ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_yeast4.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_yeast4
///////////////////////////////////////////

Load 'data_input/folding_yeast4'
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 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 21
LR fn, tp: 4, 7
LR f1 score: 0.359
LR cohens kappa score: 0.323
LR average precision score: 0.263

-> test with 'RF'
RF tn, fp: 286, 1
RF fn, tp: 10, 1
RF f1 score: 0.154
RF cohens kappa score: 0.144

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 9, 2
GB f1 score: 0.267
GB cohens kappa score: 0.252

-> test with 'KNN'
KNN tn, fp: 277, 10
KNN fn, tp: 4, 7
KNN f1 score: 0.500
KNN cohens kappa score: 0.477


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 1
LR fn, tp: 9, 2
LR f1 score: 0.286
LR cohens kappa score: 0.274
LR average precision score: 0.498

-> test with 'RF'
RF tn, fp: 286, 1
RF fn, tp: 7, 4
RF f1 score: 0.500
RF cohens kappa score: 0.488

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 7, 4
GB f1 score: 0.400
GB cohens kappa score: 0.379

-> test with 'KNN'
KNN tn, fp: 263, 24
KNN fn, tp: 8, 3
KNN f1 score: 0.158
KNN cohens kappa score: 0.111


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 1
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.006
LR average precision score: 0.228

-> test with 'RF'
RF tn, fp: 287, 0
RF fn, tp: 10, 1
RF f1 score: 0.167
RF cohens kappa score: 0.162

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 10, 1
GB f1 score: 0.154
GB cohens kappa score: 0.144

-> test with 'KNN'
KNN tn, fp: 272, 15
KNN fn, tp: 8, 3
KNN f1 score: 0.207
KNN cohens kappa score: 0.169


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 17
LR fn, tp: 8, 3
LR f1 score: 0.194
LR cohens kappa score: 0.153
LR average precision score: 0.152

-> test with 'RF'
RF tn, fp: 284, 3
RF fn, tp: 8, 3
RF f1 score: 0.353
RF cohens kappa score: 0.336

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 8, 3
GB f1 score: 0.316
GB cohens kappa score: 0.294

-> test with 'KNN'
KNN tn, fp: 267, 20
KNN fn, tp: 7, 4
KNN f1 score: 0.229
KNN cohens kappa score: 0.187


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

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-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 6
LR fn, tp: 6, 1
LR f1 score: 0.143
LR cohens kappa score: 0.122
LR average precision score: 0.260

-> test with 'RF'
RF tn, fp: 285, 0
RF fn, tp: 5, 2
RF f1 score: 0.444
RF cohens kappa score: 0.438

-> test with 'GB'
GB tn, fp: 283, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.352

-> test with 'KNN'
KNN tn, fp: 275, 10
KNN fn, tp: 6, 1
KNN f1 score: 0.111
KNN cohens kappa score: 0.084


====== 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 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 271, 16
LR fn, tp: 8, 3
LR f1 score: 0.200
LR cohens kappa score: 0.161
LR average precision score: 0.162

-> test with 'RF'
RF tn, fp: 287, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 10, 1
GB f1 score: 0.133
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 272, 15
KNN fn, tp: 8, 3
KNN f1 score: 0.207
KNN cohens kappa score: 0.169


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 261, 26
LR fn, tp: 2, 9
LR f1 score: 0.391
LR cohens kappa score: 0.355
LR average precision score: 0.334

-> test with 'RF'
RF tn, fp: 284, 3
RF fn, tp: 8, 3
RF f1 score: 0.353
RF cohens kappa score: 0.336

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 6, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 263, 24
KNN fn, tp: 4, 7
KNN f1 score: 0.333
KNN cohens kappa score: 0.295


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 2
LR fn, tp: 8, 3
LR f1 score: 0.375
LR cohens kappa score: 0.360
LR average precision score: 0.382

-> test with 'RF'
RF tn, fp: 285, 2
RF fn, tp: 9, 2
RF f1 score: 0.267
RF cohens kappa score: 0.252

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 8, 3
GB f1 score: 0.375
GB cohens kappa score: 0.360

