resultRow.append(neg)
tp_rate = float(true_alerts) / pos
resultRow.append(tp_rate)
fp_rate = float(false_alerts) / neg
resultRow.append(fp_rate)
resultRow.append(sum(found))
return resultRow
if __name__ == '__main__':
ut.list_to_csv(head=head)
result = []
df_list = []
name_list = ut_light.gen_list_title(data_type='bgs')
for index, name in enumerate(name_list[0:5]):
for threshold in thresholds:
ut.checkFolderandCreate("{}\\ROC\\{}".format(main_path, algo_name))
pickle_ROC_file = '{}\\ROC\\{}\\_{}_threshold{}.pkl'.format(
main_path, algo_name, name, threshold)
if not os.path.exists(pickle_ROC_file):
result = []
data = ut_light.load_light_select_dataset(input_path=path,
file_name=name)
print("##### cal name : {} , threshold = {} #####".format(
name, threshold))
ROC_row = genROCFile(name=name, data=data, threshold=threshold)
result.append(ROC_row)
df = pd.DataFrame(result, columns=ROC_head)
with open(pickle_ROC_file, 'wb') as output:
tp_rate = float(true_alerts) / pos
resultRow.append(tp_rate)
fp_rate = float(false_alerts) / neg
resultRow.append(fp_rate)
resultRow.append(sum(found))
return resultRow
if __name__ == '__main__':
ut.list_to_csv(head=head)
result = []
df_list = []
# name_list = ut_light.gen_list_mix(main_path=dataset_path,data_type='bgs',mix_size="*sq_L3_I30_*")
name_list = ut_light.gen_list_title(data_type='pca')
for index, name in enumerate(name_list):
listWindow = []
data = ut_light.load_light_select_dataset(input_path=path,
file_name=name,
is_mix=is_mix)
# for index_tran in range(data.get_len_tran()):
for index_tran in range(2):
coreSketch = dySketch(alpha_mean=alpha_mean,
alpha_var=alpha_var,
e=e,
w_size=WINDOW_SIZE,
bin_size=bin_size)
instances = data.get_dataset_test(int(index_tran))
for i, instance in enumerate(instances):
if i == len(instances) - 1: