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Extract_logs.py
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Extract_logs.py
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import os
import json
import argparse
def main():
# input parameters
parser = argparse.ArgumentParser(description='ArgParser')
parser.add_argument('--input', type=str, default='./ProgramLog.txt', help='input file path with file name')
parser.add_argument('--output', type=str, default='./log/parsed-log.json', help='output file path with file name')
args = parser.parse_args()
str_input = args.input
str_output = args.output
str_output_all = os.path.join(os.path.dirname(args.output),"parsed-all-log_CookieLot.json")
dictionary = {}
with open(str_input) as f:
txt = f.read()
datasets_txt = txt.split('Data generators of the ')
datasets_txt=datasets_txt[1:]
for dataset_txt in datasets_txt:
dataset_name = dataset_txt.split('\n', 1)[0].split(' ')[0]
dictionary[dataset_name]={'models':{}}
models_txt = dataset_txt.split('Training of the ')
models_txt = models_txt[1:]
for model_txt in models_txt:
model_name = model_txt.split('\n', 1)[0].split(' ')[0]
sep = False
try:
pearson_nok = model_txt.split('Median Pearson Coefficient: ', 1)[1].split(' ')[0]
pearson_ok = model_txt.split('Median Pearson Coefficient: ', 2)[2].split(' ')[0]
pearson_c = model_txt.split('Coefficient ratio: ', 1)[1].split(' ')[0]
ssim_nok = model_txt.split('Median SSIM value: ', 1)[1].split(' ')[0]
ssim_ok = model_txt.split('Median SSIM value: ', 2)[2].split(' ')[0]
ssim_ratio = model_txt.split('SSIM ratio: ', 1)[1].split(' ')[0]
dictionary[dataset_name]['models'][model_name] = {
"model_metrics": {
"p_nok": float(pearson_nok),
"p_ok": float(pearson_ok),
"pearson_r": float(pearson_c),
"ssim_nok": float(ssim_nok),
"ssim_ok": float(ssim_ok),
"ssim_r": float(ssim_ratio)},
"f_exts":{}
}
except:
print(model_name, "failed evaluation at the dataset", dataset_name, "...skipping")
pearson_nok = 100
pearson_ok = 100
pearson_c = 100
ssim_nok = 100
ssim_ok = 100
ssim_ratio = 100
dictionary[dataset_name]['models'][model_name] = {
"model_metrics": {
"p_nok": float(pearson_nok),
"p_ok": float(pearson_ok),
"pearson_r": float(pearson_c),
"ssim_nok": float(ssim_nok),
"ssim_ok": float(ssim_ok),
"ssim_r": float(ssim_ratio)},
"f_exts":{}
}
if sep == False:
f_exts_txt = model_txt.split('Feature extraction method: ')
f_exts_txt = f_exts_txt[1:]
sep = True
for f_ext_txt in f_exts_txt:
sep1 = False
f_ext_name = f_ext_txt.split('\n', 1)[0].split(' ')[0]
dictionary[dataset_name]['models'][model_name]["f_exts"][f_ext_name] = {"classifiers":{}}
if sep1 == False:
algos_txt = f_ext_txt.split('Algorithm: ')
algos_txt = algos_txt[1:5]
sep1 == True
for algo_txt in algos_txt:
algo_name, metrics = algo_txt.split('\n', 1)
metrics = metrics.split('\n')[0:8]
for i in range(len(metrics)):
metrics[i] = float(metrics[i].split(' ')[-1])
# metrics[i] = metrics[i].split(' ')[-1:]
dictionary[dataset_name]['models'][model_name]["f_exts"][f_ext_name]['classifiers'][algo_name] = { "alg_metrics":{"auc-roc": metrics[0],
"auc-pre": metrics[1],
"precision": metrics[2],
"recall": metrics[3],
"f1": metrics[4],
"tpr": metrics[5],
