ConvGeNBenchmarkResults/data_result/ctGAN/folding_flare-F.log

///////////////////////////////////////////
// Running ctGAN 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
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 176, 29
LR fn, tp: 6, 3
LR f1 score: 0.146
LR cohens kappa score: 0.086
LR average precision score: 0.084

-> test with 'RF'
RF tn, fp: 178, 27
RF fn, tp: 6, 3
RF f1 score: 0.154
RF cohens kappa score: 0.095

-> test with 'GB'
GB tn, fp: 180, 25
GB fn, tp: 7, 2
GB f1 score: 0.111
GB cohens kappa score: 0.051

-> test with 'KNN'
KNN tn, fp: 174, 31
KNN fn, tp: 5, 4
KNN f1 score: 0.182
KNN cohens kappa score: 0.123


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 191, 14
LR fn, tp: 2, 7
LR f1 score: 0.467
LR cohens kappa score: 0.433
LR average precision score: 0.387

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

-> test with 'GB'
GB tn, fp: 196, 9
GB fn, tp: 3, 6
GB f1 score: 0.500
GB cohens kappa score: 0.472

-> test with 'KNN'
KNN tn, fp: 186, 19
KNN fn, tp: 1, 8
KNN f1 score: 0.444
KNN cohens kappa score: 0.407


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 19
LR fn, tp: 2, 7
LR f1 score: 0.400
LR cohens kappa score: 0.360
LR average precision score: 0.316

-> test with 'RF'
RF tn, fp: 189, 16
RF fn, tp: 5, 4
RF f1 score: 0.276
RF cohens kappa score: 0.231

-> test with 'GB'
GB tn, fp: 188, 17
GB fn, tp: 5, 4
GB f1 score: 0.267
GB cohens kappa score: 0.221

-> test with 'KNN'
KNN tn, fp: 182, 23
KNN fn, tp: 2, 7
KNN f1 score: 0.359
KNN cohens kappa score: 0.315


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 191, 14
LR fn, tp: 2, 7
LR f1 score: 0.467
LR cohens kappa score: 0.433
LR average precision score: 0.695

-> test with 'RF'
RF tn, fp: 193, 12
RF fn, tp: 5, 4
RF f1 score: 0.320
RF cohens kappa score: 0.281

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

-> test with 'KNN'
KNN tn, fp: 195, 10
KNN fn, tp: 4, 5
KNN f1 score: 0.417
KNN cohens kappa score: 0.384


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 19
LR fn, tp: 3, 4
LR f1 score: 0.267
LR cohens kappa score: 0.227
LR average precision score: 0.166

-> test with 'RF'
RF tn, fp: 181, 22
RF fn, tp: 4, 3
RF f1 score: 0.188
RF cohens kappa score: 0.143

-> test with 'GB'
GB tn, fp: 187, 16
GB fn, tp: 3, 4
GB f1 score: 0.296
GB cohens kappa score: 0.260

-> test with 'KNN'
KNN tn, fp: 181, 22
KNN fn, tp: 4, 3
KNN f1 score: 0.188
KNN cohens kappa score: 0.143


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.411

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

-> test with 'GB'
GB tn, fp: 192, 13
GB fn, tp: 6, 3
GB f1 score: 0.240
GB cohens kappa score: 0.197

-> test with 'KNN'
KNN tn, fp: 188, 17
KNN fn, tp: 6, 3
KNN f1 score: 0.207
KNN cohens kappa score: 0.158


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 23
LR fn, tp: 3, 6
LR f1 score: 0.316
LR cohens kappa score: 0.269
LR average precision score: 0.240

-> test with 'RF'
RF tn, fp: 185, 20
RF fn, tp: 4, 5
RF f1 score: 0.294
RF cohens kappa score: 0.248

-> test with 'GB'
GB tn, fp: 191, 14
GB fn, tp: 3, 6
GB f1 score: 0.414
GB cohens kappa score: 0.378

-> test with 'KNN'
KNN tn, fp: 180, 25
KNN fn, tp: 3, 6
KNN f1 score: 0.300
KNN cohens kappa score: 0.251


