ConvGeNBenchmarkResults/data_result/CTGAN/folding_abalone9-18.log

///////////////////////////////////////////
// Running ctGAN on folding_abalone9-18
///////////////////////////////////////////

Load 'data_input/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: 131, 7
LR fn, tp: 7, 2
LR f1 score: 0.222
LR cohens kappa score: 0.171
LR average precision score: 0.308

-> 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: 136, 2
GB fn, tp: 5, 4
GB f1 score: 0.533
GB cohens kappa score: 0.509

-> 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: 134, 4
LR fn, tp: 7, 2
LR f1 score: 0.267
LR cohens kappa score: 0.229
LR average precision score: 0.340

-> 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: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.312

-> 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: 134, 4
LR fn, tp: 5, 4
LR f1 score: 0.471
LR cohens kappa score: 0.438
LR average precision score: 0.575

-> 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: 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: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ Step 1/5: Slice 4/5 -------
-> 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: 0.377

-> 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: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> 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 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 3
LR fn, tp: 4, 2
LR f1 score: 0.364
LR cohens kappa score: 0.338
LR average precision score: 0.529

-> test with 'RF'
RF tn, fp: 137, 0
RF fn, tp: 4, 2
RF f1 score: 0.500
RF cohens kappa score: 0.489

-> 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: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277


====== 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: 137, 1
LR fn, tp: 6, 3
LR f1 score: 0.462
LR cohens kappa score: 0.440
LR average precision score: 0.472

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

-> 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: 121, 17
LR fn, tp: 6, 3
LR f1 score: 0.207
LR cohens kappa score: 0.134
LR average precision score: 0.087

-> test with 'RF'
RF tn, fp: 131, 7
RF fn, tp: 5, 4
RF f1 score: 0.400
RF cohens kappa score: 0.357

-> 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: 138, 0
KNN fn, tp: 7, 2
KNN f1 score: 0.364
KNN cohens kappa score: 0.349


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 112, 26
LR fn, tp: 6, 3
LR f1 score: 0.158
LR cohens kappa score: 0.071
LR average precision score: 0.194

-> 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: 134, 4
GB fn, tp: 6, 3
GB f1 score: 0.375
GB cohens kappa score: 0.340

-> 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 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 4, 5
LR f1 score: 0.625
LR cohens kappa score: 0.604
LR average precision score: 0.709

-> 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: 134, 4
GB fn, tp: 5, 4
GB f1 score: 0.471
GB cohens kappa score: 0.438

-> 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 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: 3, 3
LR f1 score: 0.353
LR cohens kappa score: 0.316
LR average precision score: 0.588

-> test with 'RF'
RF tn, fp: 132, 5
RF fn, tp: 3, 3
RF f1 score: 0.429
RF cohens kappa score: 0.400

-> test with 'GB'
GB tn, fp: 134, 3
GB fn, tp: 3, 3
GB f1 score: 0.500
GB cohens kappa score: 0.478

-> 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: 133, 5
LR fn, tp: 8, 1
LR f1 score: 0.133
LR cohens kappa score: 0.089
LR average precision score: 0.195

-> 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: 136, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.140

-> 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 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 6, 3
LR f1 score: 0.429
LR cohens kappa score: 0.402
LR average precision score: 0.545

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

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

-> 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: 7, 2
LR f1 score: 0.174
LR cohens kappa score: 0.107
LR average precision score: 0.201

-> 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: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> 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 4/5 -------
-> 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: 0.347

-> 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: 135, 3
GB fn, tp: 5, 4
GB f1 score: 0.500
GB cohens kappa score: 0.472

-> 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 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 1
LR fn, tp: 3, 3
LR f1 score: 0.600
LR cohens kappa score: 0.586
LR average precision score: 0.587

-> test with 'RF'
RF tn, fp: 135, 2
RF fn, tp: 4, 2
RF f1 score: 0.400
RF cohens kappa score: 0.379

-> test with 'GB'
GB tn, fp: 134, 3
GB fn, tp: 3, 3
GB f1 score: 0.500
GB cohens kappa score: 0.478

-> 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 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: 135, 3
LR fn, tp: 6, 3
LR f1 score: 0.400
LR cohens kappa score: 0.369
LR average precision score: 0.352

-> 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: 135, 3
GB fn, tp: 5, 4
GB f1 score: 0.500
GB cohens kappa score: 0.472

