ConvGeNBenchmarkResults/data_result/CTAB-GAN/kaggle_creditcard.log

///////////////////////////////////////////
// Running CTAB-GAN on kaggle_creditcard
///////////////////////////////////////////

Load 'data_input/kaggle_creditcard'
Data loaded.
-> Shuffling data
### Start exercise for synthetic point generator

====== Step 1/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 1/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:22,  2.54s/it]
 20%|██        | 2/10 [00:05<00:20,  2.55s/it]
 30%|███       | 3/10 [00:07<00:17,  2.48s/it]
 40%|████      | 4/10 [00:09<00:14,  2.46s/it]
 50%|█████     | 5/10 [00:12<00:11,  2.39s/it]
 60%|██████    | 6/10 [00:14<00:09,  2.32s/it]
 70%|███████   | 7/10 [00:16<00:06,  2.29s/it]
 80%|████████  | 8/10 [00:18<00:04,  2.32s/it]
 90%|█████████ | 9/10 [00:21<00:02,  2.30s/it]
100%|██████████| 10/10 [00:23<00:00,  2.29s/it]
100%|██████████| 10/10 [00:23<00:00,  2.35s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56837, 26
LR fn, tp: 30, 69
LR f1 score: 0.711
LR cohens kappa score: 0.711
LR average precision score: 0.560

-> test with 'GB'
GB tn, fp: 56848, 15
GB fn, tp: 27, 72
GB f1 score: 0.774
GB cohens kappa score: 0.774

-> test with 'KNN'
KNN tn, fp: 56509, 354
KNN fn, tp: 92, 7
KNN f1 score: 0.030
KNN cohens kappa score: 0.028


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:19,  2.16s/it]
 20%|██        | 2/10 [00:04<00:17,  2.17s/it]
 30%|███       | 3/10 [00:06<00:14,  2.10s/it]
 40%|████      | 4/10 [00:08<00:12,  2.11s/it]
 50%|█████     | 5/10 [00:10<00:10,  2.06s/it]
 60%|██████    | 6/10 [00:12<00:08,  2.13s/it]
 70%|███████   | 7/10 [00:14<00:06,  2.15s/it]
 80%|████████  | 8/10 [00:17<00:04,  2.16s/it]
 90%|█████████ | 9/10 [00:19<00:02,  2.20s/it]
100%|██████████| 10/10 [00:21<00:00,  2.24s/it]
100%|██████████| 10/10 [00:21<00:00,  2.17s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56847, 16
LR fn, tp: 20, 79
LR f1 score: 0.814
LR cohens kappa score: 0.814
LR average precision score: 0.744

-> test with 'GB'
GB tn, fp: 56856, 7
GB fn, tp: 26, 73
GB f1 score: 0.816
GB cohens kappa score: 0.815

-> test with 'KNN'
KNN tn, fp: 56523, 340
KNN fn, tp: 93, 6
KNN f1 score: 0.027
KNN cohens kappa score: 0.024


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:17,  1.92s/it]
 20%|██        | 2/10 [00:03<00:15,  1.92s/it]
 30%|███       | 3/10 [00:05<00:13,  1.97s/it]
 40%|████      | 4/10 [00:09<00:16,  2.81s/it]
 50%|█████     | 5/10 [00:12<00:12,  2.55s/it]
 60%|██████    | 6/10 [00:13<00:09,  2.34s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.23s/it]
 80%|████████  | 8/10 [00:18<00:04,  2.34s/it]
 90%|█████████ | 9/10 [00:20<00:02,  2.18s/it]
100%|██████████| 10/10 [00:22<00:00,  2.12s/it]
100%|██████████| 10/10 [00:22<00:00,  2.24s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56838, 25
LR fn, tp: 20, 79
LR f1 score: 0.778
LR cohens kappa score: 0.778
LR average precision score: 0.657

-> test with 'GB'
GB tn, fp: 56851, 12
GB fn, tp: 20, 79
GB f1 score: 0.832
GB cohens kappa score: 0.831

