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: 179, 26
LR fn, tp: 6, 3
LR f1 score: 0.158
LR cohens kappa score: 0.100
LR average precision score: 0.079

-> test with 'GB'
GB tn, fp: 182, 23
GB fn, tp: 6, 3
GB f1 score: 0.171
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 178, 27
KNN fn, tp: 5, 4
KNN f1 score: 0.200
KNN cohens kappa score: 0.144


------ Step 1/5: 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: 2, 7
LR f1 score: 0.359
LR cohens kappa score: 0.315
LR average precision score: 0.393

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

-> test with 'KNN'
KNN tn, fp: 192, 13
KNN fn, tp: 3, 6
KNN f1 score: 0.429
KNN cohens kappa score: 0.394


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 183, 22
LR fn, tp: 2, 7
LR f1 score: 0.368
LR cohens kappa score: 0.325
LR average precision score: 0.295

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

-> test with 'KNN'
KNN tn, fp: 188, 17
KNN fn, tp: 4, 5
KNN f1 score: 0.323
KNN cohens kappa score: 0.280


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 188, 17
LR fn, tp: 0, 9
LR f1 score: 0.514
LR cohens kappa score: 0.482
LR average precision score: 0.669

-> test with 'GB'
GB tn, fp: 191, 14
GB fn, tp: 2, 7
GB f1 score: 0.467
GB cohens kappa score: 0.433

-> test with 'KNN'
KNN tn, fp: 194, 11
KNN fn, tp: 5, 4
KNN f1 score: 0.333
KNN cohens kappa score: 0.296


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 181, 22
LR fn, tp: 3, 4
LR f1 score: 0.242
LR cohens kappa score: 0.200
LR average precision score: 0.169

-> test with 'GB'
GB tn, fp: 186, 17
GB fn, tp: 3, 4
GB f1 score: 0.286
GB cohens kappa score: 0.248

-> test with 'KNN'
KNN tn, fp: 184, 19
KNN fn, tp: 2, 5
KNN f1 score: 0.323
KNN cohens kappa score: 0.286


====== 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: 185, 20
LR fn, tp: 3, 6
LR f1 score: 0.343
LR cohens kappa score: 0.299
LR average precision score: 0.415

-> test with 'GB'
GB tn, fp: 191, 14
GB fn, tp: 6, 3
GB f1 score: 0.231
GB cohens kappa score: 0.186

-> test with 'KNN'
KNN tn, fp: 190, 15
KNN fn, tp: 5, 4
KNN f1 score: 0.286
KNN cohens kappa score: 0.242


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 20
LR fn, tp: 3, 6
LR f1 score: 0.343
LR cohens kappa score: 0.299
LR average precision score: 0.260

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

-> 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 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: 0.262

-> 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: 190, 15
KNN fn, tp: 6, 3
KNN f1 score: 0.222
KNN cohens kappa score: 0.176


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 187, 18
LR fn, tp: 4, 5
LR f1 score: 0.312
LR cohens kappa score: 0.268
LR average precision score: 0.231

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

-> 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
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 183, 20
LR fn, tp: 1, 6
LR f1 score: 0.364
LR cohens kappa score: 0.328
LR average precision score: 0.335

-> test with 'GB'
GB tn, fp: 187, 16
GB fn, tp: 2, 5
GB f1 score: 0.357
GB cohens kappa score: 0.323

-> 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 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: 189, 16
LR fn, tp: 1, 8
LR f1 score: 0.485
LR cohens kappa score: 0.451
LR average precision score: 0.545

-> test with 'GB'
GB tn, fp: 191, 14
GB fn, tp: 0, 9
GB f1 score: 0.562
GB cohens kappa score: 0.534

-> 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
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 176, 29
LR fn, tp: 4, 5
LR f1 score: 0.233
LR cohens kappa score: 0.178
LR average precision score: 0.238

-> 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: 177, 28
KNN fn, tp: 4, 5
KNN f1 score: 0.238
KNN cohens kappa score: 0.184


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 188, 17
LR fn, tp: 3, 6
LR f1 score: 0.375
LR cohens kappa score: 0.335
LR average precision score: 0.242

-> test with 'GB'
GB tn, fp: 197, 8
GB fn, tp: 4, 5
GB f1 score: 0.455
GB cohens kappa score: 0.426

-> 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 3/5: 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: 4, 5
LR f1 score: 0.357
LR cohens kappa score: 0.318
LR average precision score: 0.228

-> test with 'GB'
GB tn, fp: 194, 11
GB fn, tp: 7, 2
GB f1 score: 0.182
GB cohens kappa score: 0.139

-> test with 'KNN'
KNN tn, fp: 194, 11
KNN fn, tp: 5, 4
KNN f1 score: 0.333
KNN cohens kappa score: 0.296


------ Step 3/5: 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: 0.251

-> test with 'GB'
GB tn, fp: 191, 12
GB fn, tp: 3, 4
GB f1 score: 0.348
GB cohens kappa score: 0.316

