ConvGeNBenchmarkResults/data_result/CTGAN/folding_yeast4.log

///////////////////////////////////////////
// Running ctGAN 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
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 17
LR fn, tp: 3, 8
LR f1 score: 0.444
LR cohens kappa score: 0.414
LR average precision score: 0.264

-> test with 'RF'
RF tn, fp: 278, 9
RF fn, tp: 6, 5
RF f1 score: 0.400
RF cohens kappa score: 0.374

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 7, 4
GB f1 score: 0.364
GB cohens kappa score: 0.339

-> test with 'KNN'
KNN tn, fp: 283, 4
KNN fn, tp: 6, 5
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 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 259, 28
LR fn, tp: 4, 7
LR f1 score: 0.304
LR cohens kappa score: 0.263
LR average precision score: 0.255

-> test with 'RF'
RF tn, fp: 270, 17
RF fn, tp: 4, 7
RF f1 score: 0.400
RF cohens kappa score: 0.368

-> test with 'GB'
GB tn, fp: 271, 16
GB fn, tp: 7, 4
GB f1 score: 0.258
GB cohens kappa score: 0.221

-> test with 'KNN'
KNN tn, fp: 278, 9
KNN fn, tp: 5, 6
KNN f1 score: 0.462
KNN cohens kappa score: 0.438


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 260, 27
LR fn, tp: 6, 5
LR f1 score: 0.233
LR cohens kappa score: 0.188
LR average precision score: 0.134

-> test with 'RF'
RF tn, fp: 280, 7
RF fn, tp: 7, 4
RF f1 score: 0.364
RF cohens kappa score: 0.339

-> 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: 273, 14
KNN fn, tp: 6, 5
KNN f1 score: 0.333
KNN cohens kappa score: 0.301


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 17
LR fn, tp: 7, 4
LR f1 score: 0.250
LR cohens kappa score: 0.212
LR average precision score: 0.214

-> test with 'RF'
RF tn, fp: 277, 10
RF fn, tp: 6, 5
RF f1 score: 0.385
RF cohens kappa score: 0.357

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 7, 4
GB f1 score: 0.348
GB cohens kappa score: 0.322

-> test with 'KNN'
KNN tn, fp: 279, 8
KNN fn, tp: 8, 3
KNN f1 score: 0.273
KNN cohens kappa score: 0.245


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 252, 33
LR fn, tp: 0, 7
LR f1 score: 0.298
LR cohens kappa score: 0.268
LR average precision score: 0.205

-> test with 'RF'
RF tn, fp: 278, 7
RF fn, tp: 4, 3
RF f1 score: 0.353
RF cohens kappa score: 0.334

-> test with 'GB'
GB tn, fp: 281, 4
GB fn, tp: 4, 3
GB f1 score: 0.429
GB cohens kappa score: 0.415

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


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 272, 15
LR fn, tp: 6, 5
LR f1 score: 0.323
LR cohens kappa score: 0.289
LR average precision score: 0.230

-> test with 'RF'
RF tn, fp: 275, 12
RF fn, tp: 5, 6
RF f1 score: 0.414
RF cohens kappa score: 0.386

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

-> test with 'KNN'
KNN tn, fp: 278, 9
KNN fn, tp: 8, 3
KNN f1 score: 0.261
KNN cohens kappa score: 0.231


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 236, 51
LR fn, tp: 6, 5
LR f1 score: 0.149
LR cohens kappa score: 0.093
LR average precision score: 0.091

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

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

-> test with 'KNN'
KNN tn, fp: 262, 25
KNN fn, tp: 4, 7
KNN f1 score: 0.326
KNN cohens kappa score: 0.286


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 261, 26
LR fn, tp: 5, 6
LR f1 score: 0.279
LR cohens kappa score: 0.237
LR average precision score: 0.179

-> test with 'RF'
RF tn, fp: 274, 13
RF fn, tp: 3, 8
RF f1 score: 0.500
RF cohens kappa score: 0.475

