ConvGeNBenchmarkResults/data_result/CTAB-GAN/imblearn_protein_homo.log

///////////////////////////////////////////
// Running CTAB-GAN on imblearn_protein_homo
///////////////////////////////////////////

Load 'data_input/imblearn_protein_homo'
from imblearn
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

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100%|██████████| 10/10 [01:53<00:00, 11.32s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28867, 24
LR fn, tp: 60, 200
LR f1 score: 0.826
LR cohens kappa score: 0.825
LR average precision score: 0.853

-> test with 'GB'
GB tn, fp: 28861, 30
GB fn, tp: 58, 202
GB f1 score: 0.821
GB cohens kappa score: 0.820

-> test with 'KNN'
KNN tn, fp: 28883, 8
KNN fn, tp: 161, 99
KNN f1 score: 0.540
KNN cohens kappa score: 0.537


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

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100%|██████████| 10/10 [01:51<00:00, 11.19s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28879, 12
LR fn, tp: 57, 203
LR f1 score: 0.855
LR cohens kappa score: 0.854
LR average precision score: 0.888

-> test with 'GB'
GB tn, fp: 28876, 15
GB fn, tp: 60, 200
GB f1 score: 0.842
GB cohens kappa score: 0.841

-> test with 'KNN'
KNN tn, fp: 28874, 17
KNN fn, tp: 144, 116
KNN f1 score: 0.590
KNN cohens kappa score: 0.588


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

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100%|██████████| 10/10 [01:52<00:00, 11.29s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28870, 21
LR fn, tp: 62, 198
LR f1 score: 0.827
LR cohens kappa score: 0.825
LR average precision score: 0.886

-> test with 'GB'
GB tn, fp: 28867, 24
GB fn, tp: 73, 187
GB f1 score: 0.794
GB cohens kappa score: 0.792

-> test with 'KNN'
KNN tn, fp: 28881, 10
KNN fn, tp: 170, 90
KNN f1 score: 0.500
KNN cohens kappa score: 0.498


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

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-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28877, 14
LR fn, tp: 68, 192
LR f1 score: 0.824
LR cohens kappa score: 0.823
LR average precision score: 0.852

-> test with 'GB'
GB tn, fp: 28871, 20
GB fn, tp: 70, 190
GB f1 score: 0.809
GB cohens kappa score: 0.807

-> test with 'KNN'
KNN tn, fp: 28884, 7
KNN fn, tp: 160, 100
KNN f1 score: 0.545
KNN cohens kappa score: 0.543


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

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-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 28880, 11
LR fn, tp: 84, 172
LR f1 score: 0.784
LR cohens kappa score: 0.782
LR average precision score: 0.827

-> test with 'GB'
GB tn, fp: 28865, 26
GB fn, tp: 74, 182
GB f1 score: 0.784
GB cohens kappa score: 0.783

-> test with 'KNN'
KNN tn, fp: 28886, 5
KNN fn, tp: 173, 83
KNN f1 score: 0.483
KNN cohens kappa score: 0.480


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

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

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100%|██████████| 10/10 [02:11<00:00, 13.14s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28881, 10
LR fn, tp: 75, 185
LR f1 score: 0.813
LR cohens kappa score: 0.812
LR average precision score: 0.874

-> test with 'GB'
GB tn, fp: 28868, 23
GB fn, tp: 74, 186
GB f1 score: 0.793
GB cohens kappa score: 0.792

-> test with 'KNN'
KNN tn, fp: 28882, 9
KNN fn, tp: 163, 97
KNN f1 score: 0.530
KNN cohens kappa score: 0.528


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

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100%|██████████| 10/10 [02:02<00:00, 12.26s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28876, 15
LR fn, tp: 59, 201
LR f1 score: 0.845
LR cohens kappa score: 0.843
LR average precision score: 0.892

-> test with 'GB'
GB tn, fp: 28868, 23
GB fn, tp: 61, 199
GB f1 score: 0.826
GB cohens kappa score: 0.824

-> test with 'KNN'
KNN tn, fp: 28890, 1
KNN fn, tp: 162, 98
KNN f1 score: 0.546
KNN cohens kappa score: 0.544


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

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-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28872, 19
LR fn, tp: 63, 197
LR f1 score: 0.828
LR cohens kappa score: 0.826
LR average precision score: 0.844

-> test with 'GB'
GB tn, fp: 28862, 29
GB fn, tp: 67, 193
GB f1 score: 0.801
GB cohens kappa score: 0.799

