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: 0.666

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

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 4, 5
GB f1 score: 0.625
GB cohens kappa score: 0.604

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

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100%|██████████| 10/10 [00:05<00:00,  1.73it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 4, 5
LR f1 score: 0.625
LR cohens kappa score: 0.604
LR average precision score: 0.546

-> test with 'RF'
RF tn, fp: 136, 2
RF fn, tp: 7, 2
RF f1 score: 0.308
RF cohens kappa score: 0.281

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.253

-> 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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100%|██████████| 10/10 [00:06<00:00,  1.58it/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: 0.637

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

-> 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: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ 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: 131, 7
LR fn, tp: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.577
LR average precision score: 0.729

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

-> 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: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ 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: 123, 14
LR fn, tp: 3, 3
LR f1 score: 0.261
LR cohens kappa score: 0.212
LR average precision score: 0.458

-> test with 'RF'
RF tn, fp: 135, 2
RF fn, tp: 6, 0
RF f1 score: 0.000
RF cohens kappa score: -0.021

-> 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: 133, 4
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.035


====== 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 [00:06<00:00,  1.63it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 125, 13
LR fn, tp: 2, 7
LR f1 score: 0.483
LR cohens kappa score: 0.435
LR average precision score: 0.726

-> test with 'RF'
RF tn, fp: 137, 1
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.012

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.253

-> 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 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: 132, 6
LR fn, tp: 3, 6
LR f1 score: 0.571
LR cohens kappa score: 0.539
LR average precision score: 0.594

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

-> 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: 137, 1
KNN fn, tp: 7, 2
KNN f1 score: 0.333
KNN cohens kappa score: 0.312


------ 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: 131, 7
LR fn, tp: 3, 6
LR f1 score: 0.545
LR cohens kappa score: 0.510
LR average precision score: 0.532

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

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 5, 4
GB f1 score: 0.444
GB cohens kappa score: 0.408

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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 126, 12
LR fn, tp: 1, 8
LR f1 score: 0.552
LR cohens kappa score: 0.510
LR average precision score: 0.720

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

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 5, 4
GB f1 score: 0.533
GB cohens kappa score: 0.509

-> 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 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: 132, 5
LR fn, tp: 2, 4
LR f1 score: 0.533
LR cohens kappa score: 0.509
LR average precision score: 0.648

-> test with 'RF'
RF tn, fp: 136, 1
RF fn, tp: 4, 2
RF f1 score: 0.444
RF cohens kappa score: 0.428

-> test with 'GB'
GB tn, fp: 134, 3
GB fn, tp: 3, 3
GB f1 score: 0.500
GB cohens kappa score: 0.478

-> 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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100%|██████████| 10/10 [00:05<00:00,  1.68it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 127, 11
LR fn, tp: 3, 6
LR f1 score: 0.462
LR cohens kappa score: 0.415
LR average precision score: 0.398

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

-> 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: 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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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 0, 9
LR f1 score: 0.720
LR cohens kappa score: 0.696
LR average precision score: 0.707

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

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

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ Step 3/5: 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: 129, 9
LR fn, tp: 4, 5
LR f1 score: 0.435
LR cohens kappa score: 0.389
LR average precision score: 0.557

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

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.281

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

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 20%|██        | 2/10 [00:01<00:05,  1.58it/s]
 30%|███       | 3/10 [00:01<00:04,  1.54it/s]
 40%|████      | 4/10 [00:02<00:03,  1.70it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.81it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.76it/s]
 70%|███████   | 7/10 [00:04<00:01,  1.63it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.63it/s]
 90%|█████████ | 9/10 [00:05<00:00,  1.66it/s]
100%|██████████| 10/10 [00:06<00:00,  1.65it/s]
100%|██████████| 10/10 [00:06<00:00,  1.66it/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: 0.724

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

-> 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: 137, 1
KNN fn, tp: 6, 3
KNN f1 score: 0.462
KNN cohens kappa score: 0.440


