D:\software\Anaconda\envs\py37\python.exe I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py
I:\Papers\AAAI2022anomaly\TAD_SCINet\TADib\datasets\SNetDataset.py:64: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  data = torch.tensor(data).double()  # torch.Size([27, 2049])
I:\Papers\AAAI2022anomaly\TAD_SCINet\TADib\datasets\SNetDataset.py:66: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  self.raw_data = torch.tensor(self.raw_data).double()
I:\Papers\AAAI2022anomaly\TAD_SCINet\TADib\datasets\SNetDataset.py:65: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  labels = torch.tensor(labels).double()  # 2049
level number 7, level details: [[1, 1], [1, 1], [1, 1], [0, 0], [0, 0], [0, 0], [0, 0]]
Model_type: BiSeqMask
layer number: 3
layer number: 3
Namespace(INN=1, batch=16, comment='', dataset='ecg', decay=0, device=device(type='cuda'), dropout=0.2, ensemble=0, epoch=50, groups=1, hidden_size=16, kernel=3, learning_rate=0.001, load_model_path='', lradj=3, model_name='SCINet', model_type='BiSeqMask', out_layer_inter_dim=256, out_layer_num=1, point_part=4, pred_len=64, random_seed=4321, report='best', save_path='ECG_Results', save_path_pattern='msl', seq_mask_range_high=4, seq_mask_range_low=4, slide_stride=2, slide_win=256, topk=20, val_ratio=0.1, variate_index=2)
	iters: 10, epoch: 1 | loss: 0.8037223
	speed: 0.3864s/iter
	iters: 20, epoch: 1 | loss: 0.7182185
	speed: 0.1672s/iter
	iters: 30, epoch: 1 | loss: 0.5444801
	speed: 0.1117s/iter
	iters: 40, epoch: 1 | loss: 0.5612354
	speed: 0.0824s/iter
	iters: 50, epoch: 1 | loss: 0.5309291
	speed: 0.0600s/iter
	iters: 60, epoch: 1 | loss: 0.4895285
	speed: 0.0483s/iter
	iters: 70, epoch: 1 | loss: 0.4628226
	speed: 0.0410s/iter
epoch (0 / 50) (Loss:0.61106783, ACU_loss:46.44115528)
epoch (0 / 50) (val_loss --- Loss:0.98860944
	iters: 80, epoch: 2 | loss: 0.4803781
	speed: 0.0469s/iter
	iters: 90, epoch: 2 | loss: 0.4798109
	speed: 0.0369s/iter
	iters: 100, epoch: 2 | loss: 0.4506442
	speed: 0.0304s/iter
	iters: 110, epoch: 2 | loss: 0.4398035
	speed: 0.0273s/iter
	iters: 120, epoch: 2 | loss: 0.3788338
	speed: 0.0247s/iter
	iters: 130, epoch: 2 | loss: 0.4089622
	speed: 0.0229s/iter
	iters: 140, epoch: 2 | loss: 0.4227061
	speed: 0.0239s/iter
	iters: 150, epoch: 2 | loss: 0.4080069
	speed: 0.0207s/iter
epoch (1 / 50) (Loss:0.42422594, ACU_loss:32.24117133)
epoch (1 / 50) (val_loss --- Loss:0.94287357
	iters: 160, epoch: 3 | loss: 0.4038354
	speed: 0.0242s/iter
	iters: 170, epoch: 3 | loss: 0.3963347
	speed: 0.0177s/iter
	iters: 180, epoch: 3 | loss: 0.3979297
	speed: 0.0162s/iter
	iters: 190, epoch: 3 | loss: 0.4075105
	speed: 0.0171s/iter
	iters: 200, epoch: 3 | loss: 0.4041900
	speed: 0.0151s/iter
	iters: 210, epoch: 3 | loss: 0.3908788
	speed: 0.0145s/iter
	iters: 220, epoch: 3 | loss: 0.3612679
	speed: 0.0143s/iter
epoch (2 / 50) (Loss:0.38356778, ACU_loss:29.15115109)
epoch (2 / 50) (val_loss --- Loss:0.94258642
	iters: 230, epoch: 4 | loss: 0.3531950
	speed: 0.0151s/iter
	iters: 240, epoch: 4 | loss: 0.3589877
	speed: 0.0129s/iter
	iters: 250, epoch: 4 | loss: 0.3760923
	speed: 0.0116s/iter
	iters: 260, epoch: 4 | loss: 0.3618917
	speed: 0.0123s/iter
	iters: 270, epoch: 4 | loss: 0.3723910
	speed: 0.0109s/iter
	iters: 280, epoch: 4 | loss: 0.3689767
	speed: 0.0110s/iter
	iters: 290, epoch: 4 | loss: 0.3604261
	speed: 0.0103s/iter
	iters: 300, epoch: 4 | loss: 0.3310773
	speed: 0.0109s/iter
epoch (3 / 50) (Loss:0.36318197, ACU_loss:27.60182983)
epoch (3 / 50) (val_loss --- Loss:0.93324462
	iters: 310, epoch: 5 | loss: 0.3599105
	speed: 0.0120s/iter
	iters: 320, epoch: 5 | loss: 0.3115507
	speed: 0.0095s/iter
	iters: 330, epoch: 5 | loss: 0.3733158
	speed: 0.0097s/iter
	iters: 340, epoch: 5 | loss: 0.3511162
	speed: 0.0087s/iter
	iters: 350, epoch: 5 | loss: 0.3486517
	speed: 0.0085s/iter
	iters: 360, epoch: 5 | loss: 0.3320466
	speed: 0.0084s/iter
	iters: 370, epoch: 5 | loss: 0.3605581
	speed: 0.0088s/iter
	iters: 380, epoch: 5 | loss: 0.3363302
	speed: 0.0076s/iter
epoch (4 / 50) (Loss:0.34454391, ACU_loss:26.18533698)
epoch (4 / 50) (val_loss --- Loss:0.93117557