"tnr": metrics[6],
"balance_r": metrics[7]}}
json_object = json.dumps(dictionary, indent = 1)
with open(str_output_all, "w") as outfile:
outfile.write(json_object)
dictionary_top = {}
p_r1 = 100
ssim_r2 = 100
with open(str_output_all) as f:
log = json.load(f)
for dataset_name, models in log.items():
best_c_roc_roc = -1
best_c_roc_pre = -1
best_c_pre_roc = -1
best_c_pre_pre = -1
p_r1 = 100
ssim_r2 = 100
dictionary_top[dataset_name]={'best_model_pearson':[],
'best_model_ssim':[],
'best_classifier_roc':[],
'best_classifier_pre':[]}
# dictionary_top[dataset_name]['best_model_pearson']=defaultdict(list)
for model_name, f_exts in models['models'].items():
if f_exts['model_metrics']['pearson_r'] < p_r1 :
p_r1 = f_exts['model_metrics']['pearson_r']
ssim_r1 = f_exts['model_metrics']['ssim_r']
auc_roc1 = -1
auc_pre1 = -1
pre1 = 0
f1_1 = 0
recall1 = 0
for f_ext_name, f_ext in f_exts['f_exts'].items():
for classifier_name, classifiers in f_ext['classifiers'].items():
if classifiers['alg_metrics']['auc-roc'] > auc_roc1 and classifiers['alg_metrics']['auc-pre'] > auc_pre1:
auc_roc1 = classifiers['alg_metrics']['auc-roc']
auc_pre1 = classifiers['alg_metrics']['auc-pre']
pre1 = classifiers['alg_metrics']['precision']
f1_1 = classifiers['alg_metrics']['f1']
recall1 = classifiers['alg_metrics']['recall']
f_ext_name1 = f_ext_name
model_name1 = model_name
classifier_name1 = classifier_name
if f_exts['model_metrics']['ssim_r'] < ssim_r2:
p_r2 = f_exts['model_metrics']['pearson_r']
ssim_r2 = f_exts['model_metrics']['ssim_r']
auc_roc2 = -1
auc_pre2 = -1
pre2 = 0
f1_2 = 0
recall2 = 0
for f_ext_name, f_ext in f_exts['f_exts'].items():
for classifier_name, classifiers in f_ext['classifiers'].items():
if classifiers['alg_metrics']['auc-roc'] > auc_roc2 and classifiers['alg_metrics']['auc-pre'] > auc_pre2:
auc_roc2 = classifiers['alg_metrics']['auc-roc']
auc_pre2 = classifiers['alg_metrics']['auc-pre']
pre2 = classifiers['alg_metrics']['precision']
f1_2 = classifiers['alg_metrics']['f1']
recall2 = classifiers['alg_metrics']['recall']
model_name2 = model_name
f_ext_name2 = f_ext_name
classifier_name2 = classifier_name
dictionary_top[dataset_name]['best_model_ssim'].append({
'model':model_name2,
'model_metrics': {
"pearson_r": float(p_r2),
"ssim_r": float(ssim_r2)},
'f_ext':f_ext_name2,
'classifier': classifier_name2,
'classifier_metrics': {'auc-rc': auc_roc2,
'auc-pre': auc_pre2,
'f1': f1_2,
'recall': recall2,
'precision': pre2 }})
dictionary_top[dataset_name]['best_model_pearson'].append({
'model':model_name1,
'model_metrics': {
"pearson_r": float(p_r1),
"ssim_r": float(ssim_r1)},
'f_ext':f_ext_name1,
'classifier': classifier_name1,
'classifier_metrics': { 'auc-roc': auc_roc1,
'auc-pre': auc_pre1,
'f1': f1_1,
'recall': recall1,
'precision': pre1 }})
for model_name, f_exts in models['models'].items():
if f_exts['model_metrics']['pearson_r'] == p_r1 and model_name not in [item['model'] for item in dictionary_top[dataset_name]['best_model_pearson']]:
p_r1 = f_exts['model_metrics']['pearson_r']
ssim_r1 = f_exts['model_metrics']['ssim_r']
for f_ext_name, f_ext in f_exts['f_exts'].items():
for classifier_name, classifiers in f_ext['classifiers'].items():