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 21
LR fn, tp: 2, 7
LR f1 score: 0.378
LR cohens kappa score: 0.336
LR average precision score: 0.293

-> test with 'RF'
RF tn, fp: 188, 17
RF fn, tp: 5, 4
RF f1 score: 0.267
RF cohens kappa score: 0.221

-> test with 'GB'
GB tn, fp: 191, 14
GB fn, tp: 4, 5
GB f1 score: 0.357
GB cohens kappa score: 0.318

-> test with 'KNN'
KNN tn, fp: 184, 21
KNN fn, tp: 3, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.288


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 19
LR fn, tp: 5, 4
LR f1 score: 0.250
LR cohens kappa score: 0.202
LR average precision score: 0.262

-> test with 'RF'
RF tn, fp: 186, 19
RF fn, tp: 6, 3
RF f1 score: 0.194
RF cohens kappa score: 0.142

-> test with 'GB'
GB tn, fp: 187, 18
GB fn, tp: 5, 4
GB f1 score: 0.258
GB cohens kappa score: 0.211

-> test with 'KNN'
KNN tn, fp: 185, 20
KNN fn, tp: 4, 5
KNN f1 score: 0.294
KNN cohens kappa score: 0.248


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 178, 25
LR fn, tp: 1, 6
LR f1 score: 0.316
LR cohens kappa score: 0.276
LR average precision score: 0.325

-> test with 'RF'
RF tn, fp: 179, 24
RF fn, tp: 3, 4
RF f1 score: 0.229
RF cohens kappa score: 0.185

-> test with 'GB'
GB tn, fp: 183, 20
GB fn, tp: 1, 6
GB f1 score: 0.364
GB cohens kappa score: 0.328

-> test with 'KNN'
KNN tn, fp: 181, 22
KNN fn, tp: 2, 5
KNN f1 score: 0.294
KNN cohens kappa score: 0.255


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 190, 15
LR fn, tp: 1, 8
LR f1 score: 0.500
LR cohens kappa score: 0.468
LR average precision score: 0.510

-> test with 'RF'
RF tn, fp: 187, 18
RF fn, tp: 3, 6
RF f1 score: 0.364
RF cohens kappa score: 0.322

-> test with 'GB'
GB tn, fp: 190, 15
GB fn, tp: 1, 8
GB f1 score: 0.500
GB cohens kappa score: 0.468

-> test with 'KNN'
KNN tn, fp: 193, 12
KNN fn, tp: 2, 7
KNN f1 score: 0.500
KNN cohens kappa score: 0.470


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 171, 34
LR fn, tp: 3, 6
LR f1 score: 0.245
LR cohens kappa score: 0.189
LR average precision score: 0.247

-> test with 'RF'
RF tn, fp: 175, 30
RF fn, tp: 6, 3
RF f1 score: 0.143
RF cohens kappa score: 0.082

-> test with 'GB'
GB tn, fp: 179, 26
GB fn, tp: 4, 5
GB f1 score: 0.250
GB cohens kappa score: 0.198

-> test with 'KNN'
KNN tn, fp: 169, 36
KNN fn, tp: 3, 6
KNN f1 score: 0.235
KNN cohens kappa score: 0.178


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 21
LR fn, tp: 3, 6
LR f1 score: 0.333
LR cohens kappa score: 0.288
LR average precision score: 0.239

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

-> test with 'GB'
GB tn, fp: 192, 13
GB fn, tp: 4, 5
GB f1 score: 0.370
GB cohens kappa score: 0.333

-> test with 'KNN'
KNN tn, fp: 179, 26
KNN fn, tp: 3, 6
KNN f1 score: 0.293
KNN cohens kappa score: 0.243


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 190, 15
LR fn, tp: 5, 4
LR f1 score: 0.286
LR cohens kappa score: 0.242
LR average precision score: 0.211

-> test with 'RF'
RF tn, fp: 186, 19
RF fn, tp: 4, 5
RF f1 score: 0.303
RF cohens kappa score: 0.258

-> test with 'GB'
GB tn, fp: 194, 11
GB fn, tp: 6, 3
GB f1 score: 0.261
GB cohens kappa score: 0.221

