  if args.dataset == 'credit':
        args.save_path = 'Credit_Results'
        args.variate_index = 3
        args.slide_win = 128  # 256
        args.slide_stride = 64
        args.hidden_size = 2
        args.batch = 16
        # args.pred_len = 64  # 64
        # args.seq_mask_range_low = 4
        # args.seq_mask_range_high = 4
        args.point_part = 4
        args.model_type = 'BiPointMask'  # BiPointMask'#'BiSeqMask'

        args.epoch = 15

'Credit.csv'

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=15, 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: 0.4675336
	speed: 0.7330s/iter
	iters: 20, epoch: 1 | loss: 0.2999768
	speed: 0.3415s/iter
	iters: 30, epoch: 1 | loss: 0.2661558
	speed: 0.2237s/iter
	iters: 40, epoch: 1 | loss: 0.2387996
	speed: 0.1663s/iter
	iters: 50, epoch: 1 | loss: 0.2157615
	speed: 0.1327s/iter
	iters: 60, epoch: 1 | loss: 0.1945893
	speed: 0.1108s/iter
	iters: 70, epoch: 1 | loss: 0.1748884
	speed: 0.0947s/iter
	iters: 80, epoch: 1 | loss: 0.1645272
	speed: 0.0946s/iter
	iters: 90, epoch: 1 | loss: 0.1500996
	speed: 0.0720s/iter
	iters: 100, epoch: 1 | loss: 0.1356748
	speed: 0.0648s/iter
	iters: 110, epoch: 1 | loss: 0.1284179
	speed: 0.0586s/iter
	iters: 120, epoch: 1 | loss: 0.1169643
	speed: 0.0550s/iter
epoch (0 / 15) (Loss:0.22827208, ACU_loss:28.76228187)
epoch (0 / 15) (val_loss --- Loss:0.01543536
	iters: 130, epoch: 2 | loss: 0.1043777
	speed: 0.0978s/iter
	iters: 140, epoch: 2 | loss: 0.0947070
	speed: 0.0474s/iter
	iters: 150, epoch: 2 | loss: 0.0897541
	speed: 0.0445s/iter
	iters: 160, epoch: 2 | loss: 0.0881290
	speed: 0.0419s/iter
	iters: 170, epoch: 2 | loss: 0.0727840
	speed: 0.0390s/iter
	iters: 180, epoch: 2 | loss: 0.0747380
	speed: 0.0374s/iter
	iters: 190, epoch: 2 | loss: 0.0729114
	speed: 0.0359s/iter
	iters: 200, epoch: 2 | loss: 0.0682613
	speed: 0.0334s/iter
	iters: 210, epoch: 2 | loss: 0.0619712
	speed: 0.0320s/iter
	iters: 220, epoch: 2 | loss: 0.0626101
	speed: 0.0302s/iter
	iters: 230, epoch: 2 | loss: 0.0551026
	speed: 0.0283s/iter
	iters: 240, epoch: 2 | loss: 0.0487659
	speed: 0.0271s/iter
	iters: 250, epoch: 2 | loss: 0.0520574
	speed: 0.0264s/iter
epoch (1 / 15) (Loss:0.07201625, ACU_loss:9.07404760)
epoch (1 / 15) (val_loss --- Loss:0.00393151
	iters: 260, epoch: 3 | loss: 0.0532145
	speed: 0.0480s/iter
	iters: 270, epoch: 3 | loss: 0.0484713
	speed: 0.0241s/iter
	iters: 280, epoch: 3 | loss: 0.0456629
	speed: 0.0234s/iter
	iters: 290, epoch: 3 | loss: 0.0437417
	speed: 0.0225s/iter
	iters: 300, epoch: 3 | loss: 0.0439816
	speed: 0.0218s/iter
	iters: 310, epoch: 3 | loss: 0.0396393
	speed: 0.0213s/iter
	iters: 320, epoch: 3 | loss: 0.0432461
	speed: 0.0205s/iter
	iters: 330, epoch: 3 | loss: 0.0424760
	speed: 0.0229s/iter
	iters: 340, epoch: 3 | loss: 0.0387070
	speed: 0.0189s/iter
	iters: 350, epoch: 3 | loss: 0.0370808
	speed: 0.0184s/iter
	iters: 360, epoch: 3 | loss: 0.0391712
	speed: 0.0179s/iter
	iters: 370, epoch: 3 | loss: 0.0348173
	speed: 0.0178s/iter
epoch (2 / 15) (Loss:0.04215767, ACU_loss:5.31186678)
epoch (2 / 15) (val_loss --- Loss:0.00201933
	iters: 380, epoch: 4 | loss: 0.0367363
	speed: 0.0334s/iter
	iters: 390, epoch: 4 | loss: 0.0351787
	speed: 0.0169s/iter
	iters: 400, epoch: 4 | loss: 0.0321735
	speed: 0.0163s/iter
	iters: 410, epoch: 4 | loss: 0.0308751
	speed: 0.0161s/iter
	iters: 420, epoch: 4 | loss: 0.0346033
	speed: 0.0155s/iter
	iters: 430, epoch: 4 | loss: 0.0314439
	speed: 0.0150s/iter
	iters: 440, epoch: 4 | loss: 0.0325439
	speed: 0.0152s/iter
	iters: 450, epoch: 4 | loss: 0.0344428
	speed: 0.0149s/iter
	iters: 460, epoch: 4 | loss: 0.0327072
	speed: 0.0148s/iter
	iters: 470, epoch: 4 | loss: 0.0311748
	speed: 0.0137s/iter
	iters: 480, epoch: 4 | loss: 0.0322269
	speed: 0.0135s/iter