if classifiers['alg_metrics']['auc-roc'] >= auc_roc1 and classifiers['alg_metrics']['auc-pre'] >= auc_pre1:
auc_roc1 = classifiers['alg_metrics']['auc-roc']
auc_pre1 = classifiers['alg_metrics']['auc-pre']
pre1 = classifiers['alg_metrics']['precision']
f1_1 = classifiers['alg_metrics']['f1']
recall1 = classifiers['alg_metrics']['recall']
f_ext_name1 = f_ext_name
model_name1 = model_name
classifier_name1 = classifier_name
dictionary_top[dataset_name]['best_model_pearson'].append({
'model':model_name1,
'model_metrics': {
"pearson_r": float(p_r1),
"ssim_r": float(ssim_r1)},
'f_ext':f_ext_name1,
'classifier': classifier_name1,
'classifier_metrics': { 'auc-roc': auc_roc1,
'auc-pre': auc_pre1,
'f1': f1_2,
'recall': recall2,
'precision': pre2 }})
if f_exts['model_metrics']['ssim_r'] == ssim_r2 and model_name not in [item['model'] for item in dictionary_top[dataset_name]['best_model_ssim']]:
p_r2 = f_exts['model_metrics']['pearson_r']
ssim_r2 = f_exts['model_metrics']['ssim_r']
for f_ext_name, f_ext in f_exts['f_exts'].items():
for classifier_name, classifiers in f_ext['classifiers'].items():
if classifiers['alg_metrics']['auc-roc'] >= auc_roc2 and classifiers['alg_metrics']['auc-pre'] >= auc_pre2:
auc_roc2 = classifiers['alg_metrics']['auc-roc']
auc_pre2 = classifiers['alg_metrics']['auc-pre']
pre2 = classifiers['alg_metrics']['precision']
f1_2 = classifiers['alg_metrics']['f1']
recall2 = classifiers['alg_metrics']['recall']
model_name2 = model_name
f_ext_name2 = f_ext_name
classifier_name2 = classifier_name
dictionary_top[dataset_name]['best_model_ssim'].append({
'model':model_name2,
'model_metrics': {
"pearson_r": float(p_r2),
"ssim_r": float(ssim_r2)},
'f_ext':f_ext_name2,
'classifier': classifier_name2,
'classifier_metrics': {'auc-rc': auc_roc2,
'auc-pre': auc_pre2,
'f1': f1_2,
'recall': recall2,
'precision': pre2 }})
best_c_roc_roc = -1
best_c_roc_pre = -1
best_c_pre_roc = -1
best_c_pre_pre = -1
for model_name, f_exts in models['models'].items():
for f_ext_name, f_ext in f_exts['f_exts'].items():
for classifier_name, classifiers in f_ext['classifiers'].items():
if classifiers['alg_metrics']['auc-roc'] > best_c_roc_roc:
best_c_roc_roc = classifiers['alg_metrics']['auc-roc']
best_c_roc_pre = classifiers['alg_metrics']['auc-pre']
best_pre2 = classifiers['alg_metrics']['precision']
best_f1_2 = classifiers['alg_metrics']['f1']
best_recall2 = classifiers['alg_metrics']['recall']
best_c_model_name1 = model_name
best_c_p_r2 = f_exts['model_metrics']['pearson_r']
best_c_ssim_r2 = f_exts['model_metrics']['ssim_r']
best_c_f_ext_name1 = f_ext_name
best_c_classifier_name1 = classifier_name
if classifiers['alg_metrics']['auc-pre'] > best_c_pre_pre:
best_c_pre_roc = classifiers['alg_metrics']['auc-roc']
best_c_pre_pre = classifiers['alg_metrics']['auc-pre']
best_pre1 = classifiers['alg_metrics']['precision']
best_f1_1 = classifiers['alg_metrics']['f1']
best_recall1 = classifiers['alg_metrics']['recall']
best_c_model_name2 = model_name
best_c_p_r1 = f_exts['model_metrics']['pearson_r']
best_c_ssim_r1 = f_exts['model_metrics']['ssim_r']
best_c_f_ext_name2 = f_ext_name
best_c_classifier_name2 = classifier_name
dictionary_top[dataset_name]['best_classifier_roc'].append({
