ConvGeNBenchmarkResults/data_result/ctGAN/folding_hypothyroid.log

///////////////////////////////////////////
// Running ctGAN on folding_hypothyroid
///////////////////////////////////////////

Load 'data_input/folding_hypothyroid'
from pickle file
non empty cut in data_input/folding_hypothyroid! (1 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 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 484, 119
LR fn, tp: 7, 24
LR f1 score: 0.276
LR cohens kappa score: 0.213
LR average precision score: 0.246

-> test with 'RF'
RF tn, fp: 593, 10
RF fn, tp: 3, 28
RF f1 score: 0.812
RF cohens kappa score: 0.801

-> test with 'GB'
GB tn, fp: 589, 14
GB fn, tp: 2, 29
GB f1 score: 0.784
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 581, 22
KNN fn, tp: 7, 24
KNN f1 score: 0.623
KNN cohens kappa score: 0.600


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 489, 114
LR fn, tp: 9, 22
LR f1 score: 0.263
LR cohens kappa score: 0.200
LR average precision score: 0.169

-> test with 'RF'
RF tn, fp: 587, 16
RF fn, tp: 2, 29
RF f1 score: 0.763
RF cohens kappa score: 0.749

-> test with 'GB'
GB tn, fp: 584, 19
GB fn, tp: 2, 29
GB f1 score: 0.734
GB cohens kappa score: 0.717

-> test with 'KNN'
KNN tn, fp: 580, 23
KNN fn, tp: 4, 27
KNN f1 score: 0.667
KNN cohens kappa score: 0.645


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 481, 122
LR fn, tp: 7, 24
LR f1 score: 0.271
LR cohens kappa score: 0.207
LR average precision score: 0.200

-> test with 'RF'
RF tn, fp: 586, 17
RF fn, tp: 1, 30
RF f1 score: 0.769
RF cohens kappa score: 0.755

-> test with 'GB'
GB tn, fp: 585, 18
GB fn, tp: 1, 30
GB f1 score: 0.759
GB cohens kappa score: 0.744

-> test with 'KNN'
KNN tn, fp: 578, 25
KNN fn, tp: 12, 19
KNN f1 score: 0.507
KNN cohens kappa score: 0.477


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 498, 105
LR fn, tp: 6, 25
LR f1 score: 0.311
LR cohens kappa score: 0.251
LR average precision score: 0.198

-> test with 'RF'
RF tn, fp: 592, 11
RF fn, tp: 3, 28
RF f1 score: 0.800
RF cohens kappa score: 0.788

-> test with 'GB'
GB tn, fp: 589, 14
GB fn, tp: 3, 28
GB f1 score: 0.767
GB cohens kappa score: 0.753

-> test with 'KNN'
KNN tn, fp: 583, 20
KNN fn, tp: 9, 22
KNN f1 score: 0.603
KNN cohens kappa score: 0.579


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 492, 108
LR fn, tp: 5, 22
LR f1 score: 0.280
LR cohens kappa score: 0.225
LR average precision score: 0.286

-> test with 'RF'
RF tn, fp: 589, 11
RF fn, tp: 3, 24
RF f1 score: 0.774
RF cohens kappa score: 0.763

-> test with 'GB'
GB tn, fp: 587, 13
GB fn, tp: 3, 24
GB f1 score: 0.750
GB cohens kappa score: 0.737

-> test with 'KNN'
KNN tn, fp: 581, 19
KNN fn, tp: 8, 19
KNN f1 score: 0.585
KNN cohens kappa score: 0.563


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 476, 127
LR fn, tp: 7, 24
LR f1 score: 0.264
LR cohens kappa score: 0.199
LR average precision score: 0.194

-> test with 'RF'
RF tn, fp: 589, 14
RF fn, tp: 2, 29
RF f1 score: 0.784
RF cohens kappa score: 0.771

-> test with 'GB'
GB tn, fp: 587, 16
GB fn, tp: 2, 29
GB f1 score: 0.763
GB cohens kappa score: 0.749

-> test with 'KNN'
KNN tn, fp: 583, 20
KNN fn, tp: 10, 21
KNN f1 score: 0.583
KNN cohens kappa score: 0.559


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 494, 109
LR fn, tp: 7, 24
LR f1 score: 0.293
LR cohens kappa score: 0.232
LR average precision score: 0.260

-> test with 'RF'
RF tn, fp: 581, 22
RF fn, tp: 1, 30
RF f1 score: 0.723
RF cohens kappa score: 0.705