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:04,  1.81it/s]
 20%|██        | 2/10 [00:01<00:04,  1.68it/s]
 30%|███       | 3/10 [00:01<00:04,  1.71it/s]
 40%|████      | 4/10 [00:02<00:03,  1.67it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.54it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.64it/s]
 70%|███████   | 7/10 [00:04<00:01,  1.71it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.75it/s]
 90%|█████████ | 9/10 [00:05<00:00,  1.58it/s]
100%|██████████| 10/10 [00:06<00:00,  1.63it/s]
100%|██████████| 10/10 [00:06<00:00,  1.65it/s]
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 127, 10
LR fn, tp: 2, 4
LR f1 score: 0.400
LR cohens kappa score: 0.363
LR average precision score: 0.610

-> test with 'RF'
RF tn, fp: 136, 1
RF fn, tp: 5, 1
RF f1 score: 0.250
RF cohens kappa score: 0.234

-> test with 'GB'
GB tn, fp: 135, 2
GB fn, tp: 4, 2
GB f1 score: 0.400
GB cohens kappa score: 0.379

-> 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 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 [00:00<00:05,  1.51it/s]
 20%|██        | 2/10 [00:01<00:05,  1.56it/s]
 30%|███       | 3/10 [00:01<00:04,  1.60it/s]
 40%|████      | 4/10 [00:02<00:03,  1.65it/s]
 50%|█████     | 5/10 [00:03<00:02,  1.69it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.68it/s]
 70%|███████   | 7/10 [00:04<00:01,  1.71it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.68it/s]
 90%|█████████ | 9/10 [00:05<00:00,  1.77it/s]
100%|██████████| 10/10 [00:05<00:00,  1.86it/s]
100%|██████████| 10/10 [00:05<00:00,  1.72it/s]
-> 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: 0.624

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

-> 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: 7, 2
KNN f1 score: 0.364
KNN cohens kappa score: 0.349


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.31it/s]
 20%|██        | 2/10 [00:01<00:05,  1.47it/s]
 30%|███       | 3/10 [00:01<00:04,  1.55it/s]
 40%|████      | 4/10 [00:02<00:03,  1.50it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.61it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.64it/s]
 70%|███████   | 7/10 [00:04<00:01,  1.69it/s]
 80%|████████  | 8/10 [00:05<00:01,  1.65it/s]
 90%|█████████ | 9/10 [00:05<00:00,  1.61it/s]
100%|██████████| 10/10 [00:06<00:00,  1.63it/s]
100%|██████████| 10/10 [00:06<00:00,  1.60it/s]
-> 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: 0.632

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

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 5, 4
GB f1 score: 0.421
GB cohens kappa score: 0.381

-> 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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.43it/s]
 20%|██        | 2/10 [00:01<00:05,  1.35it/s]
 30%|███       | 3/10 [00:02<00:05,  1.31it/s]
 40%|████      | 4/10 [00:03<00:04,  1.28it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.28it/s]
 60%|██████    | 6/10 [00:04<00:02,  1.37it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.38it/s]
 80%|████████  | 8/10 [00:05<00:01,  1.46it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.49it/s]
100%|██████████| 10/10 [00:07<00:00,  1.53it/s]
100%|██████████| 10/10 [00:07<00:00,  1.42it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.577
LR average precision score: 0.752

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

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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:05,  1.69it/s]
 20%|██        | 2/10 [00:01<00:04,  1.89it/s]
 30%|███       | 3/10 [00:01<00:03,  1.98it/s]
 40%|████      | 4/10 [00:02<00:03,  1.67it/s]
 50%|█████     | 5/10 [00:02<00:03,  1.64it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.59it/s]
 70%|███████   | 7/10 [00:04<00:01,  1.61it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.67it/s]
 90%|█████████ | 9/10 [00:05<00:00,  1.43it/s]
100%|██████████| 10/10 [00:06<00:00,  1.38it/s]
100%|██████████| 10/10 [00:06<00:00,  1.55it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 2, 7
LR f1 score: 0.700
LR cohens kappa score: 0.678
LR average precision score: 0.780