	iters: 490, epoch: 4 | loss: 0.0295483
	speed: 0.0134s/iter
	iters: 500, epoch: 4 | loss: 0.0302284
	speed: 0.0133s/iter
epoch (3 / 15) (Loss:0.03269031, ACU_loss:4.11897890)
epoch (3 / 15) (val_loss --- Loss:0.00178670
	iters: 510, epoch: 5 | loss: 0.0314928
	speed: 0.0250s/iter
	iters: 520, epoch: 5 | loss: 0.0309132
	speed: 0.0127s/iter
	iters: 530, epoch: 5 | loss: 0.0276299
	speed: 0.0125s/iter
	iters: 540, epoch: 5 | loss: 0.0286287
	speed: 0.0123s/iter
	iters: 550, epoch: 5 | loss: 0.0267938
	speed: 0.0121s/iter
	iters: 560, epoch: 5 | loss: 0.0253631
	speed: 0.0119s/iter
	iters: 570, epoch: 5 | loss: 0.0270711
	speed: 0.0116s/iter
	iters: 580, epoch: 5 | loss: 0.0287572
	speed: 0.0114s/iter
	iters: 590, epoch: 5 | loss: 0.0257758
	speed: 0.0113s/iter
	iters: 600, epoch: 5 | loss: 0.0274337
	speed: 0.0110s/iter
	iters: 610, epoch: 5 | loss: 0.0252333
	speed: 0.0107s/iter
	iters: 620, epoch: 5 | loss: 0.0271632
	speed: 0.0105s/iter
	iters: 630, epoch: 5 | loss: 0.0289228
	speed: 0.0100s/iter
epoch (4 / 15) (Loss:0.02770016, ACU_loss:3.49022065)
epoch (4 / 15) (val_loss --- Loss:0.00126826
	iters: 640, epoch: 6 | loss: 0.0262213
	speed: 0.0190s/iter
	iters: 650, epoch: 6 | loss: 0.0263100
	speed: 0.0099s/iter
	iters: 660, epoch: 6 | loss: 0.0231966
	speed: 0.0098s/iter
	iters: 670, epoch: 6 | loss: 0.0243929
	speed: 0.0098s/iter
	iters: 680, epoch: 6 | loss: 0.0266174
	speed: 0.0096s/iter
	iters: 690, epoch: 6 | loss: 0.0244365
	speed: 0.0094s/iter
	iters: 700, epoch: 6 | loss: 0.0234162
	speed: 0.0093s/iter
	iters: 710, epoch: 6 | loss: 0.0243792
	speed: 0.0093s/iter
	iters: 720, epoch: 6 | loss: 0.0247900
	speed: 0.0093s/iter
	iters: 730, epoch: 6 | loss: 0.0236261
	speed: 0.0092s/iter
	iters: 740, epoch: 6 | loss: 0.0241314
	speed: 0.0091s/iter
	iters: 750, epoch: 6 | loss: 0.0237534
	speed: 0.0089s/iter
epoch (5 / 15) (Loss:0.02552706, ACU_loss:3.21640895)
epoch (5 / 15) (val_loss --- Loss:0.00122089
	iters: 760, epoch: 7 | loss: 0.0272478
	speed: 0.0165s/iter
	iters: 770, epoch: 7 | loss: 0.0232345
	speed: 0.0085s/iter
	iters: 780, epoch: 7 | loss: 0.0247867
	speed: 0.0084s/iter
	iters: 790, epoch: 7 | loss: 0.0245938
	speed: 0.0083s/iter
	iters: 800, epoch: 7 | loss: 0.0235206
	speed: 0.0083s/iter
	iters: 810, epoch: 7 | loss: 0.0256755
	speed: 0.0081s/iter
	iters: 820, epoch: 7 | loss: 0.0224895
	speed: 0.0081s/iter
	iters: 830, epoch: 7 | loss: 0.0224705
	speed: 0.0078s/iter
	iters: 840, epoch: 7 | loss: 0.0278120
	speed: 0.0078s/iter
	iters: 850, epoch: 7 | loss: 0.0225940
	speed: 0.0076s/iter
	iters: 860, epoch: 7 | loss: 0.0235109
	speed: 0.0075s/iter
	iters: 870, epoch: 7 | loss: 0.0233010
	speed: 0.0074s/iter
	iters: 880, epoch: 7 | loss: 0.0243105
	speed: 0.0074s/iter
epoch (6 / 15) (Loss:0.02381042, ACU_loss:3.00011317)
epoch (6 / 15) (val_loss --- Loss:0.00122059
	iters: 890, epoch: 8 | loss: 0.0229744
	speed: 0.0141s/iter
	iters: 900, epoch: 8 | loss: 0.0210334
	speed: 0.0073s/iter
	iters: 910, epoch: 8 | loss: 0.0237620
	speed: 0.0072s/iter
	iters: 920, epoch: 8 | loss: 0.0233214
	speed: 0.0071s/iter
	iters: 930, epoch: 8 | loss: 0.0219286
	speed: 0.0071s/iter
	iters: 940, epoch: 8 | loss: 0.0225861
	speed: 0.0069s/iter
	iters: 950, epoch: 8 | loss: 0.0226976
	speed: 0.0069s/iter
	iters: 960, epoch: 8 | loss: 0.0211929
	speed: 0.0068s/iter
	iters: 970, epoch: 8 | loss: 0.0209601
	speed: 0.0069s/iter
	iters: 980, epoch: 8 | loss: 0.0207730
	speed: 0.0069s/iter
	iters: 990, epoch: 8 | loss: 0.0210262
	speed: 0.0068s/iter
	iters: 1000, epoch: 8 | loss: 0.0235626
	speed: 0.0064s/iter
epoch (7 / 15) (Loss:0.02253149, ACU_loss:2.83896750)
epoch (7 / 15) (val_loss --- Loss:0.00106227
	iters: 1010, epoch: 9 | loss: 0.0204835
	speed: 0.0121s/iter
	iters: 1020, epoch: 9 | loss: 0.0206954