-> test with 'GB'
GB tn, fp: 586, 17
GB fn, tp: 0, 31
GB f1 score: 0.785
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 588, 15
KNN fn, tp: 4, 27
KNN f1 score: 0.740
KNN cohens kappa score: 0.724


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 511, 92
LR fn, tp: 4, 27
LR f1 score: 0.360
LR cohens kappa score: 0.306
LR average precision score: 0.289

-> test with 'RF'
RF tn, fp: 595, 8
RF fn, tp: 2, 29
RF f1 score: 0.853
RF cohens kappa score: 0.845

-> test with 'GB'
GB tn, fp: 589, 14
GB fn, tp: 2, 29
GB f1 score: 0.784
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 579, 24
KNN fn, tp: 8, 23
KNN f1 score: 0.590
KNN cohens kappa score: 0.564


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 509, 94
LR fn, tp: 11, 20
LR f1 score: 0.276
LR cohens kappa score: 0.216
LR average precision score: 0.191

-> test with 'RF'
RF tn, fp: 586, 17
RF fn, tp: 5, 26
RF f1 score: 0.703
RF cohens kappa score: 0.685

-> test with 'GB'
GB tn, fp: 585, 18
GB fn, tp: 5, 26
GB f1 score: 0.693
GB cohens kappa score: 0.675

-> test with 'KNN'
KNN tn, fp: 579, 24
KNN fn, tp: 11, 20
KNN f1 score: 0.533
KNN cohens kappa score: 0.505


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 498, 102
LR fn, tp: 5, 22
LR f1 score: 0.291
LR cohens kappa score: 0.237
LR average precision score: 0.218

-> test with 'RF'
RF tn, fp: 585, 15
RF fn, tp: 2, 25
RF f1 score: 0.746
RF cohens kappa score: 0.733

-> test with 'GB'
GB tn, fp: 587, 13
GB fn, tp: 2, 25
GB f1 score: 0.769
GB cohens kappa score: 0.757

-> test with 'KNN'
KNN tn, fp: 580, 20
KNN fn, tp: 5, 22
KNN f1 score: 0.638
KNN cohens kappa score: 0.618


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 498, 105
LR fn, tp: 7, 24
LR f1 score: 0.300
LR cohens kappa score: 0.240
LR average precision score: 0.221

-> test with 'RF'
RF tn, fp: 594, 9
RF fn, tp: 0, 31
RF f1 score: 0.873
RF cohens kappa score: 0.866

-> test with 'GB'
GB tn, fp: 593, 10
GB fn, tp: 1, 30
GB f1 score: 0.845
GB cohens kappa score: 0.836

-> test with 'KNN'
KNN tn, fp: 594, 9
KNN fn, tp: 10, 21
KNN f1 score: 0.689
KNN cohens kappa score: 0.673


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 508, 95
LR fn, tp: 8, 23
LR f1 score: 0.309
LR cohens kappa score: 0.251
LR average precision score: 0.212

-> test with 'RF'
RF tn, fp: 580, 23
RF fn, tp: 3, 28
RF f1 score: 0.683
RF cohens kappa score: 0.662

-> test with 'GB'
GB tn, fp: 576, 27
GB fn, tp: 3, 28
GB f1 score: 0.651
GB cohens kappa score: 0.628

-> test with 'KNN'
KNN tn, fp: 570, 33
KNN fn, tp: 6, 25
KNN f1 score: 0.562
KNN cohens kappa score: 0.532


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 494, 109
LR fn, tp: 5, 26
LR f1 score: 0.313
LR cohens kappa score: 0.254
LR average precision score: 0.306

-> test with 'RF'
RF tn, fp: 585, 18
RF fn, tp: 6, 25
RF f1 score: 0.676
RF cohens kappa score: 0.656

-> test with 'GB'
GB tn, fp: 581, 22
GB fn, tp: 5, 26
GB f1 score: 0.658
GB cohens kappa score: 0.637

-> test with 'KNN'
KNN tn, fp: 575, 28
KNN fn, tp: 10, 21
KNN f1 score: 0.525
KNN cohens kappa score: 0.495


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 512, 91
LR fn, tp: 6, 25
LR f1 score: 0.340
LR cohens kappa score: 0.285
LR average precision score: 0.307

-> test with 'RF'
RF tn, fp: 588, 15
RF fn, tp: 2, 29
RF f1 score: 0.773
RF cohens kappa score: 0.760

-> test with 'GB'
GB tn, fp: 588, 15
GB fn, tp: 3, 28
GB f1 score: 0.757
GB cohens kappa score: 0.742

-> test with 'KNN'
KNN tn, fp: 575, 28
KNN fn, tp: 7, 24
KNN f1 score: 0.578
KNN cohens kappa score: 0.551


