def genROCFile(name, data, threshold=0.05):
resultRow = []
resultRow.append(name)
resultRow.append(threshold)
true_alerts = 0.0
false_alerts = 0.0
total_alert = 0.0
pos = 0.0
neg = 0.0
found = []
for i, file_name in enumerate(data.get_files_test()):
ut.checkFolderandCreate("{}\\Changepoint\\{}".format(main_path, algo_name))
pickle_file = '{}\\Changepoint\\{}\\_{}_threshold{}_index_{:03d}.pkl'.format \
(main_path, algo_name, name, threshold, i)
instances = data.get_dataset_test(i)
if not os.path.exists(pickle_file):
change_points,max_num, max_bin = dybin(instances=instances, threshold=threshold)
ut.checkFolderandCreate("{}\\listbin".format(main_path))
ut.checkFolderandCreate("{}\\listbin\\{}".format(main_path, algo_name))
txt_file = '{}\\listbin\\{}\\_{}_threshold{}_index_{:03d}.txt'.format \
(main_path, algo_name, name, threshold, i)
ut_light.export_list_bin_txt(max_num=max_num,max_bin=max_bin,txt_file=txt_file)
with open(pickle_file, 'wb') as output:
pickle.dump(change_points, output)
else:
with open(pickle_file, 'rb') as pickle_input:
change_points = pickle.load(pickle_input)
start_end = data.get_dataset_answer_st_ed(index=i)[0]
row = ut_light.calRowTF_NoBin(start=start_end[0], end=start_end[1], change_points=change_points,
size_instances=len(instances),
bin_size=bin_size)
true_alerts = true_alerts + row[0]
false_alerts = false_alerts + row[1]
total_alert = total_alert + row[2]
pos = pos + row[3]
neg = neg + row[4]
found.append(row[9])
resultRow = []
resultRow.append(name)
resultRow.append(threshold)
resultRow.append(true_alerts)
resultRow.append(false_alerts)
resultRow.append(total_alert)
resultRow.append(pos)
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
def genROCFile(name, data, threshold=0.05):
resultRow = []
resultRow.append(name)
resultRow.append(threshold)
true_alerts = 0.0
false_alerts = 0.0
total_alert = 0.0
pos = 0.0
neg = 0.0
found = []
for i, file_name in enumerate(data.get_files_test()):
instances = data.get_dataset_test(i)
start_end = data.get_dataset_answer_st_ed(index=i)[0]
change_points_list = []
for bin_size in bin_sizes:
algo_name = "skbin_{}".format(bin_size)
pickle_file = '{}\\Changepoint\\{}\\_{}_threshold{}_index_{:03d}.pkl'.format \
(main_path, algo_name, name, threshold, i)
if os.path.exists(pickle_file):
with open(pickle_file, 'rb') as pickle_input:
change_points = pickle.load(pickle_input)
change_points = ut_light.gen_change_points_list(
change_points=change_points, bin_size=bin_size)
change_points_list.append(change_points)
SEA_change_points = union_block(change_points_list=change_points_list)
row = ut_light.calRowTF_NoBin(start=start_end[0],
end=start_end[1],
change_points=SEA_change_points,
size_instances=len(instances),
bin_size=1)
true_alerts = true_alerts + row[0]
false_alerts = false_alerts + row[1]
total_alert = total_alert + row[2]
pos = pos + row[3]
neg = neg + row[4]
found.append(row[9])
resultRow = []
resultRow.append(name)
resultRow.append(threshold)
resultRow.append(true_alerts)
resultRow.append(false_alerts)
resultRow.append(total_alert)
resultRow.append(pos)
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