-> test with 'KNN'
KNN tn, fp: 56600, 263
KNN fn, tp: 97, 2
KNN f1 score: 0.011
KNN cohens kappa score: 0.008


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:21,  2.41s/it]
 20%|██        | 2/10 [00:04<00:18,  2.33s/it]
 30%|███       | 3/10 [00:06<00:15,  2.22s/it]
 40%|████      | 4/10 [00:08<00:13,  2.20s/it]
 50%|█████     | 5/10 [00:10<00:10,  2.12s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.11s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.18s/it]
 80%|████████  | 8/10 [00:17<00:04,  2.14s/it]
 90%|█████████ | 9/10 [00:19<00:02,  2.09s/it]
100%|██████████| 10/10 [00:21<00:00,  2.12s/it]
100%|██████████| 10/10 [00:21<00:00,  2.16s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56829, 34
LR fn, tp: 15, 84
LR f1 score: 0.774
LR cohens kappa score: 0.774
LR average precision score: 0.757

-> test with 'GB'
GB tn, fp: 56851, 12
GB fn, tp: 17, 82
GB f1 score: 0.850
GB cohens kappa score: 0.849

-> test with 'KNN'
KNN tn, fp: 56548, 315
KNN fn, tp: 98, 1
KNN f1 score: 0.005
KNN cohens kappa score: 0.002


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:22,  2.48s/it]
 20%|██        | 2/10 [00:04<00:19,  2.42s/it]
 30%|███       | 3/10 [00:06<00:15,  2.18s/it]
 40%|████      | 4/10 [00:08<00:13,  2.20s/it]
 50%|█████     | 5/10 [00:11<00:10,  2.16s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.19s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.08s/it]
 80%|████████  | 8/10 [00:17<00:04,  2.07s/it]
 90%|█████████ | 9/10 [00:19<00:02,  2.12s/it]
100%|██████████| 10/10 [00:21<00:00,  2.11s/it]
100%|██████████| 10/10 [00:21<00:00,  2.15s/it]
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56850, 13
LR fn, tp: 19, 77
LR f1 score: 0.828
LR cohens kappa score: 0.828
LR average precision score: 0.812

-> test with 'GB'
GB tn, fp: 56856, 7
GB fn, tp: 18, 78
GB f1 score: 0.862
GB cohens kappa score: 0.862

-> test with 'KNN'
KNN tn, fp: 56543, 320
KNN fn, tp: 94, 2
KNN f1 score: 0.010
KNN cohens kappa score: 0.007


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:20,  2.28s/it]
 20%|██        | 2/10 [00:04<00:19,  2.38s/it]
 30%|███       | 3/10 [00:06<00:16,  2.30s/it]
 40%|████      | 4/10 [00:09<00:13,  2.24s/it]
 50%|█████     | 5/10 [00:10<00:10,  2.11s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.15s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.12s/it]
 80%|████████  | 8/10 [00:17<00:04,  2.13s/it]
 90%|█████████ | 9/10 [00:19<00:02,  2.15s/it]
100%|██████████| 10/10 [00:21<00:00,  2.20s/it]
100%|██████████| 10/10 [00:21<00:00,  2.19s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56810, 53
LR fn, tp: 15, 84
LR f1 score: 0.712
LR cohens kappa score: 0.711
LR average precision score: 0.694

-> test with 'GB'
GB tn, fp: 56847, 16
GB fn, tp: 19, 80
GB f1 score: 0.821
GB cohens kappa score: 0.820

-> test with 'KNN'
KNN tn, fp: 56522, 341
KNN fn, tp: 96, 3
KNN f1 score: 0.014
KNN cohens kappa score: 0.011


