///////////////////////////////////////////
// Running CTAB-GAN on folding_yeast5
///////////////////////////////////////////

Load 'data_input/folding_yeast5'
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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100%|██████████| 10/10 [00:05<00:00,  1.85it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 5
LR fn, tp: 2, 7
LR f1 score: 0.667
LR cohens kappa score: 0.655
LR average precision score: 0.872

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 5, 4
RF f1 score: 0.615
RF cohens kappa score: 0.608

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 3, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 2, 7
KNN f1 score: 0.636
KNN cohens kappa score: 0.623


------ 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,  1.96it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 9
LR fn, tp: 1, 8
LR f1 score: 0.615
LR cohens kappa score: 0.600
LR average precision score: 0.714

-> test with 'RF'
RF tn, fp: 285, 3
RF fn, tp: 3, 6
RF f1 score: 0.667
RF cohens kappa score: 0.656

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 1, 8
GB f1 score: 0.800
GB cohens kappa score: 0.793

-> test with 'KNN'
KNN tn, fp: 283, 5
KNN fn, tp: 0, 9
KNN f1 score: 0.783
KNN cohens kappa score: 0.774


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

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100%|██████████| 10/10 [00:05<00:00,  1.81it/s]
100%|██████████| 10/10 [00:05<00:00,  1.82it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 1, 8
LR f1 score: 0.640
LR cohens kappa score: 0.625
LR average precision score: 0.586

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 3, 6
RF f1 score: 0.706
RF cohens kappa score: 0.697

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 2, 7
GB f1 score: 0.737
GB cohens kappa score: 0.728

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 2, 7
KNN f1 score: 0.636
KNN cohens kappa score: 0.623


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

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100%|██████████| 10/10 [00:05<00:00,  1.87it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 4
LR fn, tp: 4, 5
LR f1 score: 0.556
LR cohens kappa score: 0.542
LR average precision score: 0.720

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 6, 3
RF f1 score: 0.500
RF cohens kappa score: 0.492

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 5, 4
GB f1 score: 0.615
GB cohens kappa score: 0.608

-> test with 'KNN'
KNN tn, fp: 287, 1
KNN fn, tp: 4, 5
KNN f1 score: 0.667
KNN cohens kappa score: 0.658


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

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-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 9
LR fn, tp: 1, 7
LR f1 score: 0.583
LR cohens kappa score: 0.568
LR average precision score: 0.616

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 4, 4
RF f1 score: 0.571
RF cohens kappa score: 0.561

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 3, 5
GB f1 score: 0.667
GB cohens kappa score: 0.658

-> test with 'KNN'
KNN tn, fp: 281, 7
KNN fn, tp: 1, 7
KNN f1 score: 0.636
KNN cohens kappa score: 0.623


====== 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 [00:12<00:00,  1.02s/it]
100%|██████████| 10/10 [00:12<00:00,  1.21s/it]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 9
LR fn, tp: 1, 8
LR f1 score: 0.615
LR cohens kappa score: 0.600
LR average precision score: 0.757

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 1, 8
RF f1 score: 0.889
RF cohens kappa score: 0.885

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 2, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 283, 5
KNN fn, tp: 1, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.717


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

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100%|██████████| 10/10 [00:05<00:00,  1.83it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 5, 4
LR f1 score: 0.381
LR cohens kappa score: 0.359
LR average precision score: 0.331

-> test with 'RF'
RF tn, fp: 284, 4
RF fn, tp: 6, 3
RF f1 score: 0.375
RF cohens kappa score: 0.358

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 4, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 6, 3
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 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 2
LR fn, tp: 1, 8
LR f1 score: 0.842
LR cohens kappa score: 0.837
LR average precision score: 0.930

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 2, 7
RF f1 score: 0.875
RF cohens kappa score: 0.872

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 2, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 287, 1
KNN fn, tp: 3, 6
KNN f1 score: 0.750
KNN cohens kappa score: 0.743


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

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100%|██████████| 10/10 [00:05<00:00,  1.77it/s]
100%|██████████| 10/10 [00:05<00:00,  1.78it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 5
LR fn, tp: 1, 8
LR f1 score: 0.727
LR cohens kappa score: 0.717
LR average precision score: 0.870

