///////////////////////////////////////////
// Running CTAB-GAN on folding_kr-vs-k-three_vs_eleven
///////////////////////////////////////////

Load 'data_input/folding_kr-vs-k-three_vs_eleven'
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 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 548, 23
LR fn, tp: 0, 17
LR f1 score: 0.596
LR cohens kappa score: 0.579
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 559, 12
KNN fn, tp: 0, 17
KNN f1 score: 0.739
KNN cohens kappa score: 0.729


------ 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,  1.87it/s]
100%|██████████| 10/10 [00:05<00:00,  1.89it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 565, 6
LR fn, tp: 1, 16
LR f1 score: 0.821
LR cohens kappa score: 0.814
LR average precision score: 0.978

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 1, 16
RF f1 score: 0.970
RF cohens kappa score: 0.969

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 568, 3
KNN fn, tp: 0, 17
KNN f1 score: 0.919
KNN cohens kappa score: 0.916


------ 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,  1.94it/s]
100%|██████████| 10/10 [00:05<00:00,  1.97it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 558, 13
LR fn, tp: 0, 17
LR f1 score: 0.723
LR cohens kappa score: 0.713
LR average precision score: 0.991

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 565, 6
KNN fn, tp: 0, 17
KNN f1 score: 0.850
KNN cohens kappa score: 0.845


------ 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,  1.96it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 545, 26
LR fn, tp: 0, 17
LR f1 score: 0.567
LR cohens kappa score: 0.548
LR average precision score: 0.990

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 564, 7
KNN fn, tp: 0, 17
KNN f1 score: 0.829
KNN cohens kappa score: 0.823


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

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-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 563, 7
LR fn, tp: 0, 13
LR f1 score: 0.788
LR cohens kappa score: 0.782
LR average precision score: 0.986

-> test with 'RF'
RF tn, fp: 570, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 568, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.927

-> test with 'KNN'
KNN tn, fp: 565, 5
KNN fn, tp: 1, 12
KNN f1 score: 0.800
KNN cohens kappa score: 0.795


====== 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 [00:05<00:00,  1.98it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 557, 14
LR fn, tp: 0, 17
LR f1 score: 0.708
LR cohens kappa score: 0.697
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 564, 7
KNN fn, tp: 1, 16
KNN f1 score: 0.800
KNN cohens kappa score: 0.793


------ 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,  1.99it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 15
LR fn, tp: 0, 17
LR f1 score: 0.694
LR cohens kappa score: 0.682
LR average precision score: 0.997

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 564, 7
KNN fn, tp: 0, 17
KNN f1 score: 0.829
KNN cohens kappa score: 0.823


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

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-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 549, 22
LR fn, tp: 0, 17
LR f1 score: 0.607
LR cohens kappa score: 0.591
LR average precision score: 0.975

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 562, 9
KNN fn, tp: 0, 17
KNN f1 score: 0.791
KNN cohens kappa score: 0.783


------ 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,  1.98it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 563, 8
LR fn, tp: 0, 17
LR f1 score: 0.810
LR cohens kappa score: 0.803
LR average precision score: 0.981

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 567, 4
KNN fn, tp: 3, 14
KNN f1 score: 0.800
KNN cohens kappa score: 0.794


------ 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,  1.96it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 557, 13
LR fn, tp: 0, 13
LR f1 score: 0.667
LR cohens kappa score: 0.656
LR average precision score: 0.995

-> test with 'RF'
RF tn, fp: 570, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 565, 5
KNN fn, tp: 0, 13
KNN f1 score: 0.839
KNN cohens kappa score: 0.834


====== 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,  1.97it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 560, 11
LR fn, tp: 0, 17
LR f1 score: 0.756
LR cohens kappa score: 0.746
LR average precision score: 0.987

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 566, 5
KNN fn, tp: 0, 17
KNN f1 score: 0.872
KNN cohens kappa score: 0.867


------ 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,  1.95it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 546, 25
LR fn, tp: 0, 17
LR f1 score: 0.576
LR cohens kappa score: 0.558
LR average precision score: 0.990

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 561, 10
KNN fn, tp: 0, 17
KNN f1 score: 0.773
KNN cohens kappa score: 0.764


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

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-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 561, 10
LR fn, tp: 0, 17
LR f1 score: 0.773
LR cohens kappa score: 0.764
LR average precision score: 0.989

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 568, 3
KNN fn, tp: 0, 17
KNN f1 score: 0.919
KNN cohens kappa score: 0.916