	iters: 390, epoch: 6 | loss: 0.3399652
	speed: 0.0088s/iter
	iters: 400, epoch: 6 | loss: 0.3623479
	speed: 0.0077s/iter
	iters: 410, epoch: 6 | loss: 0.3175914
	speed: 0.0079s/iter
	iters: 420, epoch: 6 | loss: 0.3521560
	speed: 0.0073s/iter
	iters: 430, epoch: 6 | loss: 0.3340150
	speed: 0.0074s/iter
	iters: 440, epoch: 6 | loss: 0.3531510
	speed: 0.0068s/iter
	iters: 450, epoch: 6 | loss: 0.3344774
	speed: 0.0071s/iter
epoch (5 / 50) (Loss:0.32773369, ACU_loss:24.90776053)
epoch (5 / 50) (val_loss --- Loss:0.90358977
	iters: 460, epoch: 7 | loss: 0.3087769
	speed: 0.0085s/iter
	iters: 470, epoch: 7 | loss: 0.3278274
	speed: 0.0065s/iter
	iters: 480, epoch: 7 | loss: 0.2977687
	speed: 0.0062s/iter
	iters: 490, epoch: 7 | loss: 0.3286762
	speed: 0.0056s/iter
	iters: 500, epoch: 7 | loss: 0.2919122
	speed: 0.0059s/iter
	iters: 510, epoch: 7 | loss: 0.3126569
	speed: 0.0059s/iter
	iters: 520, epoch: 7 | loss: 0.2848373
	speed: 0.0060s/iter
	iters: 530, epoch: 7 | loss: 0.2938410
	speed: 0.0056s/iter
epoch (6 / 50) (Loss:0.31570621, ACU_loss:23.99367231)
epoch (6 / 50) (val_loss --- Loss:0.89792011
	iters: 540, epoch: 8 | loss: 0.2859401
	speed: 0.0067s/iter
	iters: 550, epoch: 8 | loss: 0.2740407
	speed: 0.0058s/iter
	iters: 560, epoch: 8 | loss: 0.3422293
	speed: 0.0053s/iter
	iters: 570, epoch: 8 | loss: 0.3283663
	speed: 0.0053s/iter
	iters: 580, epoch: 8 | loss: 0.2368582
	speed: 0.0051s/iter
	iters: 590, epoch: 8 | loss: 0.3078502
	speed: 0.0053s/iter
	iters: 600, epoch: 8 | loss: 0.2836710
	speed: 0.0051s/iter
epoch (7 / 50) (Loss:0.30890225, ACU_loss:23.47657081)
epoch (7 / 50) (val_loss --- Loss:0.88923223
	iters: 610, epoch: 9 | loss: 0.2885125
	speed: 0.0059s/iter
	iters: 620, epoch: 9 | loss: 0.2773306
	speed: 0.0044s/iter
	iters: 630, epoch: 9 | loss: 0.2998188
	speed: 0.0050s/iter
	iters: 640, epoch: 9 | loss: 0.3090559
	speed: 0.0046s/iter
	iters: 650, epoch: 9 | loss: 0.2977720
	speed: 0.0045s/iter
	iters: 660, epoch: 9 | loss: 0.3299947
	speed: 0.0048s/iter
	iters: 670, epoch: 9 | loss: 0.3286090
	speed: 0.0044s/iter
	iters: 680, epoch: 9 | loss: 0.3056757
	speed: 0.0051s/iter
epoch (8 / 50) (Loss:0.30210056, ACU_loss:22.95964226)
epoch (8 / 50) (val_loss --- Loss:0.87906169
	iters: 690, epoch: 10 | loss: 0.2908507
	speed: 0.0051s/iter
	iters: 700, epoch: 10 | loss: 0.3015905
	speed: 0.0046s/iter
	iters: 710, epoch: 10 | loss: 0.2835201
	speed: 0.0045s/iter
	iters: 720, epoch: 10 | loss: 0.2821807
	speed: 0.0043s/iter
	iters: 730, epoch: 10 | loss: 0.3154893
	speed: 0.0039s/iter
	iters: 740, epoch: 10 | loss: 0.3067256
	speed: 0.0040s/iter
	iters: 750, epoch: 10 | loss: 0.2730041
	speed: 0.0039s/iter
	iters: 760, epoch: 10 | loss: 0.2632248
	speed: 0.0040s/iter
epoch (9 / 50) (Loss:0.29763192, ACU_loss:22.62002629)
epoch (9 / 50) (val_loss --- Loss:0.87105514
	iters: 770, epoch: 11 | loss: 0.2942288
	speed: 0.0046s/iter
	iters: 780, epoch: 11 | loss: 0.3107002
	speed: 0.0037s/iter
	iters: 790, epoch: 11 | loss: 0.3053339
	speed: 0.0042s/iter
	iters: 800, epoch: 11 | loss: 0.2815217
	speed: 0.0039s/iter
	iters: 810, epoch: 11 | loss: 0.2931145
	speed: 0.0038s/iter
	iters: 820, epoch: 11 | loss: 0.2852653
	speed: 0.0034s/iter
	iters: 830, epoch: 11 | loss: 0.3007628
	speed: 0.0037s/iter
epoch (10 / 50) (Loss:0.29273172, ACU_loss:22.24761081)
epoch (10 / 50) (val_loss --- Loss:0.86446434
	iters: 840, epoch: 12 | loss: 0.3183612
	speed: 0.0046s/iter
	iters: 850, epoch: 12 | loss: 0.2513798
	speed: 0.0033s/iter
	iters: 860, epoch: 12 | loss: 0.2931048
	speed: 0.0033s/iter
	iters: 870, epoch: 12 | loss: 0.3116801
	speed: 0.0033s/iter
	iters: 880, epoch: 12 | loss: 0.2958695
	speed: 0.0032s/iter
	iters: 890, epoch: 12 | loss: 0.2554393
	speed: 0.0032s/iter
	iters: 900, epoch: 12 | loss: 0.2677438
	speed: 0.0034s/iter
	iters: 910, epoch: 12 | loss: 0.3013564
	speed: 0.0032s/iter
epoch (11 / 50) (Loss:0.28992113, ACU_loss:22.03400603)
epoch (11 / 50) (val_loss --- Loss:0.85370622
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.7981446664480196
=========================** Result **============================

