ConvGeNBenchmarkResults/data_result/ctGAN/folding_yeast6.log

///////////////////////////////////////////
// Running ctGAN on folding_yeast6
///////////////////////////////////////////

Load 'data_input/folding_yeast6'
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
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 16
LR fn, tp: 3, 4
LR f1 score: 0.296
LR cohens kappa score: 0.271
LR average precision score: 0.356

-> test with 'RF'
RF tn, fp: 286, 4
RF fn, tp: 3, 4
RF f1 score: 0.533
RF cohens kappa score: 0.521

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

-> test with 'KNN'
KNN tn, fp: 279, 11
KNN fn, tp: 1, 6
KNN f1 score: 0.500
KNN cohens kappa score: 0.483


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 235, 55
LR fn, tp: 2, 5
LR f1 score: 0.149
LR cohens kappa score: 0.112
LR average precision score: 0.215

-> test with 'RF'
RF tn, fp: 274, 16
RF fn, tp: 4, 3
RF f1 score: 0.231
RF cohens kappa score: 0.203

-> test with 'GB'
GB tn, fp: 277, 13
GB fn, tp: 4, 3
GB f1 score: 0.261
GB cohens kappa score: 0.236

-> test with 'KNN'
KNN tn, fp: 258, 32
KNN fn, tp: 2, 5
KNN f1 score: 0.227
KNN cohens kappa score: 0.195


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 11
LR fn, tp: 2, 5
LR f1 score: 0.435
LR cohens kappa score: 0.416
LR average precision score: 0.309

-> test with 'RF'
RF tn, fp: 283, 7
RF fn, tp: 3, 4
RF f1 score: 0.444
RF cohens kappa score: 0.428

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 3, 4
GB f1 score: 0.500
GB cohens kappa score: 0.486

-> test with 'KNN'
KNN tn, fp: 282, 8
KNN fn, tp: 3, 4
KNN f1 score: 0.421
KNN cohens kappa score: 0.403


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 11
LR fn, tp: 2, 5
LR f1 score: 0.435
LR cohens kappa score: 0.416
LR average precision score: 0.384

-> test with 'RF'
RF tn, fp: 282, 8
RF fn, tp: 4, 3
RF f1 score: 0.333
RF cohens kappa score: 0.314

-> test with 'GB'
GB tn, fp: 284, 6
GB fn, tp: 5, 2
GB f1 score: 0.267
GB cohens kappa score: 0.248

-> test with 'KNN'
KNN tn, fp: 282, 8
KNN fn, tp: 1, 6
KNN f1 score: 0.571
KNN cohens kappa score: 0.558


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 242, 47
LR fn, tp: 0, 7
LR f1 score: 0.230
LR cohens kappa score: 0.196
LR average precision score: 0.353

-> test with 'RF'
RF tn, fp: 273, 16
RF fn, tp: 1, 6
RF f1 score: 0.414
RF cohens kappa score: 0.392

-> test with 'GB'
GB tn, fp: 285, 4
GB fn, tp: 2, 5
GB f1 score: 0.625
GB cohens kappa score: 0.615

-> test with 'KNN'
KNN tn, fp: 273, 16
KNN fn, tp: 0, 7
KNN f1 score: 0.467
KNN cohens kappa score: 0.447


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 264, 26
LR fn, tp: 1, 6
LR f1 score: 0.308
LR cohens kappa score: 0.280
LR average precision score: 0.398

-> test with 'RF'
RF tn, fp: 278, 12
RF fn, tp: 3, 4
RF f1 score: 0.348
RF cohens kappa score: 0.326

-> test with 'GB'
GB tn, fp: 282, 8
GB fn, tp: 2, 5
GB f1 score: 0.500
GB cohens kappa score: 0.484

-> test with 'KNN'
KNN tn, fp: 276, 14
KNN fn, tp: 1, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.424


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 23
LR fn, tp: 0, 7
LR f1 score: 0.378
LR cohens kappa score: 0.354
LR average precision score: 0.242

-> test with 'RF'
RF tn, fp: 283, 7
RF fn, tp: 1, 6
RF f1 score: 0.600
RF cohens kappa score: 0.587

-> test with 'GB'
GB tn, fp: 284, 6
GB fn, tp: 3, 4
GB f1 score: 0.471
GB cohens kappa score: 0.455

