ConvGeNBenchmarkResults/data_result/CTGAN/folding_abalone9-18.log

///////////////////////////////////////////
// Running CTGAN on folding_abalone9-18
///////////////////////////////////////////

Load 'folding_abalone9-18'
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 518 synthetic samples
-> test with 'LR'
LR tn, fp: 125, 13
LR fn, tp: 3, 6
LR f1 score: 0.429
LR cohens kappa score: 0.377
LR average precision score: 0.655

-> test with 'RF'
RF tn, fp: 134, 4
RF fn, tp: 4, 5
RF f1 score: 0.556
RF cohens kappa score: 0.527

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 4, 5
GB f1 score: 0.526
GB cohens kappa score: 0.494

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 116, 22
LR fn, tp: 3, 6
LR f1 score: 0.324
LR cohens kappa score: 0.255
LR average precision score: 0.505

-> test with 'RF'
RF tn, fp: 134, 4
RF fn, tp: 6, 3
RF f1 score: 0.375
RF cohens kappa score: 0.340

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 6, 3
GB f1 score: 0.429
GB cohens kappa score: 0.402

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 128, 10
LR fn, tp: 6, 3
LR f1 score: 0.273
LR cohens kappa score: 0.216
LR average precision score: 0.334

-> test with 'RF'
RF tn, fp: 132, 6
RF fn, tp: 5, 4
RF f1 score: 0.421
RF cohens kappa score: 0.381

-> test with 'GB'
GB tn, fp: 130, 8
GB fn, tp: 5, 4
GB f1 score: 0.381
GB cohens kappa score: 0.334

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 5, 4
LR f1 score: 0.444
LR cohens kappa score: 0.408
LR average precision score: 0.457

-> test with 'RF'
RF tn, fp: 136, 2
RF fn, tp: 6, 3
RF f1 score: 0.429
RF cohens kappa score: 0.402

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 6, 3
KNN f1 score: 0.500
KNN cohens kappa score: 0.484


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 127, 10
LR fn, tp: 2, 4
LR f1 score: 0.400
LR cohens kappa score: 0.363
LR average precision score: 0.296

-> test with 'RF'
RF tn, fp: 132, 5
RF fn, tp: 4, 2
RF f1 score: 0.308
RF cohens kappa score: 0.275

-> test with 'GB'
GB tn, fp: 133, 4
GB fn, tp: 5, 1
GB f1 score: 0.182
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 135, 2
KNN fn, tp: 5, 1
KNN f1 score: 0.222
KNN cohens kappa score: 0.200


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 3, 6
LR f1 score: 0.545
LR cohens kappa score: 0.510
LR average precision score: 0.573

-> test with 'RF'
RF tn, fp: 135, 3
RF fn, tp: 6, 3
RF f1 score: 0.400
RF cohens kappa score: 0.369

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 8, 1
KNN f1 score: 0.182
KNN cohens kappa score: 0.163


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 5, 4
LR f1 score: 0.444
LR cohens kappa score: 0.408
LR average precision score: 0.423

-> test with 'RF'
RF tn, fp: 134, 4
RF fn, tp: 5, 4
RF f1 score: 0.471
RF cohens kappa score: 0.438

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 5, 4
GB f1 score: 0.421
GB cohens kappa score: 0.381

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 114, 24
LR fn, tp: 4, 5
LR f1 score: 0.263
LR cohens kappa score: 0.187
LR average precision score: 0.285

-> test with 'RF'
RF tn, fp: 132, 6
RF fn, tp: 6, 3
RF f1 score: 0.333
RF cohens kappa score: 0.290

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 6, 3
GB f1 score: 0.375
GB cohens kappa score: 0.340

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 7, 2
KNN f1 score: 0.286
KNN cohens kappa score: 0.253


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 119, 19
LR fn, tp: 5, 4
LR f1 score: 0.250
LR cohens kappa score: 0.178
LR average precision score: 0.475

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 5, 4
RF f1 score: 0.444
RF cohens kappa score: 0.408

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 5, 4
GB f1 score: 0.444
GB cohens kappa score: 0.408

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 7, 2
KNN f1 score: 0.333
KNN cohens kappa score: 0.312


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 129, 8
LR fn, tp: 5, 1
LR f1 score: 0.133
LR cohens kappa score: 0.087
LR average precision score: 0.091

-> test with 'RF'
RF tn, fp: 130, 7
RF fn, tp: 3, 3
RF f1 score: 0.375
RF cohens kappa score: 0.340

