///////////////////////////////////////////
// Running CTAB-GAN on folding_flare-F
///////////////////////////////////////////

Load 'data_input/folding_flare-F'
from pickle file
non empty cut in data_input/folding_flare-F! (23 points)
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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 188, 17
LR fn, tp: 7, 2
LR f1 score: 0.143
LR cohens kappa score: 0.091
LR average precision score: 0.120

-> test with 'RF'
RF tn, fp: 197, 8
RF fn, tp: 8, 1
RF f1 score: 0.111
RF cohens kappa score: 0.072

-> test with 'GB'
GB tn, fp: 200, 5
GB fn, tp: 8, 1
GB f1 score: 0.133
GB cohens kappa score: 0.103

-> test with 'KNN'
KNN tn, fp: 177, 28
KNN fn, tp: 5, 4
KNN f1 score: 0.195
KNN cohens kappa score: 0.139


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 196, 9
LR fn, tp: 3, 6
LR f1 score: 0.500
LR cohens kappa score: 0.472
LR average precision score: 0.424

-> test with 'RF'
RF tn, fp: 201, 4
RF fn, tp: 7, 2
RF f1 score: 0.267
RF cohens kappa score: 0.241

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.289

-> test with 'KNN'
KNN tn, fp: 188, 17
KNN fn, tp: 3, 6
KNN f1 score: 0.375
KNN cohens kappa score: 0.335


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 26
LR fn, tp: 2, 7
LR f1 score: 0.333
LR cohens kappa score: 0.286
LR average precision score: 0.285

-> test with 'RF'
RF tn, fp: 203, 2
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.016

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 183, 22
KNN fn, tp: 4, 5
KNN f1 score: 0.278
KNN cohens kappa score: 0.229


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 20
LR fn, tp: 0, 9
LR f1 score: 0.474
LR cohens kappa score: 0.438
LR average precision score: 0.642

-> test with 'RF'
RF tn, fp: 204, 1
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.008

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.319

-> test with 'KNN'
KNN tn, fp: 193, 12
KNN fn, tp: 4, 5
KNN f1 score: 0.385
KNN cohens kappa score: 0.349


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 183, 20
LR fn, tp: 3, 4
LR f1 score: 0.258
LR cohens kappa score: 0.218
LR average precision score: 0.177

-> test with 'RF'
RF tn, fp: 199, 4
RF fn, tp: 7, 0
RF f1 score: 0.000
RF cohens kappa score: -0.025

-> test with 'GB'
GB tn, fp: 200, 3
GB fn, tp: 6, 1
GB f1 score: 0.182
GB cohens kappa score: 0.161

-> test with 'KNN'
KNN tn, fp: 185, 18
KNN fn, tp: 4, 3
KNN f1 score: 0.214
KNN cohens kappa score: 0.173


====== 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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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 20
LR fn, tp: 2, 7
LR f1 score: 0.389
LR cohens kappa score: 0.348
LR average precision score: 0.427

-> test with 'RF'
RF tn, fp: 202, 3
RF fn, tp: 7, 2
RF f1 score: 0.286
RF cohens kappa score: 0.264

-> test with 'GB'
GB tn, fp: 200, 5
GB fn, tp: 7, 2
GB f1 score: 0.250
GB cohens kappa score: 0.221

-> test with 'KNN'
KNN tn, fp: 188, 17
KNN fn, tp: 3, 6
KNN f1 score: 0.375
KNN cohens kappa score: 0.335


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 193, 12
LR fn, tp: 5, 4
LR f1 score: 0.320
LR cohens kappa score: 0.281
LR average precision score: 0.275

-> test with 'RF'
RF tn, fp: 202, 3
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.021

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 193, 12
KNN fn, tp: 7, 2
KNN f1 score: 0.174
KNN cohens kappa score: 0.129


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 177, 28
LR fn, tp: 2, 7
LR f1 score: 0.318
LR cohens kappa score: 0.269
LR average precision score: 0.282

-> test with 'RF'
RF tn, fp: 203, 2
RF fn, tp: 8, 1
RF f1 score: 0.167
RF cohens kappa score: 0.149

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 186, 19
KNN fn, tp: 5, 4
KNN f1 score: 0.250
KNN cohens kappa score: 0.202


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 5
LR fn, tp: 6, 3
LR f1 score: 0.353
LR cohens kappa score: 0.326
LR average precision score: 0.242

-> test with 'RF'
RF tn, fp: 202, 3
RF fn, tp: 7, 2
RF f1 score: 0.286
RF cohens kappa score: 0.264

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 192, 13
KNN fn, tp: 5, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.267


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 173, 30
LR fn, tp: 0, 7
LR f1 score: 0.318
LR cohens kappa score: 0.278
LR average precision score: 0.389

-> test with 'RF'
RF tn, fp: 202, 1
RF fn, tp: 6, 1
RF f1 score: 0.222
RF cohens kappa score: 0.211

-> test with 'GB'
GB tn, fp: 201, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.184