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:18,  2.04s/it]
 20%|██        | 2/10 [00:04<00:19,  2.38s/it]
 30%|███       | 3/10 [00:07<00:16,  2.43s/it]
 40%|████      | 4/10 [00:09<00:14,  2.36s/it]
 50%|█████     | 5/10 [00:13<00:14,  2.87s/it]
 60%|██████    | 6/10 [00:15<00:10,  2.60s/it]
 70%|███████   | 7/10 [00:17<00:07,  2.37s/it]
 80%|████████  | 8/10 [00:19<00:04,  2.27s/it]
 90%|█████████ | 9/10 [00:21<00:02,  2.25s/it]
100%|██████████| 10/10 [00:23<00:00,  2.20s/it]
100%|██████████| 10/10 [00:23<00:00,  2.35s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56835, 28
LR fn, tp: 17, 82
LR f1 score: 0.785
LR cohens kappa score: 0.784
LR average precision score: 0.619

-> test with 'GB'
GB tn, fp: 56850, 13
GB fn, tp: 19, 80
GB f1 score: 0.833
GB cohens kappa score: 0.833

-> test with 'KNN'
KNN tn, fp: 56508, 355
KNN fn, tp: 98, 1
KNN f1 score: 0.004
KNN cohens kappa score: 0.002


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:19,  2.12s/it]
 20%|██        | 2/10 [00:04<00:17,  2.17s/it]
 30%|███       | 3/10 [00:06<00:16,  2.29s/it]
 40%|████      | 4/10 [00:08<00:13,  2.27s/it]
 50%|█████     | 5/10 [00:11<00:11,  2.22s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.23s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.20s/it]
 80%|████████  | 8/10 [00:17<00:04,  2.17s/it]
 90%|█████████ | 9/10 [00:19<00:02,  2.14s/it]
100%|██████████| 10/10 [00:21<00:00,  2.12s/it]
100%|██████████| 10/10 [00:21<00:00,  2.18s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56840, 23
LR fn, tp: 23, 76
LR f1 score: 0.768
LR cohens kappa score: 0.767
LR average precision score: 0.646

-> test with 'GB'
GB tn, fp: 56850, 13
GB fn, tp: 23, 76
GB f1 score: 0.809
GB cohens kappa score: 0.808

-> test with 'KNN'
KNN tn, fp: 56557, 306
KNN fn, tp: 94, 5
KNN f1 score: 0.024
KNN cohens kappa score: 0.022


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:24,  2.69s/it]
 20%|██        | 2/10 [00:05<00:21,  2.73s/it]
 30%|███       | 3/10 [00:07<00:18,  2.64s/it]
 40%|████      | 4/10 [00:10<00:14,  2.50s/it]
 50%|█████     | 5/10 [00:12<00:12,  2.42s/it]
 60%|██████    | 6/10 [00:15<00:10,  2.51s/it]
 70%|███████   | 7/10 [00:17<00:07,  2.46s/it]
 80%|████████  | 8/10 [00:19<00:04,  2.35s/it]
 90%|█████████ | 9/10 [00:21<00:02,  2.23s/it]
100%|██████████| 10/10 [00:23<00:00,  2.23s/it]
100%|██████████| 10/10 [00:23<00:00,  2.39s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56836, 27
LR fn, tp: 21, 78
LR f1 score: 0.765
LR cohens kappa score: 0.764
LR average precision score: 0.713

-> test with 'GB'
GB tn, fp: 56856, 7
GB fn, tp: 21, 78
GB f1 score: 0.848
GB cohens kappa score: 0.848

-> test with 'KNN'
KNN tn, fp: 56433, 430
KNN fn, tp: 95, 4
KNN f1 score: 0.015
KNN cohens kappa score: 0.012


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:20,  2.24s/it]
 20%|██        | 2/10 [00:04<00:17,  2.23s/it]
 30%|███       | 3/10 [00:06<00:15,  2.26s/it]
 40%|████      | 4/10 [00:08<00:13,  2.23s/it]
 50%|█████     | 5/10 [00:11<00:11,  2.29s/it]
 60%|██████    | 6/10 [00:13<00:09,  2.28s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.27s/it]
 80%|████████  | 8/10 [00:17<00:04,  2.22s/it]
 90%|█████████ | 9/10 [00:20<00:02,  2.24s/it]
100%|██████████| 10/10 [00:22<00:00,  2.35s/it]
100%|██████████| 10/10 [00:22<00:00,  2.28s/it]
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56850, 13
LR fn, tp: 23, 73
LR f1 score: 0.802
LR cohens kappa score: 0.802
LR average precision score: 0.750