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 491, 109
LR fn, tp: 6, 21
LR f1 score: 0.268
LR cohens kappa score: 0.211
LR average precision score: 0.199

-> test with 'RF'
RF tn, fp: 582, 18
RF fn, tp: 1, 26
RF f1 score: 0.732
RF cohens kappa score: 0.717

-> test with 'GB'
GB tn, fp: 583, 17
GB fn, tp: 1, 26
GB f1 score: 0.743
GB cohens kappa score: 0.728

-> test with 'KNN'
KNN tn, fp: 582, 18
KNN fn, tp: 4, 23
KNN f1 score: 0.676
KNN cohens kappa score: 0.659


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 501, 102
LR fn, tp: 10, 21
LR f1 score: 0.273
LR cohens kappa score: 0.211
LR average precision score: 0.178

-> test with 'RF'
RF tn, fp: 587, 16
RF fn, tp: 4, 27
RF f1 score: 0.730
RF cohens kappa score: 0.713

-> test with 'GB'
GB tn, fp: 581, 22
GB fn, tp: 3, 28
GB f1 score: 0.691
GB cohens kappa score: 0.672

-> test with 'KNN'
KNN tn, fp: 585, 18
KNN fn, tp: 9, 22
KNN f1 score: 0.620
KNN cohens kappa score: 0.598


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 492, 111
LR fn, tp: 7, 24
LR f1 score: 0.289
LR cohens kappa score: 0.228
LR average precision score: 0.209

-> test with 'RF'
RF tn, fp: 594, 9
RF fn, tp: 2, 29
RF f1 score: 0.841
RF cohens kappa score: 0.832

-> test with 'GB'
GB tn, fp: 591, 12
GB fn, tp: 1, 30
GB f1 score: 0.822
GB cohens kappa score: 0.811

-> test with 'KNN'
KNN tn, fp: 588, 15
KNN fn, tp: 8, 23
KNN f1 score: 0.667
KNN cohens kappa score: 0.648


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 487, 116
LR fn, tp: 4, 27
LR f1 score: 0.310
LR cohens kappa score: 0.250
LR average precision score: 0.270

-> test with 'RF'
RF tn, fp: 591, 12
RF fn, tp: 4, 27
RF f1 score: 0.771
RF cohens kappa score: 0.758

-> test with 'GB'
GB tn, fp: 590, 13
GB fn, tp: 3, 28
GB f1 score: 0.778
GB cohens kappa score: 0.765

-> test with 'KNN'
KNN tn, fp: 582, 21
KNN fn, tp: 6, 25
KNN f1 score: 0.649
KNN cohens kappa score: 0.628


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 479, 124
LR fn, tp: 5, 26
LR f1 score: 0.287
LR cohens kappa score: 0.224
LR average precision score: 0.212

-> test with 'RF'
RF tn, fp: 589, 14
RF fn, tp: 1, 30
RF f1 score: 0.800
RF cohens kappa score: 0.788

-> test with 'GB'
GB tn, fp: 587, 16
GB fn, tp: 0, 31
GB f1 score: 0.795
GB cohens kappa score: 0.782

-> test with 'KNN'
KNN tn, fp: 571, 32
KNN fn, tp: 9, 22
KNN f1 score: 0.518
KNN cohens kappa score: 0.486


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 506, 94
LR fn, tp: 7, 20
LR f1 score: 0.284
LR cohens kappa score: 0.230
LR average precision score: 0.237

-> test with 'RF'
RF tn, fp: 583, 17
RF fn, tp: 4, 23
RF f1 score: 0.687
RF cohens kappa score: 0.670

-> test with 'GB'
GB tn, fp: 580, 20
GB fn, tp: 4, 23
GB f1 score: 0.657
GB cohens kappa score: 0.638

-> test with 'KNN'
KNN tn, fp: 573, 27
KNN fn, tp: 5, 22
KNN f1 score: 0.579
KNN cohens kappa score: 0.554


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 483, 120
LR fn, tp: 7, 24
LR f1 score: 0.274
LR cohens kappa score: 0.211
LR average precision score: 0.186

-> test with 'RF'
RF tn, fp: 590, 13
RF fn, tp: 2, 29
RF f1 score: 0.795
RF cohens kappa score: 0.782

-> test with 'GB'
GB tn, fp: 589, 14
GB fn, tp: 2, 29
GB f1 score: 0.784
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 581, 22
KNN fn, tp: 7, 24
KNN f1 score: 0.623
KNN cohens kappa score: 0.600