-> test with 'KNN'
KNN tn, fp: 278, 9
KNN fn, tp: 7, 4
KNN f1 score: 0.333
KNN cohens kappa score: 0.306


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 4
LR fn, tp: 8, 3
LR f1 score: 0.333
LR cohens kappa score: 0.314
LR average precision score: 0.338

-> test with 'RF'
RF tn, fp: 286, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 6, 5
GB f1 score: 0.588
GB cohens kappa score: 0.577

-> test with 'KNN'
KNN tn, fp: 269, 18
KNN fn, tp: 5, 6
KNN f1 score: 0.343
KNN cohens kappa score: 0.308


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

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-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 15
LR fn, tp: 3, 4
LR f1 score: 0.308
LR cohens kappa score: 0.283
LR average precision score: 0.365

-> test with 'RF'
RF tn, fp: 285, 0
RF fn, tp: 6, 1
RF f1 score: 0.250
RF cohens kappa score: 0.245

-> test with 'GB'
GB tn, fp: 284, 1
GB fn, tp: 6, 1
GB f1 score: 0.222
GB cohens kappa score: 0.214

-> test with 'KNN'
KNN tn, fp: 268, 17
KNN fn, tp: 4, 3
KNN f1 score: 0.222
KNN cohens kappa score: 0.194


====== 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 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 3
LR fn, tp: 10, 1
LR f1 score: 0.133
LR cohens kappa score: 0.116
LR average precision score: 0.229

-> test with 'RF'
RF tn, fp: 286, 1
RF fn, tp: 10, 1
RF f1 score: 0.154
RF cohens kappa score: 0.144

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 9, 2
GB f1 score: 0.250
GB cohens kappa score: 0.232

-> test with 'KNN'
KNN tn, fp: 261, 26
KNN fn, tp: 8, 3
KNN f1 score: 0.150
KNN cohens kappa score: 0.102


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 272, 15
LR fn, tp: 3, 8
LR f1 score: 0.471
LR cohens kappa score: 0.443
LR average precision score: 0.266

-> test with 'RF'
RF tn, fp: 286, 1
RF fn, tp: 9, 2
RF f1 score: 0.286
RF cohens kappa score: 0.274

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 9, 2
GB f1 score: 0.286
GB cohens kappa score: 0.274

-> test with 'KNN'
KNN tn, fp: 277, 10
KNN fn, tp: 7, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.291


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 254, 33
LR fn, tp: 5, 6
LR f1 score: 0.240
LR cohens kappa score: 0.194
LR average precision score: 0.257

-> test with 'RF'
RF tn, fp: 284, 3
RF fn, tp: 9, 2
RF f1 score: 0.250
RF cohens kappa score: 0.232

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 8, 3
GB f1 score: 0.353
GB cohens kappa score: 0.336

-> test with 'KNN'
KNN tn, fp: 270, 17
KNN fn, tp: 8, 3
KNN f1 score: 0.194
KNN cohens kappa score: 0.153


------ 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,  1.97it/s]
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 278, 9
LR fn, tp: 6, 5
LR f1 score: 0.400
LR cohens kappa score: 0.374
LR average precision score: 0.263

-> test with 'RF'
RF tn, fp: 286, 1
RF fn, tp: 10, 1
RF f1 score: 0.154
RF cohens kappa score: 0.144

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 8, 3
GB f1 score: 0.353
GB cohens kappa score: 0.336

-> test with 'KNN'
KNN tn, fp: 269, 18
KNN fn, tp: 6, 5
KNN f1 score: 0.294
KNN cohens kappa score: 0.257


------ 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 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 5
LR fn, tp: 4, 3
LR f1 score: 0.400
LR cohens kappa score: 0.384
LR average precision score: 0.374

-> test with 'RF'
RF tn, fp: 285, 0
RF fn, tp: 5, 2
RF f1 score: 0.444
RF cohens kappa score: 0.438

-> test with 'GB'
GB tn, fp: 283, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 265, 20
KNN fn, tp: 4, 3
KNN f1 score: 0.200
KNN cohens kappa score: 0.169


