v2.0.0

Demos/demos_module/iris_demo.py → Demos/demos_module/main_iris_demo.py

@@ -3,13 +3,8 @@
 All rights reserved
 
 @author: Javier Fumanal Idocin - University of Essex
-@author: Javier Andreu-Perez - University of Essex
 
-
-This is a the source file that contains a demo for a tip computation example, where a diferent set of T1-FS are used to compute
-a t1 reasoning approach.
-
-We also show the GA to optimize the rules obtained in classification.
+This is a the source file that contains a demo for an Iris classification example using a FRBC where we will also show different possible ways to print the rules, precompute the partitions, etc.
 
 """
 
@@ -22,14 +17,10 @@
 import sys
 
 
-
-
 # In case you run this without installing the package, you need to add the path to the package
-
 # This is for launching from root folder path
 sys.path.append('./ex_fuzzy/')
 sys.path.append('./ex_fuzzy/ex_fuzzy/')
-
 # This is for launching from Demos folder
 sys.path.append('../ex_fuzzy/')
 sys.path.append('../ex_fuzzy/ex_fuzzy/')
@@ -43,45 +34,47 @@
 
 runner = 1 # 1: single thread, 2+: corresponding multi-thread
 
+# GA parameters
 n_gen = 5
 n_pop = 30
-
-nRules = 15
-nAnts = 3
-tolerance = 0.001
+
+# FRBC parameters
+nRules = 15 # Number of maximum rules
+nAnts = 3 # Number of maximum antecedents per rule
+vl = 3 # Number of linguistic variables
+tolerance = 0.001 # Minimum dominance score to accept a rule
+fz_type_studied = fs.FUZZY_SETS.t1 # Fuzzy set type
+
 
 iris = datasets.load_iris()
 X = pd.DataFrame(iris.data, columns=iris.feature_names)
 y = pd.Series(iris.target)
 class_names = iris.target_names
-class_names = np.unique(y)
-fz_type_studied = fs.FUZZY_SETS.t1
-vl = 3
-
-
 
 # Compute the fuzzy partitions using n linguistic variables
 precomputed_partitions_vl = utils.construct_partitions(X, fz_type_studied, n_partitions=vl)
+
 # Split the data into a training set and a test set
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)
 
-
-# We create a FRBC with the precomputed partitions and the specified fuzzy set type, 
+# We create a FRBC with the precomputed partitions or None (will optimize them as well in that case) and the specified fuzzy set type
 fl_classifier = GA.BaseFuzzyRulesClassifier(nRules=nRules, linguistic_variables=None, nAnts=nAnts, class_names=class_names, n_linguistic_variables=vl, fuzzy_type=fz_type_studied, verbose=True, tolerance=tolerance, runner=runner, allow_unknown=True, ds_mode=0)
 
 # fl_classifier.customized_loss(utils.mcc_loss) Use this to change the loss function, but be sure to look at the API first
 fl_classifier.fit(X_train, y_train, n_gen=n_gen, pop_size=n_pop, checkpoints=0, random_state=0, p_value_compute=True)
 
-# print(vis_rules.rules_to_latex(fl_classifier.rule_base))
+# We evaluate the fuzzy model, this will print the rules, the accuracy, the Matthew's correlation coefficient, etc.
 fuzzy_evaluator = eval_tools.FuzzyEvaluator(fl_classifier)
 str_rules = fuzzy_evaluator.eval_fuzzy_model(X_train, y_train, X_test, y_test, 
-                        plot_rules=False, print_rules=True, plot_partitions=False, return_rules=True)
-rule_matrix = fl_classifier.rule_base.get_rulebase_matrix()
+                        plot_rules=False, print_rules=True, plot_partitions=False, return_rules=True, bootstrap_results_print=True)
+
+# print(vis_rules.rules_to_latex(fl_classifier.rule_base)) # Do this to print the rules in latex format
 
 # Save the rules as a plain text file
-with open('rules_iris_t2.txt', 'w') as f:
-    f.write(str_rules)
+#with open('rules_iris_t2.txt', 'w') as f:
+#    f.write(str_rules)
 
-# Compute the explainable predictions
+# Compute the explainable predictions: this will return the predictions, the winning rules, the winning association degrees, and the confidence interval of the certainty of the predictions
 y_pred, winning_rules, winning_association_degrees, conf_interval_certainty = fl_classifier.explainable_predict(X_test, out_class_names=True)
-print('Done')
+
+print('Done!')

docs/build/html/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: bbbf35d0940a0dcfa2922ad1d1125950
+config: 7184ad333879fb5e4f199b3de50a2caf
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/build/html/_modules/ex_fuzzy/classifiers.html

@@ -54,6 +54,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../../extending.html">Extending Ex-Fuzzy</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../persistence.html">Persistence</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../classifiers.html">Advanced classifiers</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../pattern_stats.html">Bootstrapping and rule robustness</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../api.html">API</a></li>
 </ul>