ECG_Results
F1 score: 0.23553719008264462
precision: 0.19387755102040816
recall: 0.30158730158730157

	iters: 920, epoch: 13 | loss: 0.2764078
	speed: 0.0419s/iter
	iters: 930, epoch: 13 | loss: 0.2922880
	speed: 0.0032s/iter
	iters: 940, epoch: 13 | loss: 0.2728174
	speed: 0.0031s/iter
	iters: 950, epoch: 13 | loss: 0.2892188
	speed: 0.0031s/iter
	iters: 960, epoch: 13 | loss: 0.3065838
	speed: 0.0033s/iter
	iters: 970, epoch: 13 | loss: 0.3036748
	speed: 0.0032s/iter
	iters: 980, epoch: 13 | loss: 0.3023480
	speed: 0.0029s/iter
epoch (12 / 50) (Loss:0.28627278, ACU_loss:21.75673157)
epoch (12 / 50) (val_loss --- Loss:0.84219762
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
2.1951870618228857
=========================** Result **============================

ECG_Results
F1 score: 0.3234624145785877
precision: 0.285140562248996
recall: 0.37566137566137564

	iters: 990, epoch: 14 | loss: 0.2556538
	speed: 0.0389s/iter
	iters: 1000, epoch: 14 | loss: 0.2833404
	speed: 0.0033s/iter
	iters: 1010, epoch: 14 | loss: 0.2826574
	speed: 0.0029s/iter
	iters: 1020, epoch: 14 | loss: 0.2656829
	speed: 0.0031s/iter
	iters: 1030, epoch: 14 | loss: 0.3136618
	speed: 0.0028s/iter
	iters: 1040, epoch: 14 | loss: 0.2941912
	speed: 0.0028s/iter
	iters: 1050, epoch: 14 | loss: 0.2677503
	speed: 0.0027s/iter
	iters: 1060, epoch: 14 | loss: 0.2571600
	speed: 0.0028s/iter
epoch (13 / 50) (Loss:0.28213781, ACU_loss:21.44247340)
epoch (13 / 50) (val_loss --- Loss:0.83390356
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
2.2077222931563063
=========================** Result **============================

ECG_Results
F1 score: 0.2576112412177986
precision: 0.22784810126582278
recall: 0.2857142857142857

	iters: 1070, epoch: 15 | loss: 0.2686894
	speed: 0.0352s/iter
	iters: 1080, epoch: 15 | loss: 0.2842752
	speed: 0.0026s/iter
	iters: 1090, epoch: 15 | loss: 0.2659486
	speed: 0.0028s/iter
	iters: 1100, epoch: 15 | loss: 0.2773550
	speed: 0.0027s/iter
	iters: 1110, epoch: 15 | loss: 0.2657681
	speed: 0.0027s/iter
	iters: 1120, epoch: 15 | loss: 0.2990716
	speed: 0.0025s/iter
	iters: 1130, epoch: 15 | loss: 0.2848248
	speed: 0.0027s/iter
	iters: 1140, epoch: 15 | loss: 0.3100775
	speed: 0.0026s/iter
epoch (14 / 50) (Loss:0.27950750, ACU_loss:21.24257036)
epoch (14 / 50) (val_loss --- Loss:0.82604902
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.82452334748359
=========================** Result **============================

ECG_Results
F1 score: 0.30181086519114686
precision: 0.24429967426710097
recall: 0.3968253968253968

	iters: 1150, epoch: 16 | loss: 0.2577332
	speed: 0.0324s/iter
	iters: 1160, epoch: 16 | loss: 0.3075354
	speed: 0.0023s/iter
	iters: 1170, epoch: 16 | loss: 0.2658734
	speed: 0.0022s/iter
	iters: 1180, epoch: 16 | loss: 0.2575068
	speed: 0.0022s/iter
	iters: 1190, epoch: 16 | loss: 0.2936451
	speed: 0.0022s/iter
	iters: 1200, epoch: 16 | loss: 0.2907271
	speed: 0.0024s/iter
	iters: 1210, epoch: 16 | loss: 0.2670428
	speed: 0.0022s/iter
epoch (15 / 50) (Loss:0.27698821, ACU_loss:21.05110382)
epoch (15 / 50) (val_loss --- Loss:0.82019720
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.7370127206188806
=========================** Result **============================

ECG_Results
F1 score: 0.2840690978886756
precision: 0.22054380664652568
recall: 0.3862433862433862

	iters: 1220, epoch: 17 | loss: 0.2604119
	speed: 0.0315s/iter
	iters: 1230, epoch: 17 | loss: 0.2807103
	speed: 0.0023s/iter
	iters: 1240, epoch: 17 | loss: 0.2562866
	speed: 0.0023s/iter
	iters: 1250, epoch: 17 | loss: 0.2798624
	speed: 0.0023s/iter
	iters: 1260, epoch: 17 | loss: 0.2852454
	speed: 0.0023s/iter
	iters: 1270, epoch: 17 | loss: 0.2744173
	speed: 0.0023s/iter
	iters: 1280, epoch: 17 | loss: 0.2545763
	speed: 0.0023s/iter
	iters: 1290, epoch: 17 | loss: 0.2836342
	speed: 0.0023s/iter
epoch (16 / 50) (Loss:0.27406800, ACU_loss:20.82916820)
epoch (16 / 50) (val_loss --- Loss:0.81032785
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.9517366869943968
=========================** Result **============================

ECG_Results
F1 score: 0.31779661016949146
precision: 0.2624113475177305
recall: 0.3915343915343915

	iters: 1300, epoch: 18 | loss: 0.2681677
	speed: 0.0294s/iter
	iters: 1310, epoch: 18 | loss: 0.2846114
	speed: 0.0023s/iter
	iters: 1320, epoch: 18 | loss: 0.2956147
	speed: 0.0023s/iter
	iters: 1330, epoch: 18 | loss: 0.2404613
	speed: 0.0022s/iter
	iters: 1340, epoch: 18 | loss: 0.2662560
	speed: 0.0023s/iter
	iters: 1350, epoch: 18 | loss: 0.2715822
	speed: 0.0022s/iter
	iters: 1360, epoch: 18 | loss: 0.2778760
	speed: 0.0020s/iter
epoch (17 / 50) (Loss:0.27256728, ACU_loss:20.71511362)
epoch (17 / 50) (val_loss --- Loss:0.79896852
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
2.0435997388025733
=========================** Result **============================

ECG_Results
F1 score: 0.27826086956521734
precision: 0.23333333333333334
recall: 0.3333333333333333

	iters: 1370, epoch: 19 | loss: 0.2737847
	speed: 0.0295s/iter
	iters: 1380, epoch: 19 | loss: 0.2770476
	speed: 0.0020s/iter
	iters: 1390, epoch: 19 | loss: 0.2925847
	speed: 0.0021s/iter
	iters: 1400, epoch: 19 | loss: 0.2628950
	speed: 0.0021s/iter
	iters: 1410, epoch: 19 | loss: 0.2597677
	speed: 0.0020s/iter
	iters: 1420, epoch: 19 | loss: 0.3056814
	speed: 0.0021s/iter
	iters: 1430, epoch: 19 | loss: 0.2749452
	speed: 0.0021s/iter
	iters: 1440, epoch: 19 | loss: 0.2585171
	speed: 0.0020s/iter
epoch (18 / 50) (Loss:0.26946548, ACU_loss:20.47937638)
epoch (18 / 50) (val_loss --- Loss:0.77937092
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.7337265053492907
=========================** Result **============================

