rain:  0 None
test:  0 None
level number 7, level details: [[1, 1], [1, 1], [1, 1], [0, 0], [0, 0], [0, 0], [0, 0]]
Model_type: BiPointMask
layer number: 3
layer number: 3
layer number: 3
layer number: 3
layer number: 3
layer number: 3
layer number: 3
layer number: 3
layer number: 3
Namespace(INN=1, batch=16, comment='', dataset='credit', decay=0, device=device(type='cuda'), dropout=0.2, ensemble=0, epoch=5, groups=1, hidden_size=2.0, kernel=3, learning_rate=0.001, load_model_path='', lradj=3, model_name='SCINet', model_type='BiPointMask', out_layer_inter_dim=256, out_layer_num=1, point_part=4, pred_len=128, random_seed=4321, report='best', save_path='Credit_Results', save_path_pattern='msl', seq_mask_range_high=4, seq_mask_range_low=8, slide_stride=64, slide_win=128, topk=20, val_ratio=0.1, variate_index=3)
	iters: 10, epoch: 1 | loss: 0.4675336
	speed: 1.2743s/iter
	iters: 20, epoch: 1 | loss: 0.2999768
	speed: 0.5989s/iter
	iters: 30, epoch: 1 | loss: 0.2661558
	speed: 0.3911s/iter
	iters: 40, epoch: 1 | loss: 0.2387996
	speed: 0.3154s/iter
	iters: 50, epoch: 1 | loss: 0.2157615
	speed: 0.2499s/iter
	iters: 60, epoch: 1 | loss: 0.1945893
	speed: 0.2221s/iter
	iters: 70, epoch: 1 | loss: 0.1748884
	speed: 0.1799s/iter
	iters: 80, epoch: 1 | loss: 0.1645272
	speed: 0.1600s/iter
	iters: 90, epoch: 1 | loss: 0.1500996
	speed: 0.1395s/iter
	iters: 100, epoch: 1 | loss: 0.1356748
	speed: 0.1273s/iter
	iters: 110, epoch: 1 | loss: 0.1284179
	speed: 0.1171s/iter
	iters: 120, epoch: 1 | loss: 0.1169643
	speed: 0.1060s/iter
epoch (0 / 5) (Loss:0.22827208, ACU_loss:28.76228187)
epoch (0 / 5) (val_loss --- Loss:0.01543536
	iters: 130, epoch: 2 | loss: 0.1043777
	speed: 0.1833s/iter
	iters: 140, epoch: 2 | loss: 0.0947070
	speed: 0.0873s/iter
	iters: 150, epoch: 2 | loss: 0.0897541
	speed: 0.0826s/iter
	iters: 160, epoch: 2 | loss: 0.0881290
	speed: 0.0783s/iter
	iters: 170, epoch: 2 | loss: 0.0727840
	speed: 0.0729s/iter
	iters: 180, epoch: 2 | loss: 0.0747380
	speed: 0.0691s/iter
	iters: 190, epoch: 2 | loss: 0.0729114
	speed: 0.0651s/iter
	iters: 200, epoch: 2 | loss: 0.0682613
	speed: 0.0621s/iter
	iters: 210, epoch: 2 | loss: 0.0619712
	speed: 0.0588s/iter
	iters: 220, epoch: 2 | loss: 0.0626101
	speed: 0.0559s/iter
	iters: 230, epoch: 2 | loss: 0.0551026
	speed: 0.0536s/iter
	iters: 240, epoch: 2 | loss: 0.0487659
	speed: 0.0510s/iter
	iters: 250, epoch: 2 | loss: 0.0520574
	speed: 0.0486s/iter
epoch (1 / 5) (Loss:0.07201625, ACU_loss:9.07404760)
epoch (1 / 5) (val_loss --- Loss:0.00393151
	iters: 260, epoch: 3 | loss: 0.0532145
	speed: 0.0890s/iter
	iters: 270, epoch: 3 | loss: 0.0484713
	speed: 0.0458s/iter
	iters: 280, epoch: 3 | loss: 0.0456629
	speed: 0.0445s/iter
	iters: 290, epoch: 3 | loss: 0.0437417
	speed: 0.0399s/iter
	iters: 300, epoch: 3 | loss: 0.0439816
	speed: 0.0379s/iter
	iters: 310, epoch: 3 | loss: 0.0396393
	speed: 0.0375s/iter
	iters: 320, epoch: 3 | loss: 0.0432461
	speed: 0.0387s/iter
	iters: 330, epoch: 3 | loss: 0.0424760
	speed: 0.0375s/iter
	iters: 340, epoch: 3 | loss: 0.0387070
	speed: 0.0364s/iter
	iters: 350, epoch: 3 | loss: 0.0370808
	speed: 0.0355s/iter
	iters: 360, epoch: 3 | loss: 0.0391712
	speed: 0.0341s/iter
	iters: 370, epoch: 3 | loss: 0.0348173
	speed: 0.0333s/iter
epoch (2 / 5) (Loss:0.04215767, ACU_loss:5.31186678)
epoch (2 / 5) (val_loss --- Loss:0.00201933
	iters: 380, epoch: 4 | loss: 0.0367363
	speed: 0.0613s/iter
	iters: 390, epoch: 4 | loss: 0.0351787
	speed: 0.0313s/iter
	iters: 400, epoch: 4 | loss: 0.0321735
	speed: 0.0310s/iter
	iters: 410, epoch: 4 | loss: 0.0308751
	speed: 0.0301s/iter
	iters: 420, epoch: 4 | loss: 0.0346033
	speed: 0.0291s/iter
	iters: 430, epoch: 4 | loss: 0.0314439
	speed: 0.0282s/iter
	iters: 440, epoch: 4 | loss: 0.0325439
	speed: 0.0280s/iter
	iters: 450, epoch: 4 | loss: 0.0344428
	speed: 0.0272s/iter
	iters: 460, epoch: 4 | loss: 0.0327072
	speed: 0.0267s/iter
	iters: 470, epoch: 4 | loss: 0.0311748
	speed: 0.0259s/iter
	iters: 480, epoch: 4 | loss: 0.0322269
	speed: 0.0253s/iter
	iters: 490, epoch: 4 | loss: 0.0295483
	speed: 0.0251s/iter
	iters: 500, epoch: 4 | loss: 0.0302284
	speed: 0.0245s/iter
epoch (3 / 5) (Loss:0.03269031, ACU_loss:4.11897890)
epoch (3 / 5) (val_loss --- Loss:0.00178670
Total_infer_time 59.145402908325195
Credit_Results
run_credit.py:388: 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
run_credit.py:389: 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 : 0.9020005748639697
AUPRC: 0.0718599793102926
5.027308117656776
=========================** Result **============================