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 7, 4
GB f1 score: 0.348
GB cohens kappa score: 0.322

-> test with 'KNN'
KNN tn, fp: 274, 13
KNN fn, tp: 5, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.371


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 264, 23
LR fn, tp: 3, 8
LR f1 score: 0.381
LR cohens kappa score: 0.345
LR average precision score: 0.222

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

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 8, 3
GB f1 score: 0.273
GB cohens kappa score: 0.245

-> test with 'KNN'
KNN tn, fp: 279, 8
KNN fn, tp: 7, 4
KNN f1 score: 0.348
KNN cohens kappa score: 0.322


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 253, 32
LR fn, tp: 6, 1
LR f1 score: 0.050
LR cohens kappa score: 0.011
LR average precision score: 0.071

-> test with 'RF'
RF tn, fp: 276, 9
RF fn, tp: 6, 1
RF f1 score: 0.118
RF cohens kappa score: 0.092

-> test with 'GB'
GB tn, fp: 280, 5
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.020

-> test with 'KNN'
KNN tn, fp: 278, 7
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.025


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 268, 19
LR fn, tp: 6, 5
LR f1 score: 0.286
LR cohens kappa score: 0.248
LR average precision score: 0.191

-> test with 'RF'
RF tn, fp: 279, 8
RF fn, tp: 7, 4
RF f1 score: 0.348
RF cohens kappa score: 0.322

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 8, 3
GB f1 score: 0.273
GB cohens kappa score: 0.245

-> test with 'KNN'
KNN tn, fp: 274, 13
KNN fn, tp: 4, 7
KNN f1 score: 0.452
KNN cohens kappa score: 0.424


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 17
LR fn, tp: 3, 8
LR f1 score: 0.444
LR cohens kappa score: 0.414
LR average precision score: 0.345

-> test with 'RF'
RF tn, fp: 273, 14
RF fn, tp: 4, 7
RF f1 score: 0.437
RF cohens kappa score: 0.409

-> test with 'GB'
GB tn, fp: 275, 12
GB fn, tp: 7, 4
GB f1 score: 0.296
GB cohens kappa score: 0.264

-> 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
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 254, 33
LR fn, tp: 7, 4
LR f1 score: 0.167
LR cohens kappa score: 0.116
LR average precision score: 0.169

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

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 8, 3
GB f1 score: 0.273
GB cohens kappa score: 0.245

-> test with 'KNN'
KNN tn, fp: 280, 7
KNN fn, tp: 7, 4
KNN f1 score: 0.364
KNN cohens kappa score: 0.339


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 237, 50
LR fn, tp: 6, 5
LR f1 score: 0.152
LR cohens kappa score: 0.096
LR average precision score: 0.091

-> test with 'RF'
RF tn, fp: 270, 17
RF fn, tp: 5, 6
RF f1 score: 0.353
RF cohens kappa score: 0.319

-> test with 'GB'
GB tn, fp: 274, 13
GB fn, tp: 8, 3
GB f1 score: 0.222
GB cohens kappa score: 0.187

-> test with 'KNN'
KNN tn, fp: 263, 24
KNN fn, tp: 5, 6
KNN f1 score: 0.293
KNN cohens kappa score: 0.252


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 45
LR fn, tp: 5, 2
LR f1 score: 0.074
LR cohens kappa score: 0.034
LR average precision score: 0.044

-> test with 'RF'
RF tn, fp: 275, 10
RF fn, tp: 3, 4
RF f1 score: 0.381
RF cohens kappa score: 0.361

-> test with 'GB'
GB tn, fp: 280, 5
GB fn, tp: 5, 2
GB f1 score: 0.286
GB cohens kappa score: 0.268

-> test with 'KNN'
KNN tn, fp: 266, 19
KNN fn, tp: 3, 4
KNN f1 score: 0.267
KNN cohens kappa score: 0.239


