evaluation.py 13.00 KiB
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import argparse
import os
import numpy as np
from get_interval_anomaly import IntervalAnomaly
import time
from logger import Logger
class Evaluator():
def __init__(self, anomaly_score_label_file, th_range = [-10,10], th_step = 1, log_path='', log_file=''):
self.anomaly_score_label_file = anomaly_score_label_file
self.th_range = th_range
self.th_step = th_step
self.log_path = log_path
self.log_file = log_file
self.label = []
self.eval_metrics = {}
self.best_eval_metrics = ''
self.f1_best = 0
self.pr_auc = 0
self.ground_truth_anomaly_intervals = []
self.detected_anomaly_intervals = []
self.reconstructed_anomaly_intervals = []
self.detected_result = []
self.timestamp_detected_result = []
self.logger = Logger(self.log_path, self.log_file)
def get_ground_truth_anomaly_intervals(self, timestamp_anomalyscore_label):
ground_truth_anomaly_intervals = []
IA_ground_truth = IntervalAnomaly()
for i in range(len(timestamp_anomalyscore_label[0])):
if timestamp_anomalyscore_label[2][i] == "Anomaly":
isAnomaly = True
else:
isAnomaly = False
IA_ground_truth.IntervalAnomalyDetect(timestamp_anomalyscore_label[0][i], isAnomaly, ground_truth_anomaly_intervals)
del IA_ground_truth
return ground_truth_anomaly_intervals
def get_detected_anomaly_intervals(self, th, timestamp_anomalyscore_label):
anomaly_detected = []
detected_anomaly_intervals = []
for idx in range(len(timestamp_anomalyscore_label[0])):
if float(timestamp_anomalyscore_label[1][idx]) <= th:
anomaly_detected.append(True)
else:
anomaly_detected.append(False)
IA_detected = IntervalAnomaly()
for k in range(len(anomaly_detected)):
IA_detected.IntervalAnomalyDetect(timestamp_anomalyscore_label[0][k],anomaly_detected[k],detected_anomaly_intervals)
del IA_detected
return detected_anomaly_intervals
def get_reconstruct_detected_anomaly_intervals(self, timestamp_anomalyscore_label,
ground_truth_anomaly_intervals, detected_anomaly_intervals):
detected_anomaly_intervals_tmp = detected_anomaly_intervals
for i in range(len(timestamp_anomalyscore_label[0])):
current_timestamp = int(time.mktime(time.strptime(timestamp_anomalyscore_label[0][i], "%Y%m%d%H%M%S")))
for j in range(len(ground_truth_anomaly_intervals)):
start_gt = int(time.mktime(time.strptime(ground_truth_anomaly_intervals[j][0], "%Y%m%d%H%M%S")))
end_gt = int(time.mktime(time.strptime(ground_truth_anomaly_intervals[j][1], "%Y%m%d%H%M%S")))
if current_timestamp >= start_gt and current_timestamp <= end_gt:
for k in range(len(detected_anomaly_intervals)):
start_dt = int(time.mktime(time.strptime(detected_anomaly_intervals[k][0], "%Y%m%d%H%M%S")))
end_dt = int(time.mktime(time.strptime(detected_anomaly_intervals[k][1], "%Y%m%d%H%M%S")))
if current_timestamp >= start_dt and current_timestamp <= end_dt:
detected_anomaly_intervals_tmp[k] = ground_truth_anomaly_intervals[j]
return detected_anomaly_intervals_tmp
def get_detected_result(self, reconstruct_detected_anomaly_intervals,timestamp_anomalyscore_label):
timestamp_detected_result = []
detected_result = []
for i in range(len(timestamp_anomalyscore_label[0])):
current_timestamp = int(time.mktime(time.strptime(timestamp_anomalyscore_label[0][i], "%Y%m%d%H%M%S")))
flag = False
for j in range(len(reconstruct_detected_anomaly_intervals)):
start_gt = int(time.mktime(time.strptime(reconstruct_detected_anomaly_intervals[j][0], "%Y%m%d%H%M%S")))
end_gt = int(time.mktime(time.strptime(reconstruct_detected_anomaly_intervals[j][1], "%Y%m%d%H%M%S")))