'model':best_c_model_name1,
'model_metrics': {
"pearson_r": float(best_c_p_r1),
"ssim_r": float(best_c_ssim_r1)},
'f_ext':best_c_f_ext_name1,
'classifier': best_c_classifier_name1,
'classifier_metrics': { 'auc-roc': best_c_roc_roc,
'auc-pre': best_c_roc_pre,
'f1': best_f1_1,
'recall': best_recall1,
'precision': best_pre1 }})
dictionary_top[dataset_name]['best_classifier_pre'].append({
'model':best_c_model_name2,
'model_metrics': {
"pearson_r": float(best_c_p_r2),
"ssim_r": float(best_c_ssim_r2)},
'f_ext':best_c_f_ext_name2,
'classifier': best_c_classifier_name2,
'classifier_metrics': { 'auc-roc': best_c_pre_roc,
'auc-pre': best_c_pre_pre,
'f1': best_f1_2,
'recall': best_recall2,
'precision': best_pre2 }})
for model_name, f_exts in models['models'].items():
for f_ext_name, f_ext in f_exts['f_exts'].items():
for classifier_name, classifiers in f_ext['classifiers'].items():
if classifiers['alg_metrics']['auc-roc'] == best_c_roc_roc and classifier_name not in [item['classifier'] for item in dictionary_top[dataset_name]['best_classifier_roc']]:
best_c_roc_roc = classifiers['alg_metrics']['auc-roc']
best_c_roc_pre = classifiers['alg_metrics']['auc-pre']
best_pre1 = classifiers['alg_metrics']['precision']
best_f1_1 = classifiers['alg_metrics']['f1']
best_recall1 = classifiers['alg_metrics']['recall']
best_c_p_r1 = f_exts['model_metrics']['pearson_r']
best_c_ssim_r1 = f_exts['model_metrics']['ssim_r']
best_c_model_name1 = model_name
best_c_p_r1 = best_c_p_r1
best_c_ssim_r1 = best_c_ssim_r1
best_c_f_ext_name1 = f_ext_name
best_c_classifier_name1 = classifier_name
dictionary_top[dataset_name]['best_classifier_roc'].append({
'model':best_c_model_name1,
'model_metrics': {
"pearson_r": float(best_c_p_r1),
"ssim_r": float(best_c_ssim_r1)},
'f_ext':best_c_f_ext_name1,
'classifier': best_c_classifier_name1,
'classifier_metrics': { 'auc-roc': best_c_roc_roc,
'auc-pre': best_c_roc_pre,
'f1': best_f1_1,
'recall': best_recall1,
'precision': best_pre1 }})
if classifiers['alg_metrics']['auc-pre'] == best_c_pre_pre and classifier_name not in [item['classifier'] for item in dictionary_top[dataset_name]['best_classifier_pre']]:
best_c_pre_roc = classifiers['alg_metrics']['auc-roc']
best_c_pre_pre = classifiers['alg_metrics']['auc-pre']
best_pre2 = classifiers['alg_metrics']['precision']
best_f1_2 = classifiers['alg_metrics']['f1']
best_recall2 = classifiers['alg_metrics']['recall']
best_c_model_name2 = model_name
best_c_p_r2 = f_exts['model_metrics']['pearson_r']
best_c_ssim_r2 = f_exts['model_metrics']['ssim_r']
best_c_f_ext_name2 = f_ext_name
best_c_classifier_name2 = classifier_name
dictionary_top[dataset_name]['best_classifier_pre'].append({
'model':best_c_model_name2,
'model_metrics': {
"pearson_r": float(best_c_p_r2),
"ssim_r": float(best_c_ssim_r2)},
'f_ext':best_c_f_ext_name2,
'classifier': best_c_classifier_name2,
'classifier_metrics': { 'auc-roc': best_c_pre_roc,
'auc-pre': best_c_pre_pre,
'f1': best_f1_2,
'recall': best_recall2,
'precision': best_pre2 }})
json_object = json.dumps(dictionary_top, indent = 1)
with open(str_output, "w") as outfile:
outfile.write(json_object)
if __name__ == '__main__':
main()