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 220, 67
LR fn, tp: 1, 10
LR f1 score: 0.227
LR cohens kappa score: 0.174
LR average precision score: 0.285

-> test with 'RF'
RF tn, fp: 271, 16
RF fn, tp: 8, 3
RF f1 score: 0.200
RF cohens kappa score: 0.161

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 9, 2
GB f1 score: 0.167
GB cohens kappa score: 0.132

-> test with 'KNN'
KNN tn, fp: 278, 9
KNN fn, tp: 8, 3
KNN f1 score: 0.261
KNN cohens kappa score: 0.231


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 259, 28
LR fn, tp: 10, 1
LR f1 score: 0.050
LR cohens kappa score: -0.004
LR average precision score: 0.047

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

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 8, 3
GB f1 score: 0.273
GB cohens kappa score: 0.245

-> test with 'KNN'
KNN tn, fp: 277, 10
KNN fn, tp: 9, 2
KNN f1 score: 0.174
KNN cohens kappa score: 0.141


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 258, 29
LR fn, tp: 7, 4
LR f1 score: 0.182
LR cohens kappa score: 0.134
LR average precision score: 0.098

-> test with 'RF'
RF tn, fp: 271, 16
RF fn, tp: 9, 2
RF f1 score: 0.138
RF cohens kappa score: 0.097

-> test with 'GB'
GB tn, fp: 275, 12
GB fn, tp: 7, 4
GB f1 score: 0.296
GB cohens kappa score: 0.264

-> test with 'KNN'
KNN tn, fp: 275, 12
KNN fn, tp: 9, 2
KNN f1 score: 0.160
KNN cohens kappa score: 0.124


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 258, 29
LR fn, tp: 6, 5
LR f1 score: 0.222
LR cohens kappa score: 0.176
LR average precision score: 0.156

-> test with 'RF'
RF tn, fp: 271, 16
RF fn, tp: 6, 5
RF f1 score: 0.312
RF cohens kappa score: 0.277

-> test with 'GB'
GB tn, fp: 271, 16
GB fn, tp: 7, 4
GB f1 score: 0.258
GB cohens kappa score: 0.221

-> test with 'KNN'
KNN tn, fp: 270, 17
KNN fn, tp: 7, 4
KNN f1 score: 0.250
KNN cohens kappa score: 0.212


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 229, 56
LR fn, tp: 5, 2
LR f1 score: 0.062
LR cohens kappa score: 0.020
LR average precision score: 0.036

-> test with 'RF'
RF tn, fp: 279, 6
RF fn, tp: 3, 4
RF f1 score: 0.471
RF cohens kappa score: 0.455

-> test with 'GB'
GB tn, fp: 282, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.449

-> test with 'KNN'
KNN tn, fp: 276, 9
KNN fn, tp: 4, 3
KNN f1 score: 0.316
KNN cohens kappa score: 0.294


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 17
LR fn, tp: 6, 5
LR f1 score: 0.303
LR cohens kappa score: 0.267
LR average precision score: 0.172

-> test with 'RF'
RF tn, fp: 278, 9
RF fn, tp: 8, 3
RF f1 score: 0.261
RF cohens kappa score: 0.231

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 9, 2
GB f1 score: 0.235
GB cohens kappa score: 0.215

-> test with 'KNN'
KNN tn, fp: 279, 8
KNN fn, tp: 6, 5
KNN f1 score: 0.417
KNN cohens kappa score: 0.392


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 262, 25
LR fn, tp: 7, 4
LR f1 score: 0.200
LR cohens kappa score: 0.155
LR average precision score: 0.149

-> test with 'RF'
RF tn, fp: 278, 9
RF fn, tp: 3, 8
RF f1 score: 0.571
RF cohens kappa score: 0.551

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 5, 6
GB f1 score: 0.500
GB cohens kappa score: 0.479

-> test with 'KNN'
KNN tn, fp: 275, 12
KNN fn, tp: 3, 8
KNN f1 score: 0.516
KNN cohens kappa score: 0.492