-> test with 'KNN'
KNN tn, fp: 28882, 9
KNN fn, tp: 152, 108
KNN f1 score: 0.573
KNN cohens kappa score: 0.571


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

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-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28867, 24
LR fn, tp: 65, 195
LR f1 score: 0.814
LR cohens kappa score: 0.813
LR average precision score: 0.860

-> test with 'GB'
GB tn, fp: 28861, 30
GB fn, tp: 67, 193
GB f1 score: 0.799
GB cohens kappa score: 0.798

-> test with 'KNN'
KNN tn, fp: 28883, 8
KNN fn, tp: 155, 105
KNN f1 score: 0.563
KNN cohens kappa score: 0.561


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

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100%|██████████| 10/10 [02:02<00:00, 12.27s/it]
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 28876, 15
LR fn, tp: 67, 189
LR f1 score: 0.822
LR cohens kappa score: 0.820
LR average precision score: 0.854

-> test with 'GB'
GB tn, fp: 28860, 31
GB fn, tp: 74, 182
GB f1 score: 0.776
GB cohens kappa score: 0.774

-> test with 'KNN'
KNN tn, fp: 28884, 7
KNN fn, tp: 166, 90
KNN f1 score: 0.510
KNN cohens kappa score: 0.508


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

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

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100%|██████████| 10/10 [01:58<00:00, 11.84s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28875, 16
LR fn, tp: 59, 201
LR f1 score: 0.843
LR cohens kappa score: 0.841
LR average precision score: 0.875

-> test with 'GB'
GB tn, fp: 28869, 22
GB fn, tp: 66, 194
GB f1 score: 0.815
GB cohens kappa score: 0.814

-> test with 'KNN'
KNN tn, fp: 28886, 5
KNN fn, tp: 168, 92
KNN f1 score: 0.515
KNN cohens kappa score: 0.513


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

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100%|██████████| 10/10 [02:02<00:00, 12.23s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28874, 17
LR fn, tp: 67, 193
LR f1 score: 0.821
LR cohens kappa score: 0.820
LR average precision score: 0.858

-> test with 'GB'
GB tn, fp: 28874, 17
GB fn, tp: 70, 190
GB f1 score: 0.814
GB cohens kappa score: 0.812

-> test with 'KNN'
KNN tn, fp: 28886, 5
KNN fn, tp: 172, 88
KNN f1 score: 0.499
KNN cohens kappa score: 0.496


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

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100%|██████████| 10/10 [02:07<00:00, 12.76s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28868, 23
LR fn, tp: 72, 188
LR f1 score: 0.798
LR cohens kappa score: 0.797
LR average precision score: 0.838

-> test with 'GB'
GB tn, fp: 28865, 26
GB fn, tp: 70, 190
GB f1 score: 0.798
GB cohens kappa score: 0.797

-> test with 'KNN'
KNN tn, fp: 28878, 13
KNN fn, tp: 158, 102
KNN f1 score: 0.544
KNN cohens kappa score: 0.541


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

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100%|██████████| 10/10 [01:56<00:00, 11.67s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28873, 18
LR fn, tp: 73, 187
LR f1 score: 0.804
LR cohens kappa score: 0.803
LR average precision score: 0.857

-> test with 'GB'
GB tn, fp: 28864, 27
GB fn, tp: 68, 192
GB f1 score: 0.802
GB cohens kappa score: 0.800

-> test with 'KNN'
KNN tn, fp: 28886, 5
KNN fn, tp: 165, 95
KNN f1 score: 0.528
KNN cohens kappa score: 0.525


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

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 90%|█████████ | 9/10 [01:56<00:12, 12.39s/it]
100%|██████████| 10/10 [02:09<00:00, 12.46s/it]
100%|██████████| 10/10 [02:09<00:00, 12.93s/it]
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 28880, 11
LR fn, tp: 62, 194
LR f1 score: 0.842
LR cohens kappa score: 0.840
LR average precision score: 0.882

-> test with 'GB'
GB tn, fp: 28872, 19
GB fn, tp: 62, 194
GB f1 score: 0.827
GB cohens kappa score: 0.826

-> test with 'KNN'
KNN tn, fp: 28880, 11
KNN fn, tp: 146, 110
KNN f1 score: 0.584
KNN cohens kappa score: 0.581