====== 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]
100%|██████████| 10/10 [00:05<00:00,  1.92it/s]
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 3
LR fn, tp: 8, 3
LR f1 score: 0.353
LR cohens kappa score: 0.336
LR average precision score: 0.358

-> test with 'RF'
RF tn, fp: 287, 0
RF fn, tp: 8, 3
RF f1 score: 0.429
RF cohens kappa score: 0.419

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006

-> test with 'KNN'
KNN tn, fp: 273, 14
KNN fn, tp: 8, 3
KNN f1 score: 0.214
KNN cohens kappa score: 0.177


------ 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,  1.90it/s]
100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 3
LR fn, tp: 9, 2
LR f1 score: 0.250
LR cohens kappa score: 0.232
LR average precision score: 0.363

-> test with 'RF'
RF tn, fp: 286, 1
RF fn, tp: 10, 1
RF f1 score: 0.154
RF cohens kappa score: 0.144

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 6, 5
GB f1 score: 0.588
GB cohens kappa score: 0.577

-> test with 'KNN'
KNN tn, fp: 268, 19
KNN fn, tp: 5, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.297


------ 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,  1.96it/s]
100%|██████████| 10/10 [00:05<00:00,  1.93it/s]
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 264, 23
LR fn, tp: 6, 5
LR f1 score: 0.256
LR cohens kappa score: 0.215
LR average precision score: 0.176

-> test with 'RF'
RF tn, fp: 283, 4
RF fn, tp: 10, 1
RF f1 score: 0.125
RF cohens kappa score: 0.104

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 8, 3
GB f1 score: 0.316
GB cohens kappa score: 0.294

-> test with 'KNN'
KNN tn, fp: 269, 18
KNN fn, tp: 7, 4
KNN f1 score: 0.242
KNN cohens kappa score: 0.203


------ 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,  1.99it/s]
100%|██████████| 10/10 [00:05<00:00,  1.94it/s]
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 214, 73
LR fn, tp: 2, 9
LR f1 score: 0.194
LR cohens kappa score: 0.137
LR average precision score: 0.170

-> test with 'RF'
RF tn, fp: 285, 2
RF fn, tp: 9, 2
RF f1 score: 0.267
RF cohens kappa score: 0.252

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 8, 3
GB f1 score: 0.333
GB cohens kappa score: 0.314

-> test with 'KNN'
KNN tn, fp: 252, 35
KNN fn, tp: 5, 6
KNN f1 score: 0.231
KNN cohens kappa score: 0.183


------ 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,  1.95it/s]
100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 19
LR fn, tp: 3, 4
LR f1 score: 0.267
LR cohens kappa score: 0.239
LR average precision score: 0.287

-> test with 'RF'
RF tn, fp: 284, 1
RF fn, tp: 3, 4
RF f1 score: 0.667
RF cohens kappa score: 0.660

-> test with 'GB'
GB tn, fp: 281, 4
GB fn, tp: 3, 4
GB f1 score: 0.533
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 265, 20
KNN fn, tp: 3, 4
KNN f1 score: 0.258
KNN cohens kappa score: 0.229


====== 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,  1.95it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 252, 35
LR fn, tp: 8, 3
LR f1 score: 0.122
LR cohens kappa score: 0.069
LR average precision score: 0.136

-> test with 'RF'
RF tn, fp: 285, 2
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.011

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 10, 1
GB f1 score: 0.133
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 274, 13
KNN fn, tp: 9, 2
KNN f1 score: 0.154
KNN cohens kappa score: 0.116


------ 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,  1.95it/s]
100%|██████████| 10/10 [00:05<00:00,  1.94it/s]
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 276, 11
LR fn, tp: 4, 7
LR f1 score: 0.483
LR cohens kappa score: 0.458
LR average precision score: 0.305

-> test with 'RF'
RF tn, fp: 285, 2
RF fn, tp: 8, 3
RF f1 score: 0.375
RF cohens kappa score: 0.360

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 8, 3
GB f1 score: 0.400
GB cohens kappa score: 0.388

