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=32, 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: 0.4608007
	speed: 3.0238s/iter
	iters: 20, epoch: 1 | loss: 0.2928133
	speed: 1.5146s/iter
	iters: 30, epoch: 1 | loss: 0.2562451
	speed: 1.0020s/iter
	iters: 40, epoch: 1 | loss: 0.2258864
	speed: 0.7466s/iter
	iters: 50, epoch: 1 | loss: 0.2117569
	speed: 0.5936s/iter
	iters: 60, epoch: 1 | loss: 0.1880140
	speed: 0.4929s/iter
epoch (0 / 5) (Loss:0.30913024, ACU_loss:19.47520527)
epoch (0 / 5) (val_loss --- Loss:0.04146329
	iters: 70, epoch: 2 | loss: 0.1720781
	speed: 0.6425s/iter
	iters: 80, epoch: 2 | loss: 0.1557872
	speed: 0.3644s/iter
	iters: 90, epoch: 2 | loss: 0.1394716
	speed: 0.3243s/iter
	iters: 100, epoch: 2 | loss: 0.1235524
	speed: 0.2936s/iter
	iters: 110, epoch: 2 | loss: 0.1153829
	speed: 0.2703s/iter
	iters: 120, epoch: 2 | loss: 0.1037769
	speed: 0.2434s/iter
epoch (1 / 5) (Loss:0.13386668, ACU_loss:8.43360072)
epoch (1 / 5) (val_loss --- Loss:0.01329445
	iters: 130, epoch: 3 | loss: 0.0949337
	speed: 0.3419s/iter
	iters: 140, epoch: 3 | loss: 0.0897321
	speed: 0.2095s/iter
	iters: 150, epoch: 3 | loss: 0.0795825
	speed: 0.1958s/iter
	iters: 160, epoch: 3 | loss: 0.0748421
	speed: 0.1842s/iter
	iters: 170, epoch: 3 | loss: 0.0704272
	speed: 0.1731s/iter
	iters: 180, epoch: 3 | loss: 0.0639095
	speed: 0.1637s/iter
	iters: 190, epoch: 3 | loss: 0.0594007
	speed: 0.1480s/iter
epoch (2 / 5) (Loss:0.07734013, ACU_loss:4.87242795)
epoch (2 / 5) (val_loss --- Loss:0.00552794
	iters: 200, epoch: 4 | loss: 0.0587008
	speed: 0.2329s/iter
	iters: 210, epoch: 4 | loss: 0.0569916
	speed: 0.1413s/iter
	iters: 220, epoch: 4 | loss: 0.0496935
	speed: 0.1336s/iter
	iters: 230, epoch: 4 | loss: 0.0511911
	speed: 0.1298s/iter
	iters: 240, epoch: 4 | loss: 0.0470656
	speed: 0.1244s/iter
	iters: 250, epoch: 4 | loss: 0.0476917
	speed: 0.1168s/iter
epoch (3 / 5) (Loss:0.05254283, ACU_loss:3.31019847)
epoch (3 / 5) (val_loss --- Loss:0.00323225
Total_infer_time 80.14867901802063
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 : 0.7981426756354546
AUPRC: 0.025925370736272386
5.538951646734097
=========================** Result **============================

Credit_Results
F1 score: 0.18110236220472442
precision: 0.1619718309859155
recall: 0.2062780269058296

	iters: 260, epoch: 5 | loss: 0.0429340
	speed: 1.7689s/iter
	iters: 270, epoch: 5 | loss: 0.0420389
	speed: 0.1080s/iter
	iters: 280, epoch: 5 | loss: 0.0413717
	speed: 0.1046s/iter
	iters: 290, epoch: 5 | loss: 0.0398139
	speed: 0.1002s/iter
	iters: 300, epoch: 5 | loss: 0.0356947
	speed: 0.0965s/iter
	iters: 310, epoch: 5 | loss: 0.0381791
	speed: 0.0935s/iter
epoch (4 / 5) (Loss:0.04105407, ACU_loss:2.58640671)
epoch (4 / 5) (val_loss --- Loss:0.00233266
Total_infer_time 79.26903176307678
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 : 0.853299232555007
AUPRC: 0.04023958271921374
4.454144600400111
=========================** Result **============================

Credit_Results
F1 score: 0.19419924337957126
precision: 0.1335676625659051
recall: 0.34080717488789236

Credit_Results
Total_infer_time 81.24574756622314
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 : 0.8424394198813818
AUPRC: 0.03644748129166123
6.159015919785777
=========================** Result **============================

Credit_Results
F1 score: 0.20078740157480315
precision: 0.1795774647887324
recall: 0.22869955156950672

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