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metrics.py
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# benchmark metrics script (not part of the actual code for the CNN)
# it is merely used to produce some tables used in the paper
# this file is work in progress
# from itertools import product
import numpy as np
from tabulate import tabulate
import pandas as pd
# first by sample size
w_size_rnd = {
'names': [
"10krnd",
"30krnd",
"50krnd",
"100krnd"
],
'files': [
"20170113-114844",
"20170113-130314",
"20170113-154800",
"20170114-133631"
]
}
# same for 1 subreddit (android)
w_size_all = {
'names': [
"10kall",
"30kall",
"50kall",
"100kall"
],
'files': [
"20170114-144154",
"20170114-141341",
"20170114-140306",
"20170114-134905"
]
}
# -----------------------------------------------------
# min length effect
w_minmax_mix_rnd = {
'names': [
"50krnd_minlen",
"50krnd_maxlen",
"50krnd_minmax",
"50kall_minlen",
"50kall_maxlen",
"50kall_minmax"
],
'files': [
"20170113-141209",
"20170113-130509",
"20170113-150447",
"20170114-170906",
"20170114-172358",
"20170114-161409"
]
}
w_minlen_rnd = {
'names': [
"10krnd_minlen",
"30krnd_minlen",
"30krnd_minlen (SMOTE)",
"50krnd_minlen",
"100krnd_minlen"
],
'files': [
"20170113-115212",
"20170113-121330",
"20170113-121543",
"20170113-141209",
"20170114-180138"
]
}
w_minlen_all = {
'names': [
"10kall_minlen",
"10kall_maxlen",
"30kall_minlen",
"30kall_maxlen",
"50kall_minlen",
"50kall_maxlen",
"100kall_minlen",
"100kall_maxlen"
],
'files': [
"20170116-201214",
"20170116-201440",
"20170116-201440",
"20170116-202045",
"20170114-170906",
"20170114-172358",
"20170113-190343",
"20170113-160702"
]
}
# -----------------------------------------------------
# range effect
w_ran_mix = {
'names': [
"50krnd_ran-12*",
"50krnd_ran03",
"100kall_ran-12",
"100kall_ran03",
],
'files': [
"20170113-151134",
"20170113-153943",
"20170114-181604",
"20170114-185646"
]
}
# auch mit RND und vergleichen danke.
w_all_ran_03 = {
'names': [
"10kall_ran03",
"10kall_ran03 SMOTE",
"30kall_ran03",
"30kall_ran03 SMOTE",
"50kall_ran03",
"50kall_ran03 SMOTE",
"100kall_ran03",
"100kall_ran03 SMOTE"
],
'files': [
"20170124-115625",
"20170124-120011",
"20170124-122817",
"20170124-123402",
"20170124-123745",
"20170124-124624",
"20170114-185646",
"20170124-125647"
]
}
# -----------------------------------------------------
# CNN stuff
index = ['30krnd', '30kall',
'50krnd', '50kall',
'100krnd', '100kall']
w_filters = {
'3': ["20170124-152930", "20170125-141240",
"20170124-204810", "20170125-115709",
"20170124-205912", "20170125-113848"],
'4': ["20170124-153542", "20170125-141240",
"20170124-203842", "20170125-125420",
"20170124-211632", "20170125-111828"],
'5': ["20170124-162517", "20170125-150258",
"20170124-202837", "20170125-130720",
"20170124-213441", "20170125-110028"],
'6': ["20170124-163247", "20170125-140031",
"20170124-200516", "20170125-132213",
"20170124-215817", "20170125-103240"],
'7': ["20170124-171355", "20170125-135329",
"20170124-194915", "20170125-134211",
"20170125-143941", "20170125-100725"]
}
index_minlen = ['30kall minlen', '50kall minlen', '100kall minlen']
w_filters_minlen = {
'3': ["20170125-141925", "20170125-175623", "20170125-173531"],
'4': ["20170125-151256", "20170125-181841", "20170125-170955"],
'5': ["20170125-152114", "20170125-183529", "20170125-165212"],
'6': ["20170125-153758", "20170125-184440", "20170125-163327"],
'7': ["20170125-154552", "20170125-155511", "20170125-160925"]
}
index_minmax = ['50kall maxlen', '100kall maxlen',
'50kall minmax', '100kall minmax']
w_filters_minmax = {
'3': ["20170126-224653", "20170126-225630",
"20170126-233205", "20170126-231329"],
'7': ["20170127-150346", "20170127-133354",
"20170126-234846", "20170127-123951"]
}
index_minlen_ran = ['100kall 03', '100kall 03 smote',
'100kall minlen 03', '100kall minlen 03 smote']
w_filters_minlen03 = {
'3': ["20170126-111259", "20170126-165633",
"20170125-201814", "20170125-205028"],
'7': ["20170126-105331", "20170126-173117",
"20170125-193923", "20170125-212013"],
}
index_act = ['50kall minlen', '50kall minlen5',
'100kall minlen', '100kall minlen5']
w_filters_act = {
'3 relu': ["20170125-175623", "20170126-205349",
"20170125-173531", "20170126-184026"],
'3 tanh': ["20170126-164338", "20170126-212002",
"20170126-114635", "20170126-185833"],
'7 relu': ["20170125-155511", "20170126-210911",
"20170125-160925", "20170126-180551"],
