ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_abalone9-18.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_abalone9-18
///////////////////////////////////////////

Load 'data_input/folding_abalone9-18'
from pickle file
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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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 124, 14
LR fn, tp: 2, 7
LR f1 score: 0.467
LR cohens kappa score: 0.417
LR average precision score: 0.695

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 4, 5
GB f1 score: 0.526
GB cohens kappa score: 0.494

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


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

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100%|██████████| 10/10 [00:07<00:00,  1.32it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 4, 5
LR f1 score: 0.500
LR cohens kappa score: 0.464
LR average precision score: 0.521

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.312

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 127, 11
LR fn, tp: 2, 7
LR f1 score: 0.519
LR cohens kappa score: 0.476
LR average precision score: 0.704

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 3, 6
LR f1 score: 0.632
LR cohens kappa score: 0.606
LR average precision score: 0.653

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023


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

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-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 121, 16
LR fn, tp: 2, 4
LR f1 score: 0.308
LR cohens kappa score: 0.260
LR average precision score: 0.514

-> test with 'GB'
GB tn, fp: 133, 4
GB fn, tp: 5, 1
GB f1 score: 0.182
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


====== 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 [00:08<00:00,  1.13it/s]
100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 2, 7
LR f1 score: 0.667
LR cohens kappa score: 0.642
LR average precision score: 0.760

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 8, 1
GB f1 score: 0.143
GB cohens kappa score: 0.104

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 8, 1
KNN f1 score: 0.182
KNN cohens kappa score: 0.163


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 3, 6
LR f1 score: 0.522
LR cohens kappa score: 0.483
LR average precision score: 0.630

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 7, 2
KNN f1 score: 0.364
KNN cohens kappa score: 0.349


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 122, 16
LR fn, tp: 3, 6
LR f1 score: 0.387
LR cohens kappa score: 0.329
LR average precision score: 0.522

-> test with 'GB'
GB tn, fp: 130, 8
GB fn, tp: 7, 2
GB f1 score: 0.211
GB cohens kappa score: 0.156

-> test with 'KNN'
KNN tn, fp: 133, 5
KNN fn, tp: 7, 2
KNN f1 score: 0.250
KNN cohens kappa score: 0.208


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 118, 20
LR fn, tp: 1, 8
LR f1 score: 0.432
LR cohens kappa score: 0.374
LR average precision score: 0.730

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 5, 4
GB f1 score: 0.471
GB cohens kappa score: 0.438

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


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

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-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 128, 9
LR fn, tp: 2, 4
LR f1 score: 0.421
LR cohens kappa score: 0.386
LR average precision score: 0.717

-> test with 'GB'
GB tn, fp: 135, 2
GB fn, tp: 3, 3
GB f1 score: 0.545
GB cohens kappa score: 0.527

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


====== 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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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 129, 9
LR fn, tp: 4, 5
LR f1 score: 0.435
LR cohens kappa score: 0.389
LR average precision score: 0.492

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 8, 1
GB f1 score: 0.143
GB cohens kappa score: 0.104

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


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

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100%|██████████| 10/10 [00:08<00:00,  1.15it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 119, 19
LR fn, tp: 1, 8
LR f1 score: 0.444
LR cohens kappa score: 0.388
LR average precision score: 0.734

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.163

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 6, 3
KNN f1 score: 0.429
KNN cohens kappa score: 0.402


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

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100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 4, 5
LR f1 score: 0.500
LR cohens kappa score: 0.464
LR average precision score: 0.619

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 7, 2
GB f1 score: 0.267
GB cohens kappa score: 0.229

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 7, 2
KNN f1 score: 0.364
KNN cohens kappa score: 0.349


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

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100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 123, 15
LR fn, tp: 2, 7
LR f1 score: 0.452
LR cohens kappa score: 0.399
LR average precision score: 0.696

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 5, 4
GB f1 score: 0.500
GB cohens kappa score: 0.472

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 8, 1
KNN f1 score: 0.154
KNN cohens kappa score: 0.121


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

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100%|██████████| 10/10 [00:08<00:00,  1.10it/s]
100%|██████████| 10/10 [00:08<00:00,  1.15it/s]
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 119, 18
LR fn, tp: 1, 5
LR f1 score: 0.345
LR cohens kappa score: 0.298
LR average precision score: 0.730

-> test with 'GB'
GB tn, fp: 133, 4
GB fn, tp: 3, 3
GB f1 score: 0.462
GB cohens kappa score: 0.436

-> test with 'KNN'
KNN tn, fp: 136, 1
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


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

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

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100%|██████████| 10/10 [00:08<00:00,  1.27it/s]
100%|██████████| 10/10 [00:08<00:00,  1.20it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 125, 13
LR fn, tp: 3, 6
LR f1 score: 0.429
LR cohens kappa score: 0.377
LR average precision score: 0.617