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 507, 96
LR fn, tp: 8, 23
LR f1 score: 0.307
LR cohens kappa score: 0.248
LR average precision score: 0.229

-> test with 'RF'
RF tn, fp: 590, 13
RF fn, tp: 2, 29
RF f1 score: 0.795
RF cohens kappa score: 0.782

-> test with 'GB'
GB tn, fp: 585, 18
GB fn, tp: 2, 29
GB f1 score: 0.744
GB cohens kappa score: 0.728

-> test with 'KNN'
KNN tn, fp: 580, 23
KNN fn, tp: 9, 22
KNN f1 score: 0.579
KNN cohens kappa score: 0.553


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 456, 147
LR fn, tp: 3, 28
LR f1 score: 0.272
LR cohens kappa score: 0.206
LR average precision score: 0.241

-> test with 'RF'
RF tn, fp: 592, 11
RF fn, tp: 6, 25
RF f1 score: 0.746
RF cohens kappa score: 0.732

-> test with 'GB'
GB tn, fp: 582, 21
GB fn, tp: 4, 27
GB f1 score: 0.684
GB cohens kappa score: 0.664

-> test with 'KNN'
KNN tn, fp: 580, 23
KNN fn, tp: 7, 24
KNN f1 score: 0.615
KNN cohens kappa score: 0.591


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 499, 104
LR fn, tp: 4, 27
LR f1 score: 0.333
LR cohens kappa score: 0.276
LR average precision score: 0.277

-> test with 'RF'
RF tn, fp: 585, 18
RF fn, tp: 0, 31
RF f1 score: 0.775
RF cohens kappa score: 0.761

-> test with 'GB'
GB tn, fp: 586, 17
GB fn, tp: 0, 31
GB f1 score: 0.785
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 581, 22
KNN fn, tp: 7, 24
KNN f1 score: 0.623
KNN cohens kappa score: 0.600


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 523, 77
LR fn, tp: 9, 18
LR f1 score: 0.295
LR cohens kappa score: 0.244
LR average precision score: 0.165

-> test with 'RF'
RF tn, fp: 583, 17
RF fn, tp: 3, 24
RF f1 score: 0.706
RF cohens kappa score: 0.690

-> test with 'GB'
GB tn, fp: 585, 15
GB fn, tp: 3, 24
GB f1 score: 0.727
GB cohens kappa score: 0.713

-> test with 'KNN'
KNN tn, fp: 578, 22
KNN fn, tp: 9, 18
KNN f1 score: 0.537
KNN cohens kappa score: 0.512

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 523, 147
LR fn, tp: 11, 28
LR f1 score: 0.360
LR cohens kappa score: 0.306
LR average precision score: 0.307


average:
LR tn, fp: 494.72, 107.68
LR fn, tp: 6.56, 23.64
LR f1 score: 0.294
LR cohens kappa score: 0.234
LR average precision score: 0.228


minimum:
LR tn, fp: 456, 77
LR fn, tp: 3, 18
LR f1 score: 0.263
LR cohens kappa score: 0.199
LR average precision score: 0.165


-----[ RF ]-----
maximum:
RF tn, fp: 595, 23
RF fn, tp: 6, 31
RF f1 score: 0.873
RF cohens kappa score: 0.866


average:
RF tn, fp: 587.84, 14.56
RF fn, tp: 2.56, 27.64
RF f1 score: 0.764
RF cohens kappa score: 0.750


minimum:
RF tn, fp: 580, 8
RF fn, tp: 0, 23
RF f1 score: 0.676
RF cohens kappa score: 0.656


-----[ GB ]-----
maximum:
GB tn, fp: 593, 27
GB fn, tp: 5, 31
GB f1 score: 0.845
GB cohens kappa score: 0.836


average:
GB tn, fp: 585.8, 16.6
GB fn, tp: 2.28, 27.92
GB f1 score: 0.748
GB cohens kappa score: 0.733


minimum:
GB tn, fp: 576, 10
GB fn, tp: 0, 23
GB f1 score: 0.651
GB cohens kappa score: 0.628


-----[ KNN ]-----
maximum:
KNN tn, fp: 594, 33
KNN fn, tp: 12, 27
KNN f1 score: 0.740
KNN cohens kappa score: 0.724


average:
KNN tn, fp: 580.28, 22.12
KNN fn, tp: 7.64, 22.56
KNN f1 score: 0.604
KNN cohens kappa score: 0.580


minimum:
KNN tn, fp: 570, 9
KNN fn, tp: 4, 18
KNN f1 score: 0.507
KNN cohens kappa score: 0.477