager=self.data, + backend=self.backend, + seed=1, + num_run=1, + instance=instance, + resampling_strategy='partial-cv', + resampling_strategy_args={'folds': 5}, + all_scoring_functions=False, + output_y_hat_optimization=True, + include=None, + exclude=None, + disable_file_output=False, + metric=accuracy, + ) rval = get_last_result(self.queue) self.assertAlmostEqual(rval['loss'], results[fold]) self.assertEqual(rval['status'], StatusType.SUCCESS) @@ -852,3 +1013,4 @@ def test_eval_partial_cv(self): # info = self.queue.get() # self.assertAlmostEqual(info[1], results[fold]) # self.assertEqual(info[2], 1) + From 84825ad50f827817af08036b47c602134b619518 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 30 Nov 2017 15:49:11 +0100 Subject: [PATCH 48/61] load datamanager in child process --- autosklearn/evaluation/__init__.py | 4 -- autosklearn/evaluation/abstract_evaluator.py | 12 ++--- autosklearn/evaluation/test_evaluator.py | 15 +++--- autosklearn/evaluation/train_evaluator.py | 12 +---- .../test_abstract_evaluator.py | 38 ++++++++----- test/test_evaluation/test_test_evaluator.py | 42 +++++++++------ test/test_evaluation/test_train_evaluator.py | 53 ++++++++++--------- 7 files changed, 94 insertions(+), 82 deletions(-) diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index 057fb37cb4..11c762ca67 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -1,5 +1,4 @@ # -*- encoding: utf-8 -*- -import copy import functools import logging import math @@ -16,7 +15,6 @@ import autosklearn.evaluation.train_evaluator import autosklearn.evaluation.test_evaluator import autosklearn.evaluation.util -from autosklearn.constants import CLASSIFICATION_TASKS, MULTILABEL_CLASSIFICATION WORST_POSSIBLE_RESULT = 1.0 @@ -154,7 +152,6 @@ def run(self, config, instance=None, seed=12345, instance_specific=None): - D = self.backend.load_datamanager() queue = multiprocessing.Queue() if not (instance_specific is None or instance_specific == '0'): @@ -168,7 +165,6 @@ def run(self, config, instance=None, mem_in_mb=self.memory_limit) obj_kwargs = dict(queue=queue, config=config, - datamanager=D, backend=self.backend, metric=self.metric, seed=self.autosklearn_seed, diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index fb3a485466..95a136b5cf 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -87,7 +87,7 @@ def get_additional_run_info(self): class AbstractEvaluator(object): - def __init__(self, datamanager, backend, queue, metric, + def __init__(self, backend, queue, metric, configuration=None, all_scoring_functions=False, seed=1, @@ -105,15 +105,15 @@ def __init__(self, datamanager, backend, queue, metric, self.backend = backend self.queue = queue - self.datamanager = datamanager + self.datamanager = self.backend.load_datamanager() self.include = include self.exclude = exclude - self.X_valid = datamanager.data.get('X_valid') - self.X_test = datamanager.data.get('X_test') + self.X_valid = self.datamanager.data.get('X_valid') + self.X_test = self.datamanager.data.get('X_test') self.metric = metric - self.task_type = datamanager.info['task'] + self.task_type = self.datamanager.info['task'] self.seed = seed self.output_y_hat_optimization = output_y_hat_optimization @@ -136,7 +136,7 @@ def __init__(self, datamanager, backend, queue, metric, self.predict_function = self._predict_proba categorical_mask = [] - for feat in datamanager.feat_type: + for feat in self.datamanager.feat_type: if feat.lower() == 'numerical': categorical_mask.append(False) elif feat.lower() == 'categorical': diff --git a/autosklearn/evaluation/test_evaluator.py b/autosklearn/evaluation/test_evaluator.py index 574784fbd5..ba316bc4bc 100644 --- a/autosklearn/evaluation/test_evaluator.py +++ b/autosklearn/evaluation/test_evaluator.py @@ -13,7 +13,7 @@ class TestEvaluator(AbstractEvaluator): - def __init__(self, datamanager, backend, queue, metric, + def __init__(self, backend, queue, metric, configuration=None, all_scoring_functions=False, seed=1, @@ -22,7 +22,6 @@ def __init__(self, datamanager, backend, queue, metric, disable_file_output=False, init_params=None): super(TestEvaluator, self).__init__( - datamanager=datamanager, backend=backend, queue=queue, configuration=configuration, @@ -39,11 +38,11 @@ def __init__(self, datamanager, backend, queue, metric, ) self.configuration = configuration - self.X_train = datamanager.data['X_train'] - self.Y_train = datamanager.data['Y_train'] + self.X_train = self.datamanager.data['X_train'] + self.Y_train = self.datamanager.data['Y_train'] - self.X_test = datamanager.data.get('X_test') - self.Y_test = datamanager.data.get('Y_test') + self.X_test = self.datamanager.data.get('X_test') + self.Y_test = self.datamanager.data.get('Y_test') self.model = self._get_model() @@ -83,10 +82,10 @@ def predict_and_loss(self, train=False): # create closure for evaluating an algorithm # Has a stupid name so nosetests doesn't regard it as a test -def eval_t(queue, config, datamanager, backend, metric, seed, num_run, instance, +def eval_t(queue, config, backend, metric, seed, num_run, instance, all_scoring_functions, output_y_hat_optimization, include, exclude, disable_file_output, init_params=None): - evaluator = TestEvaluator(datamanager=datamanager, configuration=config, + evaluator = TestEvaluator(configuration=config, backend=backend, metric=metric, seed=seed, queue=queue, all_scoring_functions=all_scoring_functions, diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index a69730c42c..cca1080d0c 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -24,7 +24,7 @@ def _get_y_array(y, task_type): class TrainEvaluator(AbstractEvaluator): - def __init__(self, datamanager, backend, queue, metric, + def __init__(self, backend, queue, metric, configuration=None, all_scoring_functions=False, seed=1, @@ -39,7 +39,6 @@ def __init__(self, datamanager, backend, queue, metric, disable_file_output=False, init_params=None): super().__init__( - datamanager=datamanager, backend=backend, queue=queue, configuration=configuration, @@ -57,7 +56,7 @@ def __init__(self, datamanager, backend, queue, metric, self.resampling_strategy = resampling_strategy self.resampling_strategy_args = resampling_strategy_args - self.cv = self.get_splitter(datamanager) + self.cv = self.get_splitter(self.datamanager) self.cv_folds = self.cv.n_splits self.X_train = self.datamanager.data['X_train'] self.Y_train = self.datamanager.data['Y_train'] @@ -401,7 +400,6 @@ def get_splitter(self, D): def eval_holdout( queue, config, - datamanager, backend, resampling_strategy, resampling_strategy_args, @@ -420,7 +418,6 @@ def eval_holdout( instance = json.loads(instance) if instance is not None else {} subsample = instance.get('subsample') evaluator = TrainEvaluator( - datamanager=datamanager, backend=backend, queue=queue, resampling_strategy=resampling_strategy, @@ -443,7 +440,6 @@ def eval_holdout( def eval_iterative_holdout( queue, config, - datamanager, backend, resampling_strategy, resampling_strategy_args, @@ -461,7 +457,6 @@ def eval_iterative_holdout( return eval_holdout( queue=queue, config=config, - datamanager=datamanager, backend=backend, metric=metric, resampling_strategy=resampling_strategy, @@ -503,7 +498,6 @@ def eval_partial_cv( fold = instance['fold'] evaluator = TrainEvaluator( - datamanager=datamanager, backend=backend, queue=queue, metric=metric, @@ -567,7 +561,6 @@ def eval_partial_cv_iterative( def eval_cv( queue, config, - datamanager, backend, resampling_strategy, resampling_strategy_args, @@ -585,7 +578,6 @@ def eval_cv( instance = json.loads(instance) if instance is not None else {} subsample = instance.get('subsample') evaluator = TrainEvaluator( - datamanager=datamanager, backend=backend, queue=queue, metric=metric, diff --git a/test/test_evaluation/test_abstract_evaluator.py b/test/test_evaluation/test_abstract_evaluator.py index b398f65786..55a6895e62 100644 --- a/test/test_evaluation/test_abstract_evaluator.py +++ b/test/test_evaluation/test_abstract_evaluator.py @@ -24,8 +24,9 @@ def test_finish_up_model_predicts_NaN(self): D = get_multiclass_classification_datamanager() backend_api = unittest.mock.Mock() + backend_api.load_datamanager.return_value = D queue_mock = unittest.mock.Mock() - ae = AbstractEvaluator(datamanager=D, backend=backend_api, + ae = AbstractEvaluator(backend=backend_api, output_y_hat_optimization=False, queue=queue_mock, metric=accuracy) ae.Y_optimization = rs.rand(33, 3) @@ -68,14 +69,19 @@ def test_finish_up_model_predicts_NaN(self): def test_disable_file_output(self, exists_mock): backend_mock = unittest.mock.Mock() backend_mock.get_model_dir.return_value = 'abc' + D = get_multiclass_classification_datamanager() + backend_mock.load_datamanager.return_value = D queue_mock = unittest.mock.Mock() rs = np.random.RandomState(1) - D = get_multiclass_classification_datamanager() - ae = AbstractEvaluator(datamanager=D, backend=backend_mock, - queue=queue_mock, disable_file_output=True, - metric=accuracy) + + ae = AbstractEvaluator( + backend=backend_mock, + queue=queue_mock, + disable_file_output=True, + metric=accuracy, + ) predictions_ensemble = rs.rand(33, 3) predictions_test = rs.rand(25, 3) @@ -90,10 +96,13 @@ def test_disable_file_output(self, exists_mock): self.assertEqual(backend_mock.save_predictions_as_npy.call_count, 0) self.assertEqual(backend_mock.save_model.call_count, 0) - ae = AbstractEvaluator(datamanager=D, backend=backend_mock, - output_y_hat_optimization=False, - queue=queue_mock, disable_file_output=['model'], - metric=accuracy) + ae = AbstractEvaluator( + backend=backend_mock, + output_y_hat_optimization=False, + queue=queue_mock, + disable_file_output=['model'], + metric=accuracy, + ) ae.Y_optimization = predictions_ensemble loss_, additional_run_info_ = ae.file_output( @@ -105,10 +114,13 @@ def test_disable_file_output(self, exists_mock): self.assertEqual(backend_mock.save_predictions_as_npy.call_count, 3) self.assertEqual(backend_mock.save_model.call_count, 0) - ae = AbstractEvaluator(datamanager=D, backend=backend_mock, - output_y_hat_optimization=False, - queue=queue_mock, metric=accuracy, - disable_file_output=['y_optimization']) + ae = AbstractEvaluator( + backend=backend_mock, + output_y_hat_optimization=False, + queue=queue_mock, + metric=accuracy, + disable_file_output=['y_optimization'], + ) exists_mock.return_value = True ae.Y_optimization = predictions_ensemble ae.model = 'model' diff --git a/test/test_evaluation/test_test_evaluator.py b/test/test_evaluation/test_test_evaluator.py index 409888ba96..9c4d9ed0da 100644 --- a/test/test_evaluation/test_test_evaluator.py +++ b/test/test_evaluation/test_test_evaluator.py @@ -47,14 +47,18 @@ def test_datasets(self): y = D.data['Y_train'] if len(y.shape) == 2 and y.shape[1] == 1: D_.data['Y_train'] = y.flatten() + backend_mock.load_datamanager.return_value = D_ metric_lookup = {MULTILABEL_CLASSIFICATION: f1_macro, BINARY_CLASSIFICATION: accuracy, MULTICLASS_CLASSIFICATION: accuracy, REGRESSION: r2} queue_ = multiprocessing.Queue() - evaluator = TestEvaluator(D_, backend_mock, queue_, - metric=metric_lookup[D.info['task']]) + evaluator = TestEvaluator( + backend_mock, + queue_, + metric=metric_lookup[D.info['task']] + ) evaluator.fit_predict_and_loss() rval = get_last_result(evaluator.queue) @@ -72,6 +76,7 @@ def setUp(self): self.tmp_dir = os.path.join(os.path.dirname(__file__), '.test_cv_functions') self.backend = unittest.mock.Mock(spec=Backend) + self.backend.load_datamanager.return_value = self.data self.dataset_name = json.dumps({'task_id': 'test'}) def tearDown(self): @@ -84,27 +89,34 @@ def test_eval_test(self): eval_t(queue=self.queue, backend=self.backend, config=self.configuration, - datamanager=self.data, metric=accuracy, seed=1, num_run=1, - all_scoring_functions=False, output_y_hat_optimization=False, - include=None, exclude=None, disable_file_output=False, - instance=self.dataset_name) + all_scoring_functions=False, + output_y_hat_optimization=False, + include=None, + exclude=None, + disable_file_output=False, + instance=self.dataset_name + ) rval = get_last_result(self.queue) self.assertAlmostEqual(rval['loss'], 0.04) self.assertEqual(rval['status'], StatusType.SUCCESS) self.assertNotIn('bac_metric', rval['additional_run_info']) def test_eval_test_all_loss_functions(self): - eval_t(queue=self.queue, - backend=self.backend, - config=self.configuration, - datamanager=self.data, - metric=accuracy, - seed=1, num_run=1, - all_scoring_functions=True, output_y_hat_optimization=False, - include=None, exclude=None, disable_file_output=False, - instance=self.dataset_name) + eval_t( + queue=self.queue, + backend=self.backend, + config=self.configuration, + metric=accuracy, + seed=1, num_run=1, + all_scoring_functions=True, + output_y_hat_optimization=False, + include=None, + exclude=None, + disable_file_output=False, + instance=self.dataset_name, + ) rval = get_last_result(self.queue) fixture = {'accuracy': 0.04, 'balanced_accuracy': 0.0277777777778, diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 90b39bb824..9fd8af35af 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -55,9 +55,10 @@ def test_holdout(self, pipeline_mock): configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) + backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(D, backend_api, queue_, + evaluator = TrainEvaluator(backend_api, queue_, configuration=configuration, resampling_strategy='holdout', resampling_strategy_args={'train_size': 0.66}, @@ -91,7 +92,6 @@ def test_iterative_holdout(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() D.name = 'test' - kfold = ShuffleSplit(random_state=1, n_splits=1) class SideEffect(object): def __init__(self): @@ -115,9 +115,10 @@ def configuration_fully_fitted(self): configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) + backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(D, backend_api, queue_, + evaluator = TrainEvaluator(backend_api, queue_, configuration=configuration, resampling_strategy='holdout', all_scoring_functions=False, @@ -189,9 +190,10 @@ def configuration_fully_fitted(self): configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) + backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(D, backend_api, queue_, + evaluator = TrainEvaluator(backend_api, queue_, configuration=configuration, resampling_strategy='holdout-iterative-fit', all_scoring_functions=False, @@ -234,7 +236,6 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() D.name = 'test' - kfold = ShuffleSplit(random_state=1, n_splits=1) Xt_fixture = 'Xt_fixture' pipeline_mock.estimator_supports_iterative_fit.return_value = False @@ -246,9 +247,10 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) + backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(D, backend_api, queue_, + evaluator = TrainEvaluator(backend_api, queue_, configuration=configuration, resampling_strategy='holdout-iterative-fit', all_scoring_functions=False, @@ -285,9 +287,10 @@ def test_cv(self, pipeline_mock): configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) + backend_api.load_datamanager = lambda : D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(D, backend_api, queue_, + evaluator = TrainEvaluator(backend_api, queue_, configuration=configuration, resampling_strategy='cv', resampling_strategy_args={'folds': 5}, @@ -330,9 +333,10 @@ def test_partial_cv(self, pipeline_mock): configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) + backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(D, backend_api, queue_, + evaluator = TrainEvaluator(backend_api, queue_, configuration=configuration, resampling_strategy='partial-cv', resampling_strategy_args={'folds': 5}, @@ -362,7 +366,6 @@ def test_iterative_partial_cv(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() D.name = 'test' - kfold = StratifiedKFold(random_state=1, n_splits=3) class SideEffect(object): def __init__(self): @@ -386,9 +389,10 @@ def configuration_fully_fitted(self): configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) + backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(D, backend_api, queue_, + evaluator = TrainEvaluator(backend_api, queue_, configuration=configuration, resampling_strategy='partial-cv-iterative-fit', resampling_strategy_args={'folds': 5}, @@ -437,8 +441,7 @@ def test_file_output(self, makedirs_mock, backend_mock): configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=1) - evaluator = TrainEvaluator(D, backend_mock, queue=queue_, + evaluator = TrainEvaluator(backend_mock, queue=queue_, configuration=configuration, resampling_strategy='cv', resampling_strategy_args={'folds': 5}, @@ -475,11 +478,12 @@ def test_file_output(self, makedirs_mock, backend_mock): @unittest.mock.patch('autosklearn.util.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_subsample_indices_classification(self, mock, backend_mock): - D = get_binary_classification_datamanager() configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(D, backend_mock, queue_, + D = get_binary_classification_datamanager() + backend_mock.load_datamanager.return_value = D + evaluator = TrainEvaluator(backend_mock, queue_, configuration=configuration, resampling_strategy='cv', resampling_strategy_args={'folds': 10}, @@ -520,11 +524,10 @@ def test_subsample_indices_classification(self, mock, backend_mock): @unittest.mock.patch('autosklearn.util.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_subsample_indices_regression(self, mock, backend_mock): - D = get_regression_datamanager() configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(D, backend_mock, queue_, + evaluator = TrainEvaluator(backend_mock, queue_, configuration=configuration, resampling_strategy='cv', resampling_strategy_args={'folds': 10}, @@ -551,6 +554,7 @@ def test_subsample_indices_regression(self, mock, backend_mock): @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_predict_proba_binary_classification(self, mock, backend_mock): D = get_binary_classification_datamanager() + backend_mock.load_datamanager.return_value = D mock.predict_proba.side_effect = lambda y, batch_size: np.array( [[0.1, 0.9]] * y.shape[0] ) @@ -559,7 +563,7 @@ def test_predict_proba_binary_classification(self, mock, backend_mock): configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - evaluator = TrainEvaluator(D, backend_mock, queue_, + evaluator = TrainEvaluator(backend_mock, queue_, configuration=configuration, resampling_strategy='cv', resampling_strategy_args={'folds': 10}, @@ -580,6 +584,7 @@ def test_fit_predict_and_loss_additional_run_info( file_output_mock, ): D = get_binary_classification_datamanager() + backend_mock.load_datamanager.return_value = D mock.side_effect = lambda **kwargs: mock _partial_fit_and_predict_mock.return_value = ( [[0.1, 0.9]] * 23, [[0.1, 0.9]] * 7, [[0.1, 0.9]] * 7, {'a': 5} @@ -590,7 +595,7 @@ def test_fit_predict_and_loss_additional_run_info( queue_ = multiprocessing.Queue() evaluator = TrainEvaluator( - D, backend_mock, queue_, + backend_mock, queue_, configuration=configuration, resampling_strategy='holdout', output_y_hat_optimization=False, @@ -620,7 +625,7 @@ def __call__(self, *args, **kwargs): ) _partial_fit_and_predict_mock.side_effect = SideEffect() evaluator = TrainEvaluator( - D, backend_mock, queue_, + backend_mock, queue_, configuration=configuration, resampling_strategy='cv', resampling_strategy_args={'folds': 2}, @@ -639,7 +644,6 @@ def __call__(self, *args, **kwargs): def test_get_results(self): backend_mock = unittest.mock.Mock(spec=backend.Backend) backend_mock.get_model_dir.return_value = 'dutirapbdxvltcrpbdlcatepdeau' - D = get_binary_classification_datamanager() queue_ = multiprocessing.Queue() for i in range(5): queue_.put((i * 1, 1 - (i * 0.2), 0, "", StatusType.SUCCESS)) @@ -661,12 +665,13 @@ def test_datasets(self): y = D.data['Y_train'] if len(y.shape) == 2 and y.shape[1] == 1: D_.data['Y_train'] = y.flatten() + backend_mock.load_datamanager.return_value = D_ queue_ = multiprocessing.Queue() metric_lookup = {MULTILABEL_CLASSIFICATION: f1_macro, BINARY_CLASSIFICATION: accuracy, MULTICLASS_CLASSIFICATION: accuracy, REGRESSION: r2} - evaluator = TrainEvaluator(D_, backend_mock, queue_, + evaluator = TrainEvaluator(backend_mock, queue_, resampling_strategy='cv', resampling_strategy_args={'folds': 2}, output_y_hat_optimization=False, @@ -754,6 +759,7 @@ def setUp(self): self.y = self.data.data['Y_train'].flatten() self.backend = unittest.mock.Mock() self.backend.get_model_dir.return_value = 'udiaetzrpduaeirdaetr' + self.backend.load_datamanager.return_value = self.data self.backend.output_directory = 'duapdbaetpdbe' self.dataset_name = json.dumps({'task_id': 'test'}) @@ -761,7 +767,6 @@ def test_eval_holdout(self): eval_holdout( queue=self.queue, config=self.configuration, - datamanager=self.data, backend=self.backend, resampling_strategy='holdout', resampling_strategy_args=None, @@ -784,7 +789,6 @@ def test_eval_holdout_all_loss_functions(self): eval_holdout( queue=self.queue, config=self.configuration, - datamanager=self.data, backend=self.backend, resampling_strategy='holdout', resampling_strategy_args=None, @@ -839,7 +843,6 @@ def test_eval_holdout_iterative_fit_no_timeout(self): eval_iterative_holdout( queue=self.queue, config=self.configuration, - datamanager=self.data, backend=self.backend, resampling_strategy='holdout', resampling_strategy_args=None, @@ -871,7 +874,6 @@ def test_eval_cv(self): eval_cv( queue=self.queue, config=self.configuration, - datamanager=self.data, backend=self.backend, seed=1, num_run=1, @@ -894,7 +896,6 @@ def test_eval_cv_all_loss_functions(self): eval_cv( queue=self.queue, config=self.configuration, - datamanager=self.data, backend=self.backend, seed=1, num_run=1, From d69b0c9760121f48fd8a9f44e5de24e06f7d6573 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 30 Nov 2017 16:01:18 +0100 Subject: [PATCH 49/61] improve code style of abstract_evaluator --- autosklearn/evaluation/abstract_evaluator.py | 47 ++++++++++++-------- 1 file changed, 29 insertions(+), 18 deletions(-) diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index 95a136b5cf..b30765d99b 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -8,8 +8,14 @@ import autosklearn.pipeline.classification import autosklearn.pipeline.regression -from autosklearn.constants import * -from autosklearn.pipeline.implementations.util import convert_multioutput_multiclass_to_multilabel +from autosklearn.constants import ( + REGRESSION_TASKS, + MULTILABEL_CLASSIFICATION, + MULTICLASS_CLASSIFICATION, +) +from autosklearn.pipeline.implementations.util import ( + convert_multioutput_multiclass_to_multilabel +) from autosklearn.metrics import calculate_score from autosklearn.util.logging_ import get_logger @@ -29,7 +35,7 @@ def __init__(self, configuration, random_state, init_params=None): else: super(MyDummyClassifier, self).__init__(strategy="most_frequent") - def pre_transform(self, X, y, fit_params=None): + def pre_transform(self, X, y, fit_params=None): # pylint: disable=R0201 if fit_params is None: fit_params = {} return X, fit_params @@ -48,10 +54,10 @@ def predict_proba(self, X, batch_size=1000): np.float32) return probas - def estimator_supports_iterative_fit(self): + def estimator_supports_iterative_fit(self): # pylint: disable=R0201 return False - def get_additional_run_info(self): + def get_additional_run_info(self): # pylint: disable=R0201 return None @@ -79,10 +85,10 @@ def predict(self, X, batch_size=1000): new_X = np.ones((X.shape[0], 1)) return super(MyDummyRegressor, self).predict(new_X).astype(np.float32) - def estimator_supports_iterative_fit(self): + def estimator_supports_iterative_fit(self): # pylint: disable=R0201 return False - def get_additional_run_info(self): + def get_additional_run_info(self): # pylint: disable=R0201 return None @@ -131,8 +137,10 @@ def __init__(self, backend, queue, metric, if not isinstance(self.configuration, Configuration): self.model_class = MyDummyClassifier else: - self.model_class = \ - autosklearn.pipeline.classification.SimpleClassificationPipeline + self.model_class = ( + autosklearn.pipeline.classification. + SimpleClassificationPipeline + ) self.predict_function = self._predict_proba categorical_mask = [] @@ -169,12 +177,12 @@ def _get_model(self): random_state=self.seed, init_params=self._init_params) else: - dataset_properties = {'task': self.task_type, - 'sparse': self.datamanager.info['is_sparse'] == 1, - 'multilabel': self.task_type == - MULTILABEL_CLASSIFICATION, - 'multiclass': self.task_type == - MULTICLASS_CLASSIFICATION} + dataset_properties = { + 'task': self.task_type, + 'sparse': self.datamanager.info['is_sparse'] == 1, + 'multilabel': self.task_type == MULTILABEL_CLASSIFICATION, + 'multiclass': self.task_type == MULTICLASS_CLASSIFICATION, + } model = self.model_class(config=self.configuration, dataset_properties=dataset_properties, random_state=self.seed, @@ -228,7 +236,9 @@ def finish_up(self, loss, opt_pred, valid_pred, test_pred, else: loss_ = {} - additional_run_info = {} if additional_run_info is None else additional_run_info + additional_run_info = ( + {} if additional_run_info is None else additional_run_info + ) for metric_name, value in loss_.items(): additional_run_info[metric_name] = value additional_run_info['duration'] = self.duration @@ -281,8 +291,9 @@ def file_output(self, Y_optimization_pred, Y_valid_pred, Y_test_pred): pass self.backend.save_targets_ensemble(self.Y_optimization) - self.backend.save_predictions_as_npy(Y_optimization_pred, 'ensemble', - seed, num_run) + self.backend.save_predictions_as_npy( + Y_optimization_pred, 'ensemble', seed, num_run + ) if Y_valid_pred is not None: self.backend.save_predictions_as_npy(Y_valid_pred, 'valid', From 4c101a5b3b46955faaeb3dc53c9a6b6590b7aee5 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 30 Nov 2017 17:30:50 +0100 Subject: [PATCH 50/61] FIX bug in evaluation functions --- autosklearn/evaluation/train_evaluator.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index cca1080d0c..80ec06ea02 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -477,7 +477,6 @@ def eval_iterative_holdout( def eval_partial_cv( queue, config, - datamanager, backend, resampling_strategy, resampling_strategy_args, @@ -521,7 +520,6 @@ def eval_partial_cv( def eval_partial_cv_iterative( queue, config, - datamanager, backend, resampling_strategy, resampling_strategy_args, @@ -539,7 +537,6 @@ def eval_partial_cv_iterative( return eval_partial_cv( queue=queue, config=config, - datamanager=datamanager, backend=backend, metric=metric, resampling_strategy=resampling_strategy, From 020b676b0fb7946c5cab23c97efdbf36fd2e58b7 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 30 Nov 2017 17:30:50 +0100 Subject: [PATCH 51/61] FIX bug in evaluation functions --- autosklearn/evaluation/train_evaluator.py | 3 --- test/test_evaluation/test_train_evaluator.py | 1 - 2 files changed, 4 deletions(-) diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index cca1080d0c..80ec06ea02 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -477,7 +477,6 @@ def eval_iterative_holdout( def eval_partial_cv( queue, config, - datamanager, backend, resampling_strategy, resampling_strategy_args, @@ -521,7 +520,6 @@ def eval_partial_cv( def eval_partial_cv_iterative( queue, config, - datamanager, backend, resampling_strategy, resampling_strategy_args, @@ -539,7 +537,6 @@ def eval_partial_cv_iterative( return eval_partial_cv( queue=queue, config=config, - datamanager=datamanager, backend=backend, metric=metric, resampling_strategy=resampling_strategy, diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 9fd8af35af..4913c490c4 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -958,7 +958,6 @@ def test_eval_partial_cv(self): eval_partial_cv( queue=self.queue, config=self.configuration, - datamanager=self.data, backend=self.backend, seed=1, num_run=1, From 6bb4a472851daabc73c8b866925f3f57d097def3 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 13 Dec 2017 13:45:34 +0100 Subject: [PATCH 52/61] DOC clarify the use of seed --- autosklearn/estimators.py | 1 + 1 file changed, 1 insertion(+) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 5b157d73e4..1cd600ee2e 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -61,6 +61,7 @@ def __init__(self, most out of ensemble selection`. seed : int, optional (default=1) + Used to seed SMAC. Will determine the output file names. ml_memory_limit : int, optional (3072) Memory limit in MB for the machine learning algorithm. From 1ed057a2d95db6ec2b175a3d8201fb83f5c97391 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 15 Dec 2017 13:36:55 +0100 Subject: [PATCH 53/61] MAINT use regular SMAC3 release again --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index a86c23bc2d..476fcc7eaa 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,4 +19,4 @@ pandas ConfigSpace>=0.4.0,<0.5 pynisher>=0.4 pyrfr>=0.6.1,<0.8 -git+https://github.com/automl/SMAC3@development +smac From 428e0da1e843889bf391c99bf4ed2c167d7c82aa Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 3 Jan 2018 16:16:29 +0100 Subject: [PATCH 54/61] TSET reduce places for ridge regression test --- .../test_pipeline/components/regression/test_ridge_regression.py | 1 + 1 file changed, 1 insertion(+) diff --git a/test/test_pipeline/components/regression/test_ridge_regression.py b/test/test_pipeline/components/regression/test_ridge_regression.py index 483e944f41..f7b7d5b464 100644 --- a/test/test_pipeline/components/regression/test_ridge_regression.py +++ b/test/test_pipeline/components/regression/test_ridge_regression.py @@ -15,6 +15,7 @@ class RidgeComponentTest(BaseRegressionComponentTest): res["default_boston_sparse"] = 0.1086535440527433 res["default_boston_iterative_sparse"] = None res["default_diabetes"] = 0.32614416980439365 + res["default_diabetes_places"] = 5 res["default_diabetes_iterative"] = None res["default_diabetes_sparse"] = 0.1298840412368325 res["default_diabetes_iterative_sparse"] = None From e9d394cc56844f947dffbf8bc1ca5a63c7890eb9 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 3 Jan 2018 16:17:03 +0100 Subject: [PATCH 55/61] MAINT update meta-data --- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 255 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 255 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 160 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 160 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 160 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 160 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 150 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 152 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 255 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 255 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 150 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 152 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 156 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 156 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 255 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 255 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 156 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 156 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 138 +++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 146 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 257 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 255 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 255 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 138 +++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 146 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 255 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 253 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 255 +++++++------ .../configurations.csv | 261 +++++++------ .../description.txt | 84 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 158 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 160 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 158 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- .../algorithm_runs.arff | 160 ++++---- .../configurations.csv | 170 ++++----- .../description.txt | 82 ++--- .../feature_costs.arff | 346 +++++++++--------- .../feature_runstatus.arff | 118 +++--- .../feature_values.arff | 262 ++++++------- 432 files changed, 45738 insertions(+), 45842 deletions(-) diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/accuracy_binary.classification_dense/algorithm_runs.arff index aabe035f48..76b8b05099 100644 --- a/autosklearn/metalearning/files/accuracy_binary.classification_dense/algorithm_runs.arff +++ b/autosklearn/metalearning/files/accuracy_binary.classification_dense/algorithm_runs.arff @@ -7,136 +7,135 @@ @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} @DATA -2120,1.0,1,0.08297972654408303,ok -75193,1.0,2,0.05902478968148417,ok -2117,1.0,3,0.1463051436371533,ok -75156,1.0,4,0.20856911883589324,ok -75129,1.0,5,0.1029126213592233,ok +2120,1.0,1,0.07967939651107969,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.20291026677445434,ok +75129,1.0,5,0.10097087378640779,ok 75243,1.0,6,0.0,ok -75110,1.0,7,0.11039101317779221,ok +75110,1.0,7,0.11622380643767549,ok 75239,1.0,8,0.0,ok -75223,1.0,9,0.12324476128753514,ok -75221,1.0,10,0.40138751238850345,ok -258,1.0,11,0.007551240560949246,ok +75223,1.0,9,0.10304601425793913,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.009708737864077666,ok 75121,1.0,12,0.0,ok 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-38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize 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-47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize 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-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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-65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00029117569779819956,True,hinge,9,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.52445354905856,chi2,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax -116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize 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+37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +39,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302503,False,True,1,squared_hinge,ovr,l2,0.0007318628304090555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.560201454218397,948,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +85,weighting,one_hot_encoding,0.0003173723611800348,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1862,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_dense/description.txt b/autosklearn/metalearning/files/accuracy_binary.classification_dense/description.txt index e9f2bce480..5128f0ce51 100644 --- a/autosklearn/metalearning/files/accuracy_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/accuracy_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: accuracy performance_type: solution_quality diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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-244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 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+119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/description.txt index e9f2bce480..5128f0ce51 100644 --- a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: accuracy performance_type: solution_quality diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 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-62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+16,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +17,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +18,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565651,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463182 +19,weighting,one_hot_encoding,0.18137532678800652,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +20,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +21,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +22,none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10,0.542144980834302,None,0.0,20,13,0.0,233,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307263,0.13434811490315818 +23,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +24,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04093642460278944,deviance,7,0.5495014745530306,None,0.0,20,18,0.0,141,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +25,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +26,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12,11,0.0,100,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +27,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532,,,,,,,,,,,,,,,,,,none,,,, 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+31,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +32,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.051832615669195795,deviance,6,0.8807456180216267,None,0.0,7,19,0.0,366,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +33,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +34,none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3,386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615793 +35,none,one_hot_encoding,0.00011294596229850897,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062477,3,0.019002536385919935,poly,-1,False,0.010632086351533367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282,normal,, +36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +39,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 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+63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, 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+69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, 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+81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/description.txt index e9f2bce480..5128f0ce51 100644 --- a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: accuracy performance_type: solution_quality diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -2117,1.0,0.00055,0.01157,0.00069,2e-05,0.03906,0.71904,10.54724,0.00046,0.01394,0.02244,2e-05,0.00035,0.00011,1e-05,0.0103,6e-05,1e-05,5e-05,5e-05,5e-05,5e-05,0.0,0.00031,6e-05,0.00026,0.12553,0.0,0.00102,0.94012,2e-05,0.06161,0.00011,0.00023,0.00184,1e-05,0.00025,7.78747,1e-05,1e-05,6e-05,1e-05,0.00058,2e-05,315.23043,0.00011,0.00071,1e-05,0.00012,2e-05,3e-05,6e-05,5e-05 -2119,1.0,0.00051,0.00039,0.00053,1e-05,0.00087,0.01699,0.03836,0.00045,0.00053,0.00048,2e-05,0.0002,9e-05,1e-05,0.00051,4e-05,1e-05,4e-05,4e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.02023,0.0,0.00084,0.0358,2e-05,0.0014,0.00011,0.0001,0.00012,1e-05,0.00025,0.05298,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.06357,4e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -2120,1.0,0.00061,0.00169,0.00065,2e-05,0.04474,0.08877,0.53819,0.00046,0.00254,0.02526,2e-05,0.00034,0.00011,1e-05,0.00574,6e-05,1e-05,5e-05,5e-05,7e-05,5e-05,1e-05,0.00039,6e-05,0.00026,0.02733,1e-05,0.00099,0.12891,3e-05,0.07005,0.00011,0.00021,0.00056,1e-05,0.00025,0.78777,1e-05,1e-05,7e-05,2e-05,0.00034,3e-05,1.35102,5e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -2122,1.0,0.00051,0.00613,0.00057,2e-05,0.06877,0.40703,2.10666,0.00043,0.00686,0.02832,2e-05,0.0003,9e-05,1e-05,0.00579,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.2282,0.0,0.00088,0.68412,2e-05,0.09715,0.00011,0.00019,0.00063,1e-05,0.00023,2.7541,1e-05,1e-05,6e-05,1e-05,0.00014,2e-05,82.75009,5e-05,0.00064,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -2123,1.0,0.00055,0.00037,0.00231,1e-05,0.00586,0.07868,0.20326,0.00046,0.00051,0.00234,2e-05,0.0002,8e-05,1e-05,0.0005,4e-05,1e-05,4e-05,5e-05,4e-05,5e-05,0.0,0.00033,5e-05,0.00047,0.03788,0.0,0.00284,0.06438,2e-05,0.00825,0.00011,0.0001,0.00013,1e-05,0.00025,0.2846,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.13009,4e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -2350,1.0,0.0008,0.76706,0.00083,3e-05,7e-05,1e-05,0.0,0.00063,0.81845,1e-05,5e-05,0.00039,0.00018,2e-05,0.02503,5e-05,1e-05,8e-05,9e-05,0.00021,9e-05,1e-05,0.00044,9e-05,0.00027,2e-05,1e-05,0.00125,1e-05,5e-05,9e-05,0.00015,0.00026,0.04508,1e-05,0.00031,0.0,4e-05,2e-05,0.00012,7e-05,0.00648,0.0001,2.43616,1e-05,0.00182,1e-05,0.00019,4e-05,0.00015,0.00013,6e-05 -3043,1.0,0.00055,0.00098,0.00061,2e-05,0.0028,0.05945,0.57683,0.00048,0.00131,0.00233,2e-05,0.00031,0.00011,2e-05,0.00113,0.0001,1e-05,4e-05,6e-05,7e-05,7e-05,2e-05,0.00031,7e-05,0.00027,0.04173,0.0,0.00098,0.08173,2e-05,0.00519,0.00012,0.00014,0.00027,1e-05,0.00027,0.14239,1e-05,1e-05,8e-05,2e-05,0.00011,2e-05,1.06327,6e-05,0.00011,1e-05,0.00011,1e-05,4e-05,7e-05,6e-05 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+75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_values.arff +++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA -233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922847986221,0.464067220688,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 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+85,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +87,weighting,one_hot_encoding,0.001968664991689621,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911,,,,,,,,,,,,,,standardize +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/description.txt index e9f2bce480..5128f0ce51 100644 --- a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: accuracy performance_type: solution_quality diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 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a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+82,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2806.985866707319,False,True,1,squared_hinge,ovr,l2,0.03738539536055984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,standardize,,,, +83,weighting,one_hot_encoding,0.0006525467941596606,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6372778346125954,None,0.0,20,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.042160148347047115,False,True,1,squared_hinge,ovr,l1,3.668632753533501e-05,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +84,weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9,18,0.0,100,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639,normal,, diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_dense/description.txt b/autosklearn/metalearning/files/average_precision_binary.classification_dense/description.txt index 2083121516..5c83700b51 100644 --- a/autosklearn/metalearning/files/average_precision_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/average_precision_binary.classification_dense/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA 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a/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok 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+49,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,,none +51,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +52,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +58,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +59,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +60,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +63,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +65,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +67,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +68,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +69,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +81,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,standardize +82,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7,12,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/description.txt index 2083121516..5c83700b51 100644 --- a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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-244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 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-260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 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-@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, 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-75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 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75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75197,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75202,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75203,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75205,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75207,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 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a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_values.arff +++ b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA -233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922847986221,0.464067220688,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 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+17,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +18,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5,0.33885235607979314,None,0.0,6,4,0.0,125,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,none,,,, 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+31,weighting,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.170846362638446e-06,True,,0.09722688351233315,True,,constant,squared_hinge,l2,,0.00953454743007943,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,272,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +32,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +33,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +34,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +35,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +36,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +39,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, 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+43,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +44,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9529424105127217,None,0.0,10,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,27669.16213464114,False,True,1,squared_hinge,ovr,l1,0.004408124070202508,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8563847395036035,0.008522828523482722 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+51,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +52,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678113,None,0.0,20,2,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,none,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +58,weighting,one_hot_encoding,0.06906109437088473,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6358117195033023,None,0.0,20,3,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36477607756252617,fdr,chi2,quantile_transformer,38156,uniform,, +59,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6490762204984749,None,0.0,18,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8,None,8,9,1.0,47,,,,,,quantile_transformer,53985,uniform,, +60,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37215221406145077,0.35417466287560373,3,0.0009148519644429074,poly,-1,False,2.9166728983300667e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866,normal,, +63,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02674155532989549,deviance,3,0.14973922320166708,None,0.0,7,18,0.0,309,0.35532673462283193,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,exp,1536,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +66,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +67,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +68,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +69,weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8,None,13,16,1.0,28,,,,,,quantile_transformer,41502,uniform,, +70,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +71,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +74,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +75,weighting,one_hot_encoding,0.4273021248657385,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.11426689271753404,deviance,8,0.851601217943927,None,0.0,12,15,0.0,66,0.6029647610646524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +81,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2806.985866707319,False,True,1,squared_hinge,ovr,l2,0.03738539536055984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,standardize,,,, +83,weighting,one_hot_encoding,0.0006525467941596606,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6372778346125954,None,0.0,20,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.042160148347047115,False,True,1,squared_hinge,ovr,l1,3.668632753533501e-05,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +84,weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9,18,0.0,100,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639,normal,, diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/description.txt index 2083121516..5c83700b51 100644 --- a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA 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-253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 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a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+34,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +35,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +36,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +39,none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.6187293043367,,,2.0237366768707754,rbf,-1,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +40,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +41,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +42,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +43,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +44,weighting,one_hot_encoding,0.34516277500429876,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +45,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,weighting,one_hot_encoding,0.0026308117826759733,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452931,None,0.0,18,16,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +47,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +49,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,,none +51,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +52,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +58,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +59,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +60,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +63,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +65,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +67,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +68,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +69,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +81,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,standardize +82,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7,12,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/description.txt index 2083121516..5c83700b51 100644 --- a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other 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a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/configurations.csv index a177415386..0376f67e78 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10677595352882521,deviance,8,2.8434717355886883,None,17,6,0.0,484,0.9516595570116578,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.002694235697058705,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -24,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,0.7267019009424693,None,16,4,0.0,341,0.4859775922611174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,average,162,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -25,none,adaboost,SAMME,0.34864876412492174,9,352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.1435287900624051,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none -26,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,2.1253616607501353,5,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0004279036980618678,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -27,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -28,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.4934522753284643e-05,False,0.021298254696745126,0.06284789831075595,True,,invscaling,modified_huber,52,l2,0.14709162750315702,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,normalize -29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -30,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -31,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.170504104762284,,,0.10000000000000006,rbf,-1,True,1.893780321354902e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -32,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.7702815127831667e-05,False,,0.006631256638841467,True,,constant,squared_hinge,61,l2,,mean,0.3619893522448273,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.741050844267226,chi2,,,,normalize -33,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -34,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4244459875201874,False,True,1,squared_hinge,ovr,l2,0.0014473335587607684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax -35,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -36,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4570.657250638392,False,True,1,squared_hinge,ovr,l2,0.001312453259224503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.021100204578784896,deviance,2,3.2219978901408126,None,14,13,0.0,62,0.9175813938002152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0015802583114458146,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26682653239833576,fdr,f_classif,none -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.751911127640866,None,6,15,0.0,100,,,,,,,,,,,,median,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -45,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,163.38137776399626,False,True,1,squared_hinge,ovr,l2,0.06598224986727308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.3799175197381475,None,10,14,0.0,100,,,,,,,,,,,,median,0.004732616038822116,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -47,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0005474378153703474,True,,0.0709606524043735,True,,invscaling,squared_hinge,322,l2,0.25,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,5.27375986647238,False,True,1,squared_hinge,ovr,l1,1.0213500115323962e-05,,,,,,,,,,,,,,,,,,,,,,normalize -48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,434.04789312490243,-0.28598798624265775,5,0.00020813257891031506,poly,-1,True,0.08410050264911162,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,None,11,19,1.0,35,,,,,,normalize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,520.1785093386421,,,0.4094223029861982,rbf,-1,False,0.0001488588704335344,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27.824808973705593,chi2,,,,normalize -55,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7893822540456763e-05,True,,0.01920961872573097,True,,optimal,hinge,769,l2,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.0504050565968734,2076,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -56,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.283033279625623e-05,False,,0.04931533154730613,True,,constant,squared_hinge,531,l1,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.26694836626666,f_classif,,,,minmax -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.0615335158906123,12,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0006128377815426205,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40918220909952985,fdr,chi2,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.6939677427994257,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,extra_trees_preproc_for_classification,False,gini,None,1.0506261769751233,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,True,hinge,46,,,,,,,,,,,,,,,,,,,,,,median,0.0001614163818794968,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,226.06742020598978,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07123494942796214,True,,0.06640696915484627,True,1.444778397890192e-09,invscaling,perceptron,147,elasticnet,0.25,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1722034433283093,,,0.001958703488411727,rbf,-1,False,0.006262296782048029,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.53463583357125,None,2,4,0.0,100,,,,,,,,,,,,median,0.08965639912423894,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.58142545325404,f_classif,,,,standardize -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.1285583573915876,deviance,1,1.8700144159035181,None,5,19,0.0,252,0.7739014033956171,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,284,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0013066556710069719,False,0.03958867790828566,0.06657767200469752,True,,optimal,modified_huber,52,l2,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7941252766878464,5,0.0026102407098584192,poly,615,,,,,,,,,,,,,,,,,standardize -98,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12683.745517936331,,,0.2798030071977636,rbf,-1,False,9.038883737441219e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0483380823811535,deviance,3,3.5447265496885296,None,5,16,0.0,141,0.5896080302596434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0020128017963651694,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.014581209053425,f_classif,,,,none -103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,1,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.56701180897993,f_classif,,,,none -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.7368358338706456,8,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,True,,,,,,,,,,,,,,,standardize -106,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,,,,,none -107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize -110,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax -116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,None,,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.003937843574389652,rbf,545,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none -123,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.5555960331548946e-06,True,,0.03775758938844074,True,,optimal,hinge,6,l2,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize 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+35,none,one_hot_encoding,0.00011294596229850897,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062477,3,0.019002536385919935,poly,-1,False,0.010632086351533367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282,normal,, +36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.15944469021885255,None,0.0,18,19,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,14,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,6315,uniform,, +39,weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407,normal,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3233601927284725,None,0.0,3,18,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.29913129113847253,fdr,chi2,normalize,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 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+55,none,one_hot_encoding,0.010488491664540746,True,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6793271069375356,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09031561168137159,fdr,f_classif,robust_scaler,,,0.9762457833235179,0.2189244634478133 +56,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10,0.716738790505292,None,0.0,5,6,0.0,472,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,weighting,one_hot_encoding,0.006909187206474195,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00024264014379190562,True,,0.04987297125937914,True,,optimal,hinge,l2,,4.0861541221464815e-05,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.05153104953418389,False,True,1,squared_hinge,ovr,l1,0.00019393864742902846,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +65,weighting,one_hot_encoding,0.003566024581260295,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7974297391104296,None,0.0,5,8,0.0,100,,,,,,,,,,,,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,f_classif,,,,standardize,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1,True,1.1394216224323561e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9940718718674404,None,0.0,15,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +72,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.05347123056931161,deviance,3,0.2250677489759125,None,0.0,16,4,0.0,309,0.7245595517718859,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19466188848884064,fdr,chi2,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.005326467497783115,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.058055900067082,,,0.1626688094236879,rbf,-1,True,0.04838115942937064,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,cube,306,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.49329965447606194,None,0.0,2,20,0.0,100,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8,12,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, 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+86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13,20,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.9460823349230802,1.0,None,0.0,15,11,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2613520842796237,fdr,chi2,quantile_transformer,1000,uniform,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000,uniform,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2,233,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +98,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14904790197542306,deviance,6,0.7561346995577642,None,0.0,5,14,0.0,340,0.6548040792383665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37215221406145077,0.35417466287560373,3,0.0009148519644429074,poly,-1,False,2.9166728983300667e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866,normal,, +102,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3,0.7249041896998006,None,0.0,6,17,0.0,174,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,weighting,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,1367,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,weighting,one_hot_encoding,0.10324969243867223,True,decision_tree,,,,,,,gini,0.7467478023293801,1.0,None,0.0,12,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8,None,13,16,1.0,28,,,,,,quantile_transformer,41502,uniform,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +116,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4,0.6041596127474019,None,0.0,14,17,0.0,83,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705262,False,True,squared_hinge,4.4620804452838995e-05,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,quantile_transformer,57665,uniform,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.102423946884905,f_classif,,,,quantile_transformer,89842,normal,, +123,weighting,one_hot_encoding,0.010000000000000004,True,decision_tree,,,,,,,entropy,1.5841974853345435,1.0,None,0.0,8,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04994122399949775,deviance,9,0.8621747362759826,None,0.0,1,13,0.0,84,0.6449569386396325,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.53326630281591,chi2,,,,minmax,,,, +125,weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30,normal,, +126,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1166829447028879,deviance,4,0.8451196958788597,None,0.0,1,8,0.0,58,0.8652814520124915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03406306803297533,False,True,1,squared_hinge,ovr,l1,0.00019257971888767862,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +129,weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9,18,0.0,100,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639,normal,, diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/description.txt b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/description.txt index f8ff146acd..2af4e844e7 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: balanced_accuracy performance_type: solution_quality diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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-79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -87,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.786449460674678,,,,,,,,,,,,,,,,,,,,,most_frequent,0.06203188930692253,True,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09174494331902282,False,True,1,squared_hinge,ovr,l2,0.004642503579081282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0018444303476881426,True,kernel_pca,,,,,,,,,0.4783416998251251,4,0.5925934287948866,poly,1268,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0013066556710069719,False,0.03958867790828566,0.06657767200469752,True,,optimal,modified_huber,52,l2,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,0.7941252766878464,5,0.0026102407098584192,poly,615,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.312914959291577,None,11,14,0.0,100,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -99,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6962352073255054,None,3,13,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,gini,None,3.1516195509427503,10,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,,standardize -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9357354171445895e-06,True,,0.05458546144730029,True,,constant,perceptron,179,l2,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -106,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,none -107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -108,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,cosine,1211,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -110,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -116,weighting,adaboost,SAMME.R,0.1355825690029731,2,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.9983112975799235,None,3,17,0.0,100,,,,,,,,,,,,mean,0.012321190772400284,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -122,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -125,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.80178999786076,None,4,9,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+92,weighting,no_encoding,,,decision_tree,,,,,,,gini,1.7984076825537865,1.0,None,0.0,14,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2897525995758022,fwe,chi2,,none +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.93767728056358,None,0.0,18,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.00022308163276069302,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +102,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,weighting,one_hot_encoding,,False,bernoulli_nb,,,,,0.014801515930977628,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +105,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37163783119625016,False,True,1,squared_hinge,ovr,l2,1.2049944334095187e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,kitchen_sinks,,,,,,,,,,,,,,,,0.33181838105513195,7347,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,weighting,one_hot_encoding,0.3997572853391576,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21008613984919333,None,0.0,11,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833405,False,True,hinge,0.024385928857556572,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10,14,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1,squared_hinge,ovr,l1,0.01604898292029417,,,,,,,,,,,,,,,,,,,none +123,weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.3016059914162639,1.0,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +124,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7,12,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/description.txt index f8ff146acd..2af4e844e7 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: balanced_accuracy performance_type: solution_quality diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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-75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75202,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75203,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75205,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75207,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 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a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/configurations.csv index a177415386..0376f67e78 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-35,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -36,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4570.657250638392,False,True,1,squared_hinge,ovr,l2,0.001312453259224503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.021100204578784896,deviance,2,3.2219978901408126,None,14,13,0.0,62,0.9175813938002152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0015802583114458146,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26682653239833576,fdr,f_classif,none -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.751911127640866,None,6,15,0.0,100,,,,,,,,,,,,median,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -45,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,163.38137776399626,False,True,1,squared_hinge,ovr,l2,0.06598224986727308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.3799175197381475,None,10,14,0.0,100,,,,,,,,,,,,median,0.004732616038822116,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -47,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0005474378153703474,True,,0.0709606524043735,True,,invscaling,squared_hinge,322,l2,0.25,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,5.27375986647238,False,True,1,squared_hinge,ovr,l1,1.0213500115323962e-05,,,,,,,,,,,,,,,,,,,,,,normalize -48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,434.04789312490243,-0.28598798624265775,5,0.00020813257891031506,poly,-1,True,0.08410050264911162,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,None,11,19,1.0,35,,,,,,normalize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,520.1785093386421,,,0.4094223029861982,rbf,-1,False,0.0001488588704335344,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27.824808973705593,chi2,,,,normalize -55,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7893822540456763e-05,True,,0.01920961872573097,True,,optimal,hinge,769,l2,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.0504050565968734,2076,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -56,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.283033279625623e-05,False,,0.04931533154730613,True,,constant,squared_hinge,531,l1,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.26694836626666,f_classif,,,,minmax -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.0615335158906123,12,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0006128377815426205,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40918220909952985,fdr,chi2,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.6939677427994257,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,extra_trees_preproc_for_classification,False,gini,None,1.0506261769751233,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,True,hinge,46,,,,,,,,,,,,,,,,,,,,,,median,0.0001614163818794968,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,226.06742020598978,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07123494942796214,True,,0.06640696915484627,True,1.444778397890192e-09,invscaling,perceptron,147,elasticnet,0.25,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1722034433283093,,,0.001958703488411727,rbf,-1,False,0.006262296782048029,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.53463583357125,None,2,4,0.0,100,,,,,,,,,,,,median,0.08965639912423894,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.58142545325404,f_classif,,,,standardize -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+38,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.15944469021885255,None,0.0,18,19,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,14,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,6315,uniform,, +39,weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407,normal,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, 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+47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 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+55,none,one_hot_encoding,0.010488491664540746,True,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6793271069375356,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09031561168137159,fdr,f_classif,robust_scaler,,,0.9762457833235179,0.2189244634478133 +56,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10,0.716738790505292,None,0.0,5,6,0.0,472,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 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+63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,weighting,one_hot_encoding,0.006909187206474195,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00024264014379190562,True,,0.04987297125937914,True,,optimal,hinge,l2,,4.0861541221464815e-05,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.05153104953418389,False,True,1,squared_hinge,ovr,l1,0.00019393864742902846,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +65,weighting,one_hot_encoding,0.003566024581260295,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7974297391104296,None,0.0,5,8,0.0,100,,,,,,,,,,,,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,f_classif,,,,standardize,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1,True,1.1394216224323561e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9940718718674404,None,0.0,15,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +72,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.05347123056931161,deviance,3,0.2250677489759125,None,0.0,16,4,0.0,309,0.7245595517718859,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19466188848884064,fdr,chi2,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.005326467497783115,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.058055900067082,,,0.1626688094236879,rbf,-1,True,0.04838115942937064,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,cube,306,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.49329965447606194,None,0.0,2,20,0.0,100,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8,12,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +85,none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1793,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811075,0.03563842252368924 +86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13,20,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.9460823349230802,1.0,None,0.0,15,11,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2613520842796237,fdr,chi2,quantile_transformer,1000,uniform,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000,uniform,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2,233,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +98,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14904790197542306,deviance,6,0.7561346995577642,None,0.0,5,14,0.0,340,0.6548040792383665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37215221406145077,0.35417466287560373,3,0.0009148519644429074,poly,-1,False,2.9166728983300667e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866,normal,, +102,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3,0.7249041896998006,None,0.0,6,17,0.0,174,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,weighting,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,1367,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,weighting,one_hot_encoding,0.10324969243867223,True,decision_tree,,,,,,,gini,0.7467478023293801,1.0,None,0.0,12,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8,None,13,16,1.0,28,,,,,,quantile_transformer,41502,uniform,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +116,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4,0.6041596127474019,None,0.0,14,17,0.0,83,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705262,False,True,squared_hinge,4.4620804452838995e-05,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,quantile_transformer,57665,uniform,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.102423946884905,f_classif,,,,quantile_transformer,89842,normal,, +123,weighting,one_hot_encoding,0.010000000000000004,True,decision_tree,,,,,,,entropy,1.5841974853345435,1.0,None,0.0,8,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04994122399949775,deviance,9,0.8621747362759826,None,0.0,1,13,0.0,84,0.6449569386396325,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.53326630281591,chi2,,,,minmax,,,, +125,weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30,normal,, +126,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1166829447028879,deviance,4,0.8451196958788597,None,0.0,1,8,0.0,58,0.8652814520124915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03406306803297533,False,True,1,squared_hinge,ovr,l1,0.00019257971888767862,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +129,weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9,18,0.0,100,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639,normal,, diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/description.txt index f8ff146acd..2af4e844e7 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: balanced_accuracy performance_type: solution_quality diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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-79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -87,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.786449460674678,,,,,,,,,,,,,,,,,,,,,most_frequent,0.06203188930692253,True,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09174494331902282,False,True,1,squared_hinge,ovr,l2,0.004642503579081282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0018444303476881426,True,kernel_pca,,,,,,,,,0.4783416998251251,4,0.5925934287948866,poly,1268,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0013066556710069719,False,0.03958867790828566,0.06657767200469752,True,,optimal,modified_huber,52,l2,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,0.7941252766878464,5,0.0026102407098584192,poly,615,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.312914959291577,None,11,14,0.0,100,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -99,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6962352073255054,None,3,13,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,gini,None,3.1516195509427503,10,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,,standardize -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9357354171445895e-06,True,,0.05458546144730029,True,,constant,perceptron,179,l2,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -106,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,none -107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -108,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,cosine,1211,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -110,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -116,weighting,adaboost,SAMME.R,0.1355825690029731,2,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.9983112975799235,None,3,17,0.0,100,,,,,,,,,,,,mean,0.012321190772400284,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -122,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -125,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.80178999786076,None,4,9,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+92,weighting,no_encoding,,,decision_tree,,,,,,,gini,1.7984076825537865,1.0,None,0.0,14,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2897525995758022,fwe,chi2,,none +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.93767728056358,None,0.0,18,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.00022308163276069302,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +102,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,weighting,one_hot_encoding,,False,bernoulli_nb,,,,,0.014801515930977628,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +105,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37163783119625016,False,True,1,squared_hinge,ovr,l2,1.2049944334095187e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,kitchen_sinks,,,,,,,,,,,,,,,,0.33181838105513195,7347,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,weighting,one_hot_encoding,0.3997572853391576,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21008613984919333,None,0.0,11,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833405,False,True,hinge,0.024385928857556572,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10,14,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1,squared_hinge,ovr,l1,0.01604898292029417,,,,,,,,,,,,,,,,,,,none +123,weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.3016059914162639,1.0,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +124,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7,12,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/description.txt index f8ff146acd..2af4e844e7 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: balanced_accuracy performance_type: solution_quality diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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-75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other 75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75197,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75202,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75203,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75205,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75207,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 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+6,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.37594387937469453,None,0.0,7,18,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3689420905780977,fwe,f_classif,quantile_transformer,25061,uniform,, +7,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +8,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +9,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4,0.6041596127474019,None,0.0,14,17,0.0,83,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +10,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565651,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463182 +11,weighting,one_hot_encoding,0.18137532678800652,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +12,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +13,none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10,0.542144980834302,None,0.0,20,13,0.0,233,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307263,0.13434811490315818 +14,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15,11,0.0,100,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,quantile_transformer,1000,uniform,, 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+21,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +22,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +23,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +24,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +25,weighting,no_encoding,,,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.31700092389924267,rbf,1955,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8333938697866604,0.10426506601169797 +26,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +27,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +28,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +29,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +30,weighting,one_hot_encoding,0.06015518601232588,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2,None,11,20,1.0,47,,,,,,quantile_transformer,21065,uniform,, +31,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.04962265276655457,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +32,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +33,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10,0.716738790505292,None,0.0,5,6,0.0,472,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +34,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +35,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8770766409674923,None,0.0,13,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,84023,normal,, +36,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +39,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +40,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +41,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +42,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +43,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +44,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +45,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +46,none,one_hot_encoding,0.0037042565030130366,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9610802347688105,None,0.0,17,9,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,172,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +47,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +49,none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1793,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811075,0.03563842252368924 +50,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +51,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +52,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13,20,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.93767728056358,None,0.0,18,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +58,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +59,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +60,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14904790197542306,deviance,6,0.7561346995577642,None,0.0,5,14,0.0,340,0.6548040792383665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,weighting,one_hot_encoding,0.0009243833519832891,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9829888014816668,None,0.0,18,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,minmax,,,, +64,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +65,weighting,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,1367,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +66,weighting,one_hot_encoding,0.0506220137415751,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4433458237042269,None,0.0,11,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.8740590455827745,0.19483217552098045 +67,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +68,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +69,weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8,None,13,16,1.0,28,,,,,,quantile_transformer,41502,uniform,, +70,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +71,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +74,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +75,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4,0.6041596127474019,None,0.0,14,17,0.0,83,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.102423946884905,f_classif,,,,quantile_transformer,89842,normal,, +78,weighting,one_hot_encoding,0.010000000000000004,True,decision_tree,,,,,,,entropy,1.5841974853345435,1.0,None,0.0,8,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +79,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +80,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +81,weighting,one_hot_encoding,0.003777272888571546,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4583291745177553,None,0.0,14,7,0.0,100,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.27741957612588863,fdr,f_classif,none,,,, +82,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +83,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/f1_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_binary.classification_dense/description.txt index 24aa686982..f8f3d732ff 100644 --- a/autosklearn/metalearning/files/f1_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/f1_binary.classification_dense/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/f1_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC 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a/autosklearn/metalearning/files/f1_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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-75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75202,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75203,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75205,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75207,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 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+65,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +67,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +68,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +69,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +70,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10,14,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1,squared_hinge,ovr,l1,0.01604898292029417,,,,,,,,,,,,,,,,,,,none +78,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +81,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,standardize +82,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_binary.classification_sparse/description.txt index 24aa686982..f8f3d732ff 100644 --- a/autosklearn/metalearning/files/f1_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/f1_binary.classification_sparse/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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-50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.442753772090821,12,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46.45873045394655,f_classif,,,,standardize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.144799068832228,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,normalize -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.9520500591136147,None,6,8,0.0,100,,,,,,,,,,,,median,0.010449223310810166,True,extra_trees_preproc_for_classification,False,gini,None,1.033297028797688,6,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -67,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.008662251044778715,True,squared_hinge,5,,,,,,,,,,,,,,,,,,,,,,median,0.0002711702886572365,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97.69404883660206,chi2,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,weighting,adaboost,SAMME,0.015263565512611987,9,308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,65,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.578593489684234,None,9,14,0.0,100,,,,,,,,,,,,most_frequent,0.005297835726138853,True,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+15,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +16,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.4909422458748719,None,0.0,11,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +17,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4,0.6041596127474019,None,0.0,14,17,0.0,83,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +18,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565651,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463182 +19,weighting,one_hot_encoding,0.18137532678800652,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +20,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +21,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +22,none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10,0.542144980834302,None,0.0,20,13,0.0,233,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307263,0.13434811490315818 +23,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15,11,0.0,100,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +24,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04093642460278944,deviance,7,0.5495014745530306,None,0.0,20,18,0.0,141,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +25,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9727149851116395,None,0.0,10,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +26,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12,11,0.0,100,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +27,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,normalize,,,, +28,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5,0.33885235607979314,None,0.0,6,4,0.0,125,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,none,,,, +29,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +30,weighting,one_hot_encoding,0.0007038280350320556,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5,5.43004469263886,poly,-1,True,0.024555010060043934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +31,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +32,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.051832615669195795,deviance,6,0.8807456180216267,None,0.0,7,19,0.0,366,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +33,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +34,none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3,386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615793 +35,none,one_hot_encoding,0.00011294596229850897,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062477,3,0.019002536385919935,poly,-1,False,0.010632086351533367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282,normal,, +36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +39,weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407,normal,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,weighting,one_hot_encoding,0.06015518601232588,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2,None,11,20,1.0,47,,,,,,quantile_transformer,21065,uniform,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2,0.0337546254878617,poly,-1,True,0.09641299736884307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +53,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, 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+62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+109,weighting,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4569434193011598,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.102423946884905,f_classif,,,,quantile_transformer,89842,normal,, +123,weighting,one_hot_encoding,0.010000000000000004,True,decision_tree,,,,,,,entropy,1.5841974853345435,1.0,None,0.0,8,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,weighting,one_hot_encoding,0.003777272888571546,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4583291745177553,None,0.0,14,7,0.0,100,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.27741957612588863,fdr,f_classif,none,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400,uniform,, diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/description.txt index 68bfe3f7e4..6ba798f7d2 100644 --- a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: f1_macro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -2117,1.0,0.00055,0.01157,0.00069,2e-05,0.03906,0.71904,10.54724,0.00046,0.01394,0.02244,2e-05,0.00035,0.00011,1e-05,0.0103,6e-05,1e-05,5e-05,5e-05,5e-05,5e-05,0.0,0.00031,6e-05,0.00026,0.12553,0.0,0.00102,0.94012,2e-05,0.06161,0.00011,0.00023,0.00184,1e-05,0.00025,7.78747,1e-05,1e-05,6e-05,1e-05,0.00058,2e-05,315.23043,0.00011,0.00071,1e-05,0.00012,2e-05,3e-05,6e-05,5e-05 -2119,1.0,0.00051,0.00039,0.00053,1e-05,0.00087,0.01699,0.03836,0.00045,0.00053,0.00048,2e-05,0.0002,9e-05,1e-05,0.00051,4e-05,1e-05,4e-05,4e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.02023,0.0,0.00084,0.0358,2e-05,0.0014,0.00011,0.0001,0.00012,1e-05,0.00025,0.05298,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.06357,4e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -2120,1.0,0.00061,0.00169,0.00065,2e-05,0.04474,0.08877,0.53819,0.00046,0.00254,0.02526,2e-05,0.00034,0.00011,1e-05,0.00574,6e-05,1e-05,5e-05,5e-05,7e-05,5e-05,1e-05,0.00039,6e-05,0.00026,0.02733,1e-05,0.00099,0.12891,3e-05,0.07005,0.00011,0.00021,0.00056,1e-05,0.00025,0.78777,1e-05,1e-05,7e-05,2e-05,0.00034,3e-05,1.35102,5e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 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+75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA -233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922847986221,0.464067220688,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 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+80,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +84,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,,none +87,weighting,one_hot_encoding,0.001968664991689621,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911,,,,,,,,,,,,,,standardize +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.93767728056358,None,0.0,18,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +102,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9930358173100328,None,0.0,6,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,entropy,None,0.46558488262913855,None,0.0,13,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/description.txt index 68bfe3f7e4..6ba798f7d2 100644 --- a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: f1_macro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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-244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 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a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/configurations.csv index dd34ea7f32..decee42701 100644 --- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-31,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.170504104762284,,,0.10000000000000006,rbf,-1,True,1.893780321354902e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -32,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -33,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -34,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4244459875201874,False,True,1,squared_hinge,ovr,l2,0.0014473335587607684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax -35,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -36,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4570.657250638392,False,True,1,squared_hinge,ovr,l2,0.001312453259224503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -45,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,163.38137776399626,False,True,1,squared_hinge,ovr,l2,0.06598224986727308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.3799175197381475,None,10,14,0.0,100,,,,,,,,,,,,median,0.004732616038822116,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06896070360353206,True,hinge,36,,,,,,,,,,,,,,,,,,,,,,median,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,1818,,,,,,,,,,,,,,,,,standardize -48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.442753772090821,12,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46.45873045394655,f_classif,,,,standardize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.144799068832228,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,normalize -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.9520500591136147,None,6,8,0.0,100,,,,,,,,,,,,median,0.010449223310810166,True,extra_trees_preproc_for_classification,False,gini,None,1.033297028797688,6,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -67,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.008662251044778715,True,squared_hinge,5,,,,,,,,,,,,,,,,,,,,,,median,0.0002711702886572365,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97.69404883660206,chi2,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,weighting,adaboost,SAMME,0.015263565512611987,9,308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,65,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.578593489684234,None,9,14,0.0,100,,,,,,,,,,,,most_frequent,0.005297835726138853,True,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,1,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.56701180897993,f_classif,,,,none -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,2.14946379562645,16,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9528799560216878,True,,,,,,,,,,,,,,,standardize -107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax -116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none -123,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,True,,0.035386445062342765,True,,optimal,hinge,20,l2,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -126,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax 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+39,weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407,normal,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,weighting,one_hot_encoding,0.06015518601232588,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2,None,11,20,1.0,47,,,,,,quantile_transformer,21065,uniform,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2,0.0337546254878617,poly,-1,True,0.09641299736884307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +53,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1,True,1.1394216224323561e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,None,,0.0053894830448103555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,0.03528169333197684,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3416063836589199,None,0.0,9,15,0.0,100,,,,,,,,,,,,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6524770456279243,False,,,,,,,,,,,,,,,,robust_scaler,,,0.7602889314749132,0.25 +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +85,none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1793,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811075,0.03563842252368924 +86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.93767728056358,None,0.0,18,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,weighting,one_hot_encoding,0.0009243833519832891,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9829888014816668,None,0.0,18,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,minmax,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,weighting,one_hot_encoding,0.0506220137415751,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4433458237042269,None,0.0,11,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.8740590455827745,0.19483217552098045 +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,weighting,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4569434193011598,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.102423946884905,f_classif,,,,quantile_transformer,89842,normal,, +123,weighting,one_hot_encoding,0.010000000000000004,True,decision_tree,,,,,,,entropy,1.5841974853345435,1.0,None,0.0,8,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,weighting,one_hot_encoding,0.003777272888571546,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4583291745177553,None,0.0,14,7,0.0,100,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.27741957612588863,fdr,f_classif,none,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400,uniform,, diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/description.txt index 68bfe3f7e4..6ba798f7d2 100644 --- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: f1_macro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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-244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 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+112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9930358173100328,None,0.0,6,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,entropy,None,0.46558488262913855,None,0.0,13,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/description.txt index 68bfe3f7e4..6ba798f7d2 100644 --- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: f1_macro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_values.arff index d0395e7246..dd0f70cd52 100644 --- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_values.arff +++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA -233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922847986221,0.464067220688,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 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-62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+16,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +17,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +18,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565651,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463182 +19,weighting,one_hot_encoding,0.18137532678800652,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +20,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +21,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +22,none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10,0.542144980834302,None,0.0,20,13,0.0,233,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307263,0.13434811490315818 +23,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +24,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04093642460278944,deviance,7,0.5495014745530306,None,0.0,20,18,0.0,141,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +25,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +26,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12,11,0.0,100,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +27,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532,,,,,,,,,,,,,,,,,,none,,,, 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+31,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +32,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.051832615669195795,deviance,6,0.8807456180216267,None,0.0,7,19,0.0,366,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +33,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +34,none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3,386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615793 +35,none,one_hot_encoding,0.00011294596229850897,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062477,3,0.019002536385919935,poly,-1,False,0.010632086351533367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282,normal,, +36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +39,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 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+63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, 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+69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, 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+81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/description.txt index 29611ffc0c..1b25067f30 100644 --- a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: f1_micro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -2117,1.0,0.00055,0.01157,0.00069,2e-05,0.03906,0.71904,10.54724,0.00046,0.01394,0.02244,2e-05,0.00035,0.00011,1e-05,0.0103,6e-05,1e-05,5e-05,5e-05,5e-05,5e-05,0.0,0.00031,6e-05,0.00026,0.12553,0.0,0.00102,0.94012,2e-05,0.06161,0.00011,0.00023,0.00184,1e-05,0.00025,7.78747,1e-05,1e-05,6e-05,1e-05,0.00058,2e-05,315.23043,0.00011,0.00071,1e-05,0.00012,2e-05,3e-05,6e-05,5e-05 -2119,1.0,0.00051,0.00039,0.00053,1e-05,0.00087,0.01699,0.03836,0.00045,0.00053,0.00048,2e-05,0.0002,9e-05,1e-05,0.00051,4e-05,1e-05,4e-05,4e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.02023,0.0,0.00084,0.0358,2e-05,0.0014,0.00011,0.0001,0.00012,1e-05,0.00025,0.05298,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.06357,4e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -2120,1.0,0.00061,0.00169,0.00065,2e-05,0.04474,0.08877,0.53819,0.00046,0.00254,0.02526,2e-05,0.00034,0.00011,1e-05,0.00574,6e-05,1e-05,5e-05,5e-05,7e-05,5e-05,1e-05,0.00039,6e-05,0.00026,0.02733,1e-05,0.00099,0.12891,3e-05,0.07005,0.00011,0.00021,0.00056,1e-05,0.00025,0.78777,1e-05,1e-05,7e-05,2e-05,0.00034,3e-05,1.35102,5e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -2122,1.0,0.00051,0.00613,0.00057,2e-05,0.06877,0.40703,2.10666,0.00043,0.00686,0.02832,2e-05,0.0003,9e-05,1e-05,0.00579,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.2282,0.0,0.00088,0.68412,2e-05,0.09715,0.00011,0.00019,0.00063,1e-05,0.00023,2.7541,1e-05,1e-05,6e-05,1e-05,0.00014,2e-05,82.75009,5e-05,0.00064,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -2123,1.0,0.00055,0.00037,0.00231,1e-05,0.00586,0.07868,0.20326,0.00046,0.00051,0.00234,2e-05,0.0002,8e-05,1e-05,0.0005,4e-05,1e-05,4e-05,5e-05,4e-05,5e-05,0.0,0.00033,5e-05,0.00047,0.03788,0.0,0.00284,0.06438,2e-05,0.00825,0.00011,0.0001,0.00013,1e-05,0.00025,0.2846,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.13009,4e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -2350,1.0,0.0008,0.76706,0.00083,3e-05,7e-05,1e-05,0.0,0.00063,0.81845,1e-05,5e-05,0.00039,0.00018,2e-05,0.02503,5e-05,1e-05,8e-05,9e-05,0.00021,9e-05,1e-05,0.00044,9e-05,0.00027,2e-05,1e-05,0.00125,1e-05,5e-05,9e-05,0.00015,0.00026,0.04508,1e-05,0.00031,0.0,4e-05,2e-05,0.00012,7e-05,0.00648,0.0001,2.43616,1e-05,0.00182,1e-05,0.00019,4e-05,0.00015,0.00013,6e-05 -3043,1.0,0.00055,0.00098,0.00061,2e-05,0.0028,0.05945,0.57683,0.00048,0.00131,0.00233,2e-05,0.00031,0.00011,2e-05,0.00113,0.0001,1e-05,4e-05,6e-05,7e-05,7e-05,2e-05,0.00031,7e-05,0.00027,0.04173,0.0,0.00098,0.08173,2e-05,0.00519,0.00012,0.00014,0.00027,1e-05,0.00027,0.14239,1e-05,1e-05,8e-05,2e-05,0.00011,2e-05,1.06327,6e-05,0.00011,1e-05,0.00011,1e-05,4e-05,7e-05,6e-05 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+75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA -233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922847986221,0.464067220688,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 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+85,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +87,weighting,one_hot_encoding,0.001968664991689621,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911,,,,,,,,,,,,,,standardize +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/description.txt index 29611ffc0c..1b25067f30 100644 --- a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: f1_micro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 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-251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/configurations.csv index e7fbf33fad..c40b8d3bab 100644 --- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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-68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax -116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -126,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax 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+45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302503,False,True,1,squared_hinge,ovr,l2,0.0007318628304090555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.560201454218397,948,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +85,weighting,one_hot_encoding,0.0003173723611800348,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1862,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/description.txt index 29611ffc0c..1b25067f30 100644 --- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: f1_micro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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-244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other 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+119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/description.txt index 29611ffc0c..1b25067f30 100644 --- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: f1_micro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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+75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_values.arff index d0395e7246..dd0f70cd52 100644 --- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_values.arff +++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA 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-20,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -22,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -23,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -24,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -25,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.11772147933484846,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,1741,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -36,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -38,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -41,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -43,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -44,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -45,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -46,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -49,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -50,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -53,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-57,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.312914959291577,None,11,14,0.0,100,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -60,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -62,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0483380823811535,deviance,4,3.5447265496885296,None,5,16,0.0,141,0.5896080302596434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0020128017963651694,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.014581209053425,f_classif,,,,none 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+21,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +22,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +23,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, 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+27,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +28,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +29,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +30,weighting,one_hot_encoding,0.06015518601232588,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2,None,11,20,1.0,47,,,,,,quantile_transformer,21065,uniform,, +31,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.04962265276655457,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +32,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +33,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10,0.716738790505292,None,0.0,5,6,0.0,472,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +34,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +35,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8770766409674923,None,0.0,13,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,84023,normal,, +36,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +39,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +40,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +41,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +42,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.93767728056358,None,0.0,18,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, 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+64,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +65,weighting,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,1367,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +66,weighting,one_hot_encoding,0.0506220137415751,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4433458237042269,None,0.0,11,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.8740590455827745,0.19483217552098045 +67,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +68,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +69,weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8,None,13,16,1.0,28,,,,,,quantile_transformer,41502,uniform,, +70,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +71,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +74,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +75,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4,0.6041596127474019,None,0.0,14,17,0.0,83,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.102423946884905,f_classif,,,,quantile_transformer,89842,normal,, +78,weighting,one_hot_encoding,0.010000000000000004,True,decision_tree,,,,,,,entropy,1.5841974853345435,1.0,None,0.0,8,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +79,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +80,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +81,weighting,one_hot_encoding,0.003777272888571546,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4583291745177553,None,0.0,14,7,0.0,100,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.27741957612588863,fdr,f_classif,none,,,, +82,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +83,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_multiclass.classification_dense/description.txt index 24aa686982..f8f3d732ff 100644 --- a/autosklearn/metalearning/files/f1_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/f1_multiclass.classification_dense/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -2117,1.0,0.00055,0.01157,0.00069,2e-05,0.03906,0.71904,10.54724,0.00046,0.01394,0.02244,2e-05,0.00035,0.00011,1e-05,0.0103,6e-05,1e-05,5e-05,5e-05,5e-05,5e-05,0.0,0.00031,6e-05,0.00026,0.12553,0.0,0.00102,0.94012,2e-05,0.06161,0.00011,0.00023,0.00184,1e-05,0.00025,7.78747,1e-05,1e-05,6e-05,1e-05,0.00058,2e-05,315.23043,0.00011,0.00071,1e-05,0.00012,2e-05,3e-05,6e-05,5e-05 -2119,1.0,0.00051,0.00039,0.00053,1e-05,0.00087,0.01699,0.03836,0.00045,0.00053,0.00048,2e-05,0.0002,9e-05,1e-05,0.00051,4e-05,1e-05,4e-05,4e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.02023,0.0,0.00084,0.0358,2e-05,0.0014,0.00011,0.0001,0.00012,1e-05,0.00025,0.05298,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.06357,4e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -2120,1.0,0.00061,0.00169,0.00065,2e-05,0.04474,0.08877,0.53819,0.00046,0.00254,0.02526,2e-05,0.00034,0.00011,1e-05,0.00574,6e-05,1e-05,5e-05,5e-05,7e-05,5e-05,1e-05,0.00039,6e-05,0.00026,0.02733,1e-05,0.00099,0.12891,3e-05,0.07005,0.00011,0.00021,0.00056,1e-05,0.00025,0.78777,1e-05,1e-05,7e-05,2e-05,0.00034,3e-05,1.35102,5e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -2122,1.0,0.00051,0.00613,0.00057,2e-05,0.06877,0.40703,2.10666,0.00043,0.00686,0.02832,2e-05,0.0003,9e-05,1e-05,0.00579,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.2282,0.0,0.00088,0.68412,2e-05,0.09715,0.00011,0.00019,0.00063,1e-05,0.00023,2.7541,1e-05,1e-05,6e-05,1e-05,0.00014,2e-05,82.75009,5e-05,0.00064,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -2123,1.0,0.00055,0.00037,0.00231,1e-05,0.00586,0.07868,0.20326,0.00046,0.00051,0.00234,2e-05,0.0002,8e-05,1e-05,0.0005,4e-05,1e-05,4e-05,5e-05,4e-05,5e-05,0.0,0.00033,5e-05,0.00047,0.03788,0.0,0.00284,0.06438,2e-05,0.00825,0.00011,0.0001,0.00013,1e-05,0.00025,0.2846,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.13009,4e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -2350,1.0,0.0008,0.76706,0.00083,3e-05,7e-05,1e-05,0.0,0.00063,0.81845,1e-05,5e-05,0.00039,0.00018,2e-05,0.02503,5e-05,1e-05,8e-05,9e-05,0.00021,9e-05,1e-05,0.00044,9e-05,0.00027,2e-05,1e-05,0.00125,1e-05,5e-05,9e-05,0.00015,0.00026,0.04508,1e-05,0.00031,0.0,4e-05,2e-05,0.00012,7e-05,0.00648,0.0001,2.43616,1e-05,0.00182,1e-05,0.00019,4e-05,0.00015,0.00013,6e-05 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+75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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+43,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +44,weighting,one_hot_encoding,0.34516277500429876,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +45,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +47,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +49,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,none,one_hot_encoding,0.00011600321198702642,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +51,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +52,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.93767728056358,None,0.0,18,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +58,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +59,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +60,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +63,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +64,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +65,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +67,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +68,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +69,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +70,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10,14,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1,squared_hinge,ovr,l1,0.01604898292029417,,,,,,,,,,,,,,,,,,,none +78,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +81,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,standardize +82,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/description.txt index 24aa686982..f8f3d732ff 100644 --- a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok 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75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 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a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/configurations.csv index f977568ef2..9036164e61 100644 --- a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-32,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -33,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -34,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4244459875201874,False,True,1,squared_hinge,ovr,l2,0.0014473335587607684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax 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-38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -45,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,163.38137776399626,False,True,1,squared_hinge,ovr,l2,0.06598224986727308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.3799175197381475,None,10,14,0.0,100,,,,,,,,,,,,median,0.004732616038822116,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax 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-50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.578593489684234,None,9,14,0.0,100,,,,,,,,,,,,most_frequent,0.005297835726138853,True,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,decision_tree,,,,,,,gini,1.0026108621086127,1.0,None,11,15,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06384570118118357,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,363,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax -116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -126,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax 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+39,weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407,normal,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302503,False,True,1,squared_hinge,ovr,l2,0.0007318628304090555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.560201454218397,948,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,None,,0.0053894830448103555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,0.03528169333197684,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3416063836589199,None,0.0,9,15,0.0,100,,,,,,,,,,,,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +85,none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1793,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811075,0.03563842252368924 +86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9930358173100328,None,0.0,6,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,entropy,None,0.46558488262913855,None,0.0,13,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400,uniform,, diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/description.txt index ba3264ec3b..f63e543044 100644 --- a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: f1_weighted performance_type: solution_quality diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other 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+127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/description.txt index ba3264ec3b..f63e543044 100644 --- a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: f1_weighted performance_type: solution_quality diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/configurations.csv index f977568ef2..9036164e61 100644 --- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,15,18,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.2779355362654,chi2,,,,normalize -14,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -15,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.06951501822933613,True,,invscaling,log,178,l2,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.89177043535544,chi2,,,,none -16,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.1285583573915876,deviance,1,1.8700144159035181,None,5,19,0.0,252,0.7739014033956171,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,284,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -17,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -18,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -19,none,adaboost,SAMME,1.1145892870034408,5,354,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.001119064659211796,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -20,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.745101117534867,14,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06075999384970139,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.035488276053047,False,True,1,squared_hinge,ovr,l1,0.0013962817054747638,,,,,,,,,,,,,,,,,,,,,,minmax -21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.8474780804635738,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.010000000000000004,True,extra_trees_preproc_for_classification,True,entropy,None,0.5311472280328223,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -22,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.8916988265222563,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.001971708179099073,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10677595352882521,deviance,8,2.8434717355886883,None,17,6,0.0,484,0.9516595570116578,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.002694235697058705,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -24,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,0.7267019009424693,None,16,4,0.0,341,0.4859775922611174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,average,162,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -25,none,adaboost,SAMME,0.34864876412492174,9,352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.1435287900624051,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none -26,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -28,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.4934522753284643e-05,False,0.021298254696745126,0.05693436553536525,True,,invscaling,modified_huber,65,l2,0.14709162750315702,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -30,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -31,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.170504104762284,,,0.10000000000000006,rbf,-1,True,1.893780321354902e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -32,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -33,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -34,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4244459875201874,False,True,1,squared_hinge,ovr,l2,0.0014473335587607684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax -35,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -36,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9901.884556489502,False,True,1,squared_hinge,ovr,l2,0.0023772047112092503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -45,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,163.38137776399626,False,True,1,squared_hinge,ovr,l2,0.06598224986727308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.3799175197381475,None,10,14,0.0,100,,,,,,,,,,,,median,0.004732616038822116,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06896070360353206,True,hinge,36,,,,,,,,,,,,,,,,,,,,,,median,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,1818,,,,,,,,,,,,,,,,,standardize -48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00029117569779819956,True,hinge,9,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.52445354905856,chi2,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.578593489684234,None,9,14,0.0,100,,,,,,,,,,,,most_frequent,0.005297835726138853,True,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,decision_tree,,,,,,,gini,1.0026108621086127,1.0,None,11,15,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06384570118118357,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,363,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax -116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize 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+33,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +34,none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3,386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615793 +35,none,one_hot_encoding,0.00011294596229850897,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062477,3,0.019002536385919935,poly,-1,False,0.010632086351533367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282,normal,, +36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +39,weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407,normal,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302503,False,True,1,squared_hinge,ovr,l2,0.0007318628304090555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.560201454218397,948,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,None,,0.0053894830448103555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,0.03528169333197684,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3416063836589199,None,0.0,9,15,0.0,100,,,,,,,,,,,,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +85,none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1793,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811075,0.03563842252368924 +86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9930358173100328,None,0.0,6,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,entropy,None,0.46558488262913855,None,0.0,13,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400,uniform,, diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/description.txt index ba3264ec3b..f63e543044 100644 --- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: f1_weighted performance_type: solution_quality diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9930358173100328,None,0.0,6,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,entropy,None,0.46558488262913855,None,0.0,13,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/description.txt index ba3264ec3b..f63e543044 100644 --- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: f1_weighted performance_type: solution_quality diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -2117,1.0,0.00055,0.01157,0.00069,2e-05,0.03906,0.71904,10.54724,0.00046,0.01394,0.02244,2e-05,0.00035,0.00011,1e-05,0.0103,6e-05,1e-05,5e-05,5e-05,5e-05,5e-05,0.0,0.00031,6e-05,0.00026,0.12553,0.0,0.00102,0.94012,2e-05,0.06161,0.00011,0.00023,0.00184,1e-05,0.00025,7.78747,1e-05,1e-05,6e-05,1e-05,0.00058,2e-05,315.23043,0.00011,0.00071,1e-05,0.00012,2e-05,3e-05,6e-05,5e-05 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE 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-75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other 75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75197,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75202,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75203,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75205,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75207,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 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a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_values.arff +++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA -233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922847986221,0.464067220688,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 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-38,none,bernoulli_nb,,,,,1.8887385356342519,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00017391617518036328,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,normalize -39,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.091312607420228,None,8,6,0.0,100,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97.58683291411165,chi2,,,,none -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize 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-44,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.2584064375821877,None,13,6,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -48,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0642934604710979,deviance,9,0.7259458223924964,None,14,11,0.0,329,0.4989755813606485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -49,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -53,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,none -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.100042248785923,0.9980658447309616,3,2.285273071303168,poly,-1,False,0.0490176654053288,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -60,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.17454862892513714,rbf,-1,True,0.08170245871998191,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.06951501822933613,True,,invscaling,log,178,l2,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.89177043535544,chi2,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,weighting,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.906873453507057,True,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.76760111449241,,,0.3566604024973977,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00011292844125422223,True,kernel_pca,,,,,,,,,,,,,,,,,-0.15050022726399015,,,sigmoid,1802,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -68,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10509052674216676,deviance,3,4.4544722117539814,None,5,17,0.0,417,0.8774564835738486,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0001349402298471411,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.01,True,,optimal,log,20,l2,,mean,0.014988923294040933,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -74,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15403.050385516917,-0.5957548709454028,1,7.25907773892283e-05,poly,-1,False,0.01132121743493435,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,none -76,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1300.622622668229,,,0.0005377565832198095,rbf,-1,False,0.009546849059854897,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,none,adaboost,SAMME.R,0.6745533826329466,2,306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.11136190063887706,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.54888780093315,f_classif,,,,none -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax 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+55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10,0.716738790505292,None,0.0,5,6,0.0,472,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +57,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.836353439781838,None,0.0,4,14,0.0,100,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,minmax,,,, 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+63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9742178951431337,None,0.0,2,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,none,,,, +67,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8490054877538417,None,0.0,2,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,minmax,,,, +68,none,one_hot_encoding,0.0001449312804440222,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +69,none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.6187293043367,,,2.0237366768707754,rbf,-1,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +70,weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +72,weighting,one_hot_encoding,0.07589752815117898,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7727592543547,None,0.0,10,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,minmax,,,, +73,none,one_hot_encoding,0.03739654507085451,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8447775967498866,None,0.0,1,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +74,weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09818715003413463,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532,,,,,,,,,,,,,,,,,,none,,,, +76,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,cube,306,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9049836740055639,None,0.0,18,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,275,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +79,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.49329965447606194,None,0.0,2,20,0.0,100,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.0023537043977842477,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7766828206753251,True,,,,,,,,,,,,,,,,normalize,,,, +81,weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8,12,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +82,none,one_hot_encoding,0.0037042565030130366,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9610802347688105,None,0.0,17,9,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,172,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,0.001479024648966236,True,decision_tree,,,,,,,gini,0.002932161541051581,1.0,None,0.0,17,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.8019276898414001,0.2172955112207602 +85,none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1793,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811075,0.03563842252368924 +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +87,weighting,one_hot_encoding,0.001968664991689621,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax,,,, +89,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +90,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678113,None,0.0,20,2,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,none,,,, +91,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1340879632520081,deviance,5,0.4391917863054552,None,0.0,18,5,0.0,227,0.8290252815444993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,none,,,, +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03425735525476274,deviance,7,0.5825782146709433,None,0.0,17,6,0.0,313,0.5043438213864502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3805181287718124,fdr,f_classif,robust_scaler,,,0.8478326986019474,0.2878840415105679 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +112,none,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2020480296503026,deviance,3,0.4640458524354476,None,0.0,8,19,0.0,486,0.6791755979205191,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2,13,0.0,100,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,none,,,, +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +120,weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705262,False,True,squared_hinge,4.4620804452838995e-05,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,quantile_transformer,57665,uniform,, +121,weighting,one_hot_encoding,0.010000000000000004,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.8258052582227357e-06,True,0.00010000000000000009,0.03819810388919209,True,0.14999999999999974,invscaling,modified_huber,elasticnet,0.13714427818877545,0.04372308852525775,most_frequent,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +128,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7794633670276021,None,0.0,9,10,0.0,100,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,75840,normal,, diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_dense/description.txt b/autosklearn/metalearning/files/log_loss_binary.classification_dense/description.txt index 769dc99e0d..cfb82fec54 100644 --- a/autosklearn/metalearning/files/log_loss_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: log_loss performance_type: solution_quality diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -2117,1.0,0.00055,0.01157,0.00069,2e-05,0.03906,0.71904,10.54724,0.00046,0.01394,0.02244,2e-05,0.00035,0.00011,1e-05,0.0103,6e-05,1e-05,5e-05,5e-05,5e-05,5e-05,0.0,0.00031,6e-05,0.00026,0.12553,0.0,0.00102,0.94012,2e-05,0.06161,0.00011,0.00023,0.00184,1e-05,0.00025,7.78747,1e-05,1e-05,6e-05,1e-05,0.00058,2e-05,315.23043,0.00011,0.00071,1e-05,0.00012,2e-05,3e-05,6e-05,5e-05 -2119,1.0,0.00051,0.00039,0.00053,1e-05,0.00087,0.01699,0.03836,0.00045,0.00053,0.00048,2e-05,0.0002,9e-05,1e-05,0.00051,4e-05,1e-05,4e-05,4e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.02023,0.0,0.00084,0.0358,2e-05,0.0014,0.00011,0.0001,0.00012,1e-05,0.00025,0.05298,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.06357,4e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -2120,1.0,0.00061,0.00169,0.00065,2e-05,0.04474,0.08877,0.53819,0.00046,0.00254,0.02526,2e-05,0.00034,0.00011,1e-05,0.00574,6e-05,1e-05,5e-05,5e-05,7e-05,5e-05,1e-05,0.00039,6e-05,0.00026,0.02733,1e-05,0.00099,0.12891,3e-05,0.07005,0.00011,0.00021,0.00056,1e-05,0.00025,0.78777,1e-05,1e-05,7e-05,2e-05,0.00034,3e-05,1.35102,5e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -2122,1.0,0.00051,0.00613,0.00057,2e-05,0.06877,0.40703,2.10666,0.00043,0.00686,0.02832,2e-05,0.0003,9e-05,1e-05,0.00579,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.2282,0.0,0.00088,0.68412,2e-05,0.09715,0.00011,0.00019,0.00063,1e-05,0.00023,2.7541,1e-05,1e-05,6e-05,1e-05,0.00014,2e-05,82.75009,5e-05,0.00064,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 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+75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok 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+84,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +87,weighting,one_hot_encoding,0.001968664991689621,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911,,,,,,,,,,,,,,standardize +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2,13,0.0,100,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833405,False,True,hinge,0.024385928857556572,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/description.txt index 769dc99e0d..cfb82fec54 100644 --- a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: log_loss performance_type: solution_quality diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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-251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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index 1f5f0b0c58..a34262fcb0 100644 --- a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -30,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,True,,0.01,True,,optimal,log,82,l1,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -31,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -32,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -33,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.020938011807420828,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -34,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -35,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,2,0.02171392895210594,poly,-1,True,7.503317925389584e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.005544292258963709,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -38,none,bernoulli_nb,,,,,1.8887385356342519,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00017391617518036328,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,normalize -39,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.091312607420228,None,8,6,0.0,100,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97.58683291411165,chi2,,,,none -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.065548404220037,None,9,7,0.0,100,,,,,,,,,,,,median,0.00025884798691207033,True,fast_ica,,,,,,,,,deflation,cube,360,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.2584064375821877,None,13,6,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -48,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0642934604710979,deviance,9,0.7259458223924964,None,14,11,0.0,329,0.4989755813606485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -49,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -53,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,none -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.100042248785923,0.9980658447309616,3,2.285273071303168,poly,-1,False,0.0490176654053288,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -60,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.17454862892513714,rbf,-1,True,0.08170245871998191,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.06951501822933613,True,,invscaling,log,178,l2,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.89177043535544,chi2,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,weighting,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.906873453507057,True,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.76760111449241,,,0.3566604024973977,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00011292844125422223,True,kernel_pca,,,,,,,,,,,,,,,,,-0.15050022726399015,,,sigmoid,1802,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -68,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10509052674216676,deviance,3,4.4544722117539814,None,5,17,0.0,417,0.8774564835738486,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0001349402298471411,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.01,True,,optimal,log,20,l2,,mean,0.014988923294040933,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -74,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15403.050385516917,-0.5957548709454028,1,7.25907773892283e-05,poly,-1,False,0.01132121743493435,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,none -76,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1300.622622668229,,,0.0005377565832198095,rbf,-1,False,0.009546849059854897,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,none,adaboost,SAMME.R,0.6745533826329466,2,306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.11136190063887706,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.54888780093315,f_classif,,,,none -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09174494331902282,False,True,1,squared_hinge,ovr,l2,0.004642503579081282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0018444303476881426,True,kernel_pca,,,,,,,,,,,,,,,,,0.4783416998251251,4,0.5925934287948866,poly,1268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -91,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.373128778545669,None,6,6,0.0,100,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.013646390377719104,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.578593489684234,None,9,14,0.0,100,,,,,,,,,,,,most_frequent,0.005297835726138853,True,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03627152792976942,deviance,10,4.211238636565405,None,15,16,0.0,340,0.6289005711340923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0002148748655476835,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -101,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -102,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.03830860981109,None,17,2,0.0,100,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6629672767671623,True,,,,,,,,,,,,,,,standardize -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -107,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -108,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.7802517848045093,deviance,3,2.5054026029796663,None,7,17,0.0,124,0.5707216178482934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -113,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.924097956321882e-06,False,,0.01,True,,optimal,log,20,l2,,mean,0.010000000000000004,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.9005631277685047,7910,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.4676122454707685,None,8,3,0.0,100,,,,,,,,,,,,most_frequent,0.2356239270240324,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.022095952417762486,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08307741263309468,True,,0.06824350233113559,True,,constant,squared_hinge,16,l1,,mean,0.010000000000000004,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,None,3,2,1.0,100,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.021088101285374274,True,,invscaling,log,178,l1,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.9951290469709,chi2,,,,none -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -122,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.256288840265201,None,18,4,0.0,100,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7437347621035921,False,,,,,,,,,,,,,,,none -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize 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+36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +39,weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +40,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9397100247778197,None,0.0,3,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7272465857046146,0.24493108573961342 +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, 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+45,none,one_hot_encoding,0.00353232434213139,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9407432771644124,None,0.0,1,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,cube,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7446318916516063,0.23782974987118102 +46,weighting,one_hot_encoding,0.010000000000000004,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756120720182025,True,True,squared_hinge,0.020536827217281145,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,4188,,,,,,,,,,,,,,,,,,none,,,, 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+49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,weighting,one_hot_encoding,0.0009372940901663342,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.02608147743712296,deviance,2,0.5887618067763795,None,0.0,11,6,0.0,179,0.5309995755533717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6897958091880166,None,0.0,2,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, 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+60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7856.587651350424,,,0.0017305319997054556,rbf,-1,False,1.7622421003766457e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9742178951431337,None,0.0,2,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,none,,,, +67,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8490054877538417,None,0.0,2,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,minmax,,,, +68,none,one_hot_encoding,0.0001449312804440222,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +69,none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.6187293043367,,,2.0237366768707754,rbf,-1,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +70,weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +72,weighting,one_hot_encoding,0.07589752815117898,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7727592543547,None,0.0,10,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,minmax,,,, +73,none,one_hot_encoding,0.03739654507085451,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8447775967498866,None,0.0,1,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +74,weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09818715003413463,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532,,,,,,,,,,,,,,,,,,none,,,, +76,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,cube,306,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9049836740055639,None,0.0,18,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,275,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +79,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.49329965447606194,None,0.0,2,20,0.0,100,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.0023537043977842477,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7766828206753251,True,,,,,,,,,,,,,,,,normalize,,,, +81,weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8,12,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +82,none,one_hot_encoding,0.0037042565030130366,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9610802347688105,None,0.0,17,9,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,172,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,0.001479024648966236,True,decision_tree,,,,,,,gini,0.002932161541051581,1.0,None,0.0,17,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.8019276898414001,0.2172955112207602 +85,none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1793,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811075,0.03563842252368924 +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +87,weighting,one_hot_encoding,0.001968664991689621,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax,,,, +89,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +90,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678113,None,0.0,20,2,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,none,,,, +91,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1340879632520081,deviance,5,0.4391917863054552,None,0.0,18,5,0.0,227,0.8290252815444993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,none,,,, +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03425735525476274,deviance,7,0.5825782146709433,None,0.0,17,6,0.0,313,0.5043438213864502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3805181287718124,fdr,f_classif,robust_scaler,,,0.8478326986019474,0.2878840415105679 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +112,none,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2020480296503026,deviance,3,0.4640458524354476,None,0.0,8,19,0.0,486,0.6791755979205191,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2,13,0.0,100,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,none,,,, +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +120,weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705262,False,True,squared_hinge,4.4620804452838995e-05,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,quantile_transformer,57665,uniform,, +121,weighting,one_hot_encoding,0.010000000000000004,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.8258052582227357e-06,True,0.00010000000000000009,0.03819810388919209,True,0.14999999999999974,invscaling,modified_huber,elasticnet,0.13714427818877545,0.04372308852525775,most_frequent,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +128,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7794633670276021,None,0.0,9,10,0.0,100,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,75840,normal,, diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/description.txt index 769dc99e0d..cfb82fec54 100644 --- a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: log_loss performance_type: solution_quality diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/configurations.csv index 2da5dcf611..ca19204db5 100644 --- a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/configurations.csv +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/configurations.csv @@ -1,131 +1,130 @@ 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+113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2,13,0.0,100,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833405,False,True,hinge,0.024385928857556572,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/description.txt index 769dc99e0d..cfb82fec54 100644 --- a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: log_loss performance_type: solution_quality diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 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-50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -53,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,none -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.100042248785923,0.9980658447309616,3,2.285273071303168,poly,-1,False,0.0490176654053288,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.17454862892513714,rbf,-1,True,0.08170245871998191,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.06951501822933613,True,,invscaling,log,178,l2,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.89177043535544,chi2,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,weighting,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.906873453507057,True,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.76760111449241,,,0.3566604024973977,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00011292844125422223,True,kernel_pca,,,,,,,,,,,,,,,,,-0.15050022726399015,,,sigmoid,1802,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -68,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10509052674216676,deviance,3,4.4544722117539814,None,5,17,0.0,417,0.8774564835738486,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0001349402298471411,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.01,True,,optimal,log,20,l2,,mean,0.014988923294040933,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -74,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15403.050385516917,-0.5957548709454028,1,7.25907773892283e-05,poly,-1,False,0.01132121743493435,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,none -76,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1300.622622668229,,,0.0005377565832198095,rbf,-1,False,0.009546849059854897,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,none,adaboost,SAMME.R,0.6745533826329466,2,306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.11136190063887706,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.54888780093315,f_classif,,,,none -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09174494331902282,False,True,1,squared_hinge,ovr,l2,0.004642503579081282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0018444303476881426,True,kernel_pca,,,,,,,,,,,,,,,,,0.4783416998251251,4,0.5925934287948866,poly,1268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -91,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.373128778545669,None,6,6,0.0,100,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+15,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +16,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,standardize,,,, +17,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +18,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565651,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463182 +19,weighting,one_hot_encoding,0.18137532678800652,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +20,none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9742178951431337,None,0.0,2,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,none,,,, +21,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +22,none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10,0.542144980834302,None,0.0,20,13,0.0,233,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307263,0.13434811490315818 +23,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15,11,0.0,100,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +24,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04093642460278944,deviance,7,0.5495014745530306,None,0.0,20,18,0.0,141,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +25,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +26,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +27,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532,,,,,,,,,,,,,,,,,,none,,,, +28,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5,0.33885235607979314,None,0.0,6,4,0.0,125,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,none,,,, +29,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +30,weighting,one_hot_encoding,0.0006939450481567023,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05694967271793989,0.9005883757146016,5,6.300159702718475,poly,-1,False,0.02402691026582464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +31,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +32,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04749094903835669,deviance,3,0.6184047395714717,None,0.0,17,8,0.0,428,0.7515561640094087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +33,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.6409514454358402,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41070131983759434,fdr,f_classif,normalize,,,, +34,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9562349611127511,None,0.0,4,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9416928180674639,0.0440072111980354 +35,weighting,one_hot_encoding,0.0009664614609258606,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3714759002919618,None,0.0,18,16,0.0,100,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,917,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +39,weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +40,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9397100247778197,None,0.0,3,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7272465857046146,0.24493108573961342 +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.022939738050158573,deviance,10,0.4185394344134278,None,0.0,2,10,0.0,309,0.5979695608086252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25383213391991144 +43,none,one_hot_encoding,0.31138539716704705,True,adaboost,SAMME.R,0.3026113597945332,1,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.05578036113726603,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +44,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04310920427249598,deviance,9,0.7762532463369333,None,0.0,19,7,0.0,89,0.9651993549499902,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +45,none,one_hot_encoding,0.00353232434213139,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9407432771644124,None,0.0,1,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,cube,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7446318916516063,0.23782974987118102 +46,weighting,one_hot_encoding,0.010000000000000004,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756120720182025,True,True,squared_hinge,0.020536827217281145,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,4188,,,,,,,,,,,,,,,,,,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6897958091880166,None,0.0,2,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +52,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.1223321395075887,2,0.0035847433873211405,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46126823065673095,fpr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.836353439781838,None,0.0,4,14,0.0,100,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,minmax,,,, +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7856.587651350424,,,0.0017305319997054556,rbf,-1,False,1.7622421003766457e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9742178951431337,None,0.0,2,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,none,,,, +67,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8490054877538417,None,0.0,2,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,minmax,,,, +68,none,one_hot_encoding,0.0001449312804440222,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +69,none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.6187293043367,,,2.0237366768707754,rbf,-1,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +70,weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +72,weighting,one_hot_encoding,0.07589752815117898,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7727592543547,None,0.0,10,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,minmax,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09818715003413463,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532,,,,,,,,,,,,,,,,,,none,,,, +76,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,cube,306,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9049836740055639,None,0.0,18,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,275,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.0023537043977842477,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7766828206753251,True,,,,,,,,,,,,,,,,normalize,,,, +81,weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8,12,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +82,none,one_hot_encoding,0.0037042565030130366,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9610802347688105,None,0.0,17,9,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,172,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,0.001479024648966236,True,decision_tree,,,,,,,gini,0.002932161541051581,1.0,None,0.0,17,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.8019276898414001,0.2172955112207602 +85,none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1793,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811075,0.03563842252368924 +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +87,weighting,one_hot_encoding,0.001968664991689621,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax,,,, +89,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +90,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678113,None,0.0,20,2,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,none,,,, +91,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6856119355718316,None,0.0,2,15,0.0,100,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.9070067796375252,0.22323969787251713 +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03425735525476274,deviance,7,0.5825782146709433,None,0.0,17,6,0.0,313,0.5043438213864502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3805181287718124,fdr,f_classif,robust_scaler,,,0.8478326986019474,0.2878840415105679 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +112,none,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2020480296503026,deviance,3,0.4640458524354476,None,0.0,8,19,0.0,486,0.6791755979205191,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2,13,0.0,100,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,none,,,, +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +120,weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705262,False,True,squared_hinge,4.4620804452838995e-05,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,quantile_transformer,57665,uniform,, +121,weighting,one_hot_encoding,0.010000000000000004,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.8258052582227357e-06,True,0.00010000000000000009,0.03819810388919209,True,0.14999999999999974,invscaling,modified_huber,elasticnet,0.13714427818877545,0.04372308852525775,most_frequent,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +128,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400,uniform,, diff --git a/autosklearn/metalearning/files/pac_score_binary.classification_dense/description.txt b/autosklearn/metalearning/files/pac_score_binary.classification_dense/description.txt index 0e6466b48d..036fd4da8b 100644 --- a/autosklearn/metalearning/files/pac_score_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/pac_score_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: pac_score performance_type: solution_quality diff --git a/autosklearn/metalearning/files/pac_score_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/pac_score_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/pac_score_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/pac_score_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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+75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/pac_score_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/pac_score_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/pac_score_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/pac_score_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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+84,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +87,weighting,one_hot_encoding,0.001968664991689621,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911,,,,,,,,,,,,,,standardize +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2,13,0.0,100,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833405,False,True,hinge,0.024385928857556572,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/pac_score_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/pac_score_binary.classification_sparse/description.txt index 0e6466b48d..036fd4da8b 100644 --- a/autosklearn/metalearning/files/pac_score_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/pac_score_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: pac_score performance_type: solution_quality diff --git a/autosklearn/metalearning/files/pac_score_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/pac_score_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/pac_score_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/pac_score_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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-251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/pac_score_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/pac_score_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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-44,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.2584064375821877,None,13,6,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -48,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0642934604710979,deviance,9,0.7259458223924964,None,14,11,0.0,329,0.4989755813606485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -49,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -53,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,none -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.100042248785923,0.9980658447309616,3,2.285273071303168,poly,-1,False,0.0490176654053288,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.17454862892513714,rbf,-1,True,0.08170245871998191,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.06951501822933613,True,,invscaling,log,178,l2,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.89177043535544,chi2,,,,none 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-69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.01,True,,optimal,log,20,l2,,mean,0.014988923294040933,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -74,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15403.050385516917,-0.5957548709454028,1,7.25907773892283e-05,poly,-1,False,0.01132121743493435,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,none -76,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1300.622622668229,,,0.0005377565832198095,rbf,-1,False,0.009546849059854897,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,none,adaboost,SAMME.R,0.6745533826329466,2,306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.11136190063887706,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.54888780093315,f_classif,,,,none -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09174494331902282,False,True,1,squared_hinge,ovr,l2,0.004642503579081282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0018444303476881426,True,kernel_pca,,,,,,,,,,,,,,,,,0.4783416998251251,4,0.5925934287948866,poly,1268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -91,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.373128778545669,None,6,6,0.0,100,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03627152792976942,deviance,10,4.211238636565405,None,15,16,0.0,340,0.6289005711340923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0002148748655476835,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -101,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -102,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.03830860981109,None,17,2,0.0,100,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6629672767671623,True,,,,,,,,,,,,,,,standardize -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -108,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.7802517848045093,deviance,3,2.5054026029796663,None,7,17,0.0,124,0.5707216178482934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -113,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.924097956321882e-06,False,,0.01,True,,optimal,log,20,l2,,mean,0.010000000000000004,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.9005631277685047,7910,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.4676122454707685,None,8,3,0.0,100,,,,,,,,,,,,most_frequent,0.2356239270240324,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.022095952417762486,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08307741263309468,True,,0.06824350233113559,True,,constant,squared_hinge,16,l1,,mean,0.010000000000000004,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,None,3,2,1.0,100,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.021088101285374274,True,,invscaling,log,178,l1,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.9951290469709,chi2,,,,none -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -122,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.256288840265201,None,18,4,0.0,100,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7437347621035921,False,,,,,,,,,,,,,,,none -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize 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+39,weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +40,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9397100247778197,None,0.0,3,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7272465857046146,0.24493108573961342 +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.022939738050158573,deviance,10,0.4185394344134278,None,0.0,2,10,0.0,309,0.5979695608086252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25383213391991144 +43,none,one_hot_encoding,0.31138539716704705,True,adaboost,SAMME.R,0.3026113597945332,1,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.05578036113726603,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +44,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04310920427249598,deviance,9,0.7762532463369333,None,0.0,19,7,0.0,89,0.9651993549499902,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +45,none,one_hot_encoding,0.00353232434213139,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9407432771644124,None,0.0,1,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,cube,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7446318916516063,0.23782974987118102 +46,weighting,one_hot_encoding,0.010000000000000004,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756120720182025,True,True,squared_hinge,0.020536827217281145,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,4188,,,,,,,,,,,,,,,,,,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6897958091880166,None,0.0,2,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, 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+55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.836353439781838,None,0.0,4,14,0.0,100,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,minmax,,,, +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7856.587651350424,,,0.0017305319997054556,rbf,-1,False,1.7622421003766457e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9742178951431337,None,0.0,2,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,none,,,, +67,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8490054877538417,None,0.0,2,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,minmax,,,, +68,none,one_hot_encoding,0.0001449312804440222,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +69,none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.6187293043367,,,2.0237366768707754,rbf,-1,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +70,weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +72,weighting,one_hot_encoding,0.07589752815117898,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7727592543547,None,0.0,10,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,minmax,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09818715003413463,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532,,,,,,,,,,,,,,,,,,none,,,, +76,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,cube,306,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9049836740055639,None,0.0,18,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,275,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.0023537043977842477,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7766828206753251,True,,,,,,,,,,,,,,,,normalize,,,, +81,weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8,12,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +82,none,one_hot_encoding,0.0037042565030130366,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9610802347688105,None,0.0,17,9,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,172,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,0.001479024648966236,True,decision_tree,,,,,,,gini,0.002932161541051581,1.0,None,0.0,17,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.8019276898414001,0.2172955112207602 +85,none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1793,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811075,0.03563842252368924 +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +87,weighting,one_hot_encoding,0.001968664991689621,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax,,,, +89,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +90,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678113,None,0.0,20,2,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,none,,,, +91,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6856119355718316,None,0.0,2,15,0.0,100,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.9070067796375252,0.22323969787251713 +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03425735525476274,deviance,7,0.5825782146709433,None,0.0,17,6,0.0,313,0.5043438213864502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3805181287718124,fdr,f_classif,robust_scaler,,,0.8478326986019474,0.2878840415105679 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +112,none,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2020480296503026,deviance,3,0.4640458524354476,None,0.0,8,19,0.0,486,0.6791755979205191,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2,13,0.0,100,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,none,,,, +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +120,weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705262,False,True,squared_hinge,4.4620804452838995e-05,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,quantile_transformer,57665,uniform,, +121,weighting,one_hot_encoding,0.010000000000000004,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.8258052582227357e-06,True,0.00010000000000000009,0.03819810388919209,True,0.14999999999999974,invscaling,modified_huber,elasticnet,0.13714427818877545,0.04372308852525775,most_frequent,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +128,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400,uniform,, diff --git a/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/description.txt index 0e6466b48d..036fd4da8b 100644 --- a/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: pac_score performance_type: solution_quality diff --git a/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2,13,0.0,100,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833405,False,True,hinge,0.024385928857556572,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/description.txt index 0e6466b48d..036fd4da8b 100644 --- a/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: pac_score performance_type: solution_quality diff --git a/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -2117,1.0,0.00055,0.01157,0.00069,2e-05,0.03906,0.71904,10.54724,0.00046,0.01394,0.02244,2e-05,0.00035,0.00011,1e-05,0.0103,6e-05,1e-05,5e-05,5e-05,5e-05,5e-05,0.0,0.00031,6e-05,0.00026,0.12553,0.0,0.00102,0.94012,2e-05,0.06161,0.00011,0.00023,0.00184,1e-05,0.00025,7.78747,1e-05,1e-05,6e-05,1e-05,0.00058,2e-05,315.23043,0.00011,0.00071,1e-05,0.00012,2e-05,3e-05,6e-05,5e-05 -2119,1.0,0.00051,0.00039,0.00053,1e-05,0.00087,0.01699,0.03836,0.00045,0.00053,0.00048,2e-05,0.0002,9e-05,1e-05,0.00051,4e-05,1e-05,4e-05,4e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.02023,0.0,0.00084,0.0358,2e-05,0.0014,0.00011,0.0001,0.00012,1e-05,0.00025,0.05298,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.06357,4e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -2120,1.0,0.00061,0.00169,0.00065,2e-05,0.04474,0.08877,0.53819,0.00046,0.00254,0.02526,2e-05,0.00034,0.00011,1e-05,0.00574,6e-05,1e-05,5e-05,5e-05,7e-05,5e-05,1e-05,0.00039,6e-05,0.00026,0.02733,1e-05,0.00099,0.12891,3e-05,0.07005,0.00011,0.00021,0.00056,1e-05,0.00025,0.78777,1e-05,1e-05,7e-05,2e-05,0.00034,3e-05,1.35102,5e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -2122,1.0,0.00051,0.00613,0.00057,2e-05,0.06877,0.40703,2.10666,0.00043,0.00686,0.02832,2e-05,0.0003,9e-05,1e-05,0.00579,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.2282,0.0,0.00088,0.68412,2e-05,0.09715,0.00011,0.00019,0.00063,1e-05,0.00023,2.7541,1e-05,1e-05,6e-05,1e-05,0.00014,2e-05,82.75009,5e-05,0.00064,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 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+75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok 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75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok diff --git a/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_values.arff index d0395e7246..dd0f70cd52 100644 --- a/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_values.arff +++ b/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA 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-19,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -20,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -22,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -23,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,3.566869213708421,None,13,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -25,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -26,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -27,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.875720833375873,,,3.8464409037164655,rbf,-1,True,0.06339956504484913,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,60,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -28,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -29,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -30,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.442753772090821,12,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46.45873045394655,f_classif,,,,standardize -31,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -32,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -33,none,adaboost,SAMME.R,0.340801430264527,7,464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,none -34,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4346.137071437875,0.06630310829119646,1,5.1269202965282974e-05,poly,-1,False,0.00024452576462760927,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,333,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -36,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -38,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009710817723081867,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000006,rbf,100,,,,,,,,,,,,,,,,,standardize -39,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -40,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -41,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1722034433283093,,,0.001958703488411727,rbf,-1,False,0.006262296782048029,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -43,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -44,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.002782467639466009,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.53463583357125,None,2,4,0.0,100,,,,,,,,,,,,median,0.08965639912423894,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.58142545325404,f_classif,,,,standardize -46,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize -48,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none -49,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -50,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -53,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -54,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -55,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -62,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0483380823811535,deviance,4,3.5447265496885296,None,5,16,0.0,141,0.5896080302596434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0020128017963651694,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.014581209053425,f_classif,,,,none -63,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,1,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.56701180897993,f_classif,,,,none -64,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.03830860981109,None,17,2,0.0,100,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6629672767671623,True,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none 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+76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +79,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +80,weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30,normal,, +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9,18,0.0,100,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639,normal,, diff --git a/autosklearn/metalearning/files/precision_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_binary.classification_dense/description.txt index eb538b6df9..b136391055 100644 --- a/autosklearn/metalearning/files/precision_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/precision_binary.classification_dense/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/precision_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA 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a/autosklearn/metalearning/files/precision_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 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+46,weighting,one_hot_encoding,0.0026308117826759733,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452931,None,0.0,18,16,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +47,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +49,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +51,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +52,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +58,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +59,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +60,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +65,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +67,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +68,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +69,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_binary.classification_sparse/description.txt index eb538b6df9..b136391055 100644 --- a/autosklearn/metalearning/files/precision_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/precision_binary.classification_sparse/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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-246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 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-275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/configurations.csv index b8643fb297..2969cabb5c 100644 --- a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize 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-94,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -99,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax 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+16,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,standardize,,,, +17,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +18,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565651,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463182 +19,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +20,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +21,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +22,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +23,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +24,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04093642460278944,deviance,7,0.5495014745530306,None,0.0,20,18,0.0,141,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +25,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +26,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12,11,0.0,100,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +27,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532,,,,,,,,,,,,,,,,,,none,,,, +28,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5,0.33885235607979314,None,0.0,6,4,0.0,125,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,none,,,, +29,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +30,weighting,one_hot_encoding,0.0007038280350320556,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5,5.43004469263886,poly,-1,True,0.024555010060043934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +31,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +32,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.051832615669195795,deviance,6,0.8807456180216267,None,0.0,7,19,0.0,366,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +33,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +34,none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3,386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615793 +35,none,one_hot_encoding,0.00011294596229850897,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062477,3,0.019002536385919935,poly,-1,False,0.010632086351533367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282,normal,, +36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +39,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,weighting,one_hot_encoding,0.010000000000000004,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756120720182025,True,True,squared_hinge,0.020536827217281145,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,4188,,,,,,,,,,,,,,,,,,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,weighting,one_hot_encoding,0.06015518601232588,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2,None,11,20,1.0,47,,,,,,quantile_transformer,21065,uniform,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.836353439781838,None,0.0,4,14,0.0,100,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,minmax,,,, +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1,True,1.1394216224323561e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,None,,0.0053894830448103555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +85,weighting,one_hot_encoding,0.0003173723611800348,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1862,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, 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+101,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.745231770090801e-07,False,6.503623129182133e-05,0.034765176880257306,True,,constant,modified_huber,l2,,7.878097869194373e-05,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,751,uniform,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2806.985866707319,False,True,1,squared_hinge,ovr,l2,0.03738539536055984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,standardize,,,, +128,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400,uniform,, diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/description.txt index 2d2ec13507..5326b83bcf 100644 --- a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: precision_macro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_values.arff +++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA -233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922847986221,0.464067220688,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 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+84,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +85,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,,none +87,weighting,one_hot_encoding,0.001968664991689621,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911,,,,,,,,,,,,,,standardize +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +92,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/description.txt index 2d2ec13507..5326b83bcf 100644 --- a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: precision_macro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE 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75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 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75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75197,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok 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a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv index b8643fb297..2969cabb5c 100644 --- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-32,none,bernoulli_nb,,,,,0.1057745390543012,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0036224515761089367,True,kernel_pca,,,,,,,,,,,,,,,,,-0.15050022726399015,5,0.012951859769487931,poly,1802,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -33,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.020938011807420828,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -34,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4244459875201874,False,True,1,squared_hinge,ovr,l2,0.0014473335587607684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax 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-38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize 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-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4346.137071437875,0.06630310829119646,1,5.1269202965282974e-05,poly,-1,False,0.00024452576462760927,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,333,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-62,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.5243288674498225e-05,False,,0.006230262247014208,True,,constant,hinge,35,l1,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00029117569779819956,True,hinge,9,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.52445354905856,chi2,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,weighting,one_hot_encoding,0.010000000000000004,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756120720182025,True,True,squared_hinge,0.020536827217281145,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,4188,,,,,,,,,,,,,,,,,,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,weighting,one_hot_encoding,0.06015518601232588,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2,None,11,20,1.0,47,,,,,,quantile_transformer,21065,uniform,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.836353439781838,None,0.0,4,14,0.0,100,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,minmax,,,, +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1,True,1.1394216224323561e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,None,,0.0053894830448103555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6524770456279243,False,,,,,,,,,,,,,,,,robust_scaler,,,0.7602889314749132,0.25 +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +85,weighting,one_hot_encoding,0.0003173723611800348,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1862,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.745231770090801e-07,False,6.503623129182133e-05,0.034765176880257306,True,,constant,modified_huber,l2,,7.878097869194373e-05,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,751,uniform,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2806.985866707319,False,True,1,squared_hinge,ovr,l2,0.03738539536055984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,standardize,,,, +128,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400,uniform,, diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/description.txt index 2d2ec13507..5326b83bcf 100644 --- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: precision_macro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 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-@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other 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+116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/description.txt index 2d2ec13507..5326b83bcf 100644 --- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: precision_macro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_values.arff +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA -233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922847986221,0.464067220688,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 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-65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00029117569779819956,True,hinge,9,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.52445354905856,chi2,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax -116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none 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+31,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +32,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.051832615669195795,deviance,6,0.8807456180216267,None,0.0,7,19,0.0,366,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +33,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +34,none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3,386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615793 +35,none,one_hot_encoding,0.00011294596229850897,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062477,3,0.019002536385919935,poly,-1,False,0.010632086351533367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282,normal,, +36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +39,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 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+45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, 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+49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302503,False,True,1,squared_hinge,ovr,l2,0.0007318628304090555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.560201454218397,948,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +85,weighting,one_hot_encoding,0.0003173723611800348,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1862,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/description.txt index 9d57b906d6..a63ec71cc5 100644 --- a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: precision_micro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/description.txt index 9d57b906d6..a63ec71cc5 100644 --- a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: precision_micro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 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+75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok 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75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_values.arff index d0395e7246..dd0f70cd52 100644 --- a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_values.arff +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA 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-62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+16,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +17,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +18,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565651,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463182 +19,weighting,one_hot_encoding,0.18137532678800652,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +20,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +21,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +22,none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10,0.542144980834302,None,0.0,20,13,0.0,233,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307263,0.13434811490315818 +23,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +24,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04093642460278944,deviance,7,0.5495014745530306,None,0.0,20,18,0.0,141,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +25,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +26,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12,11,0.0,100,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +27,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532,,,,,,,,,,,,,,,,,,none,,,, 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+31,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +32,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.051832615669195795,deviance,6,0.8807456180216267,None,0.0,7,19,0.0,366,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +33,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +34,none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3,386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615793 +35,none,one_hot_encoding,0.00011294596229850897,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062477,3,0.019002536385919935,poly,-1,False,0.010632086351533367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282,normal,, +36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +39,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 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+63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, 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+69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, 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+81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/description.txt index 9d57b906d6..a63ec71cc5 100644 --- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: precision_micro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -2117,1.0,0.00055,0.01157,0.00069,2e-05,0.03906,0.71904,10.54724,0.00046,0.01394,0.02244,2e-05,0.00035,0.00011,1e-05,0.0103,6e-05,1e-05,5e-05,5e-05,5e-05,5e-05,0.0,0.00031,6e-05,0.00026,0.12553,0.0,0.00102,0.94012,2e-05,0.06161,0.00011,0.00023,0.00184,1e-05,0.00025,7.78747,1e-05,1e-05,6e-05,1e-05,0.00058,2e-05,315.23043,0.00011,0.00071,1e-05,0.00012,2e-05,3e-05,6e-05,5e-05 -2119,1.0,0.00051,0.00039,0.00053,1e-05,0.00087,0.01699,0.03836,0.00045,0.00053,0.00048,2e-05,0.0002,9e-05,1e-05,0.00051,4e-05,1e-05,4e-05,4e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.02023,0.0,0.00084,0.0358,2e-05,0.0014,0.00011,0.0001,0.00012,1e-05,0.00025,0.05298,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.06357,4e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -2120,1.0,0.00061,0.00169,0.00065,2e-05,0.04474,0.08877,0.53819,0.00046,0.00254,0.02526,2e-05,0.00034,0.00011,1e-05,0.00574,6e-05,1e-05,5e-05,5e-05,7e-05,5e-05,1e-05,0.00039,6e-05,0.00026,0.02733,1e-05,0.00099,0.12891,3e-05,0.07005,0.00011,0.00021,0.00056,1e-05,0.00025,0.78777,1e-05,1e-05,7e-05,2e-05,0.00034,3e-05,1.35102,5e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -2122,1.0,0.00051,0.00613,0.00057,2e-05,0.06877,0.40703,2.10666,0.00043,0.00686,0.02832,2e-05,0.0003,9e-05,1e-05,0.00579,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.0003,5e-05,0.00025,0.2282,0.0,0.00088,0.68412,2e-05,0.09715,0.00011,0.00019,0.00063,1e-05,0.00023,2.7541,1e-05,1e-05,6e-05,1e-05,0.00014,2e-05,82.75009,5e-05,0.00064,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -2123,1.0,0.00055,0.00037,0.00231,1e-05,0.00586,0.07868,0.20326,0.00046,0.00051,0.00234,2e-05,0.0002,8e-05,1e-05,0.0005,4e-05,1e-05,4e-05,5e-05,4e-05,5e-05,0.0,0.00033,5e-05,0.00047,0.03788,0.0,0.00284,0.06438,2e-05,0.00825,0.00011,0.0001,0.00013,1e-05,0.00025,0.2846,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.13009,4e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -2350,1.0,0.0008,0.76706,0.00083,3e-05,7e-05,1e-05,0.0,0.00063,0.81845,1e-05,5e-05,0.00039,0.00018,2e-05,0.02503,5e-05,1e-05,8e-05,9e-05,0.00021,9e-05,1e-05,0.00044,9e-05,0.00027,2e-05,1e-05,0.00125,1e-05,5e-05,9e-05,0.00015,0.00026,0.04508,1e-05,0.00031,0.0,4e-05,2e-05,0.00012,7e-05,0.00648,0.0001,2.43616,1e-05,0.00182,1e-05,0.00019,4e-05,0.00015,0.00013,6e-05 -3043,1.0,0.00055,0.00098,0.00061,2e-05,0.0028,0.05945,0.57683,0.00048,0.00131,0.00233,2e-05,0.00031,0.00011,2e-05,0.00113,0.0001,1e-05,4e-05,6e-05,7e-05,7e-05,2e-05,0.00031,7e-05,0.00027,0.04173,0.0,0.00098,0.08173,2e-05,0.00519,0.00012,0.00014,0.00027,1e-05,0.00027,0.14239,1e-05,1e-05,8e-05,2e-05,0.00011,2e-05,1.06327,6e-05,0.00011,1e-05,0.00011,1e-05,4e-05,7e-05,6e-05 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+75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_values.arff index d0395e7246..dd0f70cd52 100644 --- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_values.arff +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA -233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922847986221,0.464067220688,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 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+85,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +87,weighting,one_hot_encoding,0.001968664991689621,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911,,,,,,,,,,,,,,standardize +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/description.txt index 9d57b906d6..a63ec71cc5 100644 --- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: precision_micro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 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a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+46,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4,0.6041596127474019,None,0.0,14,17,0.0,83,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +47,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +48,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +49,none,one_hot_encoding,0.06126751481740331,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00041820552318733856,True,True,hinge,0.0007969587715456845,,,,,,,,,,,,,,,,,,,,,,,median,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9447144786798995,False,,,,,,,,,,,,,,,,standardize,,,, +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +52,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.25185682718144353,deviance,7,0.9252293870435426,None,0.0,5,8,0.0,309,0.9923577831919878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,normalize,,,, +58,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +59,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +60,none,one_hot_encoding,0.0018037858350872134,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1075752886620514,deviance,7,0.9041944194819347,None,0.0,6,20,0.0,454,0.6635283542555824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +65,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +66,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +67,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +68,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +69,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +70,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +71,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +72,none,one_hot_encoding,,False,decision_tree,,,,,,,gini,1.9629324531036463,1.0,None,0.0,13,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +74,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +79,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +80,weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30,normal,, +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9,18,0.0,100,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639,normal,, diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_multiclass.classification_dense/description.txt index eb538b6df9..b136391055 100644 --- a/autosklearn/metalearning/files/precision_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/precision_multiclass.classification_dense/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA 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a/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+68,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +69,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/description.txt index eb538b6df9..b136391055 100644 --- a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA 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a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 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-50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,434.04789312490243,-0.28598798624265775,5,0.00020813257891031506,poly,-1,True,0.08410050264911162,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,None,11,19,1.0,35,,,,,,normalize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,520.1785093386421,,,0.4094223029861982,rbf,-1,False,0.0001488588704335344,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27.824808973705593,chi2,,,,normalize -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.283033279625623e-05,False,,0.04931533154730613,True,,constant,squared_hinge,531,l1,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.26694836626666,f_classif,,,,minmax -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4346.137071437875,0.06630310829119646,1,5.1269202965282974e-05,poly,-1,False,0.00024452576462760927,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,333,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00029117569779819956,True,hinge,9,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.52445354905856,chi2,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12683.745517936331,,,0.2798030071977636,rbf,-1,False,9.038883737441219e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize 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+13,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.8384447520019118,None,0.0,13,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36637567531287824,fdr,f_classif,normalize,,,, +14,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +15,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +16,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.6291601746046639,None,0.0,11,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +17,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4,0.6041596127474019,None,0.0,14,17,0.0,83,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +18,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565651,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463182 +19,weighting,one_hot_encoding,0.18137532678800652,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +20,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +21,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +22,none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10,0.542144980834302,None,0.0,20,13,0.0,233,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307263,0.13434811490315818 +23,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15,11,0.0,100,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +24,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04093642460278944,deviance,7,0.5495014745530306,None,0.0,20,18,0.0,141,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +25,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +26,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12,11,0.0,100,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +27,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,normalize,,,, +28,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5,0.33885235607979314,None,0.0,6,4,0.0,125,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,none,,,, +29,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +30,weighting,one_hot_encoding,0.0007038280350320556,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5,5.43004469263886,poly,-1,True,0.024555010060043934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +31,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +32,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.051832615669195795,deviance,6,0.8807456180216267,None,0.0,7,19,0.0,366,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +33,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +34,none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3,386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615793 +35,none,one_hot_encoding,0.00011294596229850897,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062477,3,0.019002536385919935,poly,-1,False,0.010632086351533367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282,normal,, +36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +39,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,weighting,one_hot_encoding,0.0010446150978844174,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.21000396916509356,None,0.0,19,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,10,None,3,18,1.0,42,,,,,,quantile_transformer,44341,uniform,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2,0.0337546254878617,poly,-1,True,0.09641299736884307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +53,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.04962265276655457,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, 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+62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,None,,0.0053894830448103555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,0.000868560275005347,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +72,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7696635139179984,None,0.0,7,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, 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+77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8,12,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +85,weighting,one_hot_encoding,0.0003173723611800348,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1862,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+101,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6960837129194184,,,0.0004821713821548537,rbf,-1,False,0.040691702958042086,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6520469084729728,False,,,,,,,,,,,,,,,,quantile_transformer,97329,normal,, +102,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3,0.7249041896998006,None,0.0,6,17,0.0,174,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,weighting,no_encoding,,,bernoulli_nb,,,,,0.02310834879990973,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,gini,None,0.43664414575861454,None,0.0,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8569382605464568,0.07491671292996571 +105,weighting,one_hot_encoding,0.10324969243867223,True,decision_tree,,,,,,,gini,0.7467478023293801,1.0,None,0.0,12,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8,None,13,16,1.0,28,,,,,,quantile_transformer,41502,uniform,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.102423946884905,f_classif,,,,quantile_transformer,89842,normal,, +123,weighting,one_hot_encoding,0.010000000000000004,True,decision_tree,,,,,,,entropy,1.5841974853345435,1.0,None,0.0,8,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30,normal,, +126,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400,uniform,, diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/description.txt index a61d195c75..c9eeb707ae 100644 --- a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: precision_weighted performance_type: solution_quality diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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-244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 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-@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, 