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=64, 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: 0.4889995
	speed: 5.3302s/iter
	iters: 20, epoch: 1 | loss: 0.2655389
	speed: 2.7785s/iter
	iters: 30, epoch: 1 | loss: 0.2070586
	speed: 1.7949s/iter
epoch (0 / 5) (Loss:0.38638620, ACU_loss:12.36435835)
epoch (0 / 5) (val_loss --- Loss:0.04511623
	iters: 40, epoch: 2 | loss: 0.1702345
	speed: 1.6796s/iter
	iters: 50, epoch: 2 | loss: 0.1403526
	speed: 1.0617s/iter
	iters: 60, epoch: 2 | loss: 0.1113907
	speed: 0.8753s/iter
epoch (1 / 5) (Loss:0.14299607, ACU_loss:4.57587433)
epoch (1 / 5) (val_loss --- Loss:0.01089148
	iters: 70, epoch: 3 | loss: 0.0906018
	speed: 0.9310s/iter
	iters: 80, epoch: 3 | loss: 0.0720942
	speed: 0.6626s/iter
	iters: 90, epoch: 3 | loss: 0.0579589
	speed: 0.5806s/iter
epoch (2 / 5) (Loss:0.07131854, ACU_loss:2.28219322)
epoch (2 / 5) (val_loss --- Loss:0.00294183
	iters: 100, epoch: 4 | loss: 0.0506850
	speed: 0.6598s/iter
	iters: 110, epoch: 4 | loss: 0.0455984
	speed: 0.4843s/iter
	iters: 120, epoch: 4 | loss: 0.0417335
	speed: 0.4371s/iter
epoch (3 / 5) (Loss:0.04366851, ACU_loss:1.39739236)
epoch (3 / 5) (val_loss --- Loss:0.00178418
Total_infer_time 77.25741171836853
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 : 0.9437579233494044
AUPRC: 0.08781861247697913
6.578534467542516
=========================** Result **============================

Credit_Results
F1 score: 0.2799495586380832
precision: 0.19507908611599298
recall: 0.4977578475336323

	iters: 130, epoch: 5 | loss: 0.0382429
	speed: 3.6813s/iter
	iters: 140, epoch: 5 | loss: 0.0354769
	speed: 0.3720s/iter
	iters: 150, epoch: 5 | loss: 0.0338687
	speed: 0.3436s/iter
	iters: 160, epoch: 5 | loss: 0.0322457
	speed: 0.3227s/iter
epoch (4 / 5) (Loss:0.03447102, ACU_loss:1.10307266)
epoch (4 / 5) (val_loss --- Loss:0.00146365
Total_infer_time 77.45488333702087
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 : 0.943079801356141
AUPRC: 0.08687744210169163
7.252182540437538
=========================** Result **============================

Credit_Results
F1 score: 0.2572509457755359
precision: 0.17750439367311072
recall: 0.452914798206278

Credit_Results
Total_infer_time 51.523226499557495
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 : 0.9544323691097691
AUPRC: 0.08558848550493552
7.214479958529874
=========================** Result **============================

Credit_Results
F1 score: 0.2547288776796974
precision: 0.17750439367311072
recall: 0.452914798206278

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