====== 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 [00:10<01:38, 10.94s/it]
 20%|██        | 2/10 [00:22<01:30, 11.37s/it]
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 40%|████      | 4/10 [00:46<01:11, 11.89s/it]
 50%|█████     | 5/10 [00:59<01:00, 12.04s/it]
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100%|██████████| 10/10 [01:58<00:00, 11.73s/it]
100%|██████████| 10/10 [01:58<00:00, 11.90s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28875, 16
LR fn, tp: 62, 198
LR f1 score: 0.835
LR cohens kappa score: 0.834
LR average precision score: 0.884

-> test with 'GB'
GB tn, fp: 28859, 32
GB fn, tp: 61, 199
GB f1 score: 0.811
GB cohens kappa score: 0.809

-> test with 'KNN'
KNN tn, fp: 28888, 3
KNN fn, tp: 160, 100
KNN f1 score: 0.551
KNN cohens kappa score: 0.549


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

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 10%|█         | 1/10 [00:11<01:44, 11.66s/it]
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 40%|████      | 4/10 [00:46<01:10, 11.67s/it]
 50%|█████     | 5/10 [00:57<00:57, 11.46s/it]
 60%|██████    | 6/10 [01:09<00:46, 11.69s/it]
 70%|███████   | 7/10 [01:21<00:34, 11.67s/it]
 80%|████████  | 8/10 [01:33<00:23, 11.66s/it]
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100%|██████████| 10/10 [01:56<00:00, 11.63s/it]
100%|██████████| 10/10 [01:56<00:00, 11.64s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28870, 21
LR fn, tp: 75, 185
LR f1 score: 0.794
LR cohens kappa score: 0.792
LR average precision score: 0.841

-> test with 'GB'
GB tn, fp: 28856, 35
GB fn, tp: 72, 188
GB f1 score: 0.778
GB cohens kappa score: 0.777

-> test with 'KNN'
KNN tn, fp: 28880, 11
KNN fn, tp: 162, 98
KNN f1 score: 0.531
KNN cohens kappa score: 0.529


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:11<01:47, 11.95s/it]
 20%|██        | 2/10 [00:24<01:36, 12.09s/it]
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 40%|████      | 4/10 [00:47<01:11, 11.92s/it]
 50%|█████     | 5/10 [00:59<00:59, 11.85s/it]
 60%|██████    | 6/10 [01:11<00:47, 11.91s/it]
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100%|██████████| 10/10 [01:58<00:00, 11.70s/it]
100%|██████████| 10/10 [01:58<00:00, 11.85s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28876, 15
LR fn, tp: 60, 200
LR f1 score: 0.842
LR cohens kappa score: 0.841
LR average precision score: 0.855

-> test with 'GB'
GB tn, fp: 28866, 25
GB fn, tp: 61, 199
GB f1 score: 0.822
GB cohens kappa score: 0.821

-> test with 'KNN'
KNN tn, fp: 28883, 8
KNN fn, tp: 168, 92
KNN f1 score: 0.511
KNN cohens kappa score: 0.509


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:16<02:26, 16.24s/it]
 20%|██        | 2/10 [00:28<01:49, 13.69s/it]
 30%|███       | 3/10 [00:40<01:30, 12.89s/it]
 40%|████      | 4/10 [00:52<01:15, 12.61s/it]
 50%|█████     | 5/10 [01:04<01:02, 12.46s/it]
 60%|██████    | 6/10 [01:16<00:49, 12.41s/it]
 70%|███████   | 7/10 [01:28<00:36, 12.31s/it]
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100%|██████████| 10/10 [02:04<00:00, 12.03s/it]
100%|██████████| 10/10 [02:04<00:00, 12.50s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28875, 16
LR fn, tp: 67, 193
LR f1 score: 0.823
LR cohens kappa score: 0.822
LR average precision score: 0.877

-> test with 'GB'
GB tn, fp: 28868, 23
GB fn, tp: 75, 185
GB f1 score: 0.791
GB cohens kappa score: 0.789

-> test with 'KNN'
KNN tn, fp: 28886, 5
KNN fn, tp: 159, 101
KNN f1 score: 0.552
KNN cohens kappa score: 0.550