-> test with 'KNN'
KNN tn, fp: 193, 12
KNN fn, tp: 5, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.281


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 167, 36
LR fn, tp: 2, 5
LR f1 score: 0.208
LR cohens kappa score: 0.161
LR average precision score: 0.263

-> test with 'RF'
RF tn, fp: 179, 24
RF fn, tp: 3, 4
RF f1 score: 0.229
RF cohens kappa score: 0.185

-> test with 'GB'
GB tn, fp: 188, 15
GB fn, tp: 3, 4
GB f1 score: 0.308
GB cohens kappa score: 0.272

-> test with 'KNN'
KNN tn, fp: 179, 24
KNN fn, tp: 4, 3
KNN f1 score: 0.176
KNN cohens kappa score: 0.130


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 19
LR fn, tp: 5, 4
LR f1 score: 0.250
LR cohens kappa score: 0.202
LR average precision score: 0.145

-> test with 'RF'
RF tn, fp: 187, 18
RF fn, tp: 8, 1
RF f1 score: 0.071
RF cohens kappa score: 0.015

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

-> test with 'KNN'
KNN tn, fp: 185, 20
KNN fn, tp: 7, 2
KNN f1 score: 0.129
KNN cohens kappa score: 0.074


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 21
LR fn, tp: 3, 6
LR f1 score: 0.333
LR cohens kappa score: 0.288
LR average precision score: 0.513

-> test with 'RF'
RF tn, fp: 182, 23
RF fn, tp: 4, 5
RF f1 score: 0.270
RF cohens kappa score: 0.221

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

-> test with 'KNN'
KNN tn, fp: 184, 21
KNN fn, tp: 5, 4
KNN f1 score: 0.235
KNN cohens kappa score: 0.185


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 26
LR fn, tp: 4, 5
LR f1 score: 0.250
LR cohens kappa score: 0.198
LR average precision score: 0.235

-> test with 'RF'
RF tn, fp: 179, 26
RF fn, tp: 4, 5
RF f1 score: 0.250
RF cohens kappa score: 0.198

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

-> test with 'KNN'
KNN tn, fp: 178, 27
KNN fn, tp: 4, 5
KNN f1 score: 0.244
KNN cohens kappa score: 0.191


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.340

-> test with 'RF'
RF tn, fp: 187, 18
RF fn, tp: 4, 5
RF f1 score: 0.312
RF cohens kappa score: 0.268

-> test with 'GB'
GB tn, fp: 189, 16
GB fn, tp: 3, 6
GB f1 score: 0.387
GB cohens kappa score: 0.348

-> test with 'KNN'
KNN tn, fp: 188, 17
KNN fn, tp: 2, 7
KNN f1 score: 0.424
KNN cohens kappa score: 0.387


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 19
LR fn, tp: 0, 7
LR f1 score: 0.424
LR cohens kappa score: 0.392
LR average precision score: 0.529

-> test with 'RF'
RF tn, fp: 183, 20
RF fn, tp: 0, 7
RF f1 score: 0.412
RF cohens kappa score: 0.379

-> test with 'GB'
GB tn, fp: 184, 19
GB fn, tp: 0, 7
GB f1 score: 0.424
GB cohens kappa score: 0.392

-> test with 'KNN'
KNN tn, fp: 181, 22
KNN fn, tp: 1, 6
KNN f1 score: 0.343
KNN cohens kappa score: 0.306


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 20
LR fn, tp: 5, 4
LR f1 score: 0.242
LR cohens kappa score: 0.193
LR average precision score: 0.244

-> test with 'RF'
RF tn, fp: 189, 16
RF fn, tp: 3, 6
RF f1 score: 0.387
RF cohens kappa score: 0.348

-> test with 'GB'
GB tn, fp: 189, 16
GB fn, tp: 4, 5
GB f1 score: 0.333
GB cohens kappa score: 0.292

-> test with 'KNN'
KNN tn, fp: 185, 20
KNN fn, tp: 4, 5
KNN f1 score: 0.294
KNN cohens kappa score: 0.248


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 180, 25
LR fn, tp: 2, 7
LR f1 score: 0.341
LR cohens kappa score: 0.295
LR average precision score: 0.289