	speed: 0.0064s/iter
	iters: 1030, epoch: 9 | loss: 0.0206671
	speed: 0.0064s/iter
	iters: 1040, epoch: 9 | loss: 0.0223744
	speed: 0.0063s/iter
	iters: 1050, epoch: 9 | loss: 0.0240558
	speed: 0.0062s/iter
	iters: 1060, epoch: 9 | loss: 0.0232158
	speed: 0.0061s/iter
	iters: 1070, epoch: 9 | loss: 0.0243542
	speed: 0.0061s/iter
	iters: 1080, epoch: 9 | loss: 0.0221527
	speed: 0.0060s/iter
	iters: 1090, epoch: 9 | loss: 0.0209554
	speed: 0.0060s/iter
	iters: 1100, epoch: 9 | loss: 0.0240086
	speed: 0.0059s/iter
	iters: 1110, epoch: 9 | loss: 0.0218089
	speed: 0.0059s/iter
	iters: 1120, epoch: 9 | loss: 0.0219537
	speed: 0.0058s/iter
	iters: 1130, epoch: 9 | loss: 0.0215596
	speed: 0.0058s/iter
epoch (8 / 15) (Loss:0.02235318, ACU_loss:2.81650125)
epoch (8 / 15) (val_loss --- Loss:0.00100654
	iters: 1140, epoch: 10 | loss: 0.0232393
	speed: 0.0110s/iter
	iters: 1150, epoch: 10 | loss: 0.0220313
	speed: 0.0057s/iter
	iters: 1160, epoch: 10 | loss: 0.0222458
	speed: 0.0057s/iter
	iters: 1170, epoch: 10 | loss: 0.0228927
	speed: 0.0056s/iter
	iters: 1180, epoch: 10 | loss: 0.0220766
	speed: 0.0055s/iter
	iters: 1190, epoch: 10 | loss: 0.0195434
	speed: 0.0055s/iter
	iters: 1200, epoch: 10 | loss: 0.0215716
	speed: 0.0054s/iter
	iters: 1210, epoch: 10 | loss: 0.0206924
	speed: 0.0055s/iter
	iters: 1220, epoch: 10 | loss: 0.0205962
	speed: 0.0054s/iter
	iters: 1230, epoch: 10 | loss: 0.0188503
	speed: 0.0053s/iter
	iters: 1240, epoch: 10 | loss: 0.0203163
	speed: 0.0054s/iter
	iters: 1250, epoch: 10 | loss: 0.0210520
	speed: 0.0054s/iter
	iters: 1260, epoch: 10 | loss: 0.0205348
	speed: 0.0053s/iter
epoch (9 / 15) (Loss:0.02130520, ACU_loss:2.68445468)
epoch (9 / 15) (val_loss --- Loss:0.00100463
	iters: 1270, epoch: 11 | loss: 0.0204755
	speed: 0.0098s/iter
	iters: 1280, epoch: 11 | loss: 0.0203347
	speed: 0.0051s/iter
	iters: 1290, epoch: 11 | loss: 0.0232053
	speed: 0.0051s/iter
	iters: 1300, epoch: 11 | loss: 0.0193548
	speed: 0.0051s/iter
	iters: 1310, epoch: 11 | loss: 0.0206274
	speed: 0.0050s/iter
	iters: 1320, epoch: 11 | loss: 0.0198817
	speed: 0.0049s/iter
	iters: 1330, epoch: 11 | loss: 0.0207892
	speed: 0.0049s/iter
	iters: 1340, epoch: 11 | loss: 0.0202159
	speed: 0.0048s/iter
	iters: 1350, epoch: 11 | loss: 0.0220914
	speed: 0.0049s/iter
	iters: 1360, epoch: 11 | loss: 0.0225710
	speed: 0.0048s/iter
	iters: 1370, epoch: 11 | loss: 0.0207429
	speed: 0.0048s/iter
	iters: 1380, epoch: 11 | loss: 0.0190685
	speed: 0.0047s/iter
epoch (10 / 15) (Loss:0.02086592, ACU_loss:2.62910619)
epoch (10 / 15) (val_loss --- Loss:0.00104853
	iters: 1390, epoch: 12 | loss: 0.0206104
	speed: 0.0089s/iter
	iters: 1400, epoch: 12 | loss: 0.0211121
	speed: 0.0046s/iter
	iters: 1410, epoch: 12 | loss: 0.0225311
	speed: 0.0046s/iter
	iters: 1420, epoch: 12 | loss: 0.0196686
	speed: 0.0046s/iter
	iters: 1430, epoch: 12 | loss: 0.0193885
	speed: 0.0045s/iter
	iters: 1440, epoch: 12 | loss: 0.0200984
	speed: 0.0045s/iter
	iters: 1450, epoch: 12 | loss: 0.0208551
	speed: 0.0045s/iter
	iters: 1460, epoch: 12 | loss: 0.0184699
	speed: 0.0045s/iter
	iters: 1470, epoch: 12 | loss: 0.0199502
	speed: 0.0045s/iter
	iters: 1480, epoch: 12 | loss: 0.0216603
	speed: 0.0045s/iter
	iters: 1490, epoch: 12 | loss: 0.0211770
	speed: 0.0044s/iter
	iters: 1500, epoch: 12 | loss: 0.0202353
	speed: 0.0044s/iter
	iters: 1510, epoch: 12 | loss: 0.0206197
	speed: 0.0044s/iter
epoch (11 / 15) (Loss:0.02032974, ACU_loss:2.56154663)
epoch (11 / 15) (val_loss --- Loss:0.00099086
Credit_Results
run_credit.py:385: 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:386: 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 : 0.9565282032565577
AUPRC: 0.07943055090137406
7.721932618515828
=========================** Result **============================