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [07:09<1:04:22, 429.14s/it]
 20%|██        | 2/10 [10:09<37:41, 282.65s/it]  
 30%|███       | 3/10 [10:20<18:30, 158.60s/it]
 40%|████      | 4/10 [10:31<10:03, 100.51s/it]
 50%|█████     | 5/10 [10:43<05:43, 68.67s/it] 
 60%|██████    | 6/10 [10:55<03:16, 49.16s/it]
 70%|███████   | 7/10 [11:06<01:50, 36.88s/it]
 80%|████████  | 8/10 [11:18<00:57, 28.94s/it]
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100%|██████████| 10/10 [11:42<00:00, 19.95s/it]
100%|██████████| 10/10 [11:42<00:00, 70.22s/it]
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 28875, 16
LR fn, tp: 67, 189
LR f1 score: 0.820
LR cohens kappa score: 0.819
LR average precision score: 0.854

-> test with 'GB'
GB tn, fp: 28861, 30
GB fn, tp: 68, 188
GB f1 score: 0.793
GB cohens kappa score: 0.792

-> test with 'KNN'
KNN tn, fp: 28882, 9
KNN fn, tp: 156, 100
KNN f1 score: 0.548
KNN cohens kappa score: 0.546


====== 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 [00:13<01:59, 13.24s/it]
 20%|██        | 2/10 [00:26<01:44, 13.06s/it]
 30%|███       | 3/10 [00:38<01:28, 12.58s/it]
 40%|████      | 4/10 [00:50<01:15, 12.57s/it]
 50%|█████     | 5/10 [01:02<01:01, 12.38s/it]
 60%|██████    | 6/10 [01:15<00:49, 12.45s/it]
 70%|███████   | 7/10 [01:26<00:36, 12.11s/it]
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100%|██████████| 10/10 [02:02<00:00, 12.03s/it]
100%|██████████| 10/10 [02:02<00:00, 12.28s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28868, 23
LR fn, tp: 61, 199
LR f1 score: 0.826
LR cohens kappa score: 0.824
LR average precision score: 0.867

-> test with 'GB'
GB tn, fp: 28864, 27
GB fn, tp: 62, 198
GB f1 score: 0.816
GB cohens kappa score: 0.815

-> test with 'KNN'
KNN tn, fp: 28883, 8
KNN fn, tp: 164, 96
KNN f1 score: 0.527
KNN cohens kappa score: 0.525


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [05:15<47:16, 315.15s/it]
 20%|██        | 2/10 [05:29<18:26, 138.27s/it]
 30%|███       | 3/10 [05:43<09:28, 81.28s/it] 
 40%|████      | 4/10 [05:56<05:26, 54.45s/it]
 50%|█████     | 5/10 [06:09<03:17, 39.54s/it]
 60%|██████    | 6/10 [06:22<02:02, 30.55s/it]
 70%|███████   | 7/10 [06:34<01:13, 24.57s/it]
 80%|████████  | 8/10 [06:48<00:42, 21.18s/it]
 90%|█████████ | 9/10 [07:02<00:18, 18.71s/it]
100%|██████████| 10/10 [07:14<00:00, 16.81s/it]
100%|██████████| 10/10 [07:14<00:00, 43.46s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28880, 11
LR fn, tp: 66, 194
LR f1 score: 0.834
LR cohens kappa score: 0.833
LR average precision score: 0.866

-> test with 'GB'
GB tn, fp: 28861, 30
GB fn, tp: 71, 189
GB f1 score: 0.789
GB cohens kappa score: 0.787

-> test with 'KNN'
KNN tn, fp: 28888, 3
KNN fn, tp: 160, 100
KNN f1 score: 0.551
KNN cohens kappa score: 0.549


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:12<01:55, 12.82s/it]
 20%|██        | 2/10 [00:27<01:49, 13.74s/it]
 30%|███       | 3/10 [00:39<01:30, 12.96s/it]
 40%|████      | 4/10 [00:51<01:15, 12.58s/it]
 50%|█████     | 5/10 [01:03<01:02, 12.48s/it]
 60%|██████    | 6/10 [01:16<00:51, 12.80s/it]
 70%|███████   | 7/10 [01:29<00:38, 12.76s/it]
 80%|████████  | 8/10 [01:41<00:24, 12.49s/it]
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100%|██████████| 10/10 [02:05<00:00, 12.33s/it]
100%|██████████| 10/10 [02:05<00:00, 12.57s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28874, 17
LR fn, tp: 74, 186
LR f1 score: 0.803
LR cohens kappa score: 0.802
LR average precision score: 0.860

-> test with 'GB'
GB tn, fp: 28871, 20
GB fn, tp: 76, 184
GB f1 score: 0.793
GB cohens kappa score: 0.791