ECG_Results
F1 score: 0.29629629629629634
precision: 0.23529411764705882
recall: 0.4021164021164021

	iters: 1450, epoch: 20 | loss: 0.2640476
	speed: 0.0265s/iter
	iters: 1460, epoch: 20 | loss: 0.2643069
	speed: 0.0020s/iter
	iters: 1470, epoch: 20 | loss: 0.2826371
	speed: 0.0021s/iter
	iters: 1480, epoch: 20 | loss: 0.2423005
	speed: 0.0020s/iter
	iters: 1490, epoch: 20 | loss: 0.2668262
	speed: 0.0018s/iter
	iters: 1500, epoch: 20 | loss: 0.2839529
	speed: 0.0018s/iter
	iters: 1510, epoch: 20 | loss: 0.2639171
	speed: 0.0019s/iter
	iters: 1520, epoch: 20 | loss: 0.2583175
	speed: 0.0020s/iter
epoch (19 / 50) (Loss:0.26684127, ACU_loss:20.27993670)
epoch (19 / 50) (val_loss --- Loss:0.76731413
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.8166260248791801
=========================** Result **============================

ECG_Results
F1 score: 0.30339321357285426
precision: 0.24115755627009647
recall: 0.3968253968253968

Updating learning rate to 0.0005
	iters: 1530, epoch: 21 | loss: 0.2573376
	speed: 0.0241s/iter
	iters: 1540, epoch: 21 | loss: 0.2878394
	speed: 0.0019s/iter
	iters: 1550, epoch: 21 | loss: 0.2593485
	speed: 0.0020s/iter
	iters: 1560, epoch: 21 | loss: 0.2303244
	speed: 0.0019s/iter
	iters: 1570, epoch: 21 | loss: 0.2475822
	speed: 0.0021s/iter
	iters: 1580, epoch: 21 | loss: 0.2399127
	speed: 0.0019s/iter
	iters: 1590, epoch: 21 | loss: 0.2516926
	speed: 0.0019s/iter
epoch (20 / 50) (Loss:0.26159090, ACU_loss:19.88090825)
epoch (20 / 50) (val_loss --- Loss:0.75195167
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.9658339171450283
=========================** Result **============================

ECG_Results
F1 score: 0.2931034482758621
precision: 0.24452554744525548
recall: 0.3544973544973545

	iters: 1600, epoch: 22 | loss: 0.2686827
	speed: 0.0239s/iter
	iters: 1610, epoch: 22 | loss: 0.2503052
	speed: 0.0018s/iter
	iters: 1620, epoch: 22 | loss: 0.2460771
	speed: 0.0017s/iter
	iters: 1630, epoch: 22 | loss: 0.2595807
	speed: 0.0018s/iter
	iters: 1640, epoch: 22 | loss: 0.2610586
	speed: 0.0018s/iter
	iters: 1650, epoch: 22 | loss: 0.2787881
	speed: 0.0017s/iter
	iters: 1660, epoch: 22 | loss: 0.2788155
	speed: 0.0017s/iter
	iters: 1670, epoch: 22 | loss: 0.2862830
	speed: 0.0016s/iter
epoch (21 / 50) (Loss:0.26011779, ACU_loss:19.76895225)
epoch (21 / 50) (val_loss --- Loss:0.74041732
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
2.0540296722208624
=========================** Result **============================

ECG_Results
F1 score: 0.34022988505747126
precision: 0.2979591836734694
recall: 0.3862433862433862

	iters: 1680, epoch: 23 | loss: 0.2767891
	speed: 0.0222s/iter
	iters: 1690, epoch: 23 | loss: 0.2695042
	speed: 0.0018s/iter
	iters: 1700, epoch: 23 | loss: 0.2447757
	speed: 0.0017s/iter
	iters: 1710, epoch: 23 | loss: 0.2284135
	speed: 0.0016s/iter
	iters: 1720, epoch: 23 | loss: 0.2314764
	speed: 0.0017s/iter
	iters: 1730, epoch: 23 | loss: 0.2551220
	speed: 0.0016s/iter
	iters: 1740, epoch: 23 | loss: 0.2564551
	speed: 0.0017s/iter
epoch (22 / 50) (Loss:0.25889173, ACU_loss:19.67577118)
epoch (22 / 50) (val_loss --- Loss:0.72778491
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.9383406599704118
=========================** Result **============================

ECG_Results
F1 score: 0.3250564334085779
precision: 0.28063241106719367
recall: 0.37566137566137564

	iters: 1750, epoch: 24 | loss: 0.2518859
	speed: 0.0214s/iter
	iters: 1760, epoch: 24 | loss: 0.2854069
	speed: 0.0017s/iter
	iters: 1770, epoch: 24 | loss: 0.2626200
	speed: 0.0015s/iter
	iters: 1780, epoch: 24 | loss: 0.2698071
	speed: 0.0016s/iter
	iters: 1790, epoch: 24 | loss: 0.2424369
	speed: 0.0017s/iter
	iters: 1800, epoch: 24 | loss: 0.2679898
	speed: 0.0018s/iter
	iters: 1810, epoch: 24 | loss: 0.2354458
	speed: 0.0017s/iter
	iters: 1820, epoch: 24 | loss: 0.2424881
	speed: 0.0015s/iter
epoch (23 / 50) (Loss:0.25738350, ACU_loss:19.56114598)
epoch (23 / 50) (val_loss --- Loss:0.72044643
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.7975496255153978
=========================** Result **============================

ECG_Results
F1 score: 0.30341880341880345
precision: 0.2517985611510791
recall: 0.37037037037037035

	iters: 1830, epoch: 25 | loss: 0.2791676
	speed: 0.0205s/iter
	iters: 1840, epoch: 25 | loss: 0.2497156
	speed: 0.0015s/iter
	iters: 1850, epoch: 25 | loss: 0.2229379
	speed: 0.0016s/iter
	iters: 1860, epoch: 25 | loss: 0.2846159
	speed: 0.0016s/iter
	iters: 1870, epoch: 25 | loss: 0.2567178
	speed: 0.0016s/iter
	iters: 1880, epoch: 25 | loss: 0.2513750
	speed: 0.0015s/iter
	iters: 1890, epoch: 25 | loss: 0.2468379
	speed: 0.0015s/iter
	iters: 1900, epoch: 25 | loss: 0.2432226
	speed: 0.0014s/iter
epoch (24 / 50) (Loss:0.25654046, ACU_loss:19.49707459)
epoch (24 / 50) (val_loss --- Loss:0.70924389
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
2.2838375427070003
=========================** Result **============================

ECG_Results
F1 score: 0.3165829145728643
precision: 0.30288461538461536
recall: 0.3333333333333333