Credit_Results
F1 score: 0.21706864564007422
precision: 0.13700234192037472
recall: 0.5246636771300448

	iters: 1520, epoch: 13 | loss: 0.0196110
	speed: 0.2387s/iter
	iters: 1530, epoch: 13 | loss: 0.0195236
	speed: 0.0043s/iter
	iters: 1540, epoch: 13 | loss: 0.0181194
	speed: 0.0043s/iter
	iters: 1550, epoch: 13 | loss: 0.0184951
	speed: 0.0041s/iter
	iters: 1560, epoch: 13 | loss: 0.0195930
	speed: 0.0043s/iter
	iters: 1570, epoch: 13 | loss: 0.0214128
	speed: 0.0041s/iter
	iters: 1580, epoch: 13 | loss: 0.0220897
	speed: 0.0041s/iter
	iters: 1590, epoch: 13 | loss: 0.0202128
	speed: 0.0041s/iter
	iters: 1600, epoch: 13 | loss: 0.0201266
	speed: 0.0041s/iter
	iters: 1610, epoch: 13 | loss: 0.0200241
	speed: 0.0040s/iter
	iters: 1620, epoch: 13 | loss: 0.0186400
	speed: 0.0040s/iter
	iters: 1630, epoch: 13 | loss: 0.0203801
	speed: 0.0040s/iter
epoch (12 / 15) (Loss:0.02005776, ACU_loss:2.52727771)
epoch (12 / 15) (val_loss --- Loss:0.00094904
Credit_Results
run_credit.py:385: 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:386: 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 : 0.9543901176335828
AUPRC: 0.08760281045397914
8.87059480308527
=========================** Result **============================