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 232, 55
LR fn, tp: 3, 8
LR f1 score: 0.216
LR cohens kappa score: 0.164
LR average precision score: 0.414

-> test with 'RF'
RF tn, fp: 275, 12
RF fn, tp: 7, 4
RF f1 score: 0.296
RF cohens kappa score: 0.264

-> test with 'GB'
GB tn, fp: 273, 14
GB fn, tp: 9, 2
GB f1 score: 0.148
GB cohens kappa score: 0.109

-> test with 'KNN'
KNN tn, fp: 275, 12
KNN fn, tp: 4, 7
KNN f1 score: 0.467
KNN cohens kappa score: 0.441


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 244, 43
LR fn, tp: 2, 9
LR f1 score: 0.286
LR cohens kappa score: 0.239
LR average precision score: 0.187

-> test with 'RF'
RF tn, fp: 272, 15
RF fn, tp: 7, 4
RF f1 score: 0.267
RF cohens kappa score: 0.231

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 10, 1
GB f1 score: 0.087
GB cohens kappa score: 0.050

-> test with 'KNN'
KNN tn, fp: 269, 18
KNN fn, tp: 9, 2
KNN f1 score: 0.129
KNN cohens kappa score: 0.085


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 241, 44
LR fn, tp: 2, 5
LR f1 score: 0.179
LR cohens kappa score: 0.143
LR average precision score: 0.118

-> test with 'RF'
RF tn, fp: 275, 10
RF fn, tp: 1, 6
RF f1 score: 0.522
RF cohens kappa score: 0.505

-> test with 'GB'
GB tn, fp: 274, 11
GB fn, tp: 3, 4
GB f1 score: 0.364
GB cohens kappa score: 0.342

-> test with 'KNN'
KNN tn, fp: 273, 12
KNN fn, tp: 3, 4
KNN f1 score: 0.348
KNN cohens kappa score: 0.325

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 272, 67
LR fn, tp: 10, 10
LR f1 score: 0.444
LR cohens kappa score: 0.414
LR average precision score: 0.414


average:
LR tn, fp: 253.56, 33.04
LR fn, tp: 4.88, 5.32
LR f1 score: 0.230
LR cohens kappa score: 0.188
LR average precision score: 0.175


minimum:
LR tn, fp: 220, 15
LR fn, tp: 0, 1
LR f1 score: 0.050
LR cohens kappa score: -0.004
LR average precision score: 0.036


-----[ RF ]-----
maximum:
RF tn, fp: 280, 18
RF fn, tp: 9, 8
RF f1 score: 0.571
RF cohens kappa score: 0.551


average:
RF tn, fp: 275.08, 11.52
RF fn, tp: 5.52, 4.68
RF f1 score: 0.353
RF cohens kappa score: 0.325


minimum:
RF tn, fp: 269, 6
RF fn, tp: 1, 1
RF f1 score: 0.118
RF cohens kappa score: 0.092


-----[ GB ]-----
maximum:
GB tn, fp: 283, 17
GB fn, tp: 10, 6
GB f1 score: 0.500
GB cohens kappa score: 0.479


average:
GB tn, fp: 277.36, 9.24
GB fn, tp: 7.12, 3.08
GB f1 score: 0.276
GB cohens kappa score: 0.248


minimum:
GB tn, fp: 270, 3
GB fn, tp: 3, 0
GB f1 score: 0.000
GB cohens kappa score: -0.020


-----[ KNN ]-----
maximum:
KNN tn, fp: 283, 25
KNN fn, tp: 9, 8
KNN f1 score: 0.516
KNN cohens kappa score: 0.492


average:
KNN tn, fp: 274.52, 12.08
KNN fn, tp: 5.88, 4.32
KNN f1 score: 0.319
KNN cohens kappa score: 0.290


minimum:
KNN tn, fp: 262, 4
KNN fn, tp: 3, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.025