Updating learning rate to 0.0001
	iters: 1910, epoch: 26 | loss: 0.2369389
	speed: 0.0193s/iter
	iters: 1920, epoch: 26 | loss: 0.2689928
	speed: 0.0014s/iter
	iters: 1930, epoch: 26 | loss: 0.2369690
	speed: 0.0014s/iter
	iters: 1940, epoch: 26 | loss: 0.2634971
	speed: 0.0014s/iter
	iters: 1950, epoch: 26 | loss: 0.2352601
	speed: 0.0016s/iter
	iters: 1960, epoch: 26 | loss: 0.2351492
	speed: 0.0015s/iter
	iters: 1970, epoch: 26 | loss: 0.2572007
	speed: 0.0015s/iter
epoch (25 / 50) (Loss:0.25255675, ACU_loss:19.19431272)
epoch (25 / 50) (val_loss --- Loss:0.70331719
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.799142989642945
=========================** Result **============================

ECG_Results
F1 score: 0.32083333333333336
precision: 0.2620689655172414
recall: 0.4021164021164021

	iters: 1980, epoch: 27 | loss: 0.2658261
	speed: 0.0186s/iter
	iters: 1990, epoch: 27 | loss: 0.2511884
	speed: 0.0015s/iter
	iters: 2000, epoch: 27 | loss: 0.2663222
	speed: 0.0015s/iter
	iters: 2010, epoch: 27 | loss: 0.2424919
	speed: 0.0013s/iter
	iters: 2020, epoch: 27 | loss: 0.2856898
	speed: 0.0016s/iter
	iters: 2030, epoch: 27 | loss: 0.2307175
	speed: 0.0015s/iter
	iters: 2040, epoch: 27 | loss: 0.2516809
	speed: 0.0014s/iter
	iters: 2050, epoch: 27 | loss: 0.2534841
	speed: 0.0014s/iter
epoch (26 / 50) (Loss:0.25095123, ACU_loss:19.07229355)
epoch (26 / 50) (val_loss --- Loss:0.70008031
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.7476079577293546
=========================** Result **============================

ECG_Results
F1 score: 0.33267326732673264
precision: 0.2634920634920635
recall: 0.43915343915343913

	iters: 2060, epoch: 28 | loss: 0.2526946
	speed: 0.0175s/iter
	iters: 2070, epoch: 28 | loss: 0.2193791
	speed: 0.0014s/iter
	iters: 2080, epoch: 28 | loss: 0.2551932
	speed: 0.0015s/iter
	iters: 2090, epoch: 28 | loss: 0.2534000
	speed: 0.0014s/iter
	iters: 2100, epoch: 28 | loss: 0.2425116
	speed: 0.0013s/iter
	iters: 2110, epoch: 28 | loss: 0.2490960
	speed: 0.0014s/iter
	iters: 2120, epoch: 28 | loss: 0.2247822
	speed: 0.0014s/iter
epoch (27 / 50) (Loss:0.25102776, ACU_loss:19.07810993)
epoch (27 / 50) (val_loss --- Loss:0.69759246
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.9745877020428675
=========================** Result **============================

ECG_Results
F1 score: 0.33928571428571425
precision: 0.29457364341085274
recall: 0.4021164021164021

	iters: 2130, epoch: 29 | loss: 0.2571151
	speed: 0.0175s/iter
	iters: 2140, epoch: 29 | loss: 0.2469273
	speed: 0.0014s/iter
	iters: 2150, epoch: 29 | loss: 0.2638279
	speed: 0.0013s/iter
	iters: 2160, epoch: 29 | loss: 0.2628287
	speed: 0.0013s/iter
	iters: 2170, epoch: 29 | loss: 0.2682935
	speed: 0.0014s/iter
	iters: 2180, epoch: 29 | loss: 0.2471319
	speed: 0.0013s/iter
	iters: 2190, epoch: 29 | loss: 0.2458653
	speed: 0.0014s/iter
	iters: 2200, epoch: 29 | loss: 0.2801965
	speed: 0.0012s/iter
epoch (28 / 50) (Loss:0.25046577, ACU_loss:19.03539816)
epoch (28 / 50) (val_loss --- Loss:0.69442355
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.9114502868899286
=========================** Result **============================

ECG_Results
F1 score: 0.3318965517241379
precision: 0.2773722627737226
recall: 0.4021164021164021

	iters: 2210, epoch: 30 | loss: 0.2685806
	speed: 0.0178s/iter
	iters: 2220, epoch: 30 | loss: 0.2512294
	speed: 0.0013s/iter
	iters: 2230, epoch: 30 | loss: 0.2483771
	speed: 0.0013s/iter
	iters: 2240, epoch: 30 | loss: 0.2702525
	speed: 0.0014s/iter
	iters: 2250, epoch: 30 | loss: 0.2665831
	speed: 0.0013s/iter
	iters: 2260, epoch: 30 | loss: 0.2520783
	speed: 0.0012s/iter
	iters: 2270, epoch: 30 | loss: 0.2367344
	speed: 0.0014s/iter
	iters: 2280, epoch: 30 | loss: 0.2419887
	speed: 0.0013s/iter
epoch (29 / 50) (Loss:0.24958243, ACU_loss:18.96826489)
epoch (29 / 50) (val_loss --- Loss:0.69057425
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.8983872607369094
=========================** Result **============================

ECG_Results
F1 score: 0.32905982905982906
precision: 0.27697841726618705
recall: 0.4074074074074074

	iters: 2290, epoch: 31 | loss: 0.2476790
	speed: 0.0165s/iter
	iters: 2300, epoch: 31 | loss: 0.2425267
	speed: 0.0013s/iter
	iters: 2310, epoch: 31 | loss: 0.2372692
	speed: 0.0012s/iter
	iters: 2320, epoch: 31 | loss: 0.2639403
	speed: 0.0013s/iter
	iters: 2330, epoch: 31 | loss: 0.2580942
	speed: 0.0011s/iter
	iters: 2340, epoch: 31 | loss: 0.2552396
	speed: 0.0012s/iter
	iters: 2350, epoch: 31 | loss: 0.2346246
	speed: 0.0012s/iter
epoch (30 / 50) (Loss:0.24952104, ACU_loss:18.96359940)
epoch (30 / 50) (val_loss --- Loss:0.68660672
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.7757097247489155
=========================** Result **============================