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


====== 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: 184, 21
LR fn, tp: 5, 4
LR f1 score: 0.235
LR cohens kappa score: 0.185
LR average precision score: 0.134

-> test with 'GB'
GB tn, fp: 187, 18
GB fn, tp: 7, 2
GB f1 score: 0.138
GB cohens kappa score: 0.085

-> test with 'KNN'
KNN tn, fp: 182, 23
KNN fn, tp: 6, 3
KNN f1 score: 0.171
KNN cohens kappa score: 0.116


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 26
LR fn, tp: 3, 6
LR f1 score: 0.293
LR cohens kappa score: 0.243
LR average precision score: 0.468

-> test with 'GB'
GB tn, fp: 185, 20
GB fn, tp: 2, 7
GB f1 score: 0.389
GB cohens kappa score: 0.348

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


------ Step 4/5: 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: 4, 5
LR f1 score: 0.303
LR cohens kappa score: 0.258
LR average precision score: 0.229

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

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


------ Step 4/5: Slice 4/5 -------
-> 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: 0.350

-> test with 'GB'
GB tn, fp: 187, 18
GB fn, tp: 2, 7
GB f1 score: 0.412
GB cohens kappa score: 0.373

-> 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 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 17
LR fn, tp: 0, 7
LR f1 score: 0.452
LR cohens kappa score: 0.422
LR average precision score: 0.556

-> test with 'GB'
GB tn, fp: 189, 14
GB fn, tp: 2, 5
GB f1 score: 0.385
GB cohens kappa score: 0.353

-> test with 'KNN'
KNN tn, fp: 189, 14
KNN fn, tp: 3, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.286


====== 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: 186, 19
LR fn, tp: 5, 4
LR f1 score: 0.250
LR cohens kappa score: 0.202
LR average precision score: 0.248

-> test with 'GB'
GB tn, fp: 190, 15
GB fn, tp: 4, 5
GB f1 score: 0.345
GB cohens kappa score: 0.304

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


------ Step 5/5: 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: 2, 7
LR f1 score: 0.378
LR cohens kappa score: 0.336
LR average precision score: 0.266

-> test with 'GB'
GB tn, fp: 185, 20
GB fn, tp: 3, 6
GB f1 score: 0.343
GB cohens kappa score: 0.299

-> test with 'KNN'
KNN tn, fp: 185, 20
KNN fn, tp: 5, 4
KNN f1 score: 0.242
KNN cohens kappa score: 0.193


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 23
LR fn, tp: 0, 9
LR f1 score: 0.439
LR cohens kappa score: 0.400
LR average precision score: 0.390

-> 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: 182, 23
KNN fn, tp: 2, 7
KNN f1 score: 0.359
KNN cohens kappa score: 0.315


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 192, 13
LR fn, tp: 6, 3
LR f1 score: 0.240
LR cohens kappa score: 0.197
LR average precision score: 0.207

-> 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: 7, 2
KNN f1 score: 0.174
KNN cohens kappa score: 0.129


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 24
LR fn, tp: 2, 5
LR f1 score: 0.278
LR cohens kappa score: 0.237
LR average precision score: 0.449

-> test with 'GB'
GB tn, fp: 185, 18
GB fn, tp: 2, 5
GB f1 score: 0.333
GB cohens kappa score: 0.297

-> test with 'KNN'
KNN tn, fp: 184, 19
KNN fn, tp: 3, 4
KNN f1 score: 0.267
KNN cohens kappa score: 0.227

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 192, 29
LR fn, tp: 6, 9
LR f1 score: 0.514
LR cohens kappa score: 0.482
LR average precision score: 0.669


average:
LR tn, fp: 183.84, 20.76
LR fn, tp: 2.76, 5.84
LR f1 score: 0.332
LR cohens kappa score: 0.289
LR average precision score: 0.316


minimum:
LR tn, fp: 176, 13
LR fn, tp: 0, 3
LR f1 score: 0.158
LR cohens kappa score: 0.100
LR average precision score: 0.079


-----[ GB ]-----
maximum:
GB tn, fp: 197, 26
GB fn, tp: 7, 9
GB f1 score: 0.562
GB cohens kappa score: 0.534


average:
GB tn, fp: 188.24, 16.36
GB fn, tp: 3.72, 4.88
GB f1 score: 0.326
GB cohens kappa score: 0.285


minimum:
GB tn, fp: 179, 8
GB fn, tp: 0, 2
GB f1 score: 0.138
GB cohens kappa score: 0.085


-----[ KNN ]-----
maximum:
KNN tn, fp: 194, 28
KNN fn, tp: 7, 7
KNN f1 score: 0.444
KNN cohens kappa score: 0.411


average:
KNN tn, fp: 187.2, 17.4
KNN fn, tp: 4.2, 4.4
KNN f1 score: 0.293
KNN cohens kappa score: 0.250


minimum:
KNN tn, fp: 177, 11
KNN fn, tp: 2, 2
KNN f1 score: 0.171
KNN cohens kappa score: 0.116