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=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: 0.4946229
	speed: 1.8906s/iter
	iters: 20, epoch: 1 | loss: 0.2764398
	speed: 0.9449s/iter
	iters: 30, epoch: 1 | loss: 0.2145351
	speed: 0.6201s/iter
	iters: 40, epoch: 1 | loss: 0.1881226
	speed: 0.4575s/iter
	iters: 50, epoch: 1 | loss: 0.1574555
	speed: 0.3702s/iter
	iters: 60, epoch: 1 | loss: 0.1358595
	speed: 0.3063s/iter
	iters: 70, epoch: 1 | loss: 0.1162339
	speed: 0.2623s/iter
	iters: 80, epoch: 1 | loss: 0.0941428
	speed: 0.2292s/iter
	iters: 90, epoch: 1 | loss: 0.0795054
	speed: 0.2013s/iter
	iters: 100, epoch: 1 | loss: 0.0720797
	speed: 0.1813s/iter
	iters: 110, epoch: 1 | loss: 0.0629271
	speed: 0.1668s/iter
	iters: 120, epoch: 1 | loss: 0.0527519
	speed: 0.1515s/iter
epoch (0 / 5) (Loss:0.17987724, ACU_loss:22.66453162)
epoch (0 / 5) (val_loss --- Loss:0.00331569
	iters: 130, epoch: 2 | loss: 0.0535135
	speed: 0.2630s/iter
	iters: 140, epoch: 2 | loss: 0.0438836
	speed: 0.1307s/iter
	iters: 150, epoch: 2 | loss: 0.0422119
	speed: 0.1205s/iter
	iters: 160, epoch: 2 | loss: 0.0403159
	speed: 0.1137s/iter
	iters: 170, epoch: 2 | loss: 0.0399851
	speed: 0.1059s/iter
	iters: 180, epoch: 2 | loss: 0.0372102
	speed: 0.1008s/iter
	iters: 190, epoch: 2 | loss: 0.0341085
	speed: 0.0948s/iter
	iters: 200, epoch: 2 | loss: 0.0342082
	speed: 0.0916s/iter
	iters: 210, epoch: 2 | loss: 0.0331914
	speed: 0.0864s/iter
	iters: 220, epoch: 2 | loss: 0.0319827
	speed: 0.0815s/iter
	iters: 230, epoch: 2 | loss: 0.0304064
	speed: 0.0781s/iter
	iters: 240, epoch: 2 | loss: 0.0313391
	speed: 0.0749s/iter
	iters: 250, epoch: 2 | loss: 0.0292239
	speed: 0.0716s/iter
epoch (1 / 5) (Loss:0.03696933, ACU_loss:4.65813520)
epoch (1 / 5) (val_loss --- Loss:0.00138361
	iters: 260, epoch: 3 | loss: 0.0281412
	speed: 0.1312s/iter
	iters: 270, epoch: 3 | loss: 0.0296218
	speed: 0.0669s/iter
	iters: 280, epoch: 3 | loss: 0.0267816
	speed: 0.0650s/iter
	iters: 290, epoch: 3 | loss: 0.0275186
	speed: 0.0627s/iter
	iters: 300, epoch: 3 | loss: 0.0267871
	speed: 0.0603s/iter
	iters: 310, epoch: 3 | loss: 0.0259414
	speed: 0.0582s/iter
	iters: 320, epoch: 3 | loss: 0.0278544
	speed: 0.0568s/iter
	iters: 330, epoch: 3 | loss: 0.0246214
	speed: 0.0564s/iter
	iters: 340, epoch: 3 | loss: 0.0248708
	speed: 0.0533s/iter
	iters: 350, epoch: 3 | loss: 0.0243907
	speed: 0.0517s/iter
	iters: 360, epoch: 3 | loss: 0.0249532
	speed: 0.0513s/iter
	iters: 370, epoch: 3 | loss: 0.0245833
	speed: 0.0489s/iter
epoch (2 / 5) (Loss:0.02672786, ACU_loss:3.36771058)
epoch (2 / 5) (val_loss --- Loss:0.00130593
	iters: 380, epoch: 4 | loss: 0.0260686
	speed: 0.0917s/iter
	iters: 390, epoch: 4 | loss: 0.0249849
	speed: 0.0463s/iter
	iters: 400, epoch: 4 | loss: 0.0232069
	speed: 0.0448s/iter
	iters: 410, epoch: 4 | loss: 0.0240720
	speed: 0.0438s/iter
	iters: 420, epoch: 4 | loss: 0.0231379
	speed: 0.0424s/iter
	iters: 430, epoch: 4 | loss: 0.0245177
	speed: 0.0423s/iter
	iters: 440, epoch: 4 | loss: 0.0235600
	speed: 0.0412s/iter
	iters: 450, epoch: 4 | loss: 0.0229839
	speed: 0.0402s/iter
	iters: 460, epoch: 4 | loss: 0.0219366
	speed: 0.0391s/iter
	iters: 470, epoch: 4 | loss: 0.0222094
	speed: 0.0389s/iter
	iters: 480, epoch: 4 | loss: 0.0230356
	speed: 0.0379s/iter
	iters: 490, epoch: 4 | loss: 0.0237995
	speed: 0.0371s/iter
	iters: 500, epoch: 4 | loss: 0.0224226
	speed: 0.0361s/iter
epoch (3 / 5) (Loss:0.02330765, ACU_loss:2.93676428)
epoch (3 / 5) (val_loss --- Loss:0.00114307
Total_infer_time 84.30656242370605
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 : 0.9500709060489342
AUPRC: 0.09141454173280153
7.996297711463489
=========================** Result **============================

