ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_shuttle-2_vs_5.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_shuttle-2_vs_5
///////////////////////////////////////////

Load 'data_input/folding_shuttle-2_vs_5'
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,  1.87it/s]
100%|██████████| 10/10 [00:05<00:00,  1.90it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 652, 2
LR fn, tp: 0, 10
LR f1 score: 0.909
LR cohens kappa score: 0.908
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 0, 10
KNN f1 score: 0.952
KNN cohens kappa score: 0.952


------ 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,  1.94it/s]
100%|██████████| 10/10 [00:05<00:00,  1.98it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 2, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


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

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 10%|█         | 1/10 [00:00<00:05,  1.77it/s]
 20%|██        | 2/10 [00:01<00:04,  1.75it/s]
 30%|███       | 3/10 [00:01<00:04,  1.70it/s]
 40%|████      | 4/10 [00:02<00:03,  1.74it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.78it/s]
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 70%|███████   | 7/10 [00:03<00:01,  1.81it/s]
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100%|██████████| 10/10 [00:05<00:00,  1.80it/s]
100%|██████████| 10/10 [00:05<00:00,  1.79it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 1, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


------ Step 1/5: 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,  1.89it/s]
 20%|██        | 2/10 [00:01<00:04,  1.77it/s]
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100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
100%|██████████| 10/10 [00:05<00:00,  1.86it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 652, 2
KNN fn, tp: 0, 10
KNN f1 score: 0.909
KNN cohens kappa score: 0.908


------ Step 1/5: 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,  1.96it/s]
 20%|██        | 2/10 [00:01<00:04,  1.90it/s]
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 50%|█████     | 5/10 [00:02<00:02,  1.89it/s]
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100%|██████████| 10/10 [00:05<00:00,  1.87it/s]
100%|██████████| 10/10 [00:05<00:00,  1.88it/s]
-> create 2576 synthetic samples
-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 651, 0
RF fn, tp: 0, 9
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 650, 1
KNN fn, tp: 0, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


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

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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:04,  2.01it/s]
 20%|██        | 2/10 [00:01<00:06,  1.32it/s]
 30%|███       | 3/10 [00:08<00:24,  3.44s/it]
 40%|████      | 4/10 [00:08<00:14,  2.35s/it]
 50%|█████     | 5/10 [00:09<00:08,  1.70s/it]
 60%|██████    | 6/10 [00:09<00:05,  1.31s/it]
 70%|███████   | 7/10 [00:10<00:03,  1.06s/it]
 80%|████████  | 8/10 [00:10<00:01,  1.11it/s]
 90%|█████████ | 9/10 [00:11<00:00,  1.27it/s]
100%|██████████| 10/10 [00:12<00:00,  1.37it/s]
100%|██████████| 10/10 [00:12<00:00,  1.21s/it]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 2, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:04,  1.88it/s]
 20%|██        | 2/10 [00:01<00:04,  1.90it/s]
 30%|███       | 3/10 [00:01<00:03,  1.87it/s]
 40%|████      | 4/10 [00:02<00:03,  1.78it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.78it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.78it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.80it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.82it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.80it/s]
100%|██████████| 10/10 [00:05<00:00,  1.75it/s]
100%|██████████| 10/10 [00:05<00:00,  1.79it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:05,  1.66it/s]
 20%|██        | 2/10 [00:01<00:04,  1.64it/s]
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 50%|█████     | 5/10 [00:03<00:03,  1.64it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.68it/s]
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 80%|████████  | 8/10 [00:04<00:01,  1.78it/s]
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100%|██████████| 10/10 [00:05<00:00,  1.83it/s]
100%|██████████| 10/10 [00:05<00:00,  1.75it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 652, 2
KNN fn, tp: 2, 8
KNN f1 score: 0.800
KNN cohens kappa score: 0.797


------ Step 2/5: 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,  1.87it/s]
 20%|██        | 2/10 [00:01<00:04,  1.87it/s]
 30%|███       | 3/10 [00:01<00:03,  1.88it/s]
 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,  1.95it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.98it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.96it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.97it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
100%|██████████| 10/10 [00:05<00:00,  1.94it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 2/5: 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,  1.88it/s]
 20%|██        | 2/10 [00:01<00:04,  1.91it/s]
 30%|███       | 3/10 [00:01<00:03,  2.00it/s]
 40%|████      | 4/10 [00:02<00:02,  2.00it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.99it/s]
 60%|██████    | 6/10 [00:03<00:02,  2.00it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.97it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.94it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.94it/s]
100%|██████████| 10/10 [00:05<00:00,  1.94it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 2576 synthetic samples
-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 651, 0
RF fn, tp: 0, 9
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 649, 2
KNN fn, tp: 0, 9
KNN f1 score: 0.900
KNN cohens kappa score: 0.898