-> test with 'GB'
GB tn, fp: 56852, 11
GB fn, tp: 23, 73
GB f1 score: 0.811
GB cohens kappa score: 0.811

-> test with 'KNN'
KNN tn, fp: 56594, 269
KNN fn, tp: 93, 3
KNN f1 score: 0.016
KNN cohens kappa score: 0.014


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:19,  2.13s/it]
 20%|██        | 2/10 [00:04<00:20,  2.54s/it]
 30%|███       | 3/10 [00:07<00:17,  2.47s/it]
 40%|████      | 4/10 [00:09<00:14,  2.35s/it]
 50%|█████     | 5/10 [00:11<00:11,  2.23s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.17s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.17s/it]
 80%|████████  | 8/10 [00:17<00:04,  2.15s/it]
 90%|█████████ | 9/10 [00:20<00:02,  2.21s/it]
100%|██████████| 10/10 [00:22<00:00,  2.19s/it]
100%|██████████| 10/10 [00:22<00:00,  2.24s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56838, 25
LR fn, tp: 21, 78
LR f1 score: 0.772
LR cohens kappa score: 0.772
LR average precision score: 0.703

-> test with 'GB'
GB tn, fp: 56852, 11
GB fn, tp: 25, 74
GB f1 score: 0.804
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 56349, 514
KNN fn, tp: 95, 4
KNN f1 score: 0.013
KNN cohens kappa score: 0.010


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:19,  2.18s/it]
 20%|██        | 2/10 [00:04<00:18,  2.32s/it]
 30%|███       | 3/10 [00:06<00:16,  2.32s/it]
 40%|████      | 4/10 [00:09<00:14,  2.38s/it]
 50%|█████     | 5/10 [00:11<00:11,  2.30s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.24s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.24s/it]
 80%|████████  | 8/10 [00:18<00:04,  2.20s/it]
 90%|█████████ | 9/10 [00:20<00:02,  2.25s/it]
100%|██████████| 10/10 [00:22<00:00,  2.23s/it]
100%|██████████| 10/10 [00:22<00:00,  2.26s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56836, 27
LR fn, tp: 22, 77
LR f1 score: 0.759
LR cohens kappa score: 0.758
LR average precision score: 0.666

-> test with 'GB'
GB tn, fp: 56851, 12
GB fn, tp: 21, 78
GB f1 score: 0.825
GB cohens kappa score: 0.825

-> test with 'KNN'
KNN tn, fp: 56501, 362
KNN fn, tp: 95, 4
KNN f1 score: 0.017
KNN cohens kappa score: 0.015


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:20,  2.28s/it]
 20%|██        | 2/10 [00:04<00:20,  2.52s/it]
 30%|███       | 3/10 [00:07<00:17,  2.51s/it]
 40%|████      | 4/10 [00:10<00:15,  2.66s/it]
 50%|█████     | 5/10 [00:12<00:12,  2.51s/it]
 60%|██████    | 6/10 [00:14<00:09,  2.44s/it]
 70%|███████   | 7/10 [00:17<00:07,  2.41s/it]
 80%|████████  | 8/10 [00:19<00:04,  2.33s/it]
 90%|█████████ | 9/10 [00:21<00:02,  2.32s/it]
100%|██████████| 10/10 [00:24<00:00,  2.31s/it]
100%|██████████| 10/10 [00:24<00:00,  2.40s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56844, 19
LR fn, tp: 25, 74
LR f1 score: 0.771
LR cohens kappa score: 0.770
LR average precision score: 0.733

-> test with 'GB'
GB tn, fp: 56858, 5
GB fn, tp: 18, 81
GB f1 score: 0.876
GB cohens kappa score: 0.875

-> test with 'KNN'
KNN tn, fp: 56504, 359
KNN fn, tp: 95, 4
KNN f1 score: 0.017
KNN cohens kappa score: 0.015


