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evaluate.py
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from util.data import *
import numpy as np
from sklearn.metrics import precision_score, recall_score, roc_auc_score, f1_score
def get_full_err_scores(test_result, val_result):
np_test_result = np.array(test_result)
np_val_result = np.array(val_result)
all_scores = None
all_normals = None
feature_num = np_test_result.shape[-1]
labels = np_test_result[2, :].tolist()
for i in range(feature_num):
test_re_list = np_test_result[:2,:,i]
val_re_list = np_val_result[:2,:,i]
scores = get_err_scores(test_re_list, val_re_list)
normal_dist = get_err_scores(val_re_list, val_re_list)
if all_scores is None:
all_scores = scores
all_normals = normal_dist
else:
all_scores = np.vstack((
all_scores,
scores
))
all_normals = np.vstack((
all_normals,
normal_dist
))
return all_scores, all_normals
def get_final_err_scores(test_result, val_result):
full_scores, all_normals = get_full_err_scores(test_result, val_result, return_normal_scores=True)
all_scores = np.max(full_scores, axis=0)
return all_scores
def get_err_scores(test_res, val_res):
test_predict, test_gt = test_res
val_predict, val_gt = val_res
test_gt1=[]
test_predict1=[]
for i in range(test_predict.shape[0]):
test_gt1.append(np.array(test_gt[i]))
test_predict1.append(np.array(test_predict[i]))
#print(test_predict.shape)
#print(test_gt.shape)
test_predict1 = np.array(test_predict1)
test_gt1 = np.array(test_gt1)
n_err_mid, n_err_iqr = get_err_median_and_iqr(test_predict1, test_gt1)
test_delta = np.abs(np.subtract(
np.array(test_predict).astype(np.float64),
np.array(test_gt).astype(np.float64)
))
epsilon=1e-2
err_scores = (test_delta - n_err_mid) / ( np.abs(n_err_iqr) +epsilon)
smoothed_err_scores = np.zeros(err_scores.shape)
before_num = 3
for i in range(before_num, len(err_scores)):
smoothed_err_scores[i] = np.mean(err_scores[i-before_num:i+1])
return smoothed_err_scores
def get_loss(predict, gt):
return eval_mseloss(predict, gt)
def get_f1_scores(total_err_scores, gt_labels, topk=1):
#print('total_err_scores', total_err_scores.shape)
# remove the highest and lowest score at each timestep
total_features = total_err_scores.shape[0]
# topk_indices = np.argpartition(total_err_scores, range(total_features-1-topk, total_features-1), axis=0)[-topk-1:-1]
topk_indices = np.argpartition(total_err_scores, range(total_features-topk-1, total_features), axis=0)[-topk:]
topk_indices = np.transpose(topk_indices)
#print(topk_indices)
total_topk_err_scores = []
topk_err_score_map=[]
# topk_anomaly_sensors = []
"""
for i, indexs in enumerate(topk_indices):
sum_score = sum( score for k, score in enumerate(sorted([total_err_scores[index, i] for j, index in enumerate(indexs)])) )
total_topk_err_scores.append(sum_score)
"""
final_topk_fmeas = eval_scores(total_topk_err_scores, gt_labels, 400)
return final_topk_fmeas
def get_val_performance_data(total_err_scores, normal_scores, gt_labels, topk=1):
total_features = total_err_scores.shape[0]
#print(total_err_scores.shape)
topk_indices = np.argpartition(total_err_scores, range(total_features-topk-1, total_features), axis=0)[-topk:]
np.set_printoptions(threshold=np.inf)
#print(topk_indices.shape)
total_topk_err_scores = []
topk_err_score_map=[]
#np.save("loss_channel.npy",total_err_scores)
total_topk_err_scores = np.sum(np.take_along_axis(total_err_scores, topk_indices, axis=0), axis=0)
#np.save("loss_avg.npy", total_err_scores)
#print(total_topk_err_scores.shape)
mean = normal_scores.mean()
std = normal_scores.std()
per_list = np.percentile(normal_scores, (25, 50, 75), interpolation='midpoint')
q3 = per_list[2]
iqr = per_list[2]-per_list[0]
thresold2 = q3 + 1.5*iqr
thresold = mean + 3*std
#print(thresold)
#print(thresold2)
pred_labels = np.zeros(len(total_topk_err_scores))
pred_labels[total_topk_err_scores > thresold2] = 1
for i in range(len(pred_labels)):
pred_labels[i] = int(pred_labels[i])
#print(gt_labels)
gt_labels[i] = int(gt_labels[i])
pre = precision_score(gt_labels, pred_labels)
rec = recall_score(gt_labels, pred_labels)
f1 = f1_score(gt_labels, pred_labels)
auc_score = roc_auc_score(gt_labels, total_topk_err_scores)
#return 1
return f1, pre, rec, auc_score, thresold
def get_best_performance_data(total_err_scores, gt_labels, topk=1):
total_features = total_err_scores.shape[0]
# topk_indices = np.argpartition(total_err_scores, range(total_features-1-topk, total_features-1), axis=0)[-topk-1:-1]
topk_indices = np.argpartition(total_err_scores, range(total_features-topk-1, total_features), axis=0)[-topk:]
total_topk_err_scores = []
topk_err_score_map=[]
total_topk_err_scores = np.sum(np.take_along_axis(total_err_scores, topk_indices, axis=0), axis=0)
final_topk_fmeas ,thresolds = eval_scores(total_topk_err_scores, gt_labels, 400, return_thresold=True)
th_i = final_topk_fmeas.index(max(final_topk_fmeas))
thresold = thresolds[th_i]
pred_labels = np.zeros(len(total_topk_err_scores))
pred_labels[total_topk_err_scores > thresold] = 1
for i in range(len(pred_labels)):
#print(pred_labels[i])
#print(gt_labels[i])
pred_labels[i] = int(pred_labels[i])
gt_labels[i] = int(gt_labels[i][1])
pre = precision_score(gt_labels, pred_labels)
rec = recall_score(gt_labels, pred_labels)
auc_score = roc_auc_score(gt_labels, total_topk_err_scores)
return max(final_topk_fmeas), pre, rec, auc_score, thresold