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 2, 7
RF f1 score: 0.778
RF cohens kappa score: 0.771

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 2, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 283, 5
KNN fn, tp: 0, 9
KNN f1 score: 0.783
KNN cohens kappa score: 0.774


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

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100%|██████████| 10/10 [00:05<00:00,  1.86it/s]
100%|██████████| 10/10 [00:05<00:00,  1.88it/s]
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 5
LR fn, tp: 3, 5
LR f1 score: 0.556
LR cohens kappa score: 0.542
LR average precision score: 0.598

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 6, 2
RF f1 score: 0.333
RF cohens kappa score: 0.321

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 6, 2
GB f1 score: 0.333
GB cohens kappa score: 0.321

-> test with 'KNN'
KNN tn, fp: 284, 4
KNN fn, tp: 2, 6
KNN f1 score: 0.667
KNN cohens kappa score: 0.656


====== 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,  1.71it/s]
100%|██████████| 10/10 [00:05<00:00,  1.82it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 2, 7
LR f1 score: 0.583
LR cohens kappa score: 0.567
LR average precision score: 0.633

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 4, 5
RF f1 score: 0.667
RF cohens kappa score: 0.658

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 4, 5
GB f1 score: 0.625
GB cohens kappa score: 0.615

-> test with 'KNN'
KNN tn, fp: 286, 2
KNN fn, tp: 2, 7
KNN f1 score: 0.778
KNN cohens kappa score: 0.771


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

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100%|██████████| 10/10 [00:05<00:00,  1.85it/s]
100%|██████████| 10/10 [00:05<00:00,  1.90it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.594
LR average precision score: 0.708

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 3, 6
RF f1 score: 0.706
RF cohens kappa score: 0.697

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 3, 6
GB f1 score: 0.667
GB cohens kappa score: 0.656

-> test with 'KNN'
KNN tn, fp: 285, 3
KNN fn, tp: 2, 7
KNN f1 score: 0.737
KNN cohens kappa score: 0.728


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

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100%|██████████| 10/10 [00:05<00:00,  1.85it/s]
100%|██████████| 10/10 [00:05<00:00,  1.87it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 5
LR fn, tp: 2, 7
LR f1 score: 0.667
LR cohens kappa score: 0.655
LR average precision score: 0.753

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 4, 5
RF f1 score: 0.714
RF cohens kappa score: 0.708

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 4, 5
GB f1 score: 0.714
GB cohens kappa score: 0.708

-> test with 'KNN'
KNN tn, fp: 286, 2
KNN fn, tp: 2, 7
KNN f1 score: 0.778
KNN cohens kappa score: 0.771


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

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 40%|████      | 4/10 [00:02<00:03,  1.90it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.91it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.91it/s]
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100%|██████████| 10/10 [00:05<00:00,  1.92it/s]
100%|██████████| 10/10 [00:05<00:00,  1.90it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 5
LR fn, tp: 0, 9
LR f1 score: 0.783
LR cohens kappa score: 0.774
LR average precision score: 0.733

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 4, 5
RF f1 score: 0.714
RF cohens kappa score: 0.708

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 4, 5
GB f1 score: 0.714
GB cohens kappa score: 0.708

-> test with 'KNN'
KNN tn, fp: 285, 3
KNN fn, tp: 0, 9
KNN f1 score: 0.857
KNN cohens kappa score: 0.852


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

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100%|██████████| 10/10 [00:05<00:00,  1.93it/s]
100%|██████████| 10/10 [00:05<00:00,  1.89it/s]
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 3, 5
LR f1 score: 0.476
LR cohens kappa score: 0.458
LR average precision score: 0.427

-> test with 'RF'
RF tn, fp: 284, 4
RF fn, tp: 4, 4
RF f1 score: 0.500
RF cohens kappa score: 0.486

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 1, 7
GB f1 score: 0.700
GB cohens kappa score: 0.690

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 1, 7
KNN f1 score: 0.667
KNN cohens kappa score: 0.655


====== 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 [00:05<00:00,  1.89it/s]
100%|██████████| 10/10 [00:05<00:00,  1.92it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.594
LR average precision score: 0.750