-> test with 'KNN'
KNN tn, fp: 281, 9
KNN fn, tp: 0, 7
KNN f1 score: 0.609
KNN cohens kappa score: 0.595


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 261, 29
LR fn, tp: 1, 6
LR f1 score: 0.286
LR cohens kappa score: 0.257
LR average precision score: 0.340

-> test with 'RF'
RF tn, fp: 282, 8
RF fn, tp: 4, 3
RF f1 score: 0.333
RF cohens kappa score: 0.314

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 4, 3
GB f1 score: 0.353
GB cohens kappa score: 0.334

-> test with 'KNN'
KNN tn, fp: 276, 14
KNN fn, tp: 2, 5
KNN f1 score: 0.385
KNN cohens kappa score: 0.363


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 24
LR fn, tp: 3, 4
LR f1 score: 0.229
LR cohens kappa score: 0.198
LR average precision score: 0.318

-> test with 'RF'
RF tn, fp: 283, 7
RF fn, tp: 4, 3
RF f1 score: 0.353
RF cohens kappa score: 0.334

-> test with 'GB'
GB tn, fp: 281, 9
GB fn, tp: 4, 3
GB f1 score: 0.316
GB cohens kappa score: 0.295

-> test with 'KNN'
KNN tn, fp: 274, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.334


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 231, 58
LR fn, tp: 1, 6
LR f1 score: 0.169
LR cohens kappa score: 0.132
LR average precision score: 0.495

-> test with 'RF'
RF tn, fp: 281, 8
RF fn, tp: 4, 3
RF f1 score: 0.333
RF cohens kappa score: 0.313

-> test with 'GB'
GB tn, fp: 280, 9
GB fn, tp: 5, 2
GB f1 score: 0.222
GB cohens kappa score: 0.199

-> test with 'KNN'
KNN tn, fp: 268, 21
KNN fn, tp: 3, 4
KNN f1 score: 0.250
KNN cohens kappa score: 0.221


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 235, 55
LR fn, tp: 1, 6
LR f1 score: 0.176
LR cohens kappa score: 0.140
LR average precision score: 0.180

-> test with 'RF'
RF tn, fp: 280, 10
RF fn, tp: 4, 3
RF f1 score: 0.300
RF cohens kappa score: 0.278

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 4, 3
GB f1 score: 0.353
GB cohens kappa score: 0.334

-> test with 'KNN'
KNN tn, fp: 276, 14
KNN fn, tp: 3, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.296


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 263, 27
LR fn, tp: 0, 7
LR f1 score: 0.341
LR cohens kappa score: 0.315
LR average precision score: 0.391

-> test with 'RF'
RF tn, fp: 283, 7
RF fn, tp: 1, 6
RF f1 score: 0.600
RF cohens kappa score: 0.587

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 1, 6
GB f1 score: 0.600
GB cohens kappa score: 0.587

-> test with 'KNN'
KNN tn, fp: 277, 13
KNN fn, tp: 0, 7
KNN f1 score: 0.519
KNN cohens kappa score: 0.501


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 218, 72
LR fn, tp: 4, 3
LR f1 score: 0.073
LR cohens kappa score: 0.031
LR average precision score: 0.045

-> test with 'RF'
RF tn, fp: 278, 12
RF fn, tp: 4, 3
RF f1 score: 0.273
RF cohens kappa score: 0.249

-> test with 'GB'
GB tn, fp: 282, 8
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.026

-> test with 'KNN'
KNN tn, fp: 268, 22
KNN fn, tp: 3, 4
KNN f1 score: 0.242
KNN cohens kappa score: 0.213


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 254, 36
LR fn, tp: 4, 3
LR f1 score: 0.130
LR cohens kappa score: 0.094
LR average precision score: 0.074

-> test with 'RF'
RF tn, fp: 271, 19
RF fn, tp: 4, 3
RF f1 score: 0.207
RF cohens kappa score: 0.177

-> test with 'GB'
GB tn, fp: 276, 14
GB fn, tp: 5, 2
GB f1 score: 0.174
GB cohens kappa score: 0.146

-> test with 'KNN'
KNN tn, fp: 261, 29
KNN fn, tp: 2, 5
KNN f1 score: 0.244
KNN cohens kappa score: 0.213