-> test with 'KNN'
KNN tn, fp: 179, 24
KNN fn, tp: 2, 5
KNN f1 score: 0.278
KNN cohens kappa score: 0.237


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

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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 23
LR fn, tp: 1, 8
LR f1 score: 0.400
LR cohens kappa score: 0.358
LR average precision score: 0.620

-> test with 'RF'
RF tn, fp: 204, 1
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.008

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 193, 12
KNN fn, tp: 3, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.411


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 176, 29
LR fn, tp: 3, 6
LR f1 score: 0.273
LR cohens kappa score: 0.221
LR average precision score: 0.249

-> test with 'RF'
RF tn, fp: 198, 7
RF fn, tp: 7, 2
RF f1 score: 0.222
RF cohens kappa score: 0.188

-> test with 'GB'
GB tn, fp: 197, 8
GB fn, tp: 5, 4
GB f1 score: 0.381
GB cohens kappa score: 0.350

-> test with 'KNN'
KNN tn, fp: 179, 26
KNN fn, tp: 5, 4
KNN f1 score: 0.205
KNN cohens kappa score: 0.150


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 23
LR fn, tp: 2, 7
LR f1 score: 0.359
LR cohens kappa score: 0.315
LR average precision score: 0.297

-> test with 'RF'
RF tn, fp: 203, 2
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.016

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.008

-> test with 'KNN'
KNN tn, fp: 186, 19
KNN fn, tp: 2, 7
KNN f1 score: 0.400
KNN cohens kappa score: 0.360


------ 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,  1.90it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 188, 17
LR fn, tp: 4, 5
LR f1 score: 0.323
LR cohens kappa score: 0.280
LR average precision score: 0.308

-> test with 'RF'
RF tn, fp: 204, 1
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.008

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 190, 15
KNN fn, tp: 3, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.362


------ 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,  1.90it/s]
100%|██████████| 10/10 [00:05<00:00,  1.92it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 169, 34
LR fn, tp: 2, 5
LR f1 score: 0.217
LR cohens kappa score: 0.171
LR average precision score: 0.242

-> test with 'RF'
RF tn, fp: 198, 5
RF fn, tp: 7, 0
RF f1 score: 0.000
RF cohens kappa score: -0.029

-> test with 'GB'
GB tn, fp: 199, 4
GB fn, tp: 6, 1
GB f1 score: 0.167
GB cohens kappa score: 0.143

-> test with 'KNN'
KNN tn, fp: 187, 16
KNN fn, tp: 5, 2
KNN f1 score: 0.160
KNN cohens kappa score: 0.118


====== 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,  1.93it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 175, 30
LR fn, tp: 4, 5
LR f1 score: 0.227
LR cohens kappa score: 0.172
LR average precision score: 0.164

-> test with 'RF'
RF tn, fp: 198, 7
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.038

-> test with 'GB'
GB tn, fp: 201, 4
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.027

-> test with 'KNN'
KNN tn, fp: 188, 17
KNN fn, tp: 7, 2
KNN f1 score: 0.143
KNN cohens kappa score: 0.091


------ 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,  1.95it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 197, 8
LR fn, tp: 3, 6
LR f1 score: 0.522
LR cohens kappa score: 0.496
LR average precision score: 0.531

-> test with 'RF'
RF tn, fp: 202, 3
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.021

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 189, 16
KNN fn, tp: 7, 2
KNN f1 score: 0.148
KNN cohens kappa score: 0.098


------ 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,  1.91it/s]
100%|██████████| 10/10 [00:05<00:00,  1.93it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 194, 11
LR fn, tp: 6, 3
LR f1 score: 0.261
LR cohens kappa score: 0.221
LR average precision score: 0.214

-> test with 'RF'
RF tn, fp: 204, 1
RF fn, tp: 7, 2
RF f1 score: 0.333
RF cohens kappa score: 0.319

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 190, 15
KNN fn, tp: 7, 2
KNN f1 score: 0.154
KNN cohens kappa score: 0.105


------ 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,  1.94it/s]
100%|██████████| 10/10 [00:05<00:00,  1.93it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 19
LR fn, tp: 3, 6
LR f1 score: 0.353
LR cohens kappa score: 0.310
LR average precision score: 0.381

-> test with 'RF'
RF tn, fp: 203, 2
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.016

-> test with 'GB'
GB tn, fp: 201, 4
GB fn, tp: 7, 2
GB f1 score: 0.267
GB cohens kappa score: 0.241

-> test with 'KNN'
KNN tn, fp: 192, 13
KNN fn, tp: 5, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.267


------ 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,  1.93it/s]
100%|██████████| 10/10 [00:05<00:00,  1.92it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 194, 9
LR fn, tp: 1, 6
LR f1 score: 0.545
LR cohens kappa score: 0.524
LR average precision score: 0.624

-> test with 'RF'
RF tn, fp: 201, 2
RF fn, tp: 7, 0
RF f1 score: 0.000
RF cohens kappa score: -0.015

-> test with 'GB'
GB tn, fp: 202, 1
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.008

