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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, :, 0].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 | |
| n_err_mid, n_err_iqr = get_err_median_and_iqr(test_predict, test_gt) | |
| 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) | |
| 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] | |
| 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) | |
| thresold = np.max(normal_scores) | |
| pred_labels = np.zeros(len(total_topk_err_scores)) | |
| pred_labels[total_topk_err_scores > thresold] = 1 | |
| for i in range(len(pred_labels)): | |
| pred_labels[i] = int(pred_labels[i]) | |
| 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 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)): | |
| pred_labels[i] = int(pred_labels[i]) | |
| gt_labels[i] = int(gt_labels[i]) | |
| 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 | |