Credit_Results
F1 score: 0.2648171500630517
precision: 0.1827768014059754
recall: 0.4663677130044843

	iters: 510, epoch: 5 | loss: 0.0227339
	speed: 0.8942s/iter
	iters: 520, epoch: 5 | loss: 0.0227795
	speed: 0.0350s/iter
	iters: 530, epoch: 5 | loss: 0.0213991
	speed: 0.0343s/iter
	iters: 540, epoch: 5 | loss: 0.0219502
	speed: 0.0340s/iter
	iters: 550, epoch: 5 | loss: 0.0228177
	speed: 0.0330s/iter
	iters: 560, epoch: 5 | loss: 0.0214206
	speed: 0.0327s/iter
	iters: 570, epoch: 5 | loss: 0.0213797
	speed: 0.0314s/iter
	iters: 580, epoch: 5 | loss: 0.0201761
	speed: 0.0311s/iter
	iters: 590, epoch: 5 | loss: 0.0211494
	speed: 0.0310s/iter
	iters: 600, epoch: 5 | loss: 0.0209475
	speed: 0.0296s/iter
	iters: 610, epoch: 5 | loss: 0.0217984
	speed: 0.0296s/iter
	iters: 620, epoch: 5 | loss: 0.0217996
	speed: 0.0299s/iter
	iters: 630, epoch: 5 | loss: 0.0201453
	speed: 0.0287s/iter
epoch (4 / 5) (Loss:0.02151636, ACU_loss:2.71106076)
epoch (4 / 5) (val_loss --- Loss:0.00105370
Total_infer_time 83.61740493774414
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 : 0.9592981519750448
AUPRC: 0.08457433971656995
8.888732824557614
=========================** Result **============================

Credit_Results
F1 score: 0.2421185372005044
precision: 0.16695957820738136
recall: 0.4260089686098655

Credit_Results
Total_infer_time 84.27165675163269
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 : 0.9611816401106344
AUPRC: 0.08302852665808676
8.824404029207802
=========================** Result **============================

Credit_Results
F1 score: 0.2421185372005044
precision: 0.1687170474516696
recall: 0.4304932735426009

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