ECG_Results
F1 score: 0.32595573440643866
precision: 0.26384364820846906
recall: 0.42857142857142855

	iters: 2360, epoch: 32 | loss: 0.2097733
	speed: 0.0160s/iter
	iters: 2370, epoch: 32 | loss: 0.2419257
	speed: 0.0012s/iter
	iters: 2380, epoch: 32 | loss: 0.2470926
	speed: 0.0011s/iter
	iters: 2390, epoch: 32 | loss: 0.2522378
	speed: 0.0014s/iter
	iters: 2400, epoch: 32 | loss: 0.2499569
	speed: 0.0013s/iter
	iters: 2410, epoch: 32 | loss: 0.2604202
	speed: 0.0012s/iter
	iters: 2420, epoch: 32 | loss: 0.2577245
	speed: 0.0012s/iter
	iters: 2430, epoch: 32 | loss: 0.2751827
	speed: 0.0013s/iter
epoch (31 / 50) (Loss:0.24872163, ACU_loss:18.90284412)
epoch (31 / 50) (val_loss --- Loss:0.68729808
	iters: 2440, epoch: 33 | loss: 0.2654488
	speed: 0.0016s/iter
	iters: 2450, epoch: 33 | loss: 0.2587054
	speed: 0.0012s/iter
	iters: 2460, epoch: 33 | loss: 0.2582829
	speed: 0.0012s/iter
	iters: 2470, epoch: 33 | loss: 0.2662499
	speed: 0.0012s/iter
	iters: 2480, epoch: 33 | loss: 0.2444062
	speed: 0.0012s/iter
	iters: 2490, epoch: 33 | loss: 0.2420677
	speed: 0.0011s/iter
	iters: 2500, epoch: 33 | loss: 0.2608802
	speed: 0.0013s/iter
epoch (32 / 50) (Loss:0.24883933, ACU_loss:18.91178937)
epoch (32 / 50) (val_loss --- Loss:0.68261580
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.9358782145732776
=========================** Result **============================

ECG_Results
F1 score: 0.327433628318584
precision: 0.2786259541984733
recall: 0.3862433862433862

	iters: 2510, epoch: 34 | loss: 0.2551911
	speed: 0.0150s/iter
	iters: 2520, epoch: 34 | loss: 0.2473645
	speed: 0.0011s/iter
	iters: 2530, epoch: 34 | loss: 0.2411454
	speed: 0.0013s/iter
	iters: 2540, epoch: 34 | loss: 0.2386710
	speed: 0.0010s/iter
	iters: 2550, epoch: 34 | loss: 0.2696105
	speed: 0.0011s/iter
	iters: 2560, epoch: 34 | loss: 0.2552914
	speed: 0.0012s/iter
	iters: 2570, epoch: 34 | loss: 0.2396824
	speed: 0.0011s/iter
	iters: 2580, epoch: 34 | loss: 0.2381417
	speed: 0.0012s/iter
epoch (33 / 50) (Loss:0.24837269, ACU_loss:18.87632412)
epoch (33 / 50) (val_loss --- Loss:0.67827841
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.9513038774339144
=========================** Result **============================

ECG_Results
F1 score: 0.3464912280701754
precision: 0.29699248120300753
recall: 0.41798941798941797

	iters: 2590, epoch: 35 | loss: 0.2446866
	speed: 0.0154s/iter
	iters: 2600, epoch: 35 | loss: 0.2586869
	speed: 0.0011s/iter
	iters: 2610, epoch: 35 | loss: 0.2415631
	speed: 0.0012s/iter
	iters: 2620, epoch: 35 | loss: 0.2213483
	speed: 0.0012s/iter
	iters: 2630, epoch: 35 | loss: 0.2306576
	speed: 0.0011s/iter
	iters: 2640, epoch: 35 | loss: 0.2544244
	speed: 0.0011s/iter
	iters: 2650, epoch: 35 | loss: 0.2250142
	speed: 0.0011s/iter
	iters: 2660, epoch: 35 | loss: 0.2363019
	speed: 0.0011s/iter
epoch (34 / 50) (Loss:0.24822118, ACU_loss:18.86480987)
epoch (34 / 50) (val_loss --- Loss:0.67643570
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.7284007116562785
=========================** Result **============================

ECG_Results
F1 score: 0.3049504950495049
precision: 0.24444444444444444
recall: 0.4074074074074074

Updating learning rate to 5e-05
	iters: 2670, epoch: 36 | loss: 0.2587155
	speed: 0.0136s/iter
	iters: 2680, epoch: 36 | loss: 0.2394684
	speed: 0.0012s/iter
	iters: 2690, epoch: 36 | loss: 0.2379031
	speed: 0.0011s/iter
	iters: 2700, epoch: 36 | loss: 0.2669078
	speed: 0.0011s/iter
	iters: 2710, epoch: 36 | loss: 0.2595584
	speed: 0.0011s/iter
	iters: 2720, epoch: 36 | loss: 0.2824091
	speed: 0.0011s/iter
	iters: 2730, epoch: 36 | loss: 0.2421764
	speed: 0.0010s/iter
epoch (35 / 50) (Loss:0.24767701, ACU_loss:18.82345250)
epoch (35 / 50) (val_loss --- Loss:0.67958168
	iters: 2740, epoch: 37 | loss: 0.2321970
	speed: 0.0014s/iter
	iters: 2750, epoch: 37 | loss: 0.2156250
	speed: 0.0010s/iter
	iters: 2760, epoch: 37 | loss: 0.2548575
	speed: 0.0010s/iter
	iters: 2770, epoch: 37 | loss: 0.2579719
	speed: 0.0010s/iter
	iters: 2780, epoch: 37 | loss: 0.2542130
	speed: 0.0010s/iter
	iters: 2790, epoch: 37 | loss: 0.2617993
	speed: 0.0010s/iter
	iters: 2800, epoch: 37 | loss: 0.2358448
	speed: 0.0012s/iter
	iters: 2810, epoch: 37 | loss: 0.2234973
	speed: 0.0011s/iter
epoch (36 / 50) (Loss:0.24733319, ACU_loss:18.79732236)
epoch (36 / 50) (val_loss --- Loss:0.67471464
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.8677066800939073
=========================** Result **============================

ECG_Results
F1 score: 0.33193277310924374
precision: 0.2762237762237762
recall: 0.41798941798941797

	iters: 2820, epoch: 38 | loss: 0.2715580
	speed: 0.0139s/iter
	iters: 2830, epoch: 38 | loss: 0.2375815
	speed: 0.0010s/iter
	iters: 2840, epoch: 38 | loss: 0.2292796
	speed: 0.0009s/iter
	iters: 2850, epoch: 38 | loss: 0.2562538
	speed: 0.0010s/iter
	iters: 2860, epoch: 38 | loss: 0.2425413
	speed: 0.0010s/iter
	iters: 2870, epoch: 38 | loss: 0.2456841
	speed: 0.0009s/iter
	iters: 2880, epoch: 38 | loss: 0.2173163
	speed: 0.0011s/iter
epoch (37 / 50) (Loss:0.24694946, ACU_loss:18.76815870)
epoch (37 / 50) (val_loss --- Loss:0.67321294
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.9061526005586154
=========================** Result **============================