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 3, 6
RF f1 score: 0.706
RF cohens kappa score: 0.697

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 2, 7
GB f1 score: 0.737
GB cohens kappa score: 0.728

-> test with 'KNN'
KNN tn, fp: 281, 7
KNN fn, tp: 0, 9
KNN f1 score: 0.720
KNN cohens kappa score: 0.709


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

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100%|██████████| 10/10 [00:05<00:00,  1.93it/s]
100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 4, 5
LR f1 score: 0.455
LR cohens kappa score: 0.434
LR average precision score: 0.520

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 3, 6
RF f1 score: 0.750
RF cohens kappa score: 0.743

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 2, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 3, 6
KNN f1 score: 0.571
KNN cohens kappa score: 0.556


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

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100%|██████████| 10/10 [00:05<00:00,  1.89it/s]
100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 0, 9
LR f1 score: 0.720
LR cohens kappa score: 0.709
LR average precision score: 0.768

-> test with 'RF'
RF tn, fp: 283, 5
RF fn, tp: 4, 5
RF f1 score: 0.526
RF cohens kappa score: 0.511

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 4, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 281, 7
KNN fn, tp: 1, 8
KNN f1 score: 0.667
KNN cohens kappa score: 0.654


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

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100%|██████████| 10/10 [00:05<00:00,  1.87it/s]
100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 3
LR fn, tp: 4, 5
LR f1 score: 0.588
LR cohens kappa score: 0.576
LR average precision score: 0.731

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 4, 5
RF f1 score: 0.714
RF cohens kappa score: 0.708

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 3, 6
GB f1 score: 0.706
GB cohens kappa score: 0.697

-> test with 'KNN'
KNN tn, fp: 286, 2
KNN fn, tp: 2, 7
KNN f1 score: 0.778
KNN cohens kappa score: 0.771


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

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100%|██████████| 10/10 [00:05<00:00,  1.81it/s]
100%|██████████| 10/10 [00:05<00:00,  1.83it/s]
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 282, 6
LR fn, tp: 1, 7
LR f1 score: 0.667
LR cohens kappa score: 0.655
LR average precision score: 0.650

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 2, 6
RF f1 score: 0.857
RF cohens kappa score: 0.854

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 2, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 281, 7
KNN fn, tp: 1, 7
KNN f1 score: 0.636
KNN cohens kappa score: 0.623


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

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

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 50%|█████     | 5/10 [00:02<00:02,  1.84it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.88it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.88it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.89it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.89it/s]
100%|██████████| 10/10 [00:05<00:00,  1.88it/s]
100%|██████████| 10/10 [00:05<00:00,  1.87it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 9
LR fn, tp: 1, 8
LR f1 score: 0.615
LR cohens kappa score: 0.600
LR average precision score: 0.718

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 2, 7
RF f1 score: 0.875
RF cohens kappa score: 0.872

-> test with 'GB'
GB tn, fp: 282, 6
GB fn, tp: 2, 7
GB f1 score: 0.636
GB cohens kappa score: 0.623

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 0, 9
KNN f1 score: 0.750
KNN cohens kappa score: 0.740


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

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 40%|████      | 4/10 [00:02<00:03,  1.89it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.88it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.87it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.89it/s]
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100%|██████████| 10/10 [00:05<00:00,  1.87it/s]
100%|██████████| 10/10 [00:05<00:00,  1.86it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 3
LR fn, tp: 4, 5
LR f1 score: 0.588
LR cohens kappa score: 0.576
LR average precision score: 0.748

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 4, 5
RF f1 score: 0.714
RF cohens kappa score: 0.708

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 3, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795

-> test with 'KNN'
KNN tn, fp: 286, 2
KNN fn, tp: 4, 5
KNN f1 score: 0.625
KNN cohens kappa score: 0.615


------ 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<00:04,  2.04it/s]
 20%|██        | 2/10 [00:01<00:04,  1.95it/s]
 30%|███       | 3/10 [00:01<00:03,  1.88it/s]
 40%|████      | 4/10 [00:02<00:03,  1.86it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.89it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.89it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.87it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.87it/s]
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100%|██████████| 10/10 [00:05<00:00,  1.89it/s]
100%|██████████| 10/10 [00:05<00:00,  1.89it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 1, 8
LR f1 score: 0.667
LR cohens kappa score: 0.654
LR average precision score: 0.773