Credit_Results
F1 score: 0.2673392181588903
precision: 0.18629173989455183
recall: 0.47533632286995514

	iters: 1640, epoch: 14 | loss: 0.0208139
	speed: 0.2210s/iter
	iters: 1650, epoch: 14 | loss: 0.0187295
	speed: 0.0039s/iter
	iters: 1660, epoch: 14 | loss: 0.0191906
	speed: 0.0039s/iter
	iters: 1670, epoch: 14 | loss: 0.0190268
	speed: 0.0039s/iter
	iters: 1680, epoch: 14 | loss: 0.0196280
	speed: 0.0038s/iter
	iters: 1690, epoch: 14 | loss: 0.0188548
	speed: 0.0038s/iter
	iters: 1700, epoch: 14 | loss: 0.0182972
	speed: 0.0038s/iter
	iters: 1710, epoch: 14 | loss: 0.0193506
	speed: 0.0037s/iter
	iters: 1720, epoch: 14 | loss: 0.0183427
	speed: 0.0038s/iter
	iters: 1730, epoch: 14 | loss: 0.0193927
	speed: 0.0037s/iter
	iters: 1740, epoch: 14 | loss: 0.0198147
	speed: 0.0037s/iter
	iters: 1750, epoch: 14 | loss: 0.0187538
	speed: 0.0037s/iter
	iters: 1760, epoch: 14 | loss: 0.0198291
	speed: 0.0037s/iter
epoch (13 / 15) (Loss:0.01948773, ACU_loss:2.45545436)
epoch (13 / 15) (val_loss --- Loss:0.00092060
Credit_Results
run_credit.py:385: 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:386: 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 : 0.9508255634831906
AUPRC: 0.08436781192644904
9.37243250434694
=========================** Result **============================

Credit_Results
F1 score: 0.2547288776796974
precision: 0.17750439367311072
recall: 0.452914798206278

	iters: 1770, epoch: 15 | loss: 0.0190208
	speed: 0.2054s/iter
	iters: 1780, epoch: 15 | loss: 0.0193774
	speed: 0.0037s/iter
	iters: 1790, epoch: 15 | loss: 0.0191518
	speed: 0.0038s/iter
	iters: 1800, epoch: 15 | loss: 0.0183008
	speed: 0.0038s/iter
	iters: 1810, epoch: 15 | loss: 0.0187252
	speed: 0.0037s/iter
	iters: 1820, epoch: 15 | loss: 0.0185620
	speed: 0.0037s/iter
	iters: 1830, epoch: 15 | loss: 0.0185221
	speed: 0.0036s/iter
	iters: 1840, epoch: 15 | loss: 0.0194952
	speed: 0.0036s/iter
	iters: 1850, epoch: 15 | loss: 0.0187025
	speed: 0.0036s/iter
	iters: 1860, epoch: 15 | loss: 0.0182931
	speed: 0.0036s/iter
	iters: 1870, epoch: 15 | loss: 0.0192542
	speed: 0.0035s/iter
	iters: 1880, epoch: 15 | loss: 0.0179463
	speed: 0.0035s/iter
	iters: 1890, epoch: 15 | loss: 0.0199455
	speed: 0.0035s/iter
epoch (14 / 15) (Loss:0.01948031, ACU_loss:2.45451934)
epoch (14 / 15) (val_loss --- Loss:0.00089833
Credit_Results
run_credit.py:385: 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:386: 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 : 0.9513540698122742
AUPRC: 0.08301738680559337
9.365642073284919
=========================** Result **============================

Credit_Results
F1 score: 0.23203026481715008
precision: 0.1616871704745167
recall: 0.4125560538116592

Credit_Results
Credit_Results
run_credit.py:385: 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:386: 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