if current_timestamp >= start_gt and current_timestamp <= end_gt:
flag = True
break
timestamp_detected_result.append(str(timestamp_anomalyscore_label[0][i])+","+str(flag))
detected_result.append(flag)
return timestamp_detected_result,detected_result
def get_metrics(self, label, detected_result):
assert len(label) == len(detected_result)
TP = 0
FP = 0
TN = 0
FN = 0
for i in range(len(label)):
if label[i]==True and detected_result[i] == True:
TP = TP+1
elif label[i]==True and detected_result[i] == False:
FN = FN+1
elif label[i]==False and detected_result[i] == False:
TN = TN+1
elif label[i]==False and detected_result[i] == True:
FP = FP+1
if TP+FP-0 != 0:
precision = TP/(TP+FP)
else:
precision = 0
if TP+FN-0 != 0:
recall = TP/(TP+FN)
tpr = TP/(TP+FN)
fnr = FN/(TP+FN)
else:
recall = 0
fnr = 0
tpr = 0
if FP+TN-0 != 0:
tnr = TN/(FP+TN)
fpr = FP/(FP+TN)
else:
tnr = 0
fpr = 0
if precision+recall-0 != 0:
f1 = 2*precision*recall/(precision+recall)
else:
f1 = 0
return TP, FN, TN, FP, precision, recall, f1, fpr, tpr
def get_label(self,timestamp_anomalyscore_label):
label = []
for idx in range(len(timestamp_anomalyscore_label[2])):
if timestamp_anomalyscore_label[2][idx] == "Anomaly":
label.append(True)
else:
label.append(False)
return label
def perform_evaluating(self):
timestamp_anomalyscore_label1 = np.loadtxt(self.anomaly_score_label_file, delimiter=',', dtype=bytes, unpack=False).astype(str)
timestamp_anomalyscore_label2 = timestamp_anomalyscore_label1.tolist()
timestamp_anomalyscore_label2.sort()
timestamp_anomalyscore_label3 = [[],[],[]]
for i in range(len(timestamp_anomalyscore_label2)):
timestamp_anomalyscore_label3[0].append(timestamp_anomalyscore_label2[i][0])
timestamp_anomalyscore_label3[1].append(timestamp_anomalyscore_label2[i][1])
timestamp_anomalyscore_label3[2].append(timestamp_anomalyscore_label2[i][2])
timestamp_anomalyscore_label = np.array(timestamp_anomalyscore_label3)
anomaly_score_min = np.min(timestamp_anomalyscore_label[1].astype(float),axis=0)
anomaly_score_max = np.max(timestamp_anomalyscore_label[1].astype(float),axis=0)
if self.th_range[1] >= anomaly_score_max:
if self.th_range[0] >= anomaly_score_min:
threshold_candidates = [t for t in np.arange(self.th_range[0],anomaly_score_max,self.th_step)]
else:
threshold_candidates = [t for t in np.arange(anomaly_score_min,anomaly_score_max,self.th_step)]
else:
if self.th_range[0] >= anomaly_score_min:
threshold_candidates = [t for t in np.arange(self.th_range[0],self.th_range[1],self.th_step)]
else:
threshold_candidates = [t for t in np.arange(anomaly_score_min,self.th_range[1],self.th_step)]
self.ground_truth_anomaly_intervals = self.get_ground_truth_anomaly_intervals(timestamp_anomalyscore_label)
fscore = {}
for th in threshold_candidates:
self.detected_anomaly_intervals = self.get_detected_anomaly_intervals(th, timestamp_anomalyscore_label)
self.reconstruct_detected_anomaly_intervals = self.get_reconstruct_detected_anomaly_intervals(timestamp_anomalyscore_label,
self.ground_truth_anomaly_intervals, self.detected_anomaly_intervals)
self.timestamp_detected_result,self.detected_result = self.get_detected_result(self.reconstruct_detected_anomaly_intervals,
timestamp_anomalyscore_label)
self.label = self.get_label(timestamp_anomalyscore_label)
TP, FN, TN, FP, precision, recall, f1, fpr, tpr = self.get_metrics(self.label, self.detected_result)
fscore[f1] = "th:{}, p:{}, r:{}, f1score:{}, TP:{}, FN:{}, TN:{}, FP:{}, FPR:{}, TPR:{}".format(
th, precision, recall, f1,TP, FN, TN, FP,fpr, tpr)
self.eval_metrics['Th'] = th