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 4, 5
RF f1 score: 0.667
RF cohens kappa score: 0.658

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 2, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 283, 5
KNN fn, tp: 1, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.717


------ 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,  2.01it/s]
 20%|██        | 2/10 [00:01<00:04,  1.95it/s]
 30%|███       | 3/10 [00:01<00:03,  1.88it/s]
 40%|████      | 4/10 [00:02<00:03,  1.86it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.85it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.84it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.85it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.89it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.92it/s]
100%|██████████| 10/10 [00:05<00:00,  1.88it/s]
100%|██████████| 10/10 [00:05<00:00,  1.88it/s]
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 5
LR fn, tp: 3, 6
LR f1 score: 0.600
LR cohens kappa score: 0.586
LR average precision score: 0.594

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 5, 4
RF f1 score: 0.571
RF cohens kappa score: 0.562

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 3, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 4, 5
KNN f1 score: 0.500
KNN cohens kappa score: 0.483


------ 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<00:04,  1.86it/s]
 30%|███       | 3/10 [00:01<00:03,  1.91it/s]
 40%|████      | 4/10 [00:02<00:03,  1.91it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.87it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.90it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.89it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.89it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.90it/s]
100%|██████████| 10/10 [00:05<00:00,  1.89it/s]
100%|██████████| 10/10 [00:05<00:00,  1.89it/s]
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 2, 6
LR f1 score: 0.545
LR cohens kappa score: 0.529
LR average precision score: 0.556

-> test with 'RF'
RF tn, fp: 285, 3
RF fn, tp: 3, 5
RF f1 score: 0.625
RF cohens kappa score: 0.615

-> test with 'GB'
GB tn, fp: 282, 6
GB fn, tp: 3, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 280, 8
KNN fn, tp: 2, 6
KNN f1 score: 0.545
KNN cohens kappa score: 0.529

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 286, 9
LR fn, tp: 5, 9
LR f1 score: 0.842
LR cohens kappa score: 0.837
LR average precision score: 0.930


average:
LR tn, fp: 281.6, 6.4
LR fn, tp: 2.04, 6.76
LR f1 score: 0.614
LR cohens kappa score: 0.600
LR average precision score: 0.682


minimum:
LR tn, fp: 279, 2
LR fn, tp: 0, 4
LR f1 score: 0.381
LR cohens kappa score: 0.359
LR average precision score: 0.331


-----[ RF ]-----
maximum:
RF tn, fp: 288, 5
RF fn, tp: 6, 8
RF f1 score: 0.889
RF cohens kappa score: 0.885


average:
RF tn, fp: 286.56, 1.44
RF fn, tp: 3.64, 5.16
RF f1 score: 0.665
RF cohens kappa score: 0.656


minimum:
RF tn, fp: 283, 0
RF fn, tp: 1, 2
RF f1 score: 0.333
RF cohens kappa score: 0.321


-----[ GB ]-----
maximum:
GB tn, fp: 288, 6
GB fn, tp: 6, 8
GB f1 score: 0.824
GB cohens kappa score: 0.818


average:
GB tn, fp: 285.64, 2.36
GB fn, tp: 2.88, 5.92
GB f1 score: 0.690
GB cohens kappa score: 0.682


minimum:
GB tn, fp: 282, 0
GB fn, tp: 1, 2
GB f1 score: 0.333
GB cohens kappa score: 0.321


-----[ KNN ]-----
maximum:
KNN tn, fp: 287, 8
KNN fn, tp: 6, 9
KNN f1 score: 0.857
KNN cohens kappa score: 0.852


average:
KNN tn, fp: 283.28, 4.72
KNN fn, tp: 1.84, 6.96
KNN f1 score: 0.678
KNN cohens kappa score: 0.667


minimum:
KNN tn, fp: 280, 1
KNN fn, tp: 0, 3
KNN f1 score: 0.333
KNN cohens kappa score: 0.312