ECG_Results
F1 score: 0.33050847457627114
precision: 0.2730496453900709
recall: 0.4074074074074074

	iters: 2890, epoch: 39 | loss: 0.2448546
	speed: 0.0130s/iter
	iters: 2900, epoch: 39 | loss: 0.2407530
	speed: 0.0010s/iter
	iters: 2910, epoch: 39 | loss: 0.2485614
	speed: 0.0010s/iter
	iters: 2920, epoch: 39 | loss: 0.2724447
	speed: 0.0010s/iter
	iters: 2930, epoch: 39 | loss: 0.2638066
	speed: 0.0011s/iter
	iters: 2940, epoch: 39 | loss: 0.2520062
	speed: 0.0009s/iter
	iters: 2950, epoch: 39 | loss: 0.2466626
	speed: 0.0010s/iter
	iters: 2960, epoch: 39 | loss: 0.2462333
	speed: 0.0010s/iter
epoch (38 / 50) (Loss:0.24689089, ACU_loss:18.76370762)
epoch (38 / 50) (val_loss --- Loss:0.67140151
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.9591850517610112
=========================** Result **============================

ECG_Results
F1 score: 0.3464912280701754
precision: 0.29699248120300753
recall: 0.41798941798941797

	iters: 2970, epoch: 40 | loss: 0.2340024
	speed: 0.0125s/iter
	iters: 2980, epoch: 40 | loss: 0.2603603
	speed: 0.0009s/iter
	iters: 2990, epoch: 40 | loss: 0.2453340
	speed: 0.0011s/iter
	iters: 3000, epoch: 40 | loss: 0.2527162
	speed: 0.0011s/iter
	iters: 3010, epoch: 40 | loss: 0.2715140
	speed: 0.0011s/iter
	iters: 3020, epoch: 40 | loss: 0.2606087
	speed: 0.0010s/iter
	iters: 3030, epoch: 40 | loss: 0.2696319
	speed: 0.0010s/iter
	iters: 3040, epoch: 40 | loss: 0.2386023
	speed: 0.0010s/iter
epoch (39 / 50) (Loss:0.24693595, ACU_loss:18.76713240)
epoch (39 / 50) (val_loss --- Loss:0.67044940
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.9321288157882472
=========================** Result **============================

ECG_Results
F1 score: 0.3333333333333333
precision: 0.2857142857142857
recall: 0.4021164021164021

	iters: 3050, epoch: 41 | loss: 0.2556058
	speed: 0.0130s/iter
	iters: 3060, epoch: 41 | loss: 0.2412851
	speed: 0.0010s/iter
	iters: 3070, epoch: 41 | loss: 0.2355886
	speed: 0.0010s/iter
	iters: 3080, epoch: 41 | loss: 0.2489043
	speed: 0.0010s/iter
	iters: 3090, epoch: 41 | loss: 0.2320948
	speed: 0.0010s/iter
	iters: 3100, epoch: 41 | loss: 0.2746177
	speed: 0.0010s/iter
	iters: 3110, epoch: 41 | loss: 0.2540005
	speed: 0.0010s/iter
epoch (40 / 50) (Loss:0.24709279, ACU_loss:18.77905189)
epoch (40 / 50) (val_loss --- Loss:0.66978328
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.9158516209993937
=========================** Result **============================

ECG_Results
F1 score: 0.33620689655172414
precision: 0.28102189781021897
recall: 0.4074074074074074

	iters: 3120, epoch: 42 | loss: 0.2642304
	speed: 0.0121s/iter
	iters: 3130, epoch: 42 | loss: 0.2290531
	speed: 0.0010s/iter
	iters: 3140, epoch: 42 | loss: 0.2057170
	speed: 0.0010s/iter
	iters: 3150, epoch: 42 | loss: 0.2714115
	speed: 0.0010s/iter
	iters: 3160, epoch: 42 | loss: 0.2438105
	speed: 0.0010s/iter
	iters: 3170, epoch: 42 | loss: 0.2373421
	speed: 0.0009s/iter
	iters: 3180, epoch: 42 | loss: 0.2318446
	speed: 0.0011s/iter
	iters: 3190, epoch: 42 | loss: 0.2314546
	speed: 0.0009s/iter
epoch (41 / 50) (Loss:0.24665538, ACU_loss:18.74580896)
epoch (41 / 50) (val_loss --- Loss:0.66772199
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.7752386296080311
=========================** Result **============================

ECG_Results
F1 score: 0.31643002028397565
precision: 0.25412541254125415
recall: 0.4074074074074074

	iters: 3200, epoch: 43 | loss: 0.2543241
	speed: 0.0120s/iter
	iters: 3210, epoch: 43 | loss: 0.2597211
	speed: 0.0010s/iter
	iters: 3220, epoch: 43 | loss: 0.2269717
	speed: 0.0010s/iter
	iters: 3230, epoch: 43 | loss: 0.2192671
	speed: 0.0010s/iter
	iters: 3240, epoch: 43 | loss: 0.2666146
	speed: 0.0011s/iter
	iters: 3250, epoch: 43 | loss: 0.2693667
	speed: 0.0010s/iter
	iters: 3260, epoch: 43 | loss: 0.2469833
	speed: 0.0010s/iter
epoch (42 / 50) (Loss:0.24609242, ACU_loss:18.70302401)
epoch (42 / 50) (val_loss --- Loss:0.66573570
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.7890356568425059
=========================** Result **============================

ECG_Results
F1 score: 0.332657200811359
precision: 0.2706270627062706
recall: 0.43386243386243384