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:21,  2.41s/it]
 20%|██        | 2/10 [00:04<00:18,  2.35s/it]
 30%|███       | 3/10 [00:06<00:15,  2.18s/it]
 40%|████      | 4/10 [00:08<00:13,  2.22s/it]
 50%|█████     | 5/10 [00:11<00:10,  2.17s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.21s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.22s/it]
 80%|████████  | 8/10 [00:17<00:04,  2.20s/it]
 90%|█████████ | 9/10 [00:20<00:02,  2.24s/it]
100%|██████████| 10/10 [00:22<00:00,  2.27s/it]
100%|██████████| 10/10 [00:22<00:00,  2.24s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56839, 24
LR fn, tp: 20, 79
LR f1 score: 0.782
LR cohens kappa score: 0.782
LR average precision score: 0.747

-> test with 'GB'
GB tn, fp: 56853, 10
GB fn, tp: 16, 83
GB f1 score: 0.865
GB cohens kappa score: 0.864

-> test with 'KNN'
KNN tn, fp: 56447, 416
KNN fn, tp: 92, 7
KNN f1 score: 0.027
KNN cohens kappa score: 0.024


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:21,  2.42s/it]
 20%|██        | 2/10 [00:04<00:19,  2.39s/it]
 30%|███       | 3/10 [00:07<00:16,  2.37s/it]
 40%|████      | 4/10 [00:09<00:13,  2.26s/it]
 50%|█████     | 5/10 [00:11<00:11,  2.24s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.21s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.22s/it]
 80%|████████  | 8/10 [00:17<00:04,  2.19s/it]
 90%|█████████ | 9/10 [00:20<00:02,  2.17s/it]
100%|██████████| 10/10 [00:22<00:00,  2.31s/it]
100%|██████████| 10/10 [00:22<00:00,  2.27s/it]
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56844, 19
LR fn, tp: 21, 75
LR f1 score: 0.789
LR cohens kappa score: 0.789
LR average precision score: 0.757

-> test with 'GB'
GB tn, fp: 56853, 10
GB fn, tp: 24, 72
GB f1 score: 0.809
GB cohens kappa score: 0.809

-> test with 'KNN'
KNN tn, fp: 56518, 345
KNN fn, tp: 94, 2
KNN f1 score: 0.009
KNN cohens kappa score: 0.006


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:20,  2.23s/it]
 20%|██        | 2/10 [00:04<00:17,  2.19s/it]
 30%|███       | 3/10 [00:06<00:15,  2.22s/it]
 40%|████      | 4/10 [00:08<00:13,  2.20s/it]
 50%|█████     | 5/10 [00:11<00:12,  2.44s/it]
 60%|██████    | 6/10 [00:14<00:09,  2.47s/it]
 70%|███████   | 7/10 [00:16<00:07,  2.51s/it]
 80%|████████  | 8/10 [00:18<00:04,  2.34s/it]
 90%|█████████ | 9/10 [00:21<00:02,  2.34s/it]
100%|██████████| 10/10 [00:23<00:00,  2.38s/it]
100%|██████████| 10/10 [00:23<00:00,  2.36s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56837, 26
LR fn, tp: 16, 83
LR f1 score: 0.798
LR cohens kappa score: 0.798
LR average precision score: 0.680

-> test with 'GB'
GB tn, fp: 56849, 14
GB fn, tp: 17, 82
GB f1 score: 0.841
GB cohens kappa score: 0.841

-> test with 'KNN'
KNN tn, fp: 56571, 292
KNN fn, tp: 97, 2
KNN f1 score: 0.010
KNN cohens kappa score: 0.008


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:21,  2.42s/it]
 20%|██        | 2/10 [00:04<00:18,  2.30s/it]
 30%|███       | 3/10 [00:07<00:16,  2.43s/it]
 40%|████      | 4/10 [00:09<00:14,  2.39s/it]
 50%|█████     | 5/10 [00:11<00:11,  2.34s/it]
 60%|██████    | 6/10 [00:14<00:09,  2.38s/it]
 70%|███████   | 7/10 [00:16<00:07,  2.42s/it]
 80%|████████  | 8/10 [00:19<00:04,  2.44s/it]
 90%|█████████ | 9/10 [00:21<00:02,  2.36s/it]
100%|██████████| 10/10 [00:23<00:00,  2.34s/it]
100%|██████████| 10/10 [00:23<00:00,  2.37s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56844, 19
LR fn, tp: 32, 67
LR f1 score: 0.724
LR cohens kappa score: 0.724
LR average precision score: 0.646