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 14
LR fn, tp: 2, 5
LR f1 score: 0.385
LR cohens kappa score: 0.363
LR average precision score: 0.344

-> test with 'RF'
RF tn, fp: 284, 5
RF fn, tp: 4, 3
RF f1 score: 0.400
RF cohens kappa score: 0.384

-> test with 'GB'
GB tn, fp: 286, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 284, 5
KNN fn, tp: 4, 3
KNN f1 score: 0.400
KNN cohens kappa score: 0.384


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 259, 31
LR fn, tp: 2, 5
LR f1 score: 0.233
LR cohens kappa score: 0.201
LR average precision score: 0.248

-> test with 'RF'
RF tn, fp: 286, 4
RF fn, tp: 3, 4
RF f1 score: 0.533
RF cohens kappa score: 0.521

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 5, 2
GB f1 score: 0.333
GB cohens kappa score: 0.320

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 0, 7
KNN f1 score: 0.483
KNN cohens kappa score: 0.464


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 88
LR fn, tp: 2, 5
LR f1 score: 0.100
LR cohens kappa score: 0.059
LR average precision score: 0.063

-> test with 'RF'
RF tn, fp: 279, 11
RF fn, tp: 6, 1
RF f1 score: 0.105
RF cohens kappa score: 0.078

-> test with 'GB'
GB tn, fp: 280, 10
GB fn, tp: 5, 2
GB f1 score: 0.211
GB cohens kappa score: 0.186

-> test with 'KNN'
KNN tn, fp: 269, 21
KNN fn, tp: 1, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.328


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 238, 52
LR fn, tp: 0, 7
LR f1 score: 0.212
LR cohens kappa score: 0.177
LR average precision score: 0.260

-> test with 'RF'
RF tn, fp: 267, 23
RF fn, tp: 1, 6
RF f1 score: 0.333
RF cohens kappa score: 0.307

-> test with 'GB'
GB tn, fp: 276, 14
GB fn, tp: 4, 3
GB f1 score: 0.250
GB cohens kappa score: 0.224

-> test with 'KNN'
KNN tn, fp: 266, 24
KNN fn, tp: 1, 6
KNN f1 score: 0.324
KNN cohens kappa score: 0.297


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 15
LR fn, tp: 1, 6
LR f1 score: 0.429
LR cohens kappa score: 0.408
LR average precision score: 0.635

-> test with 'RF'
RF tn, fp: 283, 7
RF fn, tp: 4, 3
RF f1 score: 0.353
RF cohens kappa score: 0.334

-> test with 'GB'
GB tn, fp: 282, 8
GB fn, tp: 4, 3
GB f1 score: 0.333
GB cohens kappa score: 0.314

-> test with 'KNN'
KNN tn, fp: 278, 12
KNN fn, tp: 1, 6
KNN f1 score: 0.480
KNN cohens kappa score: 0.462


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 237, 52
LR fn, tp: 2, 5
LR f1 score: 0.156
LR cohens kappa score: 0.119
LR average precision score: 0.189

-> test with 'RF'
RF tn, fp: 282, 7
RF fn, tp: 4, 3
RF f1 score: 0.353
RF cohens kappa score: 0.334

-> test with 'GB'
GB tn, fp: 285, 4
GB fn, tp: 4, 3
GB f1 score: 0.429
GB cohens kappa score: 0.415

-> test with 'KNN'
KNN tn, fp: 270, 19
KNN fn, tp: 2, 5
KNN f1 score: 0.323
KNN cohens kappa score: 0.297


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 16
LR fn, tp: 2, 5
LR f1 score: 0.357
LR cohens kappa score: 0.334
LR average precision score: 0.288

-> test with 'RF'
RF tn, fp: 279, 11
RF fn, tp: 3, 4
RF f1 score: 0.364
RF cohens kappa score: 0.343

-> test with 'GB'
GB tn, fp: 279, 11
GB fn, tp: 3, 4
GB f1 score: 0.364
GB cohens kappa score: 0.343

-> test with 'KNN'
KNN tn, fp: 274, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.334


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 235, 55
LR fn, tp: 1, 6
LR f1 score: 0.176
LR cohens kappa score: 0.140
LR average precision score: 0.171