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


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

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100%|██████████| 10/10 [00:09<00:00,  1.12it/s]
100%|██████████| 10/10 [00:09<00:00,  1.11it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 114, 24
LR fn, tp: 3, 6
LR f1 score: 0.308
LR cohens kappa score: 0.236
LR average precision score: 0.647

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 4, 5
GB f1 score: 0.500
GB cohens kappa score: 0.464

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


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

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100%|██████████| 10/10 [00:07<00:00,  1.27it/s]
100%|██████████| 10/10 [00:07<00:00,  1.28it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 3, 6
LR f1 score: 0.600
LR cohens kappa score: 0.571
LR average precision score: 0.741

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


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

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100%|██████████| 10/10 [00:07<00:00,  1.24it/s]
100%|██████████| 10/10 [00:07<00:00,  1.27it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 129, 9
LR fn, tp: 3, 6
LR f1 score: 0.500
LR cohens kappa score: 0.459
LR average precision score: 0.620

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


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

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100%|██████████| 10/10 [00:07<00:00,  1.41it/s]
100%|██████████| 10/10 [00:07<00:00,  1.39it/s]
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 131, 6
LR fn, tp: 1, 5
LR f1 score: 0.588
LR cohens kappa score: 0.565
LR average precision score: 0.595

-> test with 'GB'
GB tn, fp: 134, 3
GB fn, tp: 5, 1
GB f1 score: 0.200
GB cohens kappa score: 0.172

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277


====== 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 [00:00<00:08,  1.10it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 114, 24
LR fn, tp: 2, 7
LR f1 score: 0.350
LR cohens kappa score: 0.282
LR average precision score: 0.516

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 7, 2
GB f1 score: 0.235
GB cohens kappa score: 0.189

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


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

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100%|██████████| 10/10 [00:09<00:00,  1.06it/s]
100%|██████████| 10/10 [00:09<00:00,  1.06it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 127, 11
LR fn, tp: 2, 7
LR f1 score: 0.519
LR cohens kappa score: 0.476
LR average precision score: 0.727

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 6, 3
GB f1 score: 0.375
GB cohens kappa score: 0.340

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


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

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100%|██████████| 10/10 [00:08<00:00,  1.10it/s]
100%|██████████| 10/10 [00:08<00:00,  1.14it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 5, 4
LR f1 score: 0.421
LR cohens kappa score: 0.381
LR average precision score: 0.557

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 6, 3
GB f1 score: 0.429
GB cohens kappa score: 0.402

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


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

  0%|          | 0/10 [00:00<?, ?it/s]
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 60%|██████    | 6/10 [00:04<00:03,  1.31it/s]
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100%|██████████| 10/10 [00:07<00:00,  1.41it/s]
100%|██████████| 10/10 [00:07<00:00,  1.34it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 129, 9
LR fn, tp: 2, 7
LR f1 score: 0.560
LR cohens kappa score: 0.523
LR average precision score: 0.750

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 4, 5
GB f1 score: 0.588
GB cohens kappa score: 0.563

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 7, 2
KNN f1 score: 0.364
KNN cohens kappa score: 0.349


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

  0%|          | 0/10 [00:00<?, ?it/s]
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 60%|██████    | 6/10 [00:05<00:03,  1.22it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.29it/s]
100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 130, 7
LR fn, tp: 2, 4
LR f1 score: 0.471
LR cohens kappa score: 0.440
LR average precision score: 0.722

-> test with 'GB'
GB tn, fp: 136, 1
GB fn, tp: 3, 3
GB f1 score: 0.600
GB cohens kappa score: 0.586

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 134, 24
LR fn, tp: 5, 8
LR f1 score: 0.667
LR cohens kappa score: 0.642
LR average precision score: 0.760


average:
LR tn, fp: 126.2, 11.6
LR fn, tp: 2.48, 5.92
LR f1 score: 0.471
LR cohens kappa score: 0.427
LR average precision score: 0.648


minimum:
LR tn, fp: 114, 4
LR fn, tp: 1, 4
LR f1 score: 0.308
LR cohens kappa score: 0.236
LR average precision score: 0.492


-----[ GB ]-----
maximum:
GB tn, fp: 137, 8
GB fn, tp: 8, 5
GB f1 score: 0.600
GB cohens kappa score: 0.586


average:
GB tn, fp: 134.24, 3.56
GB fn, tp: 5.64, 2.76
GB f1 score: 0.371
GB cohens kappa score: 0.340


minimum:
GB tn, fp: 130, 1
GB fn, tp: 3, 1
GB f1 score: 0.143
GB cohens kappa score: 0.104


-----[ KNN ]-----
maximum:
KNN tn, fp: 138, 5
KNN fn, tp: 9, 3
KNN f1 score: 0.429
KNN cohens kappa score: 0.402


average:
KNN tn, fp: 136.92, 0.88
KNN fn, tp: 7.64, 0.76
KNN f1 score: 0.132
KNN cohens kappa score: 0.118


minimum:
KNN tn, fp: 133, 0
KNN fn, tp: 5, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023