-> test with 'GB'
GB tn, fp: 132, 5
GB fn, tp: 4, 2
GB f1 score: 0.308
GB cohens kappa score: 0.275

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 95, 43
LR fn, tp: 2, 7
LR f1 score: 0.237
LR cohens kappa score: 0.149
LR average precision score: 0.322

-> test with 'RF'
RF tn, fp: 131, 7
RF fn, tp: 6, 3
RF f1 score: 0.316
RF cohens kappa score: 0.269

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 6, 3
GB f1 score: 0.333
GB cohens kappa score: 0.290

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 5, 4
KNN f1 score: 0.615
KNN cohens kappa score: 0.600


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 3, 6
LR f1 score: 0.571
LR cohens kappa score: 0.539
LR average precision score: 0.641

-> test with 'RF'
RF tn, fp: 136, 2
RF fn, tp: 5, 4
RF f1 score: 0.533
RF cohens kappa score: 0.509

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 4, 5
GB f1 score: 0.667
GB cohens kappa score: 0.649

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 126, 12
LR fn, tp: 6, 3
LR f1 score: 0.250
LR cohens kappa score: 0.188
LR average precision score: 0.426

-> test with 'RF'
RF tn, fp: 132, 6
RF fn, tp: 6, 3
RF f1 score: 0.333
RF cohens kappa score: 0.290

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 8, 1
KNN f1 score: 0.154
KNN cohens kappa score: 0.121


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 123, 15
LR fn, tp: 6, 3
LR f1 score: 0.222
LR cohens kappa score: 0.153
LR average precision score: 0.146

-> test with 'RF'
RF tn, fp: 134, 4
RF fn, tp: 4, 5
RF f1 score: 0.556
RF cohens kappa score: 0.527

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 5, 4
GB f1 score: 0.471
GB cohens kappa score: 0.438

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 6, 3
KNN f1 score: 0.400
KNN cohens kappa score: 0.369


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 127, 10
LR fn, tp: 4, 2
LR f1 score: 0.222
LR cohens kappa score: 0.176
LR average precision score: 0.402

-> test with 'RF'
RF tn, fp: 130, 7
RF fn, tp: 4, 2
RF f1 score: 0.267
RF cohens kappa score: 0.228

-> test with 'GB'
GB tn, fp: 133, 4
GB fn, tp: 4, 2
GB f1 score: 0.333
GB cohens kappa score: 0.304

-> test with 'KNN'
KNN tn, fp: 136, 1
KNN fn, tp: 5, 1
KNN f1 score: 0.250
KNN cohens kappa score: 0.234


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 4, 5
LR f1 score: 0.455
LR cohens kappa score: 0.412
LR average precision score: 0.648

-> test with 'RF'
RF tn, fp: 135, 3
RF fn, tp: 6, 3
RF f1 score: 0.400
RF cohens kappa score: 0.369

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 6, 3
KNN f1 score: 0.500
KNN cohens kappa score: 0.484


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 126, 12
LR fn, tp: 5, 4
LR f1 score: 0.320
LR cohens kappa score: 0.262
LR average precision score: 0.329

-> test with 'RF'
RF tn, fp: 127, 11
RF fn, tp: 4, 5
RF f1 score: 0.400
RF cohens kappa score: 0.349

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 5, 4
GB f1 score: 0.421
GB cohens kappa score: 0.381

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 6, 3
KNN f1 score: 0.462
KNN cohens kappa score: 0.440


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 102, 36
LR fn, tp: 8, 1
LR f1 score: 0.043
LR cohens kappa score: -0.061
LR average precision score: 0.084

-> test with 'RF'
RF tn, fp: 131, 7
RF fn, tp: 6, 3
RF f1 score: 0.316
RF cohens kappa score: 0.269

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 7, 2
GB f1 score: 0.250
GB cohens kappa score: 0.208

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 6, 3
LR f1 score: 0.462
LR cohens kappa score: 0.440
LR average precision score: 0.686

-> test with 'RF'
RF tn, fp: 136, 2
RF fn, tp: 7, 2
RF f1 score: 0.308
RF cohens kappa score: 0.281

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 123, 14
LR fn, tp: 2, 4
LR f1 score: 0.333
LR cohens kappa score: 0.289
LR average precision score: 0.304

-> test with 'RF'
RF tn, fp: 127, 10
RF fn, tp: 3, 3
RF f1 score: 0.316
RF cohens kappa score: 0.274

-> test with 'GB'
GB tn, fp: 126, 11
GB fn, tp: 3, 3
GB f1 score: 0.300
GB cohens kappa score: 0.256