Credit_Results
F1 score: 0.24968474148802017
precision: 0.17398945518453426
recall: 0.4439461883408072

	iters: 510, epoch: 5 | loss: 0.0280932
	speed: 0.8334s/iter
	iters: 520, epoch: 5 | loss: 0.0298833
	speed: 0.0231s/iter
	iters: 530, epoch: 5 | loss: 0.0278185
	speed: 0.0232s/iter
	iters: 540, epoch: 5 | loss: 0.0280746
	speed: 0.0227s/iter
	iters: 550, epoch: 5 | loss: 0.0257767
	speed: 0.0225s/iter
	iters: 560, epoch: 5 | loss: 0.0290951
	speed: 0.0221s/iter
	iters: 570, epoch: 5 | loss: 0.0278960
	speed: 0.0220s/iter
	iters: 580, epoch: 5 | loss: 0.0247396
	speed: 0.0216s/iter
	iters: 590, epoch: 5 | loss: 0.0257578
	speed: 0.0210s/iter
	iters: 600, epoch: 5 | loss: 0.0254946
	speed: 0.0206s/iter
	iters: 610, epoch: 5 | loss: 0.0273317
	speed: 0.0202s/iter
	iters: 620, epoch: 5 | loss: 0.0284153
	speed: 0.0202s/iter
	iters: 630, epoch: 5 | loss: 0.0241005
	speed: 0.0198s/iter
epoch (4 / 5) (Loss:0.02779624, ACU_loss:3.50232565)
epoch (4 / 5) (val_loss --- Loss:0.00127314
Total_infer_time 84.15295267105103
Credit_Results
run_credit.py:388: 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
run_credit.py:389: 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 : 0.9391569931841803
AUPRC: 0.08688819943831713
6.924338181013065
=========================** Result **============================

Credit_Results
F1 score: 0.2547288776796974
precision: 0.17750439367311072
recall: 0.452914798206278

Credit_Results
Total_infer_time 83.70314693450928
Credit_Results
run_credit.py:388: 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
run_credit.py:389: 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 : 0.9391414841471786
AUPRC: 0.07993799683303415
6.93532676112822
=========================** Result **============================

Credit_Results
F1 score: 0.2547288776796974
precision: 0.17750439367311072
recall: 0.452914798206278

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