-> test with 'GB'
GB tn, fp: 56851, 12
GB fn, tp: 21, 78
GB f1 score: 0.825
GB cohens kappa score: 0.825

-> test with 'KNN'
KNN tn, fp: 56551, 312
KNN fn, tp: 96, 3
KNN f1 score: 0.014
KNN cohens kappa score: 0.012


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:21,  2.38s/it]
 20%|██        | 2/10 [00:04<00:18,  2.30s/it]
 30%|███       | 3/10 [00:06<00:16,  2.30s/it]
 40%|████      | 4/10 [00:09<00:13,  2.24s/it]
 50%|█████     | 5/10 [00:11<00:11,  2.28s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.23s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.19s/it]
 80%|████████  | 8/10 [00:17<00:04,  2.22s/it]
 90%|█████████ | 9/10 [00:20<00:02,  2.20s/it]
100%|██████████| 10/10 [00:22<00:00,  2.17s/it]
100%|██████████| 10/10 [00:22<00:00,  2.22s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56837, 26
LR fn, tp: 22, 77
LR f1 score: 0.762
LR cohens kappa score: 0.762
LR average precision score: 0.680

-> test with 'GB'
GB tn, fp: 56854, 9
GB fn, tp: 25, 74
GB f1 score: 0.813
GB cohens kappa score: 0.813

-> test with 'KNN'
KNN tn, fp: 56591, 272
KNN fn, tp: 94, 5
KNN f1 score: 0.027
KNN cohens kappa score: 0.024


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:21,  2.42s/it]
 20%|██        | 2/10 [00:05<00:21,  2.73s/it]
 30%|███       | 3/10 [00:07<00:17,  2.49s/it]
 40%|████      | 4/10 [00:09<00:13,  2.30s/it]
 50%|█████     | 5/10 [00:11<00:11,  2.26s/it]
 60%|██████    | 6/10 [00:14<00:09,  2.27s/it]
 70%|███████   | 7/10 [00:16<00:07,  2.34s/it]
 80%|████████  | 8/10 [00:18<00:04,  2.24s/it]
 90%|█████████ | 9/10 [00:20<00:02,  2.27s/it]
100%|██████████| 10/10 [00:23<00:00,  2.27s/it]
100%|██████████| 10/10 [00:23<00:00,  2.32s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56851, 12
LR fn, tp: 19, 80
LR f1 score: 0.838
LR cohens kappa score: 0.837
LR average precision score: 0.775

-> test with 'GB'
GB tn, fp: 56852, 11
GB fn, tp: 17, 82
GB f1 score: 0.854
GB cohens kappa score: 0.854

-> test with 'KNN'
KNN tn, fp: 56552, 311
KNN fn, tp: 96, 3
KNN f1 score: 0.015
KNN cohens kappa score: 0.012


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:20,  2.33s/it]
 20%|██        | 2/10 [00:04<00:16,  2.11s/it]
 30%|███       | 3/10 [00:06<00:14,  2.03s/it]
 40%|████      | 4/10 [00:08<00:13,  2.26s/it]
 50%|█████     | 5/10 [00:11<00:11,  2.24s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.22s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.23s/it]
 80%|████████  | 8/10 [00:17<00:04,  2.22s/it]
 90%|█████████ | 9/10 [00:19<00:02,  2.21s/it]
100%|██████████| 10/10 [00:22<00:00,  2.24s/it]
100%|██████████| 10/10 [00:22<00:00,  2.22s/it]
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56847, 16
LR fn, tp: 25, 71
LR f1 score: 0.776
LR cohens kappa score: 0.776
LR average precision score: 0.710