self.eval_metrics['P'] = precision
self.eval_metrics['R'] = recall
self.eval_metrics['F1score'] = f1
self.eval_metrics['TP'] = TP
self.eval_metrics['FN']= FN
self.eval_metrics['TN'] = TN
self.eval_metrics['FP'] = FP
self.eval_metrics['Fpr'] = fpr
self.eval_metrics['Tpr'] = tpr
self.logger.log_evaluator(self.eval_metrics)
# If the recall has been reached to 1.0, we break the loop, due to the best f1-score has been achieved
# Since as the threshold increases, recall remains unchanged (1.0), while precision decreases and thus f1-score decreases
if float(recall) < 1.0:
continue
elif float(recall) == 1.0:
break
fscore_sorted_by_key = sorted(fscore.items(), key=lambda d:d[0], reverse = True)
self.f1_best = fscore_sorted_by_key[0][0]
self.best_eval_metrics = fscore_sorted_by_key[0][1]
self.logger.log_evaluator_re("f1-best is: {}".format(fscore_sorted_by_key[0][0]))
self.logger.log_evaluator_re("details: {}".format(fscore_sorted_by_key[0][1]))
def main():
parser = argparse.ArgumentParser()
# GPU option
parser.add_argument('--gpu_id', type=int, default=0)
# Dataset options
parser.add_argument('--dataset_path', type=str, default='')
parser.add_argument('--data_nums', type=int, default=0)
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--num_workers', type=int, default=4)
parser.add_argument('--T', type=int, default=20)
parser.add_argument('--win_size', type=int, default=36)
parser.add_argument('--l', type=int, default=10)
parser.add_argument('--n', type=int, default=24)
# Model options
parser.add_argument('--s_dims', type=int, default=8)
parser.add_argument('--d_dims', type=int, default=10)
parser.add_argument('--conv_dims', type=int, default=100)
parser.add_argument('--hidden_dims', type=int, default=40)
parser.add_argument('--enc_dec', type=str, default='CNN')
# Training options
parser.add_argument('--learning_rate', type=float, default=0.0002)
parser.add_argument('--epochs', type=int, default=50)
parser.add_argument('--start_epoch', type=int, default=0)
parser.add_argument('--checkpoints_path', type=str, default='')
parser.add_argument('--checkpoints_interval', type=int, default=10)
parser.add_argument('--log_path', type=str, default='log_evaluator')
parser.add_argument('--log_file', type=str, default='')
parser.add_argument('--llh_path', type=str, default='log_tester')
parser.add_argument('--llh_file', type=str, default='')
parser.add_argument('--th_min', type=float, default=-50)
parser.add_argument('--th_max', type=float, default=10)
parser.add_argument('--th_step', type=float, default=0.2)
args = parser.parse_args()
if args.llh_file == '':
args.ll_file = 'sdim{}_ddim{}_cdim{}_hdim{}_winsize{}_T{}_l{}_epochs{}_loss.txt'.format(
args.s_dims,
args.d_dims,
args.conv_dims,
args.hidden_dims,
args.win_size,
args.T,
args.l,
args.start_epoch)
if args.log_file == '':
args.log_file = 'sdim{}_ddim{}_cdim{}_hdim{}_winsize{}_T{}_l{}_epochs{}_eval_records'.format(
args.s_dims,
args.d_dims,
args.conv_dims,
args.hidden_dims,
args.win_size,
args.T,
args.l,
args.start_epoch)
if not os.path.exists(os.path.join(args.llh_path,args.llh_file)):
raise ValueError('Unknown anomaly score label file: {}'.format(args.llh_path))
if not os.path.exists(args.log_path):
os.makedirs(args.log_path)
anomaly_score_label_file = os.path.join(args.llh_path,args.ll_file)
evaluator = Evaluator(anomaly_score_label_file,
th_range = [args.th_min,args.th_max],
th_step = args.th_step,
log_path = args.log_path,
log_file = args.log_file)
evaluator.perform_evaluating()
if __name__ == '__main__':
main()