load data of: credit
train:  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=8.0, 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: 0.4337976
	speed: 1.8989s/iter
	iters: 20, epoch: 1 | loss: 0.2812497
	speed: 0.9495s/iter
	iters: 30, epoch: 1 | loss: 0.2280433
	speed: 0.6227s/iter
	iters: 40, epoch: 1 | loss: 0.1998971
	speed: 0.4562s/iter
	iters: 50, epoch: 1 | loss: 0.1742425
	speed: 0.3635s/iter
	iters: 60, epoch: 1 | loss: 0.1521976
	speed: 0.3012s/iter
	iters: 70, epoch: 1 | loss: 0.1328150
	speed: 0.2601s/iter
	iters: 80, epoch: 1 | loss: 0.1211934
	speed: 0.2271s/iter
	iters: 90, epoch: 1 | loss: 0.1083806
	speed: 0.2028s/iter
	iters: 100, epoch: 1 | loss: 0.0929993
	speed: 0.1807s/iter
	iters: 110, epoch: 1 | loss: 0.0860074
	speed: 0.1649s/iter
	iters: 120, epoch: 1 | loss: 0.0753079
	speed: 0.1509s/iter
epoch (0 / 5) (Loss:0.19096126, ACU_loss:24.06111830)
epoch (0 / 5) (val_loss --- Loss:0.00548441
	iters: 130, epoch: 2 | loss: 0.0670781
	speed: 0.2610s/iter
	iters: 140, epoch: 2 | loss: 0.0612246
	speed: 0.1291s/iter
	iters: 150, epoch: 2 | loss: 0.0558042
	speed: 0.1216s/iter
	iters: 160, epoch: 2 | loss: 0.0536869
	speed: 0.1126s/iter
	iters: 170, epoch: 2 | loss: 0.0491429
	speed: 0.1060s/iter
	iters: 180, epoch: 2 | loss: 0.0472832
	speed: 0.1006s/iter
	iters: 190, epoch: 2 | loss: 0.0430901
	speed: 0.0952s/iter
	iters: 200, epoch: 2 | loss: 0.0420155
	speed: 0.0905s/iter
	iters: 210, epoch: 2 | loss: 0.0411670
	speed: 0.0865s/iter
	iters: 220, epoch: 2 | loss: 0.0373684
	speed: 0.0820s/iter
	iters: 230, epoch: 2 | loss: 0.0377095
	speed: 0.0786s/iter
	iters: 240, epoch: 2 | loss: 0.0354490
	speed: 0.0748s/iter
	iters: 250, epoch: 2 | loss: 0.0360585
	speed: 0.0723s/iter
epoch (1 / 5) (Loss:0.04708690, ACU_loss:5.93294888)
epoch (1 / 5) (val_loss --- Loss:0.00177416
	iters: 260, epoch: 3 | loss: 0.0335623
	speed: 0.1308s/iter
	iters: 270, epoch: 3 | loss: 0.0319018
	speed: 0.0668s/iter
	iters: 280, epoch: 3 | loss: 0.0304531
	speed: 0.0644s/iter
	iters: 290, epoch: 3 | loss: 0.0304669
	speed: 0.0627s/iter
	iters: 300, epoch: 3 | loss: 0.0315468
	speed: 0.0611s/iter
	iters: 310, epoch: 3 | loss: 0.0289252
	speed: 0.0583s/iter
	iters: 320, epoch: 3 | loss: 0.0291394
	speed: 0.0571s/iter
	iters: 330, epoch: 3 | loss: 0.0285235
	speed: 0.0550s/iter
	iters: 340, epoch: 3 | loss: 0.0272880
	speed: 0.0529s/iter
	iters: 350, epoch: 3 | loss: 0.0272591
	speed: 0.0515s/iter
	iters: 360, epoch: 3 | loss: 0.0258038
	speed: 0.0503s/iter
	iters: 370, epoch: 3 | loss: 0.0256604
	speed: 0.0489s/iter
epoch (2 / 5) (Loss:0.02905834, ACU_loss:3.66135088)
epoch (2 / 5) (val_loss --- Loss:0.00142158
	iters: 380, epoch: 4 | loss: 0.0269274
	speed: 0.0897s/iter
	iters: 390, epoch: 4 | loss: 0.0249456
	speed: 0.0466s/iter
	iters: 400, epoch: 4 | loss: 0.0258871
	speed: 0.0450s/iter
	iters: 410, epoch: 4 | loss: 0.0248523
	speed: 0.0441s/iter
	iters: 420, epoch: 4 | loss: 0.0238183
	speed: 0.0429s/iter
	iters: 430, epoch: 4 | loss: 0.0237849
	speed: 0.0417s/iter
	iters: 440, epoch: 4 | loss: 0.0240718
	speed: 0.0413s/iter
	iters: 450, epoch: 4 | loss: 0.0237810
	speed: 0.0401s/iter
	iters: 460, epoch: 4 | loss: 0.0225159
	speed: 0.0394s/iter
	iters: 470, epoch: 4 | loss: 0.0229677
	speed: 0.0385s/iter
	iters: 480, epoch: 4 | loss: 0.0233405
	speed: 0.0377s/iter
	iters: 490, epoch: 4 | loss: 0.0232514
	speed: 0.0368s/iter
	iters: 500, epoch: 4 | loss: 0.0225143
	speed: 0.0361s/iter
epoch (3 / 5) (Loss:0.02379318, ACU_loss:2.99794105)
epoch (3 / 5) (val_loss --- Loss:0.00110746
Total_infer_time 83.89266705513
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 : 0.9593257937174937
AUPRC: 0.08468335148032567
8.70159694594697
=========================** Result **============================