	iters: 3270, epoch: 44 | loss: 0.2458218
	speed: 0.0116s/iter
	iters: 3280, epoch: 44 | loss: 0.2270074
	speed: 0.0009s/iter
	iters: 3290, epoch: 44 | loss: 0.2524154
	speed: 0.0010s/iter
	iters: 3300, epoch: 44 | loss: 0.2522553
	speed: 0.0008s/iter
	iters: 3310, epoch: 44 | loss: 0.2545910
	speed: 0.0008s/iter
	iters: 3320, epoch: 44 | loss: 0.2188299
	speed: 0.0008s/iter
	iters: 3330, epoch: 44 | loss: 0.2555406
	speed: 0.0008s/iter
	iters: 3340, epoch: 44 | loss: 0.2406270
	speed: 0.0009s/iter
epoch (43 / 50) (Loss:0.24618212, ACU_loss:18.70984145)
epoch (43 / 50) (val_loss --- Loss:0.66577967
	iters: 3350, epoch: 45 | loss: 0.2243228
	speed: 0.0010s/iter
	iters: 3360, epoch: 45 | loss: 0.2545798
	speed: 0.0009s/iter
	iters: 3370, epoch: 45 | loss: 0.2447020
	speed: 0.0008s/iter
	iters: 3380, epoch: 45 | loss: 0.2447865
	speed: 0.0008s/iter
	iters: 3390, epoch: 45 | loss: 0.2634062
	speed: 0.0008s/iter
	iters: 3400, epoch: 45 | loss: 0.2183653
	speed: 0.0008s/iter
	iters: 3410, epoch: 45 | loss: 0.2297211
	speed: 0.0009s/iter
	iters: 3420, epoch: 45 | loss: 0.2285394
	speed: 0.0009s/iter
epoch (44 / 50) (Loss:0.24619414, ACU_loss:18.71075477)
epoch (44 / 50) (val_loss --- Loss:0.66642409
	iters: 3430, epoch: 46 | loss: 0.2521443
	speed: 0.0010s/iter
	iters: 3440, epoch: 46 | loss: 0.2491163
	speed: 0.0008s/iter
	iters: 3450, epoch: 46 | loss: 0.2411308
	speed: 0.0008s/iter
	iters: 3460, epoch: 46 | loss: 0.2422262
	speed: 0.0008s/iter
	iters: 3470, epoch: 46 | loss: 0.2439732
	speed: 0.0009s/iter
	iters: 3480, epoch: 46 | loss: 0.2478584
	speed: 0.0008s/iter
	iters: 3490, epoch: 46 | loss: 0.2371646
	speed: 0.0008s/iter
epoch (45 / 50) (Loss:0.24527006, ACU_loss:18.64052482)
epoch (45 / 50) (val_loss --- Loss:0.66225411
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.8682329671107738
=========================** Result **============================

ECG_Results
F1 score: 0.3504273504273504
precision: 0.2949640287769784
recall: 0.43386243386243384

	iters: 3500, epoch: 47 | loss: 0.2477341
	speed: 0.0103s/iter
	iters: 3510, epoch: 47 | loss: 0.2440818
	speed: 0.0008s/iter
	iters: 3520, epoch: 47 | loss: 0.2385472
	speed: 0.0009s/iter
	iters: 3530, epoch: 47 | loss: 0.2105531
	speed: 0.0009s/iter
	iters: 3540, epoch: 47 | loss: 0.2515020
	speed: 0.0008s/iter
	iters: 3550, epoch: 47 | loss: 0.2488790
	speed: 0.0008s/iter
	iters: 3560, epoch: 47 | loss: 0.2479135
	speed: 0.0008s/iter
	iters: 3570, epoch: 47 | loss: 0.2400672
	speed: 0.0009s/iter
epoch (46 / 50) (Loss:0.24645223, ACU_loss:18.73036969)
epoch (46 / 50) (val_loss --- Loss:0.66199517
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.8394092046017483
=========================** Result **============================

ECG_Results
F1 score: 0.3403361344537815
precision: 0.28321678321678323
recall: 0.42857142857142855

	iters: 3580, epoch: 48 | loss: 0.2183205
	speed: 0.0103s/iter
	iters: 3590, epoch: 48 | loss: 0.2557867
	speed: 0.0008s/iter
	iters: 3600, epoch: 48 | loss: 0.2309871
	speed: 0.0009s/iter
	iters: 3610, epoch: 48 | loss: 0.2447686
	speed: 0.0010s/iter
	iters: 3620, epoch: 48 | loss: 0.2186157
	speed: 0.0009s/iter
	iters: 3630, epoch: 48 | loss: 0.2314579
	speed: 0.0010s/iter
	iters: 3640, epoch: 48 | loss: 0.2504007
	speed: 0.0008s/iter
epoch (47 / 50) (Loss:0.24516526, ACU_loss:18.63255960)
epoch (47 / 50) (val_loss --- Loss:0.65932647
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.7687150235252949
=========================** Result **============================

ECG_Results
F1 score: 0.3380281690140845
precision: 0.2736156351791531
recall: 0.4444444444444444

	iters: 3650, epoch: 49 | loss: 0.2205749
	speed: 0.0101s/iter
	iters: 3660, epoch: 49 | loss: 0.2575500
	speed: 0.0008s/iter
	iters: 3670, epoch: 49 | loss: 0.2501523
	speed: 0.0008s/iter
	iters: 3680, epoch: 49 | loss: 0.2509114
	speed: 0.0009s/iter
	iters: 3690, epoch: 49 | loss: 0.2433174
	speed: 0.0008s/iter
	iters: 3700, epoch: 49 | loss: 0.2398683
	speed: 0.0007s/iter
	iters: 3710, epoch: 49 | loss: 0.2514668
	speed: 0.0008s/iter
	iters: 3720, epoch: 49 | loss: 0.2554350
	speed: 0.0007s/iter
epoch (48 / 50) (Loss:0.24585410, ACU_loss:18.68491185)
epoch (48 / 50) (val_loss --- Loss:0.65860735
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.7888101401989531
=========================** Result **============================

ECG_Results
F1 score: 0.3305785123966942
precision: 0.272108843537415
recall: 0.42328042328042326

	iters: 3730, epoch: 50 | loss: 0.2360288
	speed: 0.0102s/iter
	iters: 3740, epoch: 50 | loss: 0.2621387
	speed: 0.0009s/iter
	iters: 3750, epoch: 50 | loss: 0.2545959
	speed: 0.0009s/iter
	iters: 3760, epoch: 50 | loss: 0.2195404
	speed: 0.0007s/iter
	iters: 3770, epoch: 50 | loss: 0.2644364
	speed: 0.0008s/iter
	iters: 3780, epoch: 50 | loss: 0.2231005
	speed: 0.0008s/iter
	iters: 3790, epoch: 50 | loss: 0.2410272
	speed: 0.0007s/iter
	iters: 3800, epoch: 50 | loss: 0.2510478
	speed: 0.0007s/iter
epoch (49 / 50) (Loss:0.24516798, ACU_loss:18.63276662)
epoch (49 / 50) (val_loss --- Loss:0.65805352
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.8588455054350046
=========================** Result **============================

ECG_Results
F1 score: 0.3445378151260504
precision: 0.2867132867132867
recall: 0.43386243386243384

ECG_Results
ECG_Results
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_test_result = np.array(test_result)  # 3 2034 27
I:/Papers/AAAI2022anomaly/TAD_SCINet/TADib/run_preTrain_ecg.py:400: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  np_val_result = np.array(val_result)  # 3*31*27
AUROC : 0.632253190611703
AUPRC: 0.13159739839264692
1.8588455054350046
=========================** Result **============================

ECG_Results
F1 score: 0.3445378151260504
precision: 0.2867132867132867
recall: 0.43386243386243384


Process finished with exit code 0