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

-> test with 'GB'
GB tn, fp: 189, 16
GB fn, tp: 3, 6
GB f1 score: 0.387
GB cohens kappa score: 0.348

-> test with 'KNN'
KNN tn, fp: 181, 24
KNN fn, tp: 3, 6
KNN f1 score: 0.308
KNN cohens kappa score: 0.260


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 175, 30
LR fn, tp: 0, 9
LR f1 score: 0.375
LR cohens kappa score: 0.329
LR average precision score: 0.387

-> test with 'RF'
RF tn, fp: 180, 25
RF fn, tp: 1, 8
RF f1 score: 0.381
RF cohens kappa score: 0.337

-> test with 'GB'
GB tn, fp: 183, 22
GB fn, tp: 2, 7
GB f1 score: 0.368
GB cohens kappa score: 0.325

-> test with 'KNN'
KNN tn, fp: 174, 31
KNN fn, tp: 1, 8
KNN f1 score: 0.333
KNN cohens kappa score: 0.284


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 191, 14
LR fn, tp: 6, 3
LR f1 score: 0.231
LR cohens kappa score: 0.186
LR average precision score: 0.207

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

-> test with 'GB'
GB tn, fp: 196, 9
GB fn, tp: 6, 3
GB f1 score: 0.286
GB cohens kappa score: 0.250

-> test with 'KNN'
KNN tn, fp: 193, 12
KNN fn, tp: 5, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.281


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 177, 26
LR fn, tp: 2, 5
LR f1 score: 0.263
LR cohens kappa score: 0.221
LR average precision score: 0.268

-> test with 'RF'
RF tn, fp: 178, 25
RF fn, tp: 2, 5
RF f1 score: 0.270
RF cohens kappa score: 0.229

-> test with 'GB'
GB tn, fp: 186, 17
GB fn, tp: 2, 5
GB f1 score: 0.345
GB cohens kappa score: 0.310

-> test with 'KNN'
KNN tn, fp: 178, 25
KNN fn, tp: 2, 5
KNN f1 score: 0.270
KNN cohens kappa score: 0.229

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 191, 36
LR fn, tp: 6, 9
LR f1 score: 0.500
LR cohens kappa score: 0.468
LR average precision score: 0.695


average:
LR tn, fp: 182.88, 21.72
LR fn, tp: 2.88, 5.72
LR f1 score: 0.321
LR cohens kappa score: 0.277
LR average precision score: 0.312


minimum:
LR tn, fp: 167, 14
LR fn, tp: 0, 3
LR f1 score: 0.146
LR cohens kappa score: 0.086
LR average precision score: 0.084


-----[ RF ]-----
maximum:
RF tn, fp: 193, 30
RF fn, tp: 8, 8
RF f1 score: 0.412
RF cohens kappa score: 0.379


average:
RF tn, fp: 183.72, 20.88
RF fn, tp: 4.2, 4.4
RF f1 score: 0.261
RF cohens kappa score: 0.214


minimum:
RF tn, fp: 175, 12
RF fn, tp: 0, 1
RF f1 score: 0.071
RF cohens kappa score: 0.015


-----[ GB ]-----
maximum:
GB tn, fp: 196, 26
GB fn, tp: 7, 8
GB f1 score: 0.500
GB cohens kappa score: 0.472


average:
GB tn, fp: 188.28, 16.32
GB fn, tp: 3.76, 4.84
GB f1 score: 0.326
GB cohens kappa score: 0.286


minimum:
GB tn, fp: 179, 9
GB fn, tp: 0, 2
GB f1 score: 0.111
GB cohens kappa score: 0.051


-----[ KNN ]-----
maximum:
KNN tn, fp: 195, 36
KNN fn, tp: 7, 8
KNN f1 score: 0.500
KNN cohens kappa score: 0.470


average:
KNN tn, fp: 183.04, 21.56
KNN fn, tp: 3.4, 5.2
KNN f1 score: 0.298
KNN cohens kappa score: 0.253


minimum:
KNN tn, fp: 169, 10
KNN fn, tp: 1, 2
KNN f1 score: 0.129
KNN cohens kappa score: 0.074