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

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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:04,  1.88it/s]
 20%|██        | 2/10 [00:01<00:04,  1.95it/s]
 30%|███       | 3/10 [00:01<00:03,  1.96it/s]
 40%|████      | 4/10 [00:02<00:03,  1.96it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.99it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.95it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.92it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.96it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.99it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 0, 10
KNN f1 score: 0.952
KNN cohens kappa score: 0.952


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:04,  1.98it/s]
 20%|██        | 2/10 [00:00<00:03,  2.03it/s]
 30%|███       | 3/10 [00:01<00:03,  2.08it/s]
 40%|████      | 4/10 [00:01<00:02,  2.06it/s]
 50%|█████     | 5/10 [00:02<00:02,  2.01it/s]
 60%|██████    | 6/10 [00:02<00:02,  2.00it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.97it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.90it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.90it/s]
100%|██████████| 10/10 [00:05<00:00,  1.90it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 652, 2
LR fn, tp: 0, 10
LR f1 score: 0.909
LR cohens kappa score: 0.908
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 1, 9
KNN f1 score: 0.900
KNN cohens kappa score: 0.898


------ Step 3/5: 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,  1.95it/s]
 20%|██        | 2/10 [00:01<00:04,  1.97it/s]
 30%|███       | 3/10 [00:01<00:03,  2.04it/s]
 40%|████      | 4/10 [00:01<00:02,  2.05it/s]
 50%|█████     | 5/10 [00:02<00:02,  2.00it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.96it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.93it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.99it/s]
 90%|█████████ | 9/10 [00:04<00:00,  2.04it/s]
100%|██████████| 10/10 [00:04<00:00,  2.03it/s]
100%|██████████| 10/10 [00:04<00:00,  2.01it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 3/5: 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,  1.98it/s]
 20%|██        | 2/10 [00:01<00:04,  1.98it/s]
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 40%|████      | 4/10 [00:01<00:02,  2.03it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.98it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.96it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.95it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.96it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.92it/s]
100%|██████████| 10/10 [00:05<00:00,  1.98it/s]
100%|██████████| 10/10 [00:05<00:00,  1.97it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 2, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


------ 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,  1.88it/s]
 20%|██        | 2/10 [00:01<00:04,  1.91it/s]
 30%|███       | 3/10 [00:01<00:03,  1.92it/s]
 40%|████      | 4/10 [00:02<00:03,  1.95it/s]
 50%|█████     | 5/10 [00:02<00:02,  2.00it/s]
 60%|██████    | 6/10 [00:03<00:01,  2.02it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.97it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.99it/s]
 90%|█████████ | 9/10 [00:04<00:00,  2.00it/s]
100%|██████████| 10/10 [00:05<00:00,  1.99it/s]
100%|██████████| 10/10 [00:05<00:00,  1.98it/s]
-> create 2576 synthetic samples
-> test with 'LR'
LR tn, fp: 650, 1
LR fn, tp: 0, 9
LR f1 score: 0.947
LR cohens kappa score: 0.947
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 651, 0
RF fn, tp: 0, 9
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 649, 2
KNN fn, tp: 1, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


====== 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<00:04,  1.90it/s]
 20%|██        | 2/10 [00:01<00:03,  2.01it/s]
 30%|███       | 3/10 [00:01<00:03,  2.07it/s]
 40%|████      | 4/10 [00:01<00:02,  2.01it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.98it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.95it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.94it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.93it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.92it/s]
100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 653, 1
LR fn, tp: 0, 10
LR f1 score: 0.952
LR cohens kappa score: 0.952
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ 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<00:04,  1.94it/s]
 20%|██        | 2/10 [00:01<00:04,  1.84it/s]
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 40%|████      | 4/10 [00:02<00:03,  1.97it/s]
 50%|█████     | 5/10 [00:02<00:02,  2.00it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.98it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.97it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.96it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.94it/s]
100%|██████████| 10/10 [00:05<00:00,  1.93it/s]
100%|██████████| 10/10 [00:05<00:00,  1.94it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 651, 3
KNN fn, tp: 0, 10
KNN f1 score: 0.870
KNN cohens kappa score: 0.867


------ 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<00:04,  1.95it/s]
 20%|██        | 2/10 [00:01<00:04,  1.97it/s]
 30%|███       | 3/10 [00:01<00:03,  1.95it/s]
 40%|████      | 4/10 [00:02<00:03,  1.92it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.92it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.95it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.95it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.95it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.95it/s]
100%|██████████| 10/10 [00:05<00:00,  1.98it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 1, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


------ 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<00:04,  1.81it/s]
 20%|██        | 2/10 [00:01<00:04,  1.82it/s]
 30%|███       | 3/10 [00:01<00:03,  1.85it/s]
 40%|████      | 4/10 [00:02<00:03,  1.91it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.96it/s]
 60%|██████    | 6/10 [00:03<00:02,  2.00it/s]
 70%|███████   | 7/10 [00:03<00:01,  2.02it/s]
 80%|████████  | 8/10 [00:04<00:00,  2.03it/s]
 90%|█████████ | 9/10 [00:04<00:00,  2.04it/s]
100%|██████████| 10/10 [00:05<00:00,  2.02it/s]
100%|██████████| 10/10 [00:05<00:00,  1.98it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 2, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