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75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75197,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75202,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75203,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75205,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75207,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 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+75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_values.arff index d0395e7246..dd0f70cd52 100644 --- 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+85,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,,none +87,weighting,one_hot_encoding,0.001968664991689621,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911,,,,,,,,,,,,,,standardize +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,weighting,one_hot_encoding,0.20875514426569572,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.53183372054125,None,0.0,18,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,13818.683783129034,False,True,1,squared_hinge,ovr,l1,1.009528987119941e-05,,,,,,,,,,,,,,,,,,,none +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10,14,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1,squared_hinge,ovr,l1,0.01604898292029417,,,,,,,,,,,,,,,,,,,none +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/description.txt index a61d195c75..c9eeb707ae 100644 --- a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: precision_weighted performance_type: solution_quality diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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--git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv index d8ebaf1e48..5ede7f64cf 100644 --- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-19,none,adaboost,SAMME,1.1145892870034408,5,354,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.001119064659211796,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -20,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.745101117534867,14,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06075999384970139,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.035488276053047,False,True,1,squared_hinge,ovr,l1,0.0013962817054747638,,,,,,,,,,,,,,,,,,,,,,minmax -21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.8474780804635738,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.010000000000000004,True,extra_trees_preproc_for_classification,True,entropy,None,0.5311472280328223,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -22,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.8916988265222563,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.001971708179099073,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10677595352882521,deviance,8,2.8434717355886883,None,17,6,0.0,484,0.9516595570116578,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.002694235697058705,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -24,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,0.7267019009424693,None,16,4,0.0,341,0.4859775922611174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,average,162,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -25,none,adaboost,SAMME,0.34864876412492174,9,352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.1435287900624051,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none -26,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -27,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -28,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.4934522753284643e-05,False,0.021298254696745126,0.05693436553536525,True,,invscaling,modified_huber,65,l2,0.14709162750315702,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -30,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -31,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.170504104762284,,,0.10000000000000006,rbf,-1,True,1.893780321354902e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -32,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.418744975432406,0.9877092754137076,,1.3374227649402433,sigmoid,-1,True,0.048122237675633034,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,normalize -33,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -34,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4244459875201874,False,True,1,squared_hinge,ovr,l2,0.0014473335587607684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax -35,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -36,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4570.657250638392,False,True,1,squared_hinge,ovr,l2,0.001312453259224503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.751911127640866,None,6,15,0.0,100,,,,,,,,,,,,median,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -45,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,163.38137776399626,False,True,1,squared_hinge,ovr,l2,0.06598224986727308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.3799175197381475,None,10,14,0.0,100,,,,,,,,,,,,median,0.004732616038822116,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,434.04789312490243,-0.28598798624265775,5,0.00020813257891031506,poly,-1,True,0.08410050264911162,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,None,11,19,1.0,35,,,,,,normalize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,520.1785093386421,,,0.4094223029861982,rbf,-1,False,0.0001488588704335344,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27.824808973705593,chi2,,,,normalize -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.283033279625623e-05,False,,0.04931533154730613,True,,constant,squared_hinge,531,l1,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.26694836626666,f_classif,,,,minmax -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4346.137071437875,0.06630310829119646,1,5.1269202965282974e-05,poly,-1,False,0.00024452576462760927,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,333,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00029117569779819956,True,hinge,9,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.52445354905856,chi2,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12683.745517936331,,,0.2798030071977636,rbf,-1,False,9.038883737441219e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0483380823811535,deviance,3,3.5447265496885296,None,5,16,0.0,141,0.5896080302596434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0020128017963651694,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.014581209053425,f_classif,,,,none -103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,1,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.56701180897993,f_classif,,,,none -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.7368358338706456,8,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,True,,,,,,,,,,,,,,,standardize -106,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,,,,,none -107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize -110,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax -116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none -123,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.5555960331548946e-06,True,,0.03775758938844074,True,,optimal,hinge,6,l2,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize 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+36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +39,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,weighting,one_hot_encoding,0.0010446150978844174,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.21000396916509356,None,0.0,19,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,10,None,3,18,1.0,42,,,,,,quantile_transformer,44341,uniform,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2,0.0337546254878617,poly,-1,True,0.09641299736884307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +53,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.04962265276655457,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1,True,1.1394216224323561e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,None,,0.0053894830448103555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,0.000868560275005347,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +72,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7696635139179984,None,0.0,7,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8,12,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +85,weighting,one_hot_encoding,0.0003173723611800348,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1862,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000,uniform,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2,233,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6960837129194184,,,0.0004821713821548537,rbf,-1,False,0.040691702958042086,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6520469084729728,False,,,,,,,,,,,,,,,,quantile_transformer,97329,normal,, +102,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3,0.7249041896998006,None,0.0,6,17,0.0,174,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,weighting,no_encoding,,,bernoulli_nb,,,,,0.02310834879990973,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,gini,None,0.43664414575861454,None,0.0,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8569382605464568,0.07491671292996571 +105,weighting,one_hot_encoding,0.10324969243867223,True,decision_tree,,,,,,,gini,0.7467478023293801,1.0,None,0.0,12,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8,None,13,16,1.0,28,,,,,,quantile_transformer,41502,uniform,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.102423946884905,f_classif,,,,quantile_transformer,89842,normal,, +123,weighting,one_hot_encoding,0.010000000000000004,True,decision_tree,,,,,,,entropy,1.5841974853345435,1.0,None,0.0,8,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30,normal,, +126,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400,uniform,, diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/description.txt index a61d195c75..c9eeb707ae 100644 --- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: precision_weighted performance_type: solution_quality diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA 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a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok 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+97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10,14,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1,squared_hinge,ovr,l1,0.01604898292029417,,,,,,,,,,,,,,,,,,,none +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/description.txt index a61d195c75..c9eeb707ae 100644 --- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: precision_weighted performance_type: solution_quality diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other 75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75197,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75202,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75203,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75205,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75207,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 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+9,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5263936397022638,None,0.0,6,12,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.795044474549774,False,True,1,squared_hinge,ovr,l1,0.0006963037026795237,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +10,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565651,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463182 +11,weighting,one_hot_encoding,0.18137532678800652,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +12,weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+35,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8770766409674923,None,0.0,13,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,84023,normal,, +36,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7856.587651350424,,,0.0017305319997054556,rbf,-1,False,1.7622421003766457e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,standardize,,,, +37,weighting,one_hot_encoding,0.006909187206474195,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00024264014379190562,True,,0.04987297125937914,True,,optimal,hinge,l2,,4.0861541221464815e-05,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.05153104953418389,False,True,1,squared_hinge,ovr,l1,0.00019393864742902846,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +38,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +39,none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.6187293043367,,,2.0237366768707754,rbf,-1,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, 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+43,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +44,weighting,one_hot_encoding,0.34516277500429876,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +45,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +46,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +47,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +49,none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1793,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811075,0.03563842252368924 +50,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +51,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +52,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13,20,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +53,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6856119355718316,None,0.0,2,15,0.0,100,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.9070067796375252,0.22323969787251713 +54,weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.9460823349230802,1.0,None,0.0,15,11,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2613520842796237,fdr,chi2,quantile_transformer,1000,uniform,, +55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000,uniform,, +57,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +58,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +59,weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.10845020299646906,2,248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22655400374384999,fwe,f_classif,minmax,,,, +60,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04064013793978874,deviance,7,0.33975333782743655,None,0.0,10,4,0.0,280,0.8476773466327144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,gini,None,0.9072459027692913,None,0.0,6,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8834597195173531,0.09983935639458051 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6960837129194184,,,0.0004821713821548537,rbf,-1,False,0.040691702958042086,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6520469084729728,False,,,,,,,,,,,,,,,,quantile_transformer,97329,normal,, +63,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3,0.7249041896998006,None,0.0,6,17,0.0,174,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +64,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +65,weighting,no_encoding,,,bernoulli_nb,,,,,0.02310834879990973,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,gini,None,0.43664414575861454,None,0.0,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8569382605464568,0.07491671292996571 +66,weighting,one_hot_encoding,0.10324969243867223,True,decision_tree,,,,,,,gini,0.7467478023293801,1.0,None,0.0,12,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +67,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +68,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +69,none,one_hot_encoding,0.0007295694672502942,True,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.011064778564152866,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7,None,11,18,1.0,35,,,,,,quantile_transformer,36528,normal,, +70,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +71,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +74,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +75,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4,0.6041596127474019,None,0.0,14,17,0.0,83,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7529073739756954,None,0.0,11,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,93335,normal,, +78,weighting,one_hot_encoding,0.49413952363236524,True,decision_tree,,,,,,,gini,1.7020201228673206,1.0,None,0.0,20,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,none,,,, +79,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +81,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0002525813164610038,True,,0.060155313622470105,True,0.057326377025764375,optimal,squared_hinge,elasticnet,,0.0045558808713606885,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2,None,18,4,1.0,92,,,,,,standardize,,,, +82,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +83,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1166829447028879,deviance,4,0.8451196958788597,None,0.0,1,8,0.0,58,0.8652814520124915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03406306803297533,False,True,1,squared_hinge,ovr,l1,0.00019257971888767862,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/recall_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_binary.classification_dense/description.txt index 5abe50abae..85cfeecb6e 100644 --- a/autosklearn/metalearning/files/recall_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/recall_binary.classification_dense/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -2117,1.0,0.00055,0.01157,0.00069,2e-05,0.03906,0.71904,10.54724,0.00046,0.01394,0.02244,2e-05,0.00035,0.00011,1e-05,0.0103,6e-05,1e-05,5e-05,5e-05,5e-05,5e-05,0.0,0.00031,6e-05,0.00026,0.12553,0.0,0.00102,0.94012,2e-05,0.06161,0.00011,0.00023,0.00184,1e-05,0.00025,7.78747,1e-05,1e-05,6e-05,1e-05,0.00058,2e-05,315.23043,0.00011,0.00071,1e-05,0.00012,2e-05,3e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok 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75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75197,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75202,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75203,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75205,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75207,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 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+75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok diff --git a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_values.arff index d0395e7246..dd0f70cd52 100644 --- a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_values.arff +++ 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+42,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +43,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +44,weighting,one_hot_encoding,0.34516277500429876,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +45,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +47,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +49,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,none,one_hot_encoding,0.00011600321198702642,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +51,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +52,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,weighting,no_encoding,,,decision_tree,,,,,,,gini,1.7984076825537865,1.0,None,0.0,14,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2897525995758022,fwe,chi2,,none +55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,weighting,one_hot_encoding,0.20875514426569572,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.53183372054125,None,0.0,18,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,13818.683783129034,False,True,1,squared_hinge,ovr,l1,1.009528987119941e-05,,,,,,,,,,,,,,,,,,,none +57,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +58,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +59,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +60,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +63,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +64,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +65,weighting,one_hot_encoding,,False,bernoulli_nb,,,,,0.014801515930977628,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +66,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +67,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +68,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +69,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +70,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,weighting,one_hot_encoding,0.3997572853391576,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21008613984919333,None,0.0,11,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10,14,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1,squared_hinge,ovr,l1,0.01604898292029417,,,,,,,,,,,,,,,,,,,none +78,weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.3016059914162639,1.0,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +79,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +80,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0002525813164610038,True,,0.060155313622470105,True,0.057326377025764375,optimal,squared_hinge,elasticnet,,0.0045558808713606885,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2,None,18,4,1.0,92,,,,,,,standardize +82,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7,12,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_binary.classification_sparse/description.txt index 5abe50abae..85cfeecb6e 100644 --- a/autosklearn/metalearning/files/recall_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/recall_binary.classification_sparse/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok 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75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 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a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv index a177415386..0376f67e78 100644 --- a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-32,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.7702815127831667e-05,False,,0.006631256638841467,True,,constant,squared_hinge,61,l2,,mean,0.3619893522448273,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.741050844267226,chi2,,,,normalize -33,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -34,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4244459875201874,False,True,1,squared_hinge,ovr,l2,0.0014473335587607684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax -35,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -36,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4570.657250638392,False,True,1,squared_hinge,ovr,l2,0.001312453259224503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.021100204578784896,deviance,2,3.2219978901408126,None,14,13,0.0,62,0.9175813938002152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0015802583114458146,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26682653239833576,fdr,f_classif,none -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.751911127640866,None,6,15,0.0,100,,,,,,,,,,,,median,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -45,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,163.38137776399626,False,True,1,squared_hinge,ovr,l2,0.06598224986727308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.3799175197381475,None,10,14,0.0,100,,,,,,,,,,,,median,0.004732616038822116,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -47,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0005474378153703474,True,,0.0709606524043735,True,,invscaling,squared_hinge,322,l2,0.25,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,5.27375986647238,False,True,1,squared_hinge,ovr,l1,1.0213500115323962e-05,,,,,,,,,,,,,,,,,,,,,,normalize -48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,434.04789312490243,-0.28598798624265775,5,0.00020813257891031506,poly,-1,True,0.08410050264911162,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,None,11,19,1.0,35,,,,,,normalize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,520.1785093386421,,,0.4094223029861982,rbf,-1,False,0.0001488588704335344,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27.824808973705593,chi2,,,,normalize -55,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7893822540456763e-05,True,,0.01920961872573097,True,,optimal,hinge,769,l2,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.0504050565968734,2076,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -56,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.283033279625623e-05,False,,0.04931533154730613,True,,constant,squared_hinge,531,l1,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.26694836626666,f_classif,,,,minmax -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.0615335158906123,12,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0006128377815426205,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40918220909952985,fdr,chi2,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.6939677427994257,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,extra_trees_preproc_for_classification,False,gini,None,1.0506261769751233,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,True,hinge,46,,,,,,,,,,,,,,,,,,,,,,median,0.0001614163818794968,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,226.06742020598978,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07123494942796214,True,,0.06640696915484627,True,1.444778397890192e-09,invscaling,perceptron,147,elasticnet,0.25,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1722034433283093,,,0.001958703488411727,rbf,-1,False,0.006262296782048029,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.53463583357125,None,2,4,0.0,100,,,,,,,,,,,,median,0.08965639912423894,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.58142545325404,f_classif,,,,standardize -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.1285583573915876,deviance,1,1.8700144159035181,None,5,19,0.0,252,0.7739014033956171,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,284,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0013066556710069719,False,0.03958867790828566,0.06657767200469752,True,,optimal,modified_huber,52,l2,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7941252766878464,5,0.0026102407098584192,poly,615,,,,,,,,,,,,,,,,,standardize -98,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12683.745517936331,,,0.2798030071977636,rbf,-1,False,9.038883737441219e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0483380823811535,deviance,3,3.5447265496885296,None,5,16,0.0,141,0.5896080302596434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0020128017963651694,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.014581209053425,f_classif,,,,none -103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,1,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.56701180897993,f_classif,,,,none -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.7368358338706456,8,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,True,,,,,,,,,,,,,,,standardize -106,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,,,,,none -107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize -110,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax -116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,None,,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.003937843574389652,rbf,545,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none -123,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.5555960331548946e-06,True,,0.03775758938844074,True,,optimal,hinge,6,l2,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize 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+35,none,one_hot_encoding,0.00011294596229850897,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062477,3,0.019002536385919935,poly,-1,False,0.010632086351533367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282,normal,, +36,none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10,16,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +37,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +38,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.15944469021885255,None,0.0,18,19,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,14,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,6315,uniform,, +39,weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407,normal,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +41,weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.24229264852063404,1.0,None,0.0,15,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +42,none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10,0.2797288369369436,None,0.0,14,9,0.0,480,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3233601927284725,None,0.0,3,18,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.29913129113847253,fdr,chi2,normalize,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 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+55,none,one_hot_encoding,0.010488491664540746,True,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6793271069375356,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09031561168137159,fdr,f_classif,robust_scaler,,,0.9762457833235179,0.2189244634478133 +56,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10,0.716738790505292,None,0.0,5,6,0.0,472,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,weighting,one_hot_encoding,0.006909187206474195,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00024264014379190562,True,,0.04987297125937914,True,,optimal,hinge,l2,,4.0861541221464815e-05,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.05153104953418389,False,True,1,squared_hinge,ovr,l1,0.00019393864742902846,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +65,weighting,one_hot_encoding,0.003566024581260295,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7974297391104296,None,0.0,5,8,0.0,100,,,,,,,,,,,,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,f_classif,,,,standardize,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1,True,1.1394216224323561e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9940718718674404,None,0.0,15,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +72,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.05347123056931161,deviance,3,0.2250677489759125,None,0.0,16,4,0.0,309,0.7245595517718859,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19466188848884064,fdr,chi2,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.005326467497783115,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.058055900067082,,,0.1626688094236879,rbf,-1,True,0.04838115942937064,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,cube,306,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13,20,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.9460823349230802,1.0,None,0.0,15,11,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2613520842796237,fdr,chi2,quantile_transformer,1000,uniform,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000,uniform,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2,233,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, 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+109,weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8,None,13,16,1.0,28,,,,,,quantile_transformer,41502,uniform,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, 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+124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04994122399949775,deviance,9,0.8621747362759826,None,0.0,1,13,0.0,84,0.6449569386396325,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.53326630281591,chi2,,,,minmax,,,, +125,weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30,normal,, +126,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1166829447028879,deviance,4,0.8451196958788597,None,0.0,1,8,0.0,58,0.8652814520124915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03406306803297533,False,True,1,squared_hinge,ovr,l1,0.00019257971888767862,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +129,weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9,18,0.0,100,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639,normal,, diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/description.txt index 3af354e8c3..ac4b19b431 100644 --- a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: recall_macro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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-79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -87,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.786449460674678,,,,,,,,,,,,,,,,,,,,,most_frequent,0.06203188930692253,True,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09174494331902282,False,True,1,squared_hinge,ovr,l2,0.004642503579081282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0018444303476881426,True,kernel_pca,,,,,,,,,0.4783416998251251,4,0.5925934287948866,poly,1268,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0013066556710069719,False,0.03958867790828566,0.06657767200469752,True,,optimal,modified_huber,52,l2,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,0.7941252766878464,5,0.0026102407098584192,poly,615,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.312914959291577,None,11,14,0.0,100,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -99,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6962352073255054,None,3,13,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,gini,None,3.1516195509427503,10,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,,standardize -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9357354171445895e-06,True,,0.05458546144730029,True,,constant,perceptron,179,l2,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -106,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,none -107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -108,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,cosine,1211,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -110,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -116,weighting,adaboost,SAMME.R,0.1355825690029731,2,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.9983112975799235,None,3,17,0.0,100,,,,,,,,,,,,mean,0.012321190772400284,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -122,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -125,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.80178999786076,None,4,9,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+92,weighting,no_encoding,,,decision_tree,,,,,,,gini,1.7984076825537865,1.0,None,0.0,14,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2897525995758022,fwe,chi2,,none +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.93767728056358,None,0.0,18,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.00022308163276069302,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +102,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,weighting,one_hot_encoding,,False,bernoulli_nb,,,,,0.014801515930977628,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +105,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37163783119625016,False,True,1,squared_hinge,ovr,l2,1.2049944334095187e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,kitchen_sinks,,,,,,,,,,,,,,,,0.33181838105513195,7347,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,weighting,one_hot_encoding,0.3997572853391576,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21008613984919333,None,0.0,11,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833405,False,True,hinge,0.024385928857556572,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10,14,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1,squared_hinge,ovr,l1,0.01604898292029417,,,,,,,,,,,,,,,,,,,none +123,weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.3016059914162639,1.0,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +124,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7,12,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/description.txt index 3af354e8c3..ac4b19b431 100644 --- a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: recall_macro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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-75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75202,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75203,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75205,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75207,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 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a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/configurations.csv index a177415386..0376f67e78 100644 --- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-35,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -36,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4570.657250638392,False,True,1,squared_hinge,ovr,l2,0.001312453259224503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.021100204578784896,deviance,2,3.2219978901408126,None,14,13,0.0,62,0.9175813938002152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0015802583114458146,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26682653239833576,fdr,f_classif,none -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.751911127640866,None,6,15,0.0,100,,,,,,,,,,,,median,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -45,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,163.38137776399626,False,True,1,squared_hinge,ovr,l2,0.06598224986727308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.3799175197381475,None,10,14,0.0,100,,,,,,,,,,,,median,0.004732616038822116,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -47,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0005474378153703474,True,,0.0709606524043735,True,,invscaling,squared_hinge,322,l2,0.25,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,5.27375986647238,False,True,1,squared_hinge,ovr,l1,1.0213500115323962e-05,,,,,,,,,,,,,,,,,,,,,,normalize -48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,434.04789312490243,-0.28598798624265775,5,0.00020813257891031506,poly,-1,True,0.08410050264911162,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,None,11,19,1.0,35,,,,,,normalize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,520.1785093386421,,,0.4094223029861982,rbf,-1,False,0.0001488588704335344,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27.824808973705593,chi2,,,,normalize -55,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7893822540456763e-05,True,,0.01920961872573097,True,,optimal,hinge,769,l2,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.0504050565968734,2076,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -56,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.283033279625623e-05,False,,0.04931533154730613,True,,constant,squared_hinge,531,l1,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.26694836626666,f_classif,,,,minmax -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.0615335158906123,12,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0006128377815426205,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40918220909952985,fdr,chi2,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.6939677427994257,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,extra_trees_preproc_for_classification,False,gini,None,1.0506261769751233,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,True,hinge,46,,,,,,,,,,,,,,,,,,,,,,median,0.0001614163818794968,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,226.06742020598978,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07123494942796214,True,,0.06640696915484627,True,1.444778397890192e-09,invscaling,perceptron,147,elasticnet,0.25,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1722034433283093,,,0.001958703488411727,rbf,-1,False,0.006262296782048029,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.53463583357125,None,2,4,0.0,100,,,,,,,,,,,,median,0.08965639912423894,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.58142545325404,f_classif,,,,standardize -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+38,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.15944469021885255,None,0.0,18,19,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,14,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,6315,uniform,, +39,weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407,normal,, +40,weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133568,0.62404114475118,3,1.608707699741043,poly,-1,False,3.535379282685604e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, 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+47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 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+55,none,one_hot_encoding,0.010488491664540746,True,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6793271069375356,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09031561168137159,fdr,f_classif,robust_scaler,,,0.9762457833235179,0.2189244634478133 +56,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10,0.716738790505292,None,0.0,5,6,0.0,472,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 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+63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,weighting,one_hot_encoding,0.006909187206474195,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00024264014379190562,True,,0.04987297125937914,True,,optimal,hinge,l2,,4.0861541221464815e-05,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.05153104953418389,False,True,1,squared_hinge,ovr,l1,0.00019393864742902846,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +65,weighting,one_hot_encoding,0.003566024581260295,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7974297391104296,None,0.0,5,8,0.0,100,,,,,,,,,,,,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,f_classif,,,,standardize,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1,True,1.1394216224323561e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9940718718674404,None,0.0,15,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +72,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.05347123056931161,deviance,3,0.2250677489759125,None,0.0,16,4,0.0,309,0.7245595517718859,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19466188848884064,fdr,chi2,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.005326467497783115,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.058055900067082,,,0.1626688094236879,rbf,-1,True,0.04838115942937064,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,parallel,cube,306,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.49329965447606194,None,0.0,2,20,0.0,100,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8,12,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +85,none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1793,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811075,0.03563842252368924 +86,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,none,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13,20,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.9460823349230802,1.0,None,0.0,15,11,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2613520842796237,fdr,chi2,quantile_transformer,1000,uniform,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000,uniform,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2,233,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +98,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14904790197542306,deviance,6,0.7561346995577642,None,0.0,5,14,0.0,340,0.6548040792383665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37215221406145077,0.35417466287560373,3,0.0009148519644429074,poly,-1,False,2.9166728983300667e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866,normal,, +102,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3,0.7249041896998006,None,0.0,6,17,0.0,174,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,weighting,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,1367,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,weighting,one_hot_encoding,0.10324969243867223,True,decision_tree,,,,,,,gini,0.7467478023293801,1.0,None,0.0,12,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8,None,13,16,1.0,28,,,,,,quantile_transformer,41502,uniform,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +116,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4,0.6041596127474019,None,0.0,14,17,0.0,83,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705262,False,True,squared_hinge,4.4620804452838995e-05,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,quantile_transformer,57665,uniform,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.102423946884905,f_classif,,,,quantile_transformer,89842,normal,, +123,weighting,one_hot_encoding,0.010000000000000004,True,decision_tree,,,,,,,entropy,1.5841974853345435,1.0,None,0.0,8,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04994122399949775,deviance,9,0.8621747362759826,None,0.0,1,13,0.0,84,0.6449569386396325,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.53326630281591,chi2,,,,minmax,,,, +125,weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30,normal,, +126,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1166829447028879,deviance,4,0.8451196958788597,None,0.0,1,8,0.0,58,0.8652814520124915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03406306803297533,False,True,1,squared_hinge,ovr,l1,0.00019257971888767862,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +129,weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9,18,0.0,100,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639,normal,, diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/description.txt index 3af354e8c3..ac4b19b431 100644 --- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: recall_macro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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-79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,none -82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -87,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.786449460674678,,,,,,,,,,,,,,,,,,,,,most_frequent,0.06203188930692253,True,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09174494331902282,False,True,1,squared_hinge,ovr,l2,0.004642503579081282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0018444303476881426,True,kernel_pca,,,,,,,,,0.4783416998251251,4,0.5925934287948866,poly,1268,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0013066556710069719,False,0.03958867790828566,0.06657767200469752,True,,optimal,modified_huber,52,l2,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,0.7941252766878464,5,0.0026102407098584192,poly,615,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.312914959291577,None,11,14,0.0,100,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -99,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6962352073255054,None,3,13,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,gini,None,3.1516195509427503,10,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,,standardize -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9357354171445895e-06,True,,0.05458546144730029,True,,constant,perceptron,179,l2,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -106,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,none -107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -108,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,cosine,1211,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -110,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -116,weighting,adaboost,SAMME.R,0.1355825690029731,2,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.9983112975799235,None,3,17,0.0,100,,,,,,,,,,,,mean,0.012321190772400284,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -122,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -125,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.80178999786076,None,4,9,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+92,weighting,no_encoding,,,decision_tree,,,,,,,gini,1.7984076825537865,1.0,None,0.0,14,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2897525995758022,fwe,chi2,,none +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +94,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.93767728056358,None,0.0,18,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +97,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,one_hot_encoding,0.00022308163276069302,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +102,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +103,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,weighting,one_hot_encoding,,False,bernoulli_nb,,,,,0.014801515930977628,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +105,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1,14,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +108,weighting,one_hot_encoding,,False,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37163783119625016,False,True,1,squared_hinge,ovr,l2,1.2049944334095187e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,kitchen_sinks,,,,,,,,,,,,,,,,0.33181838105513195,7347,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +110,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +112,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,weighting,one_hot_encoding,0.3997572853391576,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21008613984919333,None,0.0,11,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833405,False,True,hinge,0.024385928857556572,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10,14,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1,squared_hinge,ovr,l1,0.01604898292029417,,,,,,,,,,,,,,,,,,,none +123,weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.3016059914162639,1.0,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +124,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7,12,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/description.txt index 3af354e8c3..ac4b19b431 100644 --- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: recall_macro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 -288,1.0,0.00056,0.00136,0.00058,2e-05,0.00616,0.3313,0.68467,0.00051,0.01649,0.01298,3e-05,0.00026,0.00011,1e-05,0.00137,6e-05,1e-05,4e-05,5e-05,7e-05,5e-05,1e-05,0.00035,5e-05,0.00018,0.05596,1e-05,0.00085,0.07039,3e-05,0.01921,0.00012,0.00014,0.00049,1e-05,0.0003,2.38569,1e-05,1e-05,7e-05,2e-05,0.01469,3e-05,1.94827,6e-05,0.00015,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok 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-75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other 75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75197,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75202,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75203,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75205,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75207,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 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a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv index e7fbf33fad..c40b8d3bab 100644 --- a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00029117569779819956,True,hinge,9,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.52445354905856,chi2,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+43,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828775,None,0.0,2,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +44,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4,0.31988031161984326,None,0.0,8,13,0.0,275,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302503,False,True,1,squared_hinge,ovr,l2,0.0007318628304090555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.560201454218397,948,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +85,weighting,one_hot_encoding,0.0003173723611800348,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1862,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/description.txt index d0b54b369d..50c88c9dcc 100644 --- a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: recall_micro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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-248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok 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75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 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+129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/description.txt index d0b54b369d..50c88c9dcc 100644 --- a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: recall_micro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE 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a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 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a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/configurations.csv index e7fbf33fad..c40b8d3bab 100644 --- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-19,none,adaboost,SAMME,1.1145892870034408,5,354,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.001119064659211796,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -20,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.745101117534867,14,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06075999384970139,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.035488276053047,False,True,1,squared_hinge,ovr,l1,0.0013962817054747638,,,,,,,,,,,,,,,,,,,,,,minmax -21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.8474780804635738,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.010000000000000004,True,extra_trees_preproc_for_classification,True,entropy,None,0.5311472280328223,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -30,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -31,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.170504104762284,,,0.10000000000000006,rbf,-1,True,1.893780321354902e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -32,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -33,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -34,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4244459875201874,False,True,1,squared_hinge,ovr,l2,0.0014473335587607684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax -35,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -36,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9901.884556489502,False,True,1,squared_hinge,ovr,l2,0.0023772047112092503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -45,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.3799175197381475,None,10,14,0.0,100,,,,,,,,,,,,median,0.004732616038822116,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00029117569779819956,True,hinge,9,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.52445354905856,chi2,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax -116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -126,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax 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+45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302503,False,True,1,squared_hinge,ovr,l2,0.0007318628304090555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.560201454218397,948,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +85,weighting,one_hot_encoding,0.0003173723611800348,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1862,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/description.txt index d0b54b369d..50c88c9dcc 100644 --- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: recall_micro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other 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+119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/description.txt index d0b54b369d..50c88c9dcc 100644 --- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: recall_micro performance_type: solution_quality diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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+75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_values.arff index d0395e7246..dd0f70cd52 100644 --- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_values.arff +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA 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-18,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -19,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.020938011807420828,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -20,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -22,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -23,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -24,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -25,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.11772147933484846,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,1741,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -26,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.43773835420304896,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,standardize -27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.315931664543157,None,18,6,0.0,100,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -28,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.070144108122736,,,,,,,,,,,,,,,,,,,,,mean,0.014577587197137339,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,standardize -29,none,decision_tree,,,,,,,gini,0.2654308115213848,1.0,None,1,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,123,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -30,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,434.04789312490243,-0.28598798624265775,5,0.00020813257891031506,poly,-1,True,0.08410050264911162,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,None,11,19,1.0,35,,,,,,normalize -31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,520.1785093386421,,,0.4094223029861982,rbf,-1,False,0.0001488588704335344,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27.824808973705593,chi2,,,,normalize -32,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,522.1737827111988,False,True,1,squared_hinge,ovr,l2,0.05569113756716791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2934811012685023,fdr,chi2,normalize -33,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.5296423664586023,None,1,3,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -36,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -37,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.0615335158906123,12,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0006128377815426205,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40918220909952985,fdr,chi2,standardize -38,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,226.06742020598978,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2023.4040813008348,,,0.0010019113232392271,rbf,-1,True,0.0010248853533630416,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.037578733076375465,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,normalize -42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -43,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -44,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -49,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -50,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,none,decision_tree,,,,,,,gini,1.0026108621086127,1.0,None,11,15,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06384570118118357,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,363,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.373128778545669,None,6,6,0.0,100,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -53,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -57,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -58,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0013066556710069719,False,0.03958867790828566,0.06657767200469752,True,,optimal,modified_huber,52,l2,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7941252766878464,5,0.0026102407098584192,poly,615,,,,,,,,,,,,,,,,,standardize -59,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12683.745517936331,,,0.2798030071977636,rbf,-1,False,9.038883737441219e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,normalize -60,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -62,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.01116924019200219,True,fast_ica,,,,,,,,,deflation,cube,766,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -63,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,5,15,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23.938615614788205,f_classif,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9357354171445895e-06,True,,0.05458546144730029,True,,constant,perceptron,179,l2,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize 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+37,weighting,one_hot_encoding,0.006909187206474195,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00024264014379190562,True,,0.04987297125937914,True,,optimal,hinge,l2,,4.0861541221464815e-05,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.05153104953418389,False,True,1,squared_hinge,ovr,l1,0.00019393864742902846,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +38,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +39,none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.6187293043367,,,2.0237366768707754,rbf,-1,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, 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+43,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +44,weighting,one_hot_encoding,0.34516277500429876,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +45,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +46,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +47,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, 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+71,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +74,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +75,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4,0.6041596127474019,None,0.0,14,17,0.0,83,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7529073739756954,None,0.0,11,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,93335,normal,, +78,weighting,one_hot_encoding,0.49413952363236524,True,decision_tree,,,,,,,gini,1.7020201228673206,1.0,None,0.0,20,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,none,,,, +79,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +81,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0002525813164610038,True,,0.060155313622470105,True,0.057326377025764375,optimal,squared_hinge,elasticnet,,0.0045558808713606885,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2,None,18,4,1.0,92,,,,,,standardize,,,, +82,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +83,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1166829447028879,deviance,4,0.8451196958788597,None,0.0,1,8,0.0,58,0.8652814520124915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03406306803297533,False,True,1,squared_hinge,ovr,l1,0.00019257971888767862,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/recall_multiclass.classification_dense/description.txt index 5abe50abae..85cfeecb6e 100644 --- a/autosklearn/metalearning/files/recall_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/recall_multiclass.classification_dense/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+45,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +47,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +49,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,none,one_hot_encoding,0.00011600321198702642,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +51,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +52,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,weighting,no_encoding,,,decision_tree,,,,,,,gini,1.7984076825537865,1.0,None,0.0,14,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2897525995758022,fwe,chi2,,none +55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,weighting,one_hot_encoding,0.20875514426569572,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.53183372054125,None,0.0,18,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,13818.683783129034,False,True,1,squared_hinge,ovr,l1,1.009528987119941e-05,,,,,,,,,,,,,,,,,,,none +57,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +58,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +59,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +60,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +63,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +64,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +65,weighting,one_hot_encoding,,False,bernoulli_nb,,,,,0.014801515930977628,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +66,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +67,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +68,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +69,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +70,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,weighting,one_hot_encoding,0.3997572853391576,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21008613984919333,None,0.0,11,16,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10,14,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1,squared_hinge,ovr,l1,0.01604898292029417,,,,,,,,,,,,,,,,,,,none +78,weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.3016059914162639,1.0,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +79,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +80,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0002525813164610038,True,,0.060155313622470105,True,0.057326377025764375,optimal,squared_hinge,elasticnet,,0.0045558808713606885,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2,None,18,4,1.0,92,,,,,,,standardize +82,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7,12,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/description.txt index 5abe50abae..85cfeecb6e 100644 --- a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 -242,1.0,0.00056,0.00079,0.00055,2e-05,0.02067,0.47505,2.02385,0.00046,0.00158,0.00096,3e-05,0.00023,0.00011,1e-05,0.00071,5e-05,1e-05,5e-05,5e-05,9e-05,5e-05,1e-05,0.00032,7e-05,0.00018,0.0294,1e-05,0.00081,0.07827,3e-05,0.02168,0.00012,0.00011,0.00055,1e-05,0.00024,1.45282,1e-05,1e-05,7e-05,2e-05,0.00031,3e-05,1.20507,5e-05,0.00011,1e-05,0.00012,1e-05,5e-05,8e-05,5e-05 -244,1.0,0.00059,0.00064,0.00041,1e-05,0.00795,1.46534,0.83297,0.00045,0.00113,0.01107,0.00409,0.00019,9e-05,1e-05,0.00061,4e-05,1e-05,5e-05,5e-05,6e-05,5e-05,0.0,0.00037,6e-05,0.00016,0.03123,0.0,0.00063,0.21508,4e-05,0.0191,0.00011,0.0001,0.00035,1e-05,0.00024,3.49317,1e-05,1e-05,6e-05,2e-05,0.00018,2e-05,0.93307,8e-05,0.0001,1e-05,0.00011,1e-05,4e-05,7e-05,5e-05 -246,1.0,0.00056,0.00063,0.0048,2e-05,0.0012,0.9472,0.44851,0.00047,0.00097,0.00061,3e-05,0.00025,0.0001,1e-05,0.0007,5e-05,1e-05,5e-05,5e-05,7e-05,6e-05,1e-05,0.00032,5e-05,0.00033,0.06431,1e-05,0.00521,0.10702,3e-05,0.00189,0.00012,0.00014,0.00028,1e-05,0.00024,0.92128,1e-05,1e-05,6e-05,2e-05,0.00012,3e-05,0.43409,7e-05,0.00011,1e-05,0.00013,2e-05,4e-05,7e-05,5e-05 -248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 -258,1.0,0.00069,0.00157,0.00043,2e-05,0.01023,0.08213,2.48207,0.00048,0.00231,0.00751,3e-05,0.00026,0.0001,1e-05,0.00145,5e-05,1e-05,4e-05,5e-05,6e-05,5e-05,0.0,0.00046,7e-05,0.00016,0.02907,0.0,0.00066,0.08166,3e-05,0.01781,0.00014,0.00015,0.00054,1e-05,0.00025,0.50252,1e-05,1e-05,6e-05,2e-05,0.00025,2e-05,1.82829,6e-05,0.00018,1e-05,0.00012,2e-05,4e-05,7e-05,5e-05 -260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -262,1.0,0.00054,0.00249,0.00059,1e-05,0.0149,0.10654,0.2497,0.00043,0.00307,0.01145,2e-05,0.00025,9e-05,1e-05,0.00239,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00033,5e-05,0.00025,0.12841,0.0,0.00091,0.07772,2e-05,0.0264,0.00011,0.00014,0.00041,1e-05,0.00023,0.54223,1e-05,1e-05,6e-05,2e-05,0.00021,2e-05,0.45628,5e-05,0.00027,1e-05,0.00011,2e-05,3e-05,6e-05,5e-05 -266,1.0,0.00052,0.0006,0.00054,1e-05,0.00683,0.14738,0.24848,0.00045,0.00078,0.00095,2e-05,0.00019,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,4e-05,5e-05,4e-05,0.0,0.00032,5e-05,0.00024,0.04572,0.0,0.00084,0.16104,2e-05,0.00781,0.00011,0.0001,0.00017,1e-05,0.00025,0.45034,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.20535,3e-05,8e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -273,1.0,0.00057,0.00128,0.00081,2e-05,0.03401,0.18773,1.03398,0.00048,0.01127,0.01563,3e-05,0.00027,0.00012,1e-05,0.00139,7e-05,1e-05,5e-05,5e-05,8e-05,5e-05,1e-05,0.00035,5e-05,0.00021,0.14589,1e-05,0.00981,0.12279,3e-05,0.04972,0.00011,0.00013,0.00055,1e-05,0.00027,2.07668,1e-05,2e-05,0.00877,2e-05,0.00948,2e-05,3.6245,8e-05,0.00016,1e-05,0.00012,1e-05,6e-05,7e-05,6e-05 -275,1.0,0.0006,0.00089,0.00039,1e-05,0.00453,0.18651,4.50447,0.0005,0.01426,0.01104,3e-05,0.00019,9e-05,1e-05,0.00084,4e-05,1e-05,5e-05,6e-05,5e-05,6e-05,0.0,0.00035,6e-05,0.00016,0.07994,0.0,0.00062,0.13773,3e-05,0.01572,0.00012,0.0001,0.01327,1e-05,0.00026,0.41159,1e-05,1e-05,5e-05,2e-05,0.00014,2e-05,4.90343,0.00015,9e-05,1e-05,0.00013,1e-05,3e-05,7e-05,5e-05 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE 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75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 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75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75197,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok 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a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv index e7fbf33fad..c40b8d3bab 100644 --- a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00029117569779819956,True,hinge,9,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.52445354905856,chi2,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302503,False,True,1,squared_hinge,ovr,l2,0.0007318628304090555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.560201454218397,948,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +85,weighting,one_hot_encoding,0.0003173723611800348,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1862,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/description.txt index c782c44005..7654024793 100644 --- a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: recall_weighted performance_type: solution_quality diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other 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+129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/description.txt index c782c44005..7654024793 100644 --- a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: recall_weighted performance_type: solution_quality diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE 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a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 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a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/configurations.csv index e7fbf33fad..c40b8d3bab 100644 --- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/configurations.csv +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/configurations.csv @@ -1,131 +1,130 @@ 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-19,none,adaboost,SAMME,1.1145892870034408,5,354,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.001119064659211796,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -20,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.745101117534867,14,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06075999384970139,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.035488276053047,False,True,1,squared_hinge,ovr,l1,0.0013962817054747638,,,,,,,,,,,,,,,,,,,,,,minmax -21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.8474780804635738,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.010000000000000004,True,extra_trees_preproc_for_classification,True,entropy,None,0.5311472280328223,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -30,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -31,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.170504104762284,,,0.10000000000000006,rbf,-1,True,1.893780321354902e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -32,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -33,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -34,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4244459875201874,False,True,1,squared_hinge,ovr,l2,0.0014473335587607684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax -35,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -36,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9901.884556489502,False,True,1,squared_hinge,ovr,l2,0.0023772047112092503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize -42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -45,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.3799175197381475,None,10,14,0.0,100,,,,,,,,,,,,median,0.004732616038822116,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -62,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5869992660583874,0.8714214450056397,,1.5336002214545474,sigmoid,-1,False,0.0046925012595254315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,2000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00029117569779819956,True,hinge,9,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.52445354905856,chi2,,,,none -68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax -75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none -86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax -116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -126,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax 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+45,none,one_hot_encoding,0.010000000000000004,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118102 +46,none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870853,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +47,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2,0.33708497069988536,None,0.0,15,13,0.0,451,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000,uniform,, +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +49,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,11,11,1.0,12,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +50,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +51,weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +52,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3,0.010682839357128344,poly,-1,False,2.4851608604406576e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.934840932693321,rbf,-1,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +55,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +57,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +58,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +59,weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.059656714779183606,deviance,8,0.4858133247974158,None,0.0,14,7,0.0,480,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +60,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1,True,2.3839685780861314e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39376071555683756,fwe,chi2,robust_scaler,,,0.9410187789848541,0.2144110585080491 +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.342897164595882,,,0.2229870623330047,rbf,-1,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +63,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +65,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2,9,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.275042925685625,f_classif,,,,minmax,,,, +66,weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.511920092321888e-05,,,,,,,,,,,,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +67,none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302503,False,True,1,squared_hinge,ovr,l2,0.0007318628304090555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.560201454218397,948,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +68,weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +69,weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935253,None,0.0,2,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,standardize,,,, +70,none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +71,weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5,183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36326267876588403,fdr,chi2,robust_scaler,,,0.8826612080363588,0.21896053101710966 +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +74,none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +75,weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589424,,,0.08351549479967445,rbf,-1,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3,0.9690352514774068,None,0.0,12,3,0.0,234,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,minmax,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.010000000000000004,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +81,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +83,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +84,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +85,weighting,one_hot_encoding,0.0003173723611800348,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,exp,1862,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +86,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +87,weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156,auto,,0.00019873338528715893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +88,none,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +89,weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634,normal,, +90,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +91,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +92,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +93,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +94,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +95,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +96,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +97,none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +98,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +99,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10,0.33000096635982235,None,0.0,15,13,0.0,388,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +100,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +101,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +102,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +103,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +104,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +105,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +106,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +107,weighting,one_hot_encoding,0.0053329726928195206,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5,18,0.0,100,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270145,0.04135311355893583 +108,weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +109,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +110,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +111,weighting,one_hot_encoding,0.010000000000000004,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119375,rbf,-1,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +112,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +113,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +114,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +115,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +116,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +117,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +118,weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292295,False,True,1,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +119,none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508,normal,, +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +121,weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546483,invscaling,modified_huber,elasticnet,0.13714427818877545,0.05517964277254504,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +124,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10,0.9377656718112951,None,0.0,7,13,0.0,214,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,minmax,,,, +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +127,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,exp,10,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/description.txt index c782c44005..7654024793 100644 --- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: recall_weighted performance_type: solution_quality diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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-@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other 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+119,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +120,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +121,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +124,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +126,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +129,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/description.txt index c782c44005..7654024793 100644 --- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/description.txt @@ -1,63 +1,63 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129 algorithms_stochastic: performance_measures: recall_weighted performance_type: solution_quality diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 -236,1.0,0.00052,0.06126,0.00055,2e-05,0.04995,0.32106,0.60304,0.00043,0.06214,0.01058,2e-05,0.00028,0.0001,1e-05,0.03274,6e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.00032,5e-05,0.00018,0.20707,1e-05,0.00085,0.70861,2e-05,0.06058,0.00011,0.00016,0.00063,1e-05,0.00024,2.36639,1e-05,1e-05,0.00011,1e-05,0.00029,2e-05,7.41647,5e-05,0.00055,1e-05,0.00011,2e-05,4e-05,7e-05,5e-05 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+75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_values.arff index d0395e7246..dd0f70cd52 100644 --- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_values.arff +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA 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-16,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -17,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -18,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -19,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -20,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none -21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -22,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize -23,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -24,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -25,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -26,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.2584064375821877,None,13,6,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -28,none,adaboost,SAMME,0.10852728577735547,8,492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -29,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize -30,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.0615335158906123,12,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0006128377815426205,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40918220909952985,fdr,chi2,none -31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,346.99358578854435,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -32,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7893822540456763e-05,True,,0.01920961872573097,True,,optimal,hinge,769,l2,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.0504050565968734,2076,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -33,none,adaboost,SAMME.R,0.340801430264527,7,464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,none -34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.180833449674944,0.9270226145864255,3,4.302883844246615,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -36,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -38,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -39,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,226.06742020598978,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -40,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -41,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -43,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -44,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.0235762546783831,rbf,-1,False,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,6.391474036008549,False,True,1,squared_hinge,ovr,l1,1.0014702398825083e-05,,,,,,,,,,,,,,,,,,,,,,none -45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -49,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.9145911691745825,0.8390938017045895,2,0.12403388838921078,poly,-1,False,0.00011235090274035898,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.09940116443132659,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax -50,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize -52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -53,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -55,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.1285583573915876,deviance,1,1.8700144159035181,None,5,19,0.0,252,0.7739014033956171,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -57,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize -60,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -62,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0483380823811535,deviance,3,3.5447265496885296,None,5,16,0.0,141,0.5896080302596434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0020128017963651694,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.014581209053425,f_classif,,,,none -63,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,5,15,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23.938615614788205,f_classif,,,,none -64,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.03830860981109,None,17,2,0.0,100,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6629672767671623,True,,,,,,,,,,,,,,,standardize 