-> test with 'GB'
GB tn, fp: 56845, 18
GB fn, tp: 21, 75
GB f1 score: 0.794
GB cohens kappa score: 0.793

-> test with 'KNN'
KNN tn, fp: 56541, 322
KNN fn, tp: 88, 8
KNN f1 score: 0.038
KNN cohens kappa score: 0.035


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:22,  2.53s/it]
 20%|██        | 2/10 [00:04<00:18,  2.36s/it]
 30%|███       | 3/10 [00:07<00:16,  2.32s/it]
 40%|████      | 4/10 [00:09<00:14,  2.35s/it]
 50%|█████     | 5/10 [00:11<00:11,  2.34s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.25s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.15s/it]
 80%|████████  | 8/10 [00:18<00:04,  2.26s/it]
 90%|█████████ | 9/10 [00:20<00:02,  2.27s/it]
100%|██████████| 10/10 [00:22<00:00,  2.24s/it]
100%|██████████| 10/10 [00:22<00:00,  2.27s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56841, 22
LR fn, tp: 24, 75
LR f1 score: 0.765
LR cohens kappa score: 0.765
LR average precision score: 0.677

-> test with 'GB'
GB tn, fp: 56857, 6
GB fn, tp: 25, 74
GB f1 score: 0.827
GB cohens kappa score: 0.827

-> test with 'KNN'
KNN tn, fp: 56487, 376
KNN fn, tp: 96, 3
KNN f1 score: 0.013
KNN cohens kappa score: 0.010


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:20,  2.29s/it]
 20%|██        | 2/10 [00:04<00:17,  2.15s/it]
 30%|███       | 3/10 [00:06<00:15,  2.18s/it]
 40%|████      | 4/10 [00:08<00:13,  2.24s/it]
 50%|█████     | 5/10 [00:11<00:11,  2.24s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.18s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.20s/it]
 80%|████████  | 8/10 [00:17<00:04,  2.20s/it]
 90%|█████████ | 9/10 [00:21<00:02,  2.61s/it]
100%|██████████| 10/10 [00:23<00:00,  2.41s/it]
100%|██████████| 10/10 [00:23<00:00,  2.31s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56799, 64
LR fn, tp: 16, 83
LR f1 score: 0.675
LR cohens kappa score: 0.674
LR average precision score: 0.733

-> test with 'GB'
GB tn, fp: 56854, 9
GB fn, tp: 20, 79
GB f1 score: 0.845
GB cohens kappa score: 0.845

-> test with 'KNN'
KNN tn, fp: 56409, 454
KNN fn, tp: 93, 6
KNN f1 score: 0.021
KNN cohens kappa score: 0.019


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:22,  2.53s/it]
 20%|██        | 2/10 [00:05<00:21,  2.66s/it]
 30%|███       | 3/10 [00:07<00:17,  2.53s/it]
 40%|████      | 4/10 [00:10<00:14,  2.50s/it]
 50%|█████     | 5/10 [00:12<00:12,  2.43s/it]
 60%|██████    | 6/10 [00:14<00:09,  2.41s/it]
 70%|███████   | 7/10 [00:16<00:06,  2.31s/it]
 80%|████████  | 8/10 [00:18<00:04,  2.23s/it]
 90%|█████████ | 9/10 [00:21<00:02,  2.26s/it]
100%|██████████| 10/10 [00:23<00:00,  2.22s/it]
100%|██████████| 10/10 [00:23<00:00,  2.34s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56841, 22
LR fn, tp: 24, 75
LR f1 score: 0.765
LR cohens kappa score: 0.765
LR average precision score: 0.677

-> test with 'GB'
GB tn, fp: 56852, 11
GB fn, tp: 20, 79
GB f1 score: 0.836
GB cohens kappa score: 0.836

-> test with 'KNN'
KNN tn, fp: 56608, 255
KNN fn, tp: 96, 3
KNN f1 score: 0.017
KNN cohens kappa score: 0.014