-> test with 'KNN'
KNN tn, fp: 28883, 8
KNN fn, tp: 165, 95
KNN f1 score: 0.523
KNN cohens kappa score: 0.521


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:12<01:49, 12.17s/it]
 20%|██        | 2/10 [00:24<01:38, 12.28s/it]
 30%|███       | 3/10 [00:37<01:29, 12.77s/it]
 40%|████      | 4/10 [00:50<01:17, 12.86s/it]
 50%|█████     | 5/10 [01:03<01:03, 12.72s/it]
 60%|██████    | 6/10 [01:15<00:50, 12.62s/it]
 70%|███████   | 7/10 [01:30<00:39, 13.30s/it]
 80%|████████  | 8/10 [01:42<00:26, 13.00s/it]
 90%|█████████ | 9/10 [01:56<00:13, 13.06s/it]
100%|██████████| 10/10 [02:08<00:00, 12.98s/it]
100%|██████████| 10/10 [02:08<00:00, 12.88s/it]
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28877, 14
LR fn, tp: 65, 195
LR f1 score: 0.832
LR cohens kappa score: 0.830
LR average precision score: 0.857

-> test with 'GB'
GB tn, fp: 28863, 28
GB fn, tp: 67, 193
GB f1 score: 0.802
GB cohens kappa score: 0.801

-> test with 'KNN'
KNN tn, fp: 28884, 7
KNN fn, tp: 165, 95
KNN f1 score: 0.525
KNN cohens kappa score: 0.522


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:12<01:52, 12.52s/it]
 20%|██        | 2/10 [00:25<01:42, 12.86s/it]
 30%|███       | 3/10 [00:38<01:29, 12.81s/it]
 40%|████      | 4/10 [00:51<01:16, 12.83s/it]
 50%|█████     | 5/10 [01:03<01:02, 12.53s/it]
 60%|██████    | 6/10 [01:15<00:50, 12.58s/it]
 70%|███████   | 7/10 [01:28<00:37, 12.51s/it]
 80%|████████  | 8/10 [01:40<00:24, 12.41s/it]
 90%|█████████ | 9/10 [01:53<00:12, 12.58s/it]
100%|██████████| 10/10 [02:05<00:00, 12.42s/it]
100%|██████████| 10/10 [02:05<00:00, 12.55s/it]
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 28872, 19
LR fn, tp: 63, 193
LR f1 score: 0.825
LR cohens kappa score: 0.823
LR average precision score: 0.861

-> test with 'GB'
GB tn, fp: 28866, 25
GB fn, tp: 66, 190
GB f1 score: 0.807
GB cohens kappa score: 0.805

-> test with 'KNN'
KNN tn, fp: 28880, 11
KNN fn, tp: 147, 109
KNN f1 score: 0.580
KNN cohens kappa score: 0.577

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 28881, 24
LR fn, tp: 84, 203
LR f1 score: 0.855
LR cohens kappa score: 0.854
LR average precision score: 0.892


average:
LR tn, fp: 28874.28, 16.72
LR fn, tp: 66.12, 193.08
LR f1 score: 0.823
LR cohens kappa score: 0.822
LR average precision score: 0.862


minimum:
LR tn, fp: 28867, 10
LR fn, tp: 57, 172
LR f1 score: 0.784
LR cohens kappa score: 0.782
LR average precision score: 0.827


-----[ GB ]-----
maximum:
GB tn, fp: 28876, 35
GB fn, tp: 76, 202
GB f1 score: 0.842
GB cohens kappa score: 0.841


average:
GB tn, fp: 28865.52, 25.48
GB fn, tp: 67.72, 191.48
GB f1 score: 0.804
GB cohens kappa score: 0.803


minimum:
GB tn, fp: 28856, 15
GB fn, tp: 58, 182
GB f1 score: 0.776
GB cohens kappa score: 0.774


-----[ KNN ]-----
maximum:
KNN tn, fp: 28890, 17
KNN fn, tp: 173, 116
KNN f1 score: 0.590
KNN cohens kappa score: 0.588


average:
KNN tn, fp: 28883.28, 7.72
KNN fn, tp: 160.84, 98.36
KNN f1 score: 0.538
KNN cohens kappa score: 0.536


minimum:
KNN tn, fp: 28874, 1
KNN fn, tp: 144, 83
KNN f1 score: 0.483
KNN cohens kappa score: 0.480