-> test with 'KNN'
KNN tn, fp: 262, 25
KNN fn, tp: 3, 8
KNN f1 score: 0.364
KNN cohens kappa score: 0.326


------ 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,  1.90it/s]
100%|██████████| 10/10 [00:05<00:00,  1.87it/s]
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 1
LR fn, tp: 9, 2
LR f1 score: 0.286
LR cohens kappa score: 0.274
LR average precision score: 0.427

-> test with 'RF'
RF tn, fp: 287, 0
RF fn, tp: 10, 1
RF f1 score: 0.167
RF cohens kappa score: 0.162

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 8, 3
GB f1 score: 0.400
GB cohens kappa score: 0.388

-> test with 'KNN'
KNN tn, fp: 270, 17
KNN fn, tp: 8, 3
KNN f1 score: 0.194
KNN cohens kappa score: 0.153


------ 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,  1.97it/s]
100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 12
LR fn, tp: 6, 5
LR f1 score: 0.357
LR cohens kappa score: 0.327
LR average precision score: 0.279

-> test with 'RF'
RF tn, fp: 285, 2
RF fn, tp: 10, 1
RF f1 score: 0.143
RF cohens kappa score: 0.129

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 9, 2
GB f1 score: 0.211
GB cohens kappa score: 0.185

-> test with 'KNN'
KNN tn, fp: 267, 20
KNN fn, tp: 7, 4
KNN f1 score: 0.229
KNN cohens kappa score: 0.187


------ 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,  1.95it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 4
LR fn, tp: 5, 2
LR f1 score: 0.308
LR cohens kappa score: 0.292
LR average precision score: 0.235

-> test with 'RF'
RF tn, fp: 284, 1
RF fn, tp: 6, 1
RF f1 score: 0.222
RF cohens kappa score: 0.214

-> test with 'GB'
GB tn, fp: 281, 4
GB fn, tp: 5, 2
GB f1 score: 0.308
GB cohens kappa score: 0.292

-> test with 'KNN'
KNN tn, fp: 272, 13
KNN fn, tp: 3, 4
KNN f1 score: 0.333
KNN cohens kappa score: 0.310

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 286, 73
LR fn, tp: 11, 9
LR f1 score: 0.483
LR cohens kappa score: 0.458
LR average precision score: 0.498


average:
LR tn, fp: 272.28, 14.32
LR fn, tp: 6.2, 4.0
LR f1 score: 0.284
LR cohens kappa score: 0.257
LR average precision score: 0.284


minimum:
LR tn, fp: 214, 1
LR fn, tp: 2, 0
LR f1 score: 0.000
LR cohens kappa score: -0.006
LR average precision score: 0.136


-----[ RF ]-----
maximum:
RF tn, fp: 287, 4
RF fn, tp: 11, 4
RF f1 score: 0.667
RF cohens kappa score: 0.660


average:
RF tn, fp: 285.32, 1.28
RF fn, tp: 8.52, 1.68
RF f1 score: 0.253
RF cohens kappa score: 0.242


minimum:
RF tn, fp: 283, 0
RF fn, tp: 3, 0
RF f1 score: 0.000
RF cohens kappa score: -0.011


-----[ GB ]-----
maximum:
GB tn, fp: 286, 6
GB fn, tp: 11, 5
GB f1 score: 0.588
GB cohens kappa score: 0.577


average:
GB tn, fp: 283.92, 2.68
GB fn, tp: 7.56, 2.64
GB f1 score: 0.332
GB cohens kappa score: 0.317


minimum:
GB tn, fp: 281, 1
GB fn, tp: 3, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006


-----[ KNN ]-----
maximum:
KNN tn, fp: 278, 35
KNN fn, tp: 9, 8
KNN f1 score: 0.500
KNN cohens kappa score: 0.477


average:
KNN tn, fp: 268.72, 17.88
KNN fn, tp: 6.08, 4.12
KNN f1 score: 0.254
KNN cohens kappa score: 0.218


minimum:
KNN tn, fp: 252, 9
KNN fn, tp: 3, 1
KNN f1 score: 0.111
KNN cohens kappa score: 0.084