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

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100%|██████████| 10/10 [00:05<00:00,  1.98it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 564, 7
LR fn, tp: 0, 17
LR f1 score: 0.829
LR cohens kappa score: 0.823
LR average precision score: 0.997

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 568, 3
KNN fn, tp: 1, 16
KNN f1 score: 0.889
KNN cohens kappa score: 0.885


------ 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,  1.99it/s]
100%|██████████| 10/10 [00:05<00:00,  1.99it/s]
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 16
LR fn, tp: 0, 13
LR f1 score: 0.619
LR cohens kappa score: 0.607
LR average precision score: 0.958

-> test with 'RF'
RF tn, fp: 570, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 561, 9
KNN fn, tp: 2, 11
KNN f1 score: 0.667
KNN cohens kappa score: 0.657


====== 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,  1.96it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 552, 19
LR fn, tp: 0, 17
LR f1 score: 0.642
LR cohens kappa score: 0.627
LR average precision score: 0.994

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 561, 10
KNN fn, tp: 0, 17
KNN f1 score: 0.773
KNN cohens kappa score: 0.764


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

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100%|██████████| 10/10 [00:04<00:00,  2.01it/s]
100%|██████████| 10/10 [00:04<00:00,  2.01it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 551, 20
LR fn, tp: 0, 17
LR f1 score: 0.630
LR cohens kappa score: 0.614
LR average precision score: 0.991

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 560, 11
KNN fn, tp: 2, 15
KNN f1 score: 0.698
KNN cohens kappa score: 0.687


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

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 70%|███████   | 7/10 [00:03<00:01,  1.98it/s]
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100%|██████████| 10/10 [00:04<00:00,  2.07it/s]
100%|██████████| 10/10 [00:04<00:00,  2.03it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 15
LR fn, tp: 0, 17
LR f1 score: 0.694
LR cohens kappa score: 0.682
LR average precision score: 0.997

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 565, 6
KNN fn, tp: 0, 17
KNN f1 score: 0.850
KNN cohens kappa score: 0.845


------ 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,  1.98it/s]
100%|██████████| 10/10 [00:05<00:00,  1.99it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 561, 10
LR fn, tp: 0, 17
LR f1 score: 0.773
LR cohens kappa score: 0.764
LR average precision score: 0.991

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 569, 2
GB fn, tp: 0, 17
GB f1 score: 0.944
GB cohens kappa score: 0.943

-> test with 'KNN'
KNN tn, fp: 564, 7
KNN fn, tp: 0, 17
KNN f1 score: 0.829
KNN cohens kappa score: 0.823


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

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100%|██████████| 10/10 [00:04<00:00,  1.98it/s]
100%|██████████| 10/10 [00:04<00:00,  2.00it/s]
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 564, 6
LR fn, tp: 0, 13
LR f1 score: 0.813
LR cohens kappa score: 0.807
LR average precision score: 0.995

-> test with 'RF'
RF tn, fp: 570, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 567, 3
KNN fn, tp: 0, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.894


====== 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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 60%|██████    | 6/10 [00:03<00:02,  1.96it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.95it/s]
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 90%|█████████ | 9/10 [00:04<00:00,  2.02it/s]
100%|██████████| 10/10 [00:05<00:00,  2.02it/s]
100%|██████████| 10/10 [00:05<00:00,  1.99it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 547, 24
LR fn, tp: 0, 17
LR f1 score: 0.586
LR cohens kappa score: 0.569
LR average precision score: 0.990

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 570, 1
GB fn, tp: 0, 17
GB f1 score: 0.971
GB cohens kappa score: 0.971

-> test with 'KNN'
KNN tn, fp: 564, 7
KNN fn, tp: 0, 17
KNN f1 score: 0.829
KNN cohens kappa score: 0.823


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

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 60%|██████    | 6/10 [00:02<00:01,  2.01it/s]
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 80%|████████  | 8/10 [00:03<00:01,  1.99it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.99it/s]
100%|██████████| 10/10 [00:04<00:00,  1.99it/s]
100%|██████████| 10/10 [00:04<00:00,  2.00it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 560, 11
LR fn, tp: 0, 17
LR f1 score: 0.756
LR cohens kappa score: 0.746
LR average precision score: 0.973

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 565, 6
KNN fn, tp: 0, 17
KNN f1 score: 0.850
KNN cohens kappa score: 0.845