Credit_Results
F1 score: 0.244640605296343
precision: 0.1704745166959578
recall: 0.4349775784753363

	iters: 510, epoch: 5 | loss: 0.0226812
	speed: 0.8784s/iter
	iters: 520, epoch: 5 | loss: 0.0220489
	speed: 0.0346s/iter
	iters: 530, epoch: 5 | loss: 0.0210116
	speed: 0.0337s/iter
	iters: 540, epoch: 5 | loss: 0.0211676
	speed: 0.0329s/iter
	iters: 550, epoch: 5 | loss: 0.0218363
	speed: 0.0323s/iter
	iters: 560, epoch: 5 | loss: 0.0219252
	speed: 0.0323s/iter
	iters: 570, epoch: 5 | loss: 0.0205253
	speed: 0.0317s/iter
	iters: 580, epoch: 5 | loss: 0.0212929
	speed: 0.0308s/iter
	iters: 590, epoch: 5 | loss: 0.0204644
	speed: 0.0302s/iter
	iters: 600, epoch: 5 | loss: 0.0209255
	speed: 0.0299s/iter
	iters: 610, epoch: 5 | loss: 0.0203664
	speed: 0.0294s/iter
	iters: 620, epoch: 5 | loss: 0.0214481
	speed: 0.0290s/iter
	iters: 630, epoch: 5 | loss: 0.0202120
	speed: 0.0285s/iter
epoch (4 / 5) (Loss:0.02138221, ACU_loss:2.69415843)
epoch (4 / 5) (val_loss --- Loss:0.00106026
Total_infer_time 83.63336300849915
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 : 0.9564998357605969
AUPRC: 0.08373959094709865
8.708240900138126
=========================** Result **============================

Credit_Results
F1 score: 0.2370744010088273
precision: 0.16520210896309315
recall: 0.42152466367713004

Credit_Results
Total_infer_time 83.67425322532654
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 : 0.9633680198966437
AUPRC: 0.08651867520557634
8.680824189125785
=========================** Result **============================

Credit_Results
F1 score: 0.25220680958385877
precision: 0.17398945518453426
recall: 0.4439461883408072

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