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=16.0, 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: 0.4882233
	speed: 3.0231s/iter
	iters: 20, epoch: 1 | loss: 0.2713926
	speed: 1.5343s/iter
	iters: 30, epoch: 1 | loss: 0.2108914
	speed: 1.0174s/iter
	iters: 40, epoch: 1 | loss: 0.1803889
	speed: 0.7444s/iter
	iters: 50, epoch: 1 | loss: 0.1466075
	speed: 0.5961s/iter
	iters: 60, epoch: 1 | loss: 0.1242728
	speed: 0.4937s/iter
epoch (0 / 5) (Loss:0.27324688, ACU_loss:17.21455316)
epoch (0 / 5) (val_loss --- Loss:0.01581745
	iters: 70, epoch: 2 | loss: 0.1020855
	speed: 0.6443s/iter
	iters: 80, epoch: 2 | loss: 0.0869227
	speed: 0.3701s/iter
	iters: 90, epoch: 2 | loss: 0.0732526
	speed: 0.3367s/iter
	iters: 100, epoch: 2 | loss: 0.0619096
	speed: 0.2986s/iter
	iters: 110, epoch: 2 | loss: 0.0538042
	speed: 0.2636s/iter
	iters: 120, epoch: 2 | loss: 0.0488998
	speed: 0.2452s/iter
epoch (1 / 5) (Loss:0.06970298, ACU_loss:4.39128790)
epoch (1 / 5) (val_loss --- Loss:0.00260773
	iters: 130, epoch: 3 | loss: 0.0440508
	speed: 0.3450s/iter
	iters: 140, epoch: 3 | loss: 0.0404240
	speed: 0.2087s/iter
	iters: 150, epoch: 3 | loss: 0.0372464
	speed: 0.1937s/iter
	iters: 160, epoch: 3 | loss: 0.0366033
	speed: 0.1835s/iter
	iters: 170, epoch: 3 | loss: 0.0345142
	speed: 0.1739s/iter
	iters: 180, epoch: 3 | loss: 0.0324558
	speed: 0.1629s/iter
	iters: 190, epoch: 3 | loss: 0.0306667
	speed: 0.1490s/iter
epoch (2 / 5) (Loss:0.03649679, ACU_loss:2.29929802)
epoch (2 / 5) (val_loss --- Loss:0.00143071
	iters: 200, epoch: 4 | loss: 0.0303788
	speed: 0.2273s/iter
	iters: 210, epoch: 4 | loss: 0.0300697
	speed: 0.1408s/iter
	iters: 220, epoch: 4 | loss: 0.0283442
	speed: 0.1326s/iter
	iters: 230, epoch: 4 | loss: 0.0278289
	speed: 0.1272s/iter
	iters: 240, epoch: 4 | loss: 0.0273904
	speed: 0.1218s/iter
	iters: 250, epoch: 4 | loss: 0.0259286
	speed: 0.1174s/iter
epoch (3 / 5) (Loss:0.02861228, ACU_loss:1.80257344)
epoch (3 / 5) (val_loss --- Loss:0.00124901
Total_infer_time 79.51437711715698
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 : 0.9519905871966119
AUPRC: 0.0895387706482149
8.038576872431866
=========================** Result **============================

Credit_Results
F1 score: 0.26229508196721313
precision: 0.1827768014059754
recall: 0.4663677130044843

	iters: 260, epoch: 5 | loss: 0.0257825
	speed: 1.7677s/iter
	iters: 270, epoch: 5 | loss: 0.0251957
	speed: 0.1085s/iter
	iters: 280, epoch: 5 | loss: 0.0258281
	speed: 0.1052s/iter
	iters: 290, epoch: 5 | loss: 0.0242798
	speed: 0.1016s/iter
	iters: 300, epoch: 5 | loss: 0.0234673
	speed: 0.0987s/iter
	iters: 310, epoch: 5 | loss: 0.0234299
	speed: 0.0950s/iter
epoch (4 / 5) (Loss:0.02504311, ACU_loss:1.57771616)
epoch (4 / 5) (val_loss --- Loss:0.00111134
Total_infer_time 79.78864288330078
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 : 0.9515163230539094
AUPRC: 0.0815378767205593
8.667921726678443
=========================** Result **============================

Credit_Results
F1 score: 0.23203026481715008
precision: 0.1616871704745167
recall: 0.4125560538116592

Credit_Results
Total_infer_time 79.82454586029053
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 : 0.9584987029273108
AUPRC: 0.08263919677337353
8.557471645608436
=========================** Result **============================

Credit_Results
F1 score: 0.2370744010088273
precision: 0.16520210896309315
recall: 0.42152466367713004

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