------ 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,  1.98it/s]
 20%|██        | 2/10 [00:01<00:04,  1.99it/s]
 30%|███       | 3/10 [00:01<00:03,  1.96it/s]
 40%|████      | 4/10 [00:02<00:03,  1.99it/s]
 50%|█████     | 5/10 [00:02<00:02,  2.05it/s]
 60%|██████    | 6/10 [00:02<00:01,  2.05it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.99it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.95it/s]
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100%|██████████| 10/10 [00:05<00:00,  2.00it/s]
100%|██████████| 10/10 [00:05<00:00,  2.00it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 17
LR fn, tp: 0, 17
LR f1 score: 0.667
LR cohens kappa score: 0.653
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 563, 8
KNN fn, tp: 0, 17
KNN f1 score: 0.810
KNN cohens kappa score: 0.803


------ 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,  1.85it/s]
 20%|██        | 2/10 [00:01<00:04,  1.96it/s]
 30%|███       | 3/10 [00:01<00:03,  1.98it/s]
 40%|████      | 4/10 [00:02<00:02,  2.03it/s]
 50%|█████     | 5/10 [00:02<00:02,  2.04it/s]
 60%|██████    | 6/10 [00:02<00:01,  2.05it/s]
 70%|███████   | 7/10 [00:03<00:01,  2.03it/s]
 80%|████████  | 8/10 [00:03<00:00,  2.08it/s]
 90%|█████████ | 9/10 [00:04<00:00,  2.07it/s]
100%|██████████| 10/10 [00:04<00:00,  2.09it/s]
100%|██████████| 10/10 [00:04<00:00,  2.05it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 558, 13
LR fn, tp: 0, 17
LR f1 score: 0.723
LR cohens kappa score: 0.713
LR average precision score: 0.997

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 565, 6
KNN fn, tp: 0, 17
KNN f1 score: 0.850
KNN cohens kappa score: 0.845


------ 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,  1.98it/s]
 20%|██        | 2/10 [00:00<00:03,  2.06it/s]
 30%|███       | 3/10 [00:01<00:03,  2.02it/s]
 40%|████      | 4/10 [00:01<00:02,  2.03it/s]
 50%|█████     | 5/10 [00:02<00:02,  2.03it/s]
 60%|██████    | 6/10 [00:02<00:02,  1.99it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.97it/s]
 80%|████████  | 8/10 [00:03<00:00,  2.02it/s]
 90%|█████████ | 9/10 [00:04<00:00,  2.02it/s]
100%|██████████| 10/10 [00:04<00:00,  2.04it/s]
100%|██████████| 10/10 [00:04<00:00,  2.02it/s]
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 14
LR fn, tp: 0, 13
LR f1 score: 0.650
LR cohens kappa score: 0.639
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 570, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 562, 8
KNN fn, tp: 0, 13
KNN f1 score: 0.765
KNN cohens kappa score: 0.758

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 565, 26
LR fn, tp: 1, 17
LR f1 score: 0.829
LR cohens kappa score: 0.823
LR average precision score: 1.000


average:
LR tn, fp: 556.2, 14.6
LR fn, tp: 0.04, 16.16
LR f1 score: 0.699
LR cohens kappa score: 0.687
LR average precision score: 0.990


minimum:
LR tn, fp: 545, 6
LR fn, tp: 0, 13
LR f1 score: 0.567
LR cohens kappa score: 0.548
LR average precision score: 0.958


-----[ RF ]-----
maximum:
RF tn, fp: 571, 0
RF fn, tp: 1, 17
RF f1 score: 1.000
RF cohens kappa score: 1.000


average:
RF tn, fp: 570.8, 0.0
RF fn, tp: 0.08, 16.12
RF f1 score: 0.997
RF cohens kappa score: 0.997


minimum:
RF tn, fp: 570, 0
RF fn, tp: 0, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959


-----[ GB ]-----
maximum:
GB tn, fp: 571, 2
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 570.6, 0.2
GB fn, tp: 0.0, 16.2
GB f1 score: 0.994
GB cohens kappa score: 0.994


minimum:
GB tn, fp: 568, 0
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.927


-----[ KNN ]-----
maximum:
KNN tn, fp: 568, 12
KNN fn, tp: 3, 17
KNN f1 score: 0.919
KNN cohens kappa score: 0.916


average:
KNN tn, fp: 564.12, 6.68
KNN fn, tp: 0.4, 15.8
KNN f1 score: 0.819
KNN cohens kappa score: 0.813


minimum:
KNN tn, fp: 559, 3
KNN fn, tp: 0, 11
KNN f1 score: 0.667
KNN cohens kappa score: 0.657