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

-> 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: 137, 1
KNN fn, tp: 7, 2
KNN f1 score: 0.333
KNN cohens kappa score: 0.312


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.23it/s]
 20%|██        | 2/10 [00:01<00:06,  1.32it/s]
 30%|███       | 3/10 [00:02<00:04,  1.45it/s]
 40%|████      | 4/10 [00:02<00:04,  1.50it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.61it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.67it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.28it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.06it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.03it/s]
100%|██████████| 10/10 [00:08<00:00,  1.07it/s]
100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 132, 5
LR fn, tp: 1, 5
LR f1 score: 0.625
LR cohens kappa score: 0.604
LR average precision score: 0.631

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

-> test with 'GB'
GB tn, fp: 136, 1
GB fn, tp: 4, 2
GB f1 score: 0.444
GB cohens kappa score: 0.428

-> test with 'KNN'
KNN tn, fp: 136, 1
KNN fn, tp: 5, 1
KNN f1 score: 0.250
KNN cohens kappa score: 0.234


====== 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<00:06,  1.48it/s]
 20%|██        | 2/10 [00:01<00:05,  1.46it/s]
 30%|███       | 3/10 [00:02<00:06,  1.12it/s]
 40%|████      | 4/10 [00:03<00:05,  1.20it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.24it/s]
 60%|██████    | 6/10 [00:04<00:02,  1.36it/s]
 70%|███████   | 7/10 [00:05<00:01,  1.52it/s]
 80%|████████  | 8/10 [00:05<00:01,  1.66it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.46it/s]
100%|██████████| 10/10 [00:07<00:00,  1.33it/s]
100%|██████████| 10/10 [00:07<00:00,  1.36it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 111, 27
LR fn, tp: 1, 8
LR f1 score: 0.364
LR cohens kappa score: 0.295
LR average precision score: 0.477

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

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

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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.01it/s]
 20%|██        | 2/10 [00:01<00:07,  1.10it/s]
 30%|███       | 3/10 [00:02<00:05,  1.33it/s]
 40%|████      | 4/10 [00:03<00:04,  1.38it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.42it/s]
 60%|██████    | 6/10 [00:04<00:02,  1.51it/s]
 70%|███████   | 7/10 [00:04<00:01,  1.56it/s]
 80%|████████  | 8/10 [00:05<00:01,  1.54it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.56it/s]
100%|██████████| 10/10 [00:06<00:00,  1.67it/s]
100%|██████████| 10/10 [00:06<00:00,  1.49it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 4, 5
LR f1 score: 0.556
LR cohens kappa score: 0.527
LR average precision score: 0.614

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

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.281

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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.07it/s]
 20%|██        | 2/10 [00:01<00:07,  1.03it/s]
 30%|███       | 3/10 [00:02<00:06,  1.03it/s]
 40%|████      | 4/10 [00:03<00:06,  1.01s/it]
 50%|█████     | 5/10 [00:05<00:05,  1.16s/it]
 60%|██████    | 6/10 [00:15<00:16,  4.07s/it]
 70%|███████   | 7/10 [00:16<00:09,  3.31s/it]
 80%|████████  | 8/10 [00:18<00:05,  2.62s/it]
 90%|█████████ | 9/10 [00:19<00:02,  2.17s/it]
100%|██████████| 10/10 [00:20<00:00,  1.90s/it]
100%|██████████| 10/10 [00:20<00:00,  2.05s/it]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 126, 12
LR fn, tp: 5, 4
LR f1 score: 0.320
LR cohens kappa score: 0.262
LR average precision score: 0.498