-> test with 'RF'
RF tn, fp: 280, 10
RF fn, tp: 3, 4
RF f1 score: 0.381
RF cohens kappa score: 0.361

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.024

-> test with 'KNN'
KNN tn, fp: 271, 19
KNN fn, tp: 1, 6
KNN f1 score: 0.375
KNN cohens kappa score: 0.351


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 260, 30
LR fn, tp: 0, 7
LR f1 score: 0.318
LR cohens kappa score: 0.290
LR average precision score: 0.700

-> test with 'RF'
RF tn, fp: 276, 14
RF fn, tp: 1, 6
RF f1 score: 0.444
RF cohens kappa score: 0.424

-> test with 'GB'
GB tn, fp: 281, 9
GB fn, tp: 1, 6
GB f1 score: 0.545
GB cohens kappa score: 0.530

-> test with 'KNN'
KNN tn, fp: 273, 17
KNN fn, tp: 0, 7
KNN f1 score: 0.452
KNN cohens kappa score: 0.431


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 273, 17
LR fn, tp: 2, 5
LR f1 score: 0.345
LR cohens kappa score: 0.321
LR average precision score: 0.317

-> test with 'RF'
RF tn, fp: 286, 4
RF fn, tp: 3, 4
RF f1 score: 0.533
RF cohens kappa score: 0.521

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 3, 4
GB f1 score: 0.571
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 280, 10
KNN fn, tp: 3, 4
KNN f1 score: 0.381
KNN cohens kappa score: 0.361


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 10
LR fn, tp: 2, 5
LR f1 score: 0.455
LR cohens kappa score: 0.436
LR average precision score: 0.374

-> test with 'RF'
RF tn, fp: 285, 4
RF fn, tp: 4, 3
RF f1 score: 0.429
RF cohens kappa score: 0.415

-> test with 'GB'
GB tn, fp: 282, 7
GB fn, tp: 4, 3
GB f1 score: 0.353
GB cohens kappa score: 0.334

-> test with 'KNN'
KNN tn, fp: 282, 7
KNN fn, tp: 3, 4
KNN f1 score: 0.444
KNN cohens kappa score: 0.428

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 279, 88
LR fn, tp: 4, 7
LR f1 score: 0.455
LR cohens kappa score: 0.436
LR average precision score: 0.700


average:
LR tn, fp: 255.0, 34.8
LR fn, tp: 1.6, 5.4
LR f1 score: 0.272
LR cohens kappa score: 0.242
LR average precision score: 0.308


minimum:
LR tn, fp: 202, 10
LR fn, tp: 0, 3
LR f1 score: 0.073
LR cohens kappa score: 0.031
LR average precision score: 0.045


-----[ RF ]-----
maximum:
RF tn, fp: 286, 23
RF fn, tp: 6, 6
RF f1 score: 0.600
RF cohens kappa score: 0.587


average:
RF tn, fp: 280.16, 9.64
RF fn, tp: 3.2, 3.8
RF f1 score: 0.381
RF cohens kappa score: 0.362


minimum:
RF tn, fp: 267, 4
RF fn, tp: 1, 1
RF f1 score: 0.105
RF cohens kappa score: 0.078


-----[ GB ]-----
maximum:
GB tn, fp: 287, 14
GB fn, tp: 7, 6
GB f1 score: 0.625
GB cohens kappa score: 0.615


average:
GB tn, fp: 282.28, 7.52
GB fn, tp: 3.88, 3.12
GB f1 score: 0.355
GB cohens kappa score: 0.336


minimum:
GB tn, fp: 276, 3
GB fn, tp: 1, 0
GB f1 score: 0.000
GB cohens kappa score: -0.026


-----[ KNN ]-----
maximum:
KNN tn, fp: 284, 32
KNN fn, tp: 4, 7
KNN f1 score: 0.609
KNN cohens kappa score: 0.595


average:
KNN tn, fp: 274.12, 15.68
KNN fn, tp: 1.64, 5.36
KNN f1 score: 0.397
KNN cohens kappa score: 0.375


minimum:
KNN tn, fp: 258, 5
KNN fn, tp: 0, 3
KNN f1 score: 0.227
KNN cohens kappa score: 0.195