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:04<00:36,  4.06s/it]
 20%|██        | 2/10 [00:06<00:25,  3.15s/it]
 30%|███       | 3/10 [00:09<00:19,  2.82s/it]
 40%|████      | 4/10 [00:11<00:15,  2.60s/it]
 50%|█████     | 5/10 [00:13<00:12,  2.51s/it]
 60%|██████    | 6/10 [00:15<00:09,  2.41s/it]
 70%|███████   | 7/10 [00:18<00:07,  2.38s/it]
 80%|████████  | 8/10 [00:20<00:04,  2.41s/it]
 90%|█████████ | 9/10 [00:23<00:02,  2.41s/it]
100%|██████████| 10/10 [00:25<00:00,  2.45s/it]
100%|██████████| 10/10 [00:25<00:00,  2.56s/it]
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56841, 22
LR fn, tp: 18, 81
LR f1 score: 0.802
LR cohens kappa score: 0.802
LR average precision score: 0.734

-> test with 'GB'
GB tn, fp: 56856, 7
GB fn, tp: 20, 79
GB f1 score: 0.854
GB cohens kappa score: 0.854

-> test with 'KNN'
KNN tn, fp: 56449, 414
KNN fn, tp: 97, 2
KNN f1 score: 0.008
KNN cohens kappa score: 0.005


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:24,  2.70s/it]
 20%|██        | 2/10 [00:05<00:19,  2.50s/it]
 30%|███       | 3/10 [00:07<00:17,  2.47s/it]
 40%|████      | 4/10 [00:09<00:14,  2.40s/it]
 50%|█████     | 5/10 [00:12<00:12,  2.41s/it]
 60%|██████    | 6/10 [00:14<00:09,  2.33s/it]
 70%|███████   | 7/10 [00:16<00:06,  2.23s/it]
 80%|████████  | 8/10 [00:18<00:04,  2.30s/it]
 90%|█████████ | 9/10 [00:20<00:02,  2.20s/it]
100%|██████████| 10/10 [00:23<00:00,  2.22s/it]
100%|██████████| 10/10 [00:23<00:00,  2.31s/it]
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56843, 20
LR fn, tp: 19, 77
LR f1 score: 0.798
LR cohens kappa score: 0.798
LR average precision score: 0.661

-> test with 'GB'
GB tn, fp: 56846, 17
GB fn, tp: 22, 74
GB f1 score: 0.791
GB cohens kappa score: 0.791

-> test with 'KNN'
KNN tn, fp: 56548, 315
KNN fn, tp: 94, 2
KNN f1 score: 0.010
KNN cohens kappa score: 0.007

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 56851, 64
LR fn, tp: 32, 84
LR f1 score: 0.838
LR cohens kappa score: 0.837
LR average precision score: 0.812


average:
LR tn, fp: 56838.16, 24.84
LR fn, tp: 21.08, 77.32
LR f1 score: 0.773
LR cohens kappa score: 0.772
LR average precision score: 0.701


minimum:
LR tn, fp: 56799, 12
LR fn, tp: 15, 67
LR f1 score: 0.675
LR cohens kappa score: 0.674
LR average precision score: 0.560


-----[ GB ]-----
maximum:
GB tn, fp: 56858, 18
GB fn, tp: 27, 83
GB f1 score: 0.876
GB cohens kappa score: 0.875


average:
GB tn, fp: 56852.0, 11.0
GB fn, tp: 21.0, 77.4
GB f1 score: 0.829
GB cohens kappa score: 0.828


minimum:
GB tn, fp: 56845, 5
GB fn, tp: 16, 72
GB f1 score: 0.774
GB cohens kappa score: 0.774


-----[ KNN ]-----
maximum:
KNN tn, fp: 56608, 514
KNN fn, tp: 98, 8
KNN f1 score: 0.038
KNN cohens kappa score: 0.035


average:
KNN tn, fp: 56518.52, 344.48
KNN fn, tp: 94.72, 3.68
KNN f1 score: 0.016
KNN cohens kappa score: 0.014


minimum:
KNN tn, fp: 56349, 255
KNN fn, tp: 88, 1
KNN f1 score: 0.004
KNN cohens kappa score: 0.002