-> test with 'KNN'
KNN tn, fp: 134, 3
KNN fn, tp: 4, 2
KNN f1 score: 0.364
KNN cohens kappa score: 0.338


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 129, 9
LR fn, tp: 5, 4
LR f1 score: 0.364
LR cohens kappa score: 0.314
LR average precision score: 0.305

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 7, 2
RF f1 score: 0.250
RF cohens kappa score: 0.208

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 8, 1
GB f1 score: 0.118
GB cohens kappa score: 0.064

-> test with 'KNN'
KNN tn, fp: 134, 4
KNN fn, tp: 6, 3
KNN f1 score: 0.375
KNN cohens kappa score: 0.340


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 123, 15
LR fn, tp: 6, 3
LR f1 score: 0.222
LR cohens kappa score: 0.153
LR average precision score: 0.283

-> test with 'RF'
RF tn, fp: 130, 8
RF fn, tp: 6, 3
RF f1 score: 0.300
RF cohens kappa score: 0.249

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 6, 3
GB f1 score: 0.316
GB cohens kappa score: 0.269

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 7, 2
KNN f1 score: 0.308
KNN cohens kappa score: 0.281


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 127, 11
LR fn, tp: 5, 4
LR f1 score: 0.333
LR cohens kappa score: 0.278
LR average precision score: 0.351

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 6, 3
RF f1 score: 0.353
RF cohens kappa score: 0.313

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 7, 2
GB f1 score: 0.267
GB cohens kappa score: 0.229

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 7, 2
KNN f1 score: 0.286
KNN cohens kappa score: 0.253


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 128, 10
LR fn, tp: 1, 8
LR f1 score: 0.593
LR cohens kappa score: 0.556
LR average precision score: 0.731

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 5, 4
RF f1 score: 0.444
RF cohens kappa score: 0.408

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 4, 5
GB f1 score: 0.667
GB cohens kappa score: 0.649

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 7, 2
KNN f1 score: 0.364
KNN cohens kappa score: 0.349


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 131, 6
LR fn, tp: 3, 3
LR f1 score: 0.400
LR cohens kappa score: 0.368
LR average precision score: 0.638

-> test with 'RF'
RF tn, fp: 137, 0
RF fn, tp: 3, 3
RF f1 score: 0.667
RF cohens kappa score: 0.657

-> test with 'GB'
GB tn, fp: 137, 0
GB fn, tp: 3, 3
GB f1 score: 0.667
GB cohens kappa score: 0.657

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 137, 43
LR fn, tp: 8, 8
LR f1 score: 0.593
LR cohens kappa score: 0.556
LR average precision score: 0.731


average:
LR tn, fp: 124.56, 13.24
LR fn, tp: 4.28, 4.12
LR f1 score: 0.341
LR cohens kappa score: 0.288
LR average precision score: 0.416


minimum:
LR tn, fp: 95, 1
LR fn, tp: 1, 1
LR f1 score: 0.043
LR cohens kappa score: -0.061
LR average precision score: 0.084


-----[ RF ]-----
maximum:
RF tn, fp: 137, 11
RF fn, tp: 7, 5
RF f1 score: 0.667
RF cohens kappa score: 0.657


average:
RF tn, fp: 132.68, 5.12
RF fn, tp: 5.12, 3.28
RF f1 score: 0.395
RF cohens kappa score: 0.359


minimum:
RF tn, fp: 127, 0
RF fn, tp: 3, 2
RF f1 score: 0.250
RF cohens kappa score: 0.208


-----[ GB ]-----
maximum:
GB tn, fp: 137, 11
GB fn, tp: 8, 5
GB f1 score: 0.667
GB cohens kappa score: 0.657


average:
GB tn, fp: 133.4, 4.4
GB fn, tp: 5.28, 3.12
GB f1 score: 0.396
GB cohens kappa score: 0.362


minimum:
GB tn, fp: 126, 0
GB fn, tp: 3, 1
GB f1 score: 0.118
GB cohens kappa score: 0.064


-----[ KNN ]-----
maximum:
KNN tn, fp: 138, 4
KNN fn, tp: 9, 4
KNN f1 score: 0.615
KNN cohens kappa score: 0.600


average:
KNN tn, fp: 136.44, 1.36
KNN fn, tp: 6.76, 1.64
KNN f1 score: 0.276
KNN cohens kappa score: 0.256


minimum:
KNN tn, fp: 134, 0
KNN fn, tp: 4, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023

  wall time: 00:04:04s, process time: 00:31:24s