'7 tanh': ["20170126-163023", "20170126-214901",
"20170126-120732", "20170126-191858"]
}
index_opt = ["3 relu RMSprop", "3 relu adadelta", "3 relu adam",
"7 relu RMSprop", "7 relu adadelta", "7 relu adam"]
w_filters_opt = {
"50kall minlen": ["20170125-175623", "20170126-154119", "20170128-185935",
"20170126-155136", "20170125-155511", "20170128-185935"],
"100kall minlen": ["20170125-173531", "20170126-152423", "20170128-163747",
"20170125-160925", "20170126-150416", "20170128-175709"]
}
# -----------------------------------------------------
def create_table_filter_sizes(w, index):
d_val = {}
d_auc = {}
for i in w:
ival = []
iauc = []
for npz in w[i]:
if (npz != ""):
f = np.load("output/" + npz + "-model.npz")
cnn_metrics = f['arr_1']
ival.append(np.nanmean(cnn_metrics, axis=0)[1])
iauc.append(np.nanmean(cnn_metrics, axis=0)[2])
else:
ival.append("NaN")
iauc.append("NaN")
d_val[i] = ival
d_auc[i] = iauc
d_val = pd.DataFrame(d_val, index=index)
d_auc = pd.DataFrame(d_auc, index=index)
print(tabulate(d_val,
tablefmt="latex_booktabs",
floatfmt=".3f",
showindex=True,
headers="keys"))
print(tabulate(d_auc,
tablefmt="latex_booktabs",
floatfmt=".3f",
showindex=True,
headers="keys"))
def create_table_bench(w):
val_skl_m, val_skl_v, val_k1_m, val_k1_v, val_k2_m, val_k2_v, val_nb, \
val_svm = [], [], [], [], [], [], [], []
auc_skl_m, auc_k1_m, auc_k2_m, auc_nb, auc_svm = [], [], [], [], []
auc_skl_v, auc_k1_v, auc_k2_v = [], [], []
for npz in w['files']:
f = np.load("output/" + npz + "-bench.npz")
lr_metrics = f['arr_0'].item()
svm_metrics = f['arr_2'].item()
nb_metrics = f['arr_1'].item()
# lr method sklearn
val_skl_m.append(np.nanmean(lr_metrics['val'][0:10]))
val_skl_v.append(np.var(lr_metrics['val'][0:10]))
auc_skl_m.append(np.nanmean(lr_metrics['roc_auc'][0:10]))
auc_skl_v.append(np.var(lr_metrics['roc_auc'][0:10]))
# lr method keras1
val_k1_m.append(np.nanmean(lr_metrics['val'][10:20]))
val_k1_v.append(np.var(lr_metrics['val'][10:20]))
auc_k1_m.append(np.nanmean(lr_metrics['roc_auc'][0:10]))
auc_k1_v.append(np.var(lr_metrics['roc_auc'][0:10]))
# lr method keras2
val_k2_m.append(np.nanmean(lr_metrics['val'][20:30]))
val_k2_v.append(np.var(lr_metrics['val'][20:30]))
auc_k2_m.append(np.nanmean(lr_metrics['roc_auc'][0:10]))
auc_k2_v.append(np.var(lr_metrics['roc_auc'][0:10]))
# NB
val_nb.append(nb_metrics['val'][1])
auc_nb.append(nb_metrics['roc_auc'][0])
# SVM
val_svm.append(svm_metrics['val'][1])
auc_svm.append(svm_metrics['roc_auc'][0])
table = zip(w['names'], val_skl_m, val_k1_m, val_k2_m, val_nb, val_svm)
# add: varianz in klammern(?)
print(tabulate(
table,
tablefmt="latex_booktabs",
floatfmt=".3f",
headers=['Sample', 'LR1', 'LR2', 'LR3', 'NB', 'SVM']
))
table = zip(w['names'], auc_skl_m, auc_k1_m, auc_k2_m, auc_nb, auc_svm)
print(tabulate(
table,
tablefmt="latex_booktabs",
floatfmt=".3f",
headers=['Sample', 'LR1', 'LR2', 'LR3', 'NB', 'SVM']
))
# create_table_bench(w_size_all)
# create_table_bench(w_size_rnd)
# create_table_bench(w_minlen_rnd)
# create_table_bench(w_minmax_mix_rnd)
# create_table_bench(w_minlen_all)
# create_table_bench(w_ran_mix)
# create_table_bench(w_all_ran_03)
# create_table_filter_sizes(w_filters, index)
# create_table_filter_sizes(w_filters_act, index_act)
# create_table_filter_sizes(w_filters_minmax, index_minmax)
create_table_filter_sizes(w_filters_opt, index_opt)
# old stuff
# def create_table_cnn(w):
# val_cnn_m, val_cnn_v = [], []
# auc_cnn_m, auc_cnn_v = [], []
# for npz in w['files']:
# f = np.load("output/" + npz + "-model.npz")
# cnn_metrics = f['arr_1'][0]
# val_cnn_m.append(np.nanmean(cnn_metrics)[1])
# val_cnn_v.append(np.var(cnn_metrics)[1])
# auc_cnn_m.append(np.nanmean(cnn_metrics)[2])
# auc_cnn_v.append(np.var(cnn_metrics)[2])
# table = zip(w['names'], val_cnn_m, val_cnn_v, auc_cnn_m, auc_cnn_v)
# print(tabulate(
# table,
# tablefmt="latex_booktabs",
# floatfmt=".3f",
# headers=['Sample', 'Mean Acc.', 'Var. Acc.', 'Mean AUC', 'Var. AUC']
# ))
# NB metrics
# # calculate cv fold average here
# val = nb_metrics['val'][1] # average??
# fpr = nb_metrics['fpr'][0]
# tpr = nb_metrics['tpr'][0]
# roc_auc = nb_metrics['roc_auc'][0]
#
# # fold results
# cvresults = nb_metrics['val'][0] # folds
# col_no = np.array(range(0, len(cvresults))).astype(int)
# table = {
# 'folds': col_no, 'validation accuray': np.round(cvresults, 3)
# }
#
# print(tabulate(
# table,
# tablefmt="latex_booktabs",
# # floatfmt=".3f",
# headers="keys")
# )