-> test with 'KNN'
KNN tn, fp: 185, 18
KNN fn, tp: 3, 4
KNN f1 score: 0.276
KNN cohens kappa score: 0.237


====== 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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100%|██████████| 10/10 [00:05<00:00,  2.00it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 23
LR fn, tp: 4, 5
LR f1 score: 0.270
LR cohens kappa score: 0.221
LR average precision score: 0.214

-> test with 'RF'
RF tn, fp: 204, 1
RF fn, tp: 8, 1
RF f1 score: 0.182
RF cohens kappa score: 0.169

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.131

-> test with 'KNN'
KNN tn, fp: 186, 19
KNN fn, tp: 3, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.310


------ Step 5/5: 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]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 19
LR fn, tp: 3, 6
LR f1 score: 0.353
LR cohens kappa score: 0.310
LR average precision score: 0.327

-> test with 'RF'
RF tn, fp: 204, 1
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.008

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 7, 2
GB f1 score: 0.364
GB cohens kappa score: 0.354

-> test with 'KNN'
KNN tn, fp: 191, 14
KNN fn, tp: 5, 4
KNN f1 score: 0.296
KNN cohens kappa score: 0.254


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

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100%|██████████| 10/10 [00:05<00:00,  2.04it/s]
100%|██████████| 10/10 [00:05<00:00,  1.99it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 21
LR fn, tp: 0, 9
LR f1 score: 0.462
LR cohens kappa score: 0.424
LR average precision score: 0.427

-> test with 'RF'
RF tn, fp: 205, 0
RF fn, tp: 8, 1
RF f1 score: 0.200
RF cohens kappa score: 0.193

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 7, 2
GB f1 score: 0.364
GB cohens kappa score: 0.354

-> test with 'KNN'
KNN tn, fp: 182, 23
KNN fn, tp: 1, 8
KNN f1 score: 0.400
KNN cohens kappa score: 0.358


------ Step 5/5: Slice 4/5 -------
-> 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,  1.90it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 188, 17
LR fn, tp: 5, 4
LR f1 score: 0.267
LR cohens kappa score: 0.221
LR average precision score: 0.275

-> test with 'RF'
RF tn, fp: 202, 3
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.021

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.021

-> test with 'KNN'
KNN tn, fp: 195, 10
KNN fn, tp: 7, 2
KNN f1 score: 0.190
KNN cohens kappa score: 0.150


------ Step 5/5: Slice 5/5 -------
-> 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 21
LR fn, tp: 2, 5
LR f1 score: 0.303
LR cohens kappa score: 0.264
LR average precision score: 0.283

-> test with 'RF'
RF tn, fp: 197, 6
RF fn, tp: 7, 0
RF f1 score: 0.000
RF cohens kappa score: -0.032

-> test with 'GB'
GB tn, fp: 199, 4
GB fn, tp: 6, 1
GB f1 score: 0.167
GB cohens kappa score: 0.143

-> test with 'KNN'
KNN tn, fp: 188, 15
KNN fn, tp: 6, 1
KNN f1 score: 0.087
KNN cohens kappa score: 0.043

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 200, 34
LR fn, tp: 7, 9
LR f1 score: 0.545
LR cohens kappa score: 0.524
LR average precision score: 0.642


average:
LR tn, fp: 184.96, 19.64
LR fn, tp: 2.92, 5.68
LR f1 score: 0.342
LR cohens kappa score: 0.301
LR average precision score: 0.337


minimum:
LR tn, fp: 169, 5
LR fn, tp: 0, 2
LR f1 score: 0.143
LR cohens kappa score: 0.091
LR average precision score: 0.120


-----[ RF ]-----
maximum:
RF tn, fp: 205, 8
RF fn, tp: 9, 2
RF f1 score: 0.333
RF cohens kappa score: 0.319


average:
RF tn, fp: 201.68, 2.92
RF fn, tp: 8.0, 0.6
RF f1 score: 0.091
RF cohens kappa score: 0.071


minimum:
RF tn, fp: 197, 0
RF fn, tp: 6, 0
RF f1 score: 0.000
RF cohens kappa score: -0.038


-----[ GB ]-----
maximum:
GB tn, fp: 205, 8
GB fn, tp: 9, 4
GB f1 score: 0.381
GB cohens kappa score: 0.354


average:
GB tn, fp: 202.32, 2.28
GB fn, tp: 7.52, 1.08
GB f1 score: 0.166
GB cohens kappa score: 0.149


minimum:
GB tn, fp: 197, 0
GB fn, tp: 5, 0
GB f1 score: 0.000
GB cohens kappa score: -0.027


-----[ KNN ]-----
maximum:
KNN tn, fp: 195, 28
KNN fn, tp: 7, 8
KNN f1 score: 0.444
KNN cohens kappa score: 0.411


average:
KNN tn, fp: 187.4, 17.2
KNN fn, tp: 4.44, 4.16
KNN f1 score: 0.272
KNN cohens kappa score: 0.228


minimum:
KNN tn, fp: 177, 10
KNN fn, tp: 1, 1
KNN f1 score: 0.087
KNN cohens kappa score: 0.043