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+69,weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8,None,13,16,1.0,28,,,,,,quantile_transformer,41502,uniform,, +70,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +71,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +74,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +81,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2806.985866707319,False,True,1,squared_hinge,ovr,l2,0.03738539536055984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,standardize,,,, +83,weighting,one_hot_encoding,0.0006079518254197427,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5357045097570147,None,0.0,19,13,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.061215867328733256,False,True,1,squared_hinge,ovr,l1,0.00014224609210090503,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +84,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7794633670276021,None,0.0,9,10,0.0,100,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,75840,normal,, diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/description.txt b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/description.txt index d7f0f02920..3d00f9b82d 100644 --- a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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+75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 % % % \ No newline at end of file diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_runstatus.arff index 5b94d90fff..9d6128843f 100644 --- a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_runstatus.arff +++ b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+43,weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +44,weighting,one_hot_encoding,0.34516277500429876,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +45,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,weighting,one_hot_encoding,0.0026308117826759733,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452931,None,0.0,18,16,0.0,100,,,,,,,,,,,,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +47,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +49,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17,7,0.0,100,,,,,,,,,,,,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41656327663888065,fpr,chi2,,none +51,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +52,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.93767728056358,None,0.0,18,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +58,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +59,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +60,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,weighting,one_hot_encoding,0.03953306390719093,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9,6,0.0,100,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18788055192455086,fdr,chi2,,standardize +63,weighting,one_hot_encoding,0.001856820833094005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4,15,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +64,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +65,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +67,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +68,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +69,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,standardize +82,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7,12,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/description.txt index d7f0f02920..3d00f9b82d 100644 --- a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA 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-253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 -254,1.0,0.00067,0.00191,0.00079,1e-05,0.00207,0.06512,3.23532,0.00046,0.00248,0.00165,2e-05,0.00021,8e-05,1e-05,0.00183,3e-05,1e-05,6e-05,5e-05,5e-05,6e-05,0.0,0.00041,7e-05,0.00019,0.02997,1e-05,0.00104,0.06705,2e-05,0.00378,0.00012,0.00012,0.00038,1e-05,0.00023,0.20068,1e-05,1e-05,5e-05,2e-05,0.00022,2e-05,6.86616,6e-05,0.00014,1e-05,0.00013,1e-05,3e-05,6e-05,6e-05 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a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, 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75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 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75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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-75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75202,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75203,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75205,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75207,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 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+10,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565651,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463182 +11,weighting,one_hot_encoding,0.18137532678800652,True,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +12,weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +13,none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10,0.542144980834302,None,0.0,20,13,0.0,233,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307263,0.13434811490315818 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+33,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +34,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512536,False,True,1,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,robust_scaler,,,0.9135115200783679,0.27422293254554436 +35,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17,7,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, 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+51,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +52,none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678113,None,0.0,20,2,0.0,100,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,none,,,, +53,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +54,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +55,weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1,2,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +56,weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5855957814188109,None,0.0,17,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,109,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +57,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.26362013742534607,deviance,7,0.8344964130784466,None,0.0,9,2,0.0,298,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,normalize,,,, +58,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +59,weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2,233,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +60,weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416368,deviance,7,0.8603242247379981,None,0.0,2,6,0.0,500,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,none,,,, +61,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +62,weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37215221406145077,0.35417466287560373,3,0.0009148519644429074,poly,-1,False,2.9166728983300667e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866,normal,, +63,weighting,one_hot_encoding,0.010000000000000004,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02674155532989549,deviance,3,0.14973922320166708,None,0.0,7,18,0.0,309,0.35532673462283193,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +64,weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9457745734341919,None,0.0,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209,normal,, +65,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +66,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +67,none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,standardize,,,, +68,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +69,weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8,None,13,16,1.0,28,,,,,,quantile_transformer,41502,uniform,, +70,weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20,15,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +71,none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.24729845478857812,deviance,3,0.6564306719064884,None,0.0,15,14,0.0,220,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000,uniform,, +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +74,weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5,0.2281306656230014,None,0.0,14,13,0.0,493,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382,normal,, +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +77,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +78,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +80,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +81,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,,,,,standardize,,,, +82,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2806.985866707319,False,True,1,squared_hinge,ovr,l2,0.03738539536055984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,standardize,,,, +83,weighting,one_hot_encoding,0.0006079518254197427,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5357045097570147,None,0.0,19,13,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.061215867328733256,False,True,1,squared_hinge,ovr,l1,0.00014224609210090503,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +84,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7794633670276021,None,0.0,9,10,0.0,100,,,,,,,,,,,,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,75840,normal,, diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/description.txt index d7f0f02920..3d00f9b82d 100644 --- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_costs.arff +++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA -233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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-248,1.0,0.00048,0.0005,0.00044,1e-05,0.0009,0.04186,0.03988,0.0004,0.00064,0.00049,2e-05,0.0002,9e-05,1e-05,0.00067,4e-05,1e-05,4e-05,5e-05,5e-05,5e-05,0.0,0.00027,5e-05,0.00018,0.03061,0.0,0.00069,0.03734,2e-05,0.00143,0.0001,0.0001,0.00013,1e-05,0.00021,0.06444,1e-05,1e-05,6e-05,1e-05,4e-05,2e-05,0.04907,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -251,1.0,0.0005,0.00045,0.00046,1e-05,0.00623,0.034,0.19832,0.00044,0.00056,0.00259,2e-05,0.00021,9e-05,1e-05,0.00059,5e-05,1e-05,4e-05,5e-05,6e-05,4e-05,1e-05,0.0003,5e-05,0.00017,0.07776,1e-05,0.00071,0.07463,2e-05,0.00886,0.00011,0.0001,0.00012,1e-05,0.00024,0.06413,1e-05,1e-05,6e-05,2e-05,3e-05,2e-05,0.35706,4e-05,7e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 -252,1.0,0.00059,0.00058,0.00484,1e-05,0.00113,1.45253,0.51035,0.00043,0.0009,0.0006,2e-05,0.0002,9e-05,1e-05,0.00062,4e-05,1e-05,4e-05,5e-05,5e-05,6e-05,0.0,0.00036,5e-05,0.0004,0.09033,0.0,0.0053,0.13835,2e-05,0.00178,0.00011,0.0001,0.00023,1e-05,0.00023,2.09983,1e-05,1e-05,5e-05,2e-05,0.00012,2e-05,0.55826,5e-05,9e-05,1e-05,0.00012,1e-05,3e-05,7e-05,5e-05 -253,1.0,0.00054,0.00041,0.00068,2e-05,0.01558,0.01273,0.0731,0.00048,0.00058,0.01637,2e-05,0.0003,0.00014,1e-05,0.00056,5e-05,1e-05,4e-05,5e-05,5e-05,4e-05,1e-05,0.00033,5e-05,0.00026,0.03101,0.0,0.00101,0.02491,2e-05,0.032,0.00011,0.00013,0.00015,1e-05,0.00028,0.04835,1e-05,1e-05,6e-05,1e-05,5e-05,2e-05,0.21111,4e-05,0.0001,1e-05,0.00011,1e-05,3e-05,7e-05,0.00012 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-260,1.0,0.00049,0.00125,0.00067,1e-05,0.00544,0.06007,0.04355,0.00037,0.00151,0.00063,2e-05,0.00024,0.0001,1e-05,0.00146,7e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00028,6e-05,0.00018,0.02702,1e-05,0.00094,0.03175,2e-05,0.00612,0.0001,0.00011,0.00023,1e-05,0.00019,0.33067,1e-05,1e-05,7e-05,1e-05,7e-05,2e-05,0.17688,4e-05,0.00013,1e-05,0.00011,1e-05,3e-05,7e-05,5e-05 -261,1.0,0.00048,0.0003,0.00039,1e-05,0.01481,0.05429,0.3471,0.00044,0.00045,0.01923,2e-05,0.00019,8e-05,1e-05,0.0004,4e-05,1e-05,4e-05,5e-05,5e-05,4e-05,0.0,0.00027,5e-05,0.00016,0.1403,0.0,0.00061,0.10514,2e-05,0.0341,0.00011,9e-05,0.00013,1e-05,0.00024,0.23797,1e-05,1e-05,5e-05,1e-05,6e-05,2e-05,0.49577,6e-05,6e-05,1e-05,0.00011,1e-05,3e-05,6e-05,5e-05 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE 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75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok 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75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -132,10 +132,10 @@ 75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75168,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75172,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok 75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -148,20 +148,20 @@ 75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75188,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75189,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok -75191,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok 75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other 75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75197,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75198,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75201,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75202,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75203,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75205,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75207,1.0,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 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a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_values.arff +++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_values.arff @@ -50,139 +50,139 @@ @ATTRIBUTE SymbolsSum NUMERIC @DATA -233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922847986221,0.464067220688,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 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+68,weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6,467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +69,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16,14,0.0,100,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +72,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +79,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161,,,,,,,,,,,,,,standardize +82,weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7,12,0.0,100,,,,,,,,,,,,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +84,none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,100,,,,,,,,,,,,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/description.txt index d7f0f02920..3d00f9b82d 100644 --- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/description.txt @@ -1,61 +1,61 @@ features_cutoff_time: 3600 features_cutoff_memory: 3072 number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD feature_step KurtosisMin: KurtosisMin feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 algorithms_stochastic: diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_costs.arff index ff02941ae9..598aa45a81 100644 --- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_costs.arff +++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_costs.arff @@ -2,193 +2,193 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC @ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC @ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC @ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC @ATTRIBUTE KurtosisMin NUMERIC @ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC @ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC @ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC @ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC @ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC @ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC @DATA 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a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_runstatus.arff +++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_runstatus.arff @@ -2,58 +2,58 @@ @ATTRIBUTE instance_id STRING @ATTRIBUTE repetition NUMERIC -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} @ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} @DATA 233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -79,22 +79,22 @@ 2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -2350,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75097,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other 75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75105,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok -75106,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,ok -75107,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other 75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok @@ -111,7 +111,7 @@ 75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -75127,1.0,ok,ok,ok,ok,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,other,other,ok,other,ok,other,other,ok,other,ok,ok,other,ok,other,other,ok,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,other,ok,ok,other,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other 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\ No newline at end of file From 3b371241420e62c2c025338c08dfb37afce45191 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 3 Jan 2018 16:19:39 +0100 Subject: [PATCH 56/61] CI automatically test examples --- .travis.yml | 2 ++ ci_scripts/test.sh | 6 ++++++ 2 files changed, 8 insertions(+) diff --git a/.travis.yml b/.travis.yml index f8271cab72..f299f314f9 100644 --- a/.travis.yml +++ b/.travis.yml @@ -17,6 +17,8 @@ matrix: env: DISTRIB="conda" PYTHON_VERSION="3.5" COVERAGE="true" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh" - os: linux env: DISTRIB="conda" PYTHON_VERSION="3.6" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh" + - os: linux + env: DISTRIB="conda" PYTHON_VERSION="3.6" EXAMPLES="true" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh" # Temporarily disabling OSX builds because thy take too long # Set language to generic to not break travis-ci diff --git a/ci_scripts/test.sh b/ci_scripts/test.sh index 2a0a9d2174..370c6444ca 100644 --- a/ci_scripts/test.sh +++ b/ci_scripts/test.sh @@ -5,12 +5,18 @@ set -e mkdir -p $TEST_DIR cwd=`pwd` +examples_dir=$pwd/example test_dir=$cwd/test/ cd $TEST_DIR if [[ "$COVERAGE" == "true" ]]; then nosetests --no-path-adjustment -sv --with-coverage --cover-package=$MODULE $test_dir +elif [[ "$EXAMPLES" == "true" ]]; then + for example in `find $examples_dir -name '*.py'` + do + python $example + done else nosetests --no-path-adjustment -sv $test_dir fi From 4c7dee130245868dd984a99a0819c3f1340f3868 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 3 Jan 2018 16:48:08 +0100 Subject: [PATCH 57/61] Fix path --- ci_scripts/test.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ci_scripts/test.sh b/ci_scripts/test.sh index 370c6444ca..1da3952045 100644 --- a/ci_scripts/test.sh +++ b/ci_scripts/test.sh @@ -5,7 +5,7 @@ set -e mkdir -p $TEST_DIR cwd=`pwd` -examples_dir=$pwd/example +examples_dir=$cwd/example test_dir=$cwd/test/ cd $TEST_DIR From ac6608d2069010f18aee88fddd4fbdeff782da33 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 4 Jan 2018 15:07:01 +0100 Subject: [PATCH 58/61] Update examples --- example/example_crossvalidation.py | 6 +- example/example_eips.py | 7 +- example/example_feature_types.py | 7 +- example/example_holdout.py | 6 +- example/example_metrics.py | 59 ++++++++------ example/example_parallel.py | 41 +++++----- example/example_random_search.py | 23 ++++-- example/example_regression.py | 6 +- example/example_sequential.py | 7 +- example/test_new_smbo.py | 119 +++++++++++++++++++++++++++++ 10 files changed, 220 insertions(+), 61 deletions(-) create mode 100644 example/test_new_smbo.py diff --git a/example/example_crossvalidation.py b/example/example_crossvalidation.py index 73f67ff9d9..06299b48db 100644 --- a/example/example_crossvalidation.py +++ b/example/example_crossvalidation.py @@ -12,12 +12,14 @@ def main(): sklearn.model_selection.train_test_split(X, y, random_state=1) automl = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task=120, per_run_time_limit=30, + time_left_for_this_task=120, + per_run_time_limit=30, tmp_folder='/tmp/autosklearn_cv_example_tmp', output_folder='/tmp/autosklearn_cv_example_out', delete_tmp_folder_after_terminate=False, resampling_strategy='cv', - resampling_strategy_arguments={'folds': 5}) + resampling_strategy_arguments={'folds': 5}, + ) # fit() changes the data in place, but refit needs the original data. We # therefore copy the data. In practice, one should reload the data diff --git a/example/example_eips.py b/example/example_eips.py index 854ccabea4..969746cda1 100644 --- a/example/example_eips.py +++ b/example/example_eips.py @@ -49,7 +49,8 @@ def get_eips_object_callback( tae_runner=ta, runhistory2epm=rh2EPM, model=model, - acquisition_function=acquisition_function + acquisition_function=acquisition_function, + **kwargs ) @@ -59,11 +60,11 @@ def main(): sklearn.model_selection.train_test_split(X, y, random_state=1) automl = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task=120, per_run_time_limit=30, + time_left_for_this_task=120, + per_run_time_limit=30, tmp_folder='/tmp/autosklearn_eips_example_tmp', output_folder='/tmp/autosklearn_eips_example_out', get_smac_object_callback=get_eips_object_callback, - delete_tmp_folder_after_terminate=False, initial_configurations_via_metalearning=0, ) automl.fit(X_train, y_train, dataset_name='digits') diff --git a/example/example_feature_types.py b/example/example_feature_types.py index baa1bf2387..53b0b70b07 100644 --- a/example/example_feature_types.py +++ b/example/example_feature_types.py @@ -39,9 +39,10 @@ def main(): feat_type = ['Categorical' if ci else 'Numerical' for ci in categorical_indicator] - cls = autosklearn.classification.\ - AutoSklearnClassifier(time_left_for_this_task=120, - per_run_time_limit=30) + cls = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=120, + per_run_time_limit=30, + ) cls.fit(X_train, y_train, feat_type=feat_type) predictions = cls.predict(X_test) diff --git a/example/example_holdout.py b/example/example_holdout.py index 0dbff8efea..a17707aac1 100644 --- a/example/example_holdout.py +++ b/example/example_holdout.py @@ -11,10 +11,12 @@ def main(): sklearn.model_selection.train_test_split(X, y, random_state=1) automl = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task=120, per_run_time_limit=30, + time_left_for_this_task=120, + per_run_time_limit=30, tmp_folder='/tmp/autosklearn_holdout_example_tmp', output_folder='/tmp/autosklearn_holdout_example_out', - disable_evaluator_output=False) + disable_evaluator_output=False, + ) automl.fit(X_train, y_train, dataset_name='digits') # Print the final ensemble constructed by auto-sklearn. diff --git a/example/example_metrics.py b/example/example_metrics.py index 8e58137879..603b82f946 100644 --- a/example/example_metrics.py +++ b/example/example_metrics.py @@ -37,9 +37,11 @@ def main(): # First example: Use predefined accuracy metric print("#"*80) print("Use predefined accuracy metric") - cls = autosklearn.classification.\ - AutoSklearnClassifier(time_left_for_this_task=60, - per_run_time_limit=30, seed=1) + cls = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=60, + per_run_time_limit=30, + seed=1, + ) cls.fit(X_train, y_train, metric=autosklearn.metrics.accuracy) predictions = cls.predict(X_test) @@ -50,14 +52,18 @@ def main(): # Second example: Use own accuracy metric print("#"*80) print("Use self defined accuracy metric") - accuracy_scorer = autosklearn.metrics.make_scorer(name="accu", - score_func=accuracy, - greater_is_better=True, - needs_proba=False, - needs_threshold=False) - cls = autosklearn.classification.\ - AutoSklearnClassifier(time_left_for_this_task=60, - per_run_time_limit=30, seed=1) + accuracy_scorer = autosklearn.metrics.make_scorer( + name="accu", + score_func=accuracy, + greater_is_better=True, + needs_proba=False, + needs_threshold=False, + ) + cls = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=60, + per_run_time_limit=30, + seed=1, + ) cls.fit(X_train, y_train, metric=accuracy_scorer) predictions = cls.predict(X_test) @@ -68,21 +74,28 @@ def main(): # Third example: Use own accuracy metric with additional argument print("#"*80) print("Use self defined accuracy with additional argument") - accuracy_scorer = autosklearn.metrics.make_scorer(name="accu_add", - score_func=accuracy_wk, - greater_is_better=True, - needs_proba=False, - needs_threshold=False, - dummy=None) - cls = autosklearn.classification.\ - AutoSklearnClassifier(time_left_for_this_task=60, - per_run_time_limit=30, seed=1) + accuracy_scorer = autosklearn.metrics.make_scorer( + name="accu_add", + score_func=accuracy_wk, + greater_is_better=True, + needs_proba=False, + needs_threshold=False, + dummy=None, + ) + cls = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=60, + per_run_time_limit=30, + seed=1, + ) cls.fit(X_train, y_train, metric=accuracy_scorer) predictions = cls.predict(X_test) - print("Accuracy score {:g} using {:s}". - format(sklearn.metrics.accuracy_score(y_test, predictions), - cls._automl._metric.name)) + print( + "Accuracy score {:g} using {:s}".format( + sklearn.metrics.accuracy_score(y_test, predictions), + cls._automl._metric.name + ) + ) if __name__ == "__main__": diff --git a/example/example_parallel.py b/example/example_parallel.py index a3db16d928..f448d7f293 100644 --- a/example/example_parallel.py +++ b/example/example_parallel.py @@ -55,7 +55,8 @@ def spawn_classifier(seed, dataset_name): ensemble_size=0, # ensembles will be built when all optimization runs are finished initial_configurations_via_metalearning=initial_configurations_via_metalearning, seed=seed, - smac_scenario_args=smac_scenario_args) + smac_scenario_args=smac_scenario_args, + ) automl.fit(X_train, y_train, dataset_name=dataset_name) if __name__ == '__main__': @@ -73,26 +74,30 @@ def spawn_classifier(seed, dataset_name): p.join() print('Starting to build an ensemble!') - automl = AutoSklearnClassifier(time_left_for_this_task=15, - per_run_time_limit=15, - ml_memory_limit=1024, - shared_mode=True, - ensemble_size=50, - ensemble_nbest=200, - tmp_folder=tmp_folder, - output_folder=output_folder, - initial_configurations_via_metalearning=0, - seed=1) + automl = AutoSklearnClassifier( + time_left_for_this_task=15, + per_run_time_limit=15, + ml_memory_limit=1024, + shared_mode=True, + ensemble_size=50, + ensemble_nbest=200, + tmp_folder=tmp_folder, + output_folder=output_folder, + initial_configurations_via_metalearning=0, + seed=1, + ) # Both the ensemble_size and ensemble_nbest parameters can be changed now if # necessary - automl.fit_ensemble(y_train, - task=MULTICLASS_CLASSIFICATION, - metric=accuracy, - precision='32', - dataset_name='digits', - ensemble_size=20, - ensemble_nbest=50) + automl.fit_ensemble( + y_train, + task=MULTICLASS_CLASSIFICATION, + metric=accuracy, + precision='32', + dataset_name='digits', + ensemble_size=20, + ensemble_nbest=50, + ) predictions = automl.predict(X_test) print(automl.show_models()) diff --git a/example/example_random_search.py b/example/example_random_search.py index 3d1ac10efc..8d14c8c410 100644 --- a/example/example_random_search.py +++ b/example/example_random_search.py @@ -12,7 +12,10 @@ def get_roar_object_callback( scenario_dict, seed, ta, - **kwargs + backend, + metalearning_configurations, + runhistory, + run_id, ): """Random online adaptive racing. @@ -22,14 +25,19 @@ def get_roar_object_callback( scenario=scenario, rng=seed, tae_runner=ta, + runhistory=runhistory, + run_id=run_id, ) def get_random_search_object_callback( - scenario_dict, - seed, - ta, - **kwargs + scenario_dict, + seed, + ta, + backend, + metalearning_configurations, + runhistory, + run_id, ): """Random search. @@ -41,6 +49,8 @@ def get_random_search_object_callback( scenario=scenario, rng=seed, tae_runner=ta, + runhistory=runhistory, + run_id=run_id, ) @@ -69,7 +79,8 @@ def main(): print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions)) automl = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task=120, per_run_time_limit=30, + time_left_for_this_task=120, + per_run_time_limit=30, tmp_folder='/tmp/autosklearn_random_search_example_tmp', output_folder='/tmp/autosklearn_random_search_example_out', get_smac_object_callback=get_random_search_object_callback, diff --git a/example/example_regression.py b/example/example_regression.py index 1cc6c367d3..9420d88f83 100644 --- a/example/example_regression.py +++ b/example/example_regression.py @@ -13,9 +13,11 @@ def main(): sklearn.model_selection.train_test_split(X, y, random_state=1) automl = autosklearn.regression.AutoSklearnRegressor( - time_left_for_this_task=120, per_run_time_limit=30, + time_left_for_this_task=120, + per_run_time_limit=30, tmp_folder='/tmp/autosklearn_regression_example_tmp', - output_folder='/tmp/autosklearn_regression_example_out') + output_folder='/tmp/autosklearn_regression_example_out', + ) automl.fit(X_train, y_train, dataset_name='boston', feat_type=feature_types) diff --git a/example/example_sequential.py b/example/example_sequential.py index fcd976ff52..019ad3be9c 100644 --- a/example/example_sequential.py +++ b/example/example_sequential.py @@ -11,13 +11,16 @@ def main(): sklearn.model_selection.train_test_split(X, y, random_state=1) automl = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task=120, per_run_time_limit=30, + time_left_for_this_task=120, + per_run_time_limit=30, tmp_folder='/tmp/autosklearn_sequential_example_tmp', output_folder='/tmp/autosklearn_sequential_example_out', # Do not construct ensembles in parallel to avoid using more than one # core at a time. The ensemble will be constructed after auto-sklearn # finished fitting all machine learning models. - ensemble_size=0, delete_tmp_folder_after_terminate=False) + ensemble_size=0, + delete_tmp_folder_after_terminate=False, + ) automl.fit(X_train, y_train, dataset_name='digits') # This call to fit_ensemble uses all models trained in the previous call # to fit to build an ensemble which can be used with automl.predict() diff --git a/example/test_new_smbo.py b/example/test_new_smbo.py new file mode 100644 index 0000000000..c7a6a42b7e --- /dev/null +++ b/example/test_new_smbo.py @@ -0,0 +1,119 @@ +import glob + +import time + +import sklearn.model_selection +import sklearn.datasets +import sklearn.metrics + +from smac.facade.smac_facade import SMAC +from smac.optimizer.smbo import SMBO +from smac.optimizer import pSMAC + +import autosklearn.classification + + +class AutoSklearnSMBO(SMBO): + def run(self): + """Runs the Bayesian optimization loop + + Returns + ---------- + incumbent: np.array(1, H) + The best found configuration + """ + self.start() + + # Main BO loop + while True: + if self.scenario.shared_model: + pSMAC.read(run_history=self.runhistory, + output_dirs=self.scenario.input_psmac_dirs, + configuration_space=self.config_space, + logger=self.logger) + + start_time = time.time() + X, Y = self.rh2EPM.transform(self.runhistory) + + self.logger.debug("Search for next configuration") + # get all found configurations sorted according to acq + challengers = self.choose_next(X, Y) + + time_spent = time.time() - start_time + time_left = self._get_timebound_for_intensification(time_spent) + + self.logger.debug("Intensify") + + self.incumbent, inc_perf = self.intensifier.intensify( + challengers=challengers, + incumbent=self.incumbent, + run_history=self.runhistory, + aggregate_func=self.aggregate_func, + time_bound=max(self.intensifier._min_time, time_left)) + + if self.scenario.shared_model: + pSMAC.write(run_history=self.runhistory, + output_directory=self.scenario.output_dir) + + logging.debug( + "Remaining budget: %f (wallclock), %f (ta costs), %f (target runs)" % ( + self.stats.get_remaing_time_budget(), + self.stats.get_remaining_ta_budget(), + self.stats.get_remaining_ta_runs())) + + if self.stats.is_budget_exhausted(): + break + + self.stats.print_stats(debug_out=True) + + return self.incumbent + + + +def get_smac_object_callback( + scenario_dict, + seed, + ta, + backend, + metalearning_configurations, +): + scenario = Scenario(scenario_dict) + default_config = scenario.cs.get_default_configuration() + initial_configurations = [default_config] + metalearning_configurations + smac = SMAC( + scenario=scenario, + rng=seed, + tae_runner=ta, + initial_configurations=initial_configurations, + smbo_class=AutoSklearnSMBO, + ) + smac.solver.backend = backend + return smac + + +def main(): + X, y = sklearn.datasets.load_digits(return_X_y=True) + X_train, X_test, y_train, y_test = \ + sklearn.model_selection.train_test_split(X, y, random_state=1) + + automl = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=120, per_run_time_limit=30, + tmp_folder='/tmp/autosklearn_eips_example_tmp', + output_folder='/tmp/autosklearn_eips_example_out', + get_smac_object_callback=get_smac_object_callback, + delete_tmp_folder_after_terminate=False, + initial_configurations_via_metalearning=2, + ) + automl.fit(X_train, y_train, dataset_name='digits') + + # Print the final ensemble constructed by auto-sklearn via ROAR. + print(automl.show_models()) + predictions = automl.predict(X_test) + # Print statistics about the auto-sklearn run such as number of + # iterations, number of models failed with a time out. + print(automl.sprint_statistics()) + print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions)) + + +if __name__ == '__main__': + main() From 13e1ef5025000d4bbe8d99a1cc7419a088ad3291 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 4 Jan 2018 15:24:27 +0100 Subject: [PATCH 59/61] DOC update documentation --- autosklearn/__version__.py | 2 +- doc/index.rst | 16 ++++++++-------- doc/installation.rst | 6 +++--- doc/releases.rst | 17 ++++++++++++++++- requirements.txt | 2 +- 5 files changed, 29 insertions(+), 14 deletions(-) diff --git a/autosklearn/__version__.py b/autosklearn/__version__.py index 36509b4a75..f9484c6b61 100644 --- a/autosklearn/__version__.py +++ b/autosklearn/__version__.py @@ -1,4 +1,4 @@ """Version information.""" # The following line *must* be the last in the module, exactly as formatted: -__version__ = "0.2.1" +__version__ = "0.3.0" diff --git a/doc/index.rst b/doc/index.rst index 4c074c9907..bb4826625d 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -1,6 +1,6 @@ -********************* -What is auto-sklearn? -********************* +************ +auto-sklearn +************ .. role:: bash(code) :language: bash @@ -19,8 +19,8 @@ replacement for a scikit-learn estimator: *auto-sklearn* frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in *Bayesian optimization*, *meta-learning* and *ensemble construction*. Learn more about -the technology behind *auto-sklearn* by reading this paper published at -the `NIPS 2015 `_ +the technology behind *auto-sklearn* by reading our paper published at +`NIPS 2015 `_ . Example @@ -39,7 +39,7 @@ Example >>> print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_hat)) -This will run for one hour should result in an accuracy above 0.98. +This will run for one hour and should result in an accuracy above 0.98. Manual @@ -59,8 +59,8 @@ namely the 3-clause BSD license. Citing auto-sklearn ******************* -If you use auto-sklearn in a scientific publication, we would appreciate -references to the following paper: +If you use auto-sklearn in a scientific publication, we would appreciate a +reference to the following paper: `Efficient and Robust Automated Machine Learning diff --git a/doc/installation.rst b/doc/installation.rst index 3c60842509..bb24ea7cb2 100644 --- a/doc/installation.rst +++ b/doc/installation.rst @@ -12,7 +12,7 @@ System requirements auto-sklearn has the following system requirements: * Linux operating system (for example Ubuntu), -* Python (>=3.4). +* Python (>=3.5). * C++ compiler (with C++11 supports) and SWIG (version 3.0 or later) For an explanation of missing Microsoft Windows and MAC OSX support please @@ -63,7 +63,7 @@ be solved by installing the *gcc* compiler shipped with AnaConda (as well as .. code:: bash - conda install gcc swig + conda install gxx_linux-64 gcc_linux-64 swig Windows/OSX compability @@ -86,7 +86,7 @@ Possible solutions (not tested): Mac OSX ~~~~~~~ -Auto-sklearn is known to work on OSX systems. Nevertheless, there are two +We currently do not know if *auto-sklearn* works on OSX. There are at least two issues holding us back from actively supporting OSX: * The ``resource`` module cannot enforce a memory limit on a Python process diff --git a/doc/releases.rst b/doc/releases.rst index 35062350ea..8b6d4f8bbb 100644 --- a/doc/releases.rst +++ b/doc/releases.rst @@ -11,11 +11,26 @@ Releases ======== -Version 0.2.2 +Version 0.3.0 ============= +* Upgrade to scikit-learn 0.19.1. * Do not use the ``DummyClassifier`` or ``DummyRegressor`` as part of an ensemble. Fixes `#140 `_. +* Fixes #295 by loading the data in the subprocess instead of the main process. +* Fixes #326: refitting could result in a type error. This is now fixed by + better type checking in the classification components. +* Updated search space for ``RandomForestClassifier``, ``ExtraTreesClassifier`` + and ``GradientBoostingClassifier`` (fixes #358). +* Removal of constant features is now a part of the pipeline. +* Allow passing an SMBO object into the ``AutoSklearnClassifier`` and + ``AutoSklearnRegressor``. + +Contributors +~~~~~~~~~~~~ + +* Matthias Feurer +* Jesper van Engelen Version 0.2.1 ============= diff --git a/requirements.txt b/requirements.txt index 476fcc7eaa..b0a2b0be9b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -18,5 +18,5 @@ pandas ConfigSpace>=0.4.0,<0.5 pynisher>=0.4 -pyrfr>=0.6.1,<0.8 +pyrfr>=0.7,<0.8 smac From afb542db2655dce71e940ebc3aa1408f6a77c501 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 4 Jan 2018 15:37:04 +0100 Subject: [PATCH 60/61] FIX examples --- example/example_eips.py | 9 +++++++-- example/example_random_search.py | 2 ++ 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/example/example_eips.py b/example/example_eips.py index 969746cda1..3e0019c4c2 100644 --- a/example/example_eips.py +++ b/example/example_eips.py @@ -18,8 +18,12 @@ def get_eips_object_callback( scenario_dict, seed, ta, - **kwargs + backend, + metalearning_configurations, + runhistory, + run_id, ): + scenario_dict['input_psmac_dirs'] = backend.get_smac_output_glob() scenario = Scenario(scenario_dict) rh2EPM = RunHistory2EPM4EIPS( num_params=len(scenario.cs.get_hyperparameters()), @@ -44,13 +48,14 @@ def get_eips_object_callback( ) acquisition_function = EIPS(model) return SMAC( + runhistory=runhistory, scenario=scenario, rng=seed, tae_runner=ta, runhistory2epm=rh2EPM, model=model, acquisition_function=acquisition_function, - **kwargs + run_id=run_id, ) diff --git a/example/example_random_search.py b/example/example_random_search.py index 8d14c8c410..1c62a190dd 100644 --- a/example/example_random_search.py +++ b/example/example_random_search.py @@ -20,6 +20,7 @@ def get_roar_object_callback( """Random online adaptive racing. http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf""" + scenario_dict['input_psmac_dirs'] = backend.get_smac_output_glob() scenario = Scenario(scenario_dict) return ROAR( scenario=scenario, @@ -42,6 +43,7 @@ def get_random_search_object_callback( """Random search. http://www.jmlr.org/papers/v13/bergstra12a.html""" + scenario_dict['input_psmac_dirs'] = backend.get_smac_output_glob() scenario_dict['minR'] = len(scenario_dict['instances']) scenario_dict['initial_incumbent'] = 'RANDOM' scenario = Scenario(scenario_dict) From 3d5bfe6acc595782fbd0ca34406116f1d3c08bdc Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Thu, 4 Jan 2018 16:39:32 +0100 Subject: [PATCH 61/61] FIX remove accidentaly commited file --- example/test_new_smbo.py | 119 --------------------------------------- 1 file changed, 119 deletions(-) delete mode 100644 example/test_new_smbo.py diff --git a/example/test_new_smbo.py b/example/test_new_smbo.py deleted file mode 100644 index c7a6a42b7e..0000000000 --- a/example/test_new_smbo.py +++ /dev/null @@ -1,119 +0,0 @@ -import glob - -import time - -import sklearn.model_selection -import sklearn.datasets -import sklearn.metrics - -from smac.facade.smac_facade import SMAC -from smac.optimizer.smbo import SMBO -from smac.optimizer import pSMAC - -import autosklearn.classification - - -class AutoSklearnSMBO(SMBO): - def run(self): - """Runs the Bayesian optimization loop - - Returns - ---------- - incumbent: np.array(1, H) - The best found configuration - """ - self.start() - - # Main BO loop - while True: - if self.scenario.shared_model: - pSMAC.read(run_history=self.runhistory, - output_dirs=self.scenario.input_psmac_dirs, - configuration_space=self.config_space, - logger=self.logger) - - start_time = time.time() - X, Y = self.rh2EPM.transform(self.runhistory) - - self.logger.debug("Search for next configuration") - # get all found configurations sorted according to acq - challengers = self.choose_next(X, Y) - - time_spent = time.time() - start_time - time_left = self._get_timebound_for_intensification(time_spent) - - self.logger.debug("Intensify") - - self.incumbent, inc_perf = self.intensifier.intensify( - challengers=challengers, - incumbent=self.incumbent, - run_history=self.runhistory, - aggregate_func=self.aggregate_func, - time_bound=max(self.intensifier._min_time, time_left)) - - if self.scenario.shared_model: - pSMAC.write(run_history=self.runhistory, - output_directory=self.scenario.output_dir) - - logging.debug( - "Remaining budget: %f (wallclock), %f (ta costs), %f (target runs)" % ( - self.stats.get_remaing_time_budget(), - self.stats.get_remaining_ta_budget(), - self.stats.get_remaining_ta_runs())) - - if self.stats.is_budget_exhausted(): - break - - self.stats.print_stats(debug_out=True) - - return self.incumbent - - - -def get_smac_object_callback( - scenario_dict, - seed, - ta, - backend, - metalearning_configurations, -): - scenario = Scenario(scenario_dict) - default_config = scenario.cs.get_default_configuration() - initial_configurations = [default_config] + metalearning_configurations - smac = SMAC( - scenario=scenario, - rng=seed, - tae_runner=ta, - initial_configurations=initial_configurations, - smbo_class=AutoSklearnSMBO, - ) - smac.solver.backend = backend - return smac - - -def main(): - X, y = sklearn.datasets.load_digits(return_X_y=True) - X_train, X_test, y_train, y_test = \ - sklearn.model_selection.train_test_split(X, y, random_state=1) - - automl = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task=120, per_run_time_limit=30, - tmp_folder='/tmp/autosklearn_eips_example_tmp', - output_folder='/tmp/autosklearn_eips_example_out', - get_smac_object_callback=get_smac_object_callback, - delete_tmp_folder_after_terminate=False, - initial_configurations_via_metalearning=2, - ) - automl.fit(X_train, y_train, dataset_name='digits') - - # Print the final ensemble constructed by auto-sklearn via ROAR. - print(automl.show_models()) - predictions = automl.predict(X_test) - # Print statistics about the auto-sklearn run such as number of - # iterations, number of models failed with a time out. - print(automl.sprint_statistics()) - print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions)) - - -if __name__ == '__main__': - main()