-> 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 4/5: 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: 4, 5
LR f1 score: 0.500
LR cohens kappa score: 0.464
LR average precision score: 0.561

-> 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: 134, 4
GB fn, tp: 5, 4
GB f1 score: 0.471
GB cohens kappa score: 0.438

-> 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 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 3
LR fn, tp: 5, 4
LR f1 score: 0.500
LR cohens kappa score: 0.472
LR average precision score: 0.343

-> 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: 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: 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: 134, 4
LR fn, tp: 6, 3
LR f1 score: 0.375
LR cohens kappa score: 0.340
LR average precision score: 0.471

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

-> 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: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 122, 15
LR fn, tp: 4, 2
LR f1 score: 0.174
LR cohens kappa score: 0.119
LR average precision score: 0.119

-> test with 'RF'
RF tn, fp: 128, 9
RF fn, tp: 2, 4
RF f1 score: 0.421
RF cohens kappa score: 0.386

-> test with 'GB'
GB tn, fp: 128, 9
GB fn, tp: 6, 0
GB f1 score: 0.000
GB cohens kappa score: -0.053

-> test with 'KNN'
KNN tn, fp: 134, 3
KNN fn, tp: 5, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.172


====== 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: 118, 20
LR fn, tp: 3, 6
LR f1 score: 0.343
LR cohens kappa score: 0.277
LR average precision score: 0.335

-> test with 'RF'
RF tn, fp: 128, 10
RF fn, tp: 6, 3
RF f1 score: 0.273
RF cohens kappa score: 0.216

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

-> 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 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 122, 16
LR fn, tp: 6, 3
LR f1 score: 0.214
LR cohens kappa score: 0.143
LR average precision score: 0.338

-> 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: 136, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.281

-> 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 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 124, 14
LR fn, tp: 5, 4
LR f1 score: 0.296
LR cohens kappa score: 0.234
LR average precision score: 0.274

-> 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: 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 5/5: 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: 4, 5
LR f1 score: 0.667
LR cohens kappa score: 0.649
LR average precision score: 0.767

-> test with 'RF'
RF tn, fp: 135, 3
RF fn, tp: 5, 4
RF f1 score: 0.500
RF cohens kappa score: 0.472

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

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 132, 5
LR fn, tp: 6, 0
LR f1 score: 0.000
LR cohens kappa score: -0.040
LR average precision score: 0.032

-> test with 'RF'
RF tn, fp: 136, 1
RF fn, tp: 3, 3
RF f1 score: 0.600
RF cohens kappa score: 0.586

-> 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, 26
LR fn, tp: 8, 6
LR f1 score: 0.667
LR cohens kappa score: 0.649
LR average precision score: 0.767


average:
LR tn, fp: 129.2, 8.6
LR fn, tp: 5.2, 3.2
LR f1 score: 0.341
LR cohens kappa score: 0.296
LR average precision score: 0.386


minimum:
LR tn, fp: 112, 1
LR fn, tp: 3, 0
LR f1 score: 0.000
LR cohens kappa score: -0.040
LR average precision score: 0.032


-----[ RF ]-----
maximum:
RF tn, fp: 137, 10
RF fn, tp: 8, 4
RF f1 score: 0.600
RF cohens kappa score: 0.586


average:
RF tn, fp: 133.92, 3.88
RF fn, tp: 5.4, 3.0
RF f1 score: 0.398
RF cohens kappa score: 0.366


minimum:
RF tn, fp: 128, 0
RF fn, tp: 2, 1
RF f1 score: 0.167
RF cohens kappa score: 0.140


-----[ GB ]-----
maximum:
GB tn, fp: 137, 9
GB fn, tp: 8, 4
GB f1 score: 0.667
GB cohens kappa score: 0.657


average:
GB tn, fp: 134.88, 2.92
GB fn, tp: 5.6, 2.8
GB f1 score: 0.398
GB cohens kappa score: 0.370


minimum:
GB tn, fp: 128, 0
GB fn, tp: 3, 0
GB f1 score: 0.000
GB cohens kappa score: -0.053


-----[ KNN ]-----
maximum:
KNN tn, fp: 138, 3
KNN fn, tp: 9, 3
KNN f1 score: 0.500
KNN cohens kappa score: 0.484


average:
KNN tn, fp: 137.12, 0.68
KNN fn, tp: 7.32, 1.08
KNN f1 score: 0.212
KNN cohens kappa score: 0.197


minimum:
KNN tn, fp: 134, 0
KNN fn, tp: 5, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023