------ 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<00:04,  1.97it/s]
 20%|██        | 2/10 [00:01<00:04,  1.98it/s]
 30%|███       | 3/10 [00:01<00:03,  1.94it/s]
 40%|████      | 4/10 [00:02<00:03,  1.95it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.97it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.99it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.95it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.93it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.93it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
-> create 2576 synthetic samples
-> test with 'LR'
LR tn, fp: 650, 1
LR fn, tp: 0, 9
LR f1 score: 0.947
LR cohens kappa score: 0.947
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 651, 0
RF fn, tp: 0, 9
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 650, 1
KNN fn, tp: 0, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


====== 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<00:04,  1.89it/s]
 20%|██        | 2/10 [00:01<00:04,  1.91it/s]
 30%|███       | 3/10 [00:01<00:03,  1.93it/s]
 40%|████      | 4/10 [00:02<00:03,  1.97it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.98it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.96it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.93it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.94it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.93it/s]
100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
100%|██████████| 10/10 [00:05<00:00,  1.93it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 0, 10
KNN f1 score: 0.952
KNN cohens kappa score: 0.952


------ 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<00:05,  1.74it/s]
 20%|██        | 2/10 [00:01<00:04,  1.82it/s]
 30%|███       | 3/10 [00:01<00:03,  1.84it/s]
 40%|████      | 4/10 [00:02<00:03,  1.82it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.84it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.84it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.84it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.76it/s]
 90%|█████████ | 9/10 [00:05<00:00,  1.74it/s]
100%|██████████| 10/10 [00:05<00:00,  1.78it/s]
100%|██████████| 10/10 [00:05<00:00,  1.80it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ 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,  1.89it/s]
 20%|██        | 2/10 [00:01<00:04,  1.89it/s]
 30%|███       | 3/10 [00:01<00:03,  1.97it/s]
 40%|████      | 4/10 [00:02<00:03,  2.00it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.95it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.92it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.94it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.95it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.92it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 650, 4
KNN fn, tp: 2, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.723


------ 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,  1.96it/s]
 20%|██        | 2/10 [00:01<00:04,  1.97it/s]
 30%|███       | 3/10 [00:01<00:03,  1.97it/s]
 40%|████      | 4/10 [00:02<00:03,  1.96it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.90it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.89it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.93it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.91it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.91it/s]
100%|██████████| 10/10 [00:05<00:00,  1.93it/s]
100%|██████████| 10/10 [00:05<00:00,  1.93it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ 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,  2.05it/s]
 20%|██        | 2/10 [00:01<00:04,  1.98it/s]
 30%|███       | 3/10 [00:01<00:03,  1.95it/s]
 40%|████      | 4/10 [00:02<00:03,  1.96it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.94it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.98it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.96it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.93it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.95it/s]
100%|██████████| 10/10 [00:05<00:00,  1.97it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 2576 synthetic samples
-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 651, 0
RF fn, tp: 0, 9
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 650, 1
KNN fn, tp: 1, 8
KNN f1 score: 0.889
KNN cohens kappa score: 0.887

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 654, 2
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000


average:
LR tn, fp: 653.12, 0.28
LR fn, tp: 0.0, 9.8
LR f1 score: 0.987
LR cohens kappa score: 0.986
LR average precision score: 1.000


minimum:
LR tn, fp: 650, 0
LR fn, tp: 0, 9
LR f1 score: 0.909
LR cohens kappa score: 0.908
LR average precision score: 1.000


-----[ RF ]-----
maximum:
RF tn, fp: 654, 0
RF fn, tp: 0, 10
RF f1 score: 1.000
RF cohens kappa score: 1.000


average:
RF tn, fp: 653.4, 0.0
RF fn, tp: 0.0, 9.8
RF f1 score: 1.000
RF cohens kappa score: 1.000


minimum:
RF tn, fp: 651, 0
RF fn, tp: 0, 9
RF f1 score: 1.000
RF cohens kappa score: 1.000


-----[ GB ]-----
maximum:
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 653.4, 0.0
GB fn, tp: 0.0, 9.8
GB f1 score: 1.000
GB cohens kappa score: 1.000


minimum:
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000


-----[ KNN ]-----
maximum:
KNN tn, fp: 654, 4
KNN fn, tp: 2, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


average:
KNN tn, fp: 652.36, 1.04
KNN fn, tp: 0.68, 9.12
KNN f1 score: 0.914
KNN cohens kappa score: 0.913


minimum:
KNN tn, fp: 649, 0
KNN fn, tp: 0, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.723