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

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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:04,  1.92it/s]
 20%|██        | 2/10 [00:01<00:07,  1.14it/s]
 30%|███       | 3/10 [00:02<00:07,  1.07s/it]
 40%|████      | 4/10 [00:03<00:06,  1.05s/it]
 50%|█████     | 5/10 [00:05<00:05,  1.08s/it]
 60%|██████    | 6/10 [00:06<00:04,  1.20s/it]
 70%|███████   | 7/10 [00:07<00:03,  1.23s/it]
 80%|████████  | 8/10 [00:08<00:02,  1.20s/it]
 90%|█████████ | 9/10 [00:10<00:01,  1.19s/it]
100%|██████████| 10/10 [00:11<00:00,  1.23s/it]
100%|██████████| 10/10 [00:11<00:00,  1.15s/it]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 2, 7
LR f1 score: 0.636
LR cohens kappa score: 0.608
LR average precision score: 0.751

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

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 5, 4
GB f1 score: 0.533
GB cohens kappa score: 0.509

-> 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]
 10%|█         | 1/10 [00:00<00:04,  1.84it/s]
 20%|██        | 2/10 [00:01<00:05,  1.48it/s]
 30%|███       | 3/10 [00:02<00:05,  1.25it/s]
 40%|████      | 4/10 [00:03<00:05,  1.09it/s]
 50%|█████     | 5/10 [00:04<00:05,  1.05s/it]
 60%|██████    | 6/10 [00:06<00:04,  1.16s/it]
 70%|███████   | 7/10 [00:06<00:03,  1.10s/it]
 80%|████████  | 8/10 [00:08<00:02,  1.21s/it]
 90%|█████████ | 9/10 [00:09<00:01,  1.17s/it]
100%|██████████| 10/10 [00:10<00:00,  1.08s/it]
100%|██████████| 10/10 [00:10<00:00,  1.04s/it]
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 131, 6
LR fn, tp: 2, 4
LR f1 score: 0.500
LR cohens kappa score: 0.472
LR average precision score: 0.706

-> test with 'RF'
RF tn, fp: 136, 1
RF fn, tp: 4, 2
RF f1 score: 0.444
RF cohens kappa score: 0.428

-> 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: 4, 2
KNN f1 score: 0.444
KNN cohens kappa score: 0.428

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 136, 27
LR fn, tp: 5, 9
LR f1 score: 0.720
LR cohens kappa score: 0.696
LR average precision score: 0.780


average:
LR tn, fp: 128.56, 9.24
LR fn, tp: 2.44, 5.96
LR f1 score: 0.517
LR cohens kappa score: 0.479
LR average precision score: 0.629


minimum:
LR tn, fp: 111, 2
LR fn, tp: 0, 3
LR f1 score: 0.261
LR cohens kappa score: 0.212
LR average precision score: 0.398


-----[ RF ]-----
maximum:
RF tn, fp: 138, 3
RF fn, tp: 9, 3
RF f1 score: 0.462
RF cohens kappa score: 0.440


average:
RF tn, fp: 136.72, 1.08
RF fn, tp: 6.92, 1.48
RF f1 score: 0.252
RF cohens kappa score: 0.235


minimum:
RF tn, fp: 135, 0
RF fn, tp: 4, 0
RF f1 score: 0.000
RF cohens kappa score: -0.023


-----[ GB ]-----
maximum:
GB tn, fp: 138, 6
GB fn, tp: 8, 5
GB f1 score: 0.625
GB cohens kappa score: 0.604


average:
GB tn, fp: 134.88, 2.92
GB fn, tp: 5.6, 2.8
GB f1 score: 0.389
GB cohens kappa score: 0.361


minimum:
GB tn, fp: 132, 0
GB fn, tp: 3, 1
GB f1 score: 0.154
GB cohens kappa score: 0.121


-----[ KNN ]-----
maximum:
KNN tn, fp: 138, 4
KNN fn, tp: 9, 3
KNN f1 score: 0.462
KNN cohens kappa score: 0.440


average:
KNN tn, fp: 137.04, 0.76
KNN fn, tp: 7.56, 0.84
KNN f1 score: 0.153
KNN cohens kappa score: 0.140


minimum:
KNN tn, fp: 133, 0
KNN fn, tp: 4, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.035