oad 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=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: 0.4398414
	speed: 2.9813s/iter
	iters: 20, epoch: 1 | loss: 0.2745905
	speed: 1.5268s/iter
	iters: 30, epoch: 1 | loss: 0.2287266
	speed: 1.0040s/iter
	iters: 40, epoch: 1 | loss: 0.1917728
	speed: 0.7445s/iter
	iters: 50, epoch: 1 | loss: 0.1660707
	speed: 0.5933s/iter
	iters: 60, epoch: 1 | loss: 0.1436489
	speed: 0.4931s/iter
epoch (0 / 5) (Loss:0.27413932, ACU_loss:17.27077696)
epoch (0 / 5) (val_loss --- Loss:0.01988561
	iters: 70, epoch: 2 | loss: 0.1248341
	speed: 0.6465s/iter
	iters: 80, epoch: 2 | loss: 0.1099525
	speed: 0.3718s/iter
	iters: 90, epoch: 2 | loss: 0.0947812
	speed: 0.3313s/iter
	iters: 100, epoch: 2 | loss: 0.0827605
	speed: 0.2937s/iter
	iters: 110, epoch: 2 | loss: 0.0699440
	speed: 0.2737s/iter
	iters: 120, epoch: 2 | loss: 0.0641998
	speed: 0.2470s/iter
epoch (1 / 5) (Loss:0.08976733, ACU_loss:5.65534164)
epoch (1 / 5) (val_loss --- Loss:0.00489277
	iters: 130, epoch: 3 | loss: 0.0578040
	speed: 0.3455s/iter
	iters: 140, epoch: 3 | loss: 0.0513239
	speed: 0.2123s/iter
	iters: 150, epoch: 3 | loss: 0.0476445
	speed: 0.1950s/iter
	iters: 160, epoch: 3 | loss: 0.0451227
	speed: 0.1846s/iter
	iters: 170, epoch: 3 | loss: 0.0411996
	speed: 0.1727s/iter
	iters: 180, epoch: 3 | loss: 0.0396708
	speed: 0.1652s/iter
	iters: 190, epoch: 3 | loss: 0.0371809
	speed: 0.1490s/iter
epoch (2 / 5) (Loss:0.04622968, ACU_loss:2.91246985)
epoch (2 / 5) (val_loss --- Loss:0.00219223
	iters: 200, epoch: 4 | loss: 0.0363182
	speed: 0.2281s/iter
	iters: 210, epoch: 4 | loss: 0.0345769
	speed: 0.1401s/iter
	iters: 220, epoch: 4 | loss: 0.0322340
	speed: 0.1341s/iter
	iters: 230, epoch: 4 | loss: 0.0317723
	speed: 0.1293s/iter
	iters: 240, epoch: 4 | loss: 0.0300058
	speed: 0.1226s/iter
	iters: 250, epoch: 4 | loss: 0.0302963
	speed: 0.1177s/iter
epoch (3 / 5) (Loss:0.03269453, ACU_loss:2.05975520)
epoch (3 / 5) (val_loss --- Loss:0.00144451
Total_infer_time 80.03298830986023
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 : 0.9509937331936718
AUPRC: 0.09137051107129301
7.130378135293882
=========================** Result **============================

Credit_Results
F1 score: 0.2774274905422446
precision: 0.19332161687170474
recall: 0.49327354260089684

	iters: 260, epoch: 5 | loss: 0.0283024
	speed: 1.7562s/iter
	iters: 270, epoch: 5 | loss: 0.0274562
	speed: 0.1096s/iter
	iters: 280, epoch: 5 | loss: 0.0264672
	speed: 0.1046s/iter
	iters: 290, epoch: 5 | loss: 0.0272086
	speed: 0.1017s/iter
	iters: 300, epoch: 5 | loss: 0.0251483
	speed: 0.0979s/iter
	iters: 310, epoch: 5 | loss: 0.0261870
	speed: 0.0946s/iter
epoch (4 / 5) (Loss:0.02728060, ACU_loss:1.71867768)
epoch (4 / 5) (val_loss --- Loss:0.00120393
Total_infer_time 77.378089427948
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 : 0.9519097445662988
AUPRC: 0.0863667704929227
8.239236056796473
=========================** Result **============================

Credit_Results
F1 score: 0.244640605296343
precision: 0.1704745166959578
recall: 0.4349775784753363

Credit_Results
Total_infer_time 80.2374427318573
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 : 0.9616972564262588
AUPRC: 0.08674250901775854
8.154400031492727
=========================** Result **============================

Credit_Results
F1 score: 0.24968474148802017
precision: 0.17398945518453426
recall: 0.4439461883408072

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