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: 0.256

-> test with 'GB'
GB tn, fp: 587, 16
GB fn, tp: 3, 28
GB f1 score: 0.747
GB cohens kappa score: 0.731

-> test with 'KNN'
KNN tn, fp: 584, 19
KNN fn, tp: 6, 25
KNN f1 score: 0.667
KNN cohens kappa score: 0.646


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 487, 116
LR fn, tp: 9, 22
LR f1 score: 0.260
LR cohens kappa score: 0.196
LR average precision score: 0.191

-> test with 'GB'
GB tn, fp: 582, 21
GB fn, tp: 2, 29
GB f1 score: 0.716
GB cohens kappa score: 0.698

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


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 493, 110
LR fn, tp: 8, 23
LR f1 score: 0.280
LR cohens kappa score: 0.219
LR average precision score: 0.202

-> test with 'GB'
GB tn, fp: 587, 16
GB fn, tp: 1, 30
GB f1 score: 0.779
GB cohens kappa score: 0.766

-> 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 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 507, 96
LR fn, tp: 7, 24
LR f1 score: 0.318
LR cohens kappa score: 0.260
LR average precision score: 0.196

-> test with 'GB'
GB tn, fp: 582, 21
GB fn, tp: 3, 28
GB f1 score: 0.700
GB cohens kappa score: 0.681

-> 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: 0.295

-> test with 'GB'
GB tn, fp: 586, 14
GB fn, tp: 3, 24
GB f1 score: 0.738
GB cohens kappa score: 0.725

-> 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: 482, 121
LR fn, tp: 8, 23
LR f1 score: 0.263
LR cohens kappa score: 0.198
LR average precision score: 0.206

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

-> 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: 0.251

-> test with 'GB'
GB tn, fp: 589, 14
GB fn, tp: 0, 31
GB f1 score: 0.816
GB cohens kappa score: 0.804

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


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 500, 103
LR fn, tp: 3, 28
LR f1 score: 0.346
LR cohens kappa score: 0.289
LR average precision score: 0.284

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

-> 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 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 510, 93
LR fn, tp: 12, 19
LR f1 score: 0.266
LR cohens kappa score: 0.205
LR average precision score: 0.192

-> test with 'GB'
GB tn, fp: 582, 21
GB fn, tp: 5, 26
GB f1 score: 0.667
GB cohens kappa score: 0.646

-> test with 'KNN'
KNN tn, fp: 583, 20
KNN fn, tp: 11, 20
KNN f1 score: 0.563
KNN cohens kappa score: 0.538


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 489, 111
LR fn, tp: 4, 23
LR f1 score: 0.286
LR cohens kappa score: 0.231
LR average precision score: 0.185

-> test with 'GB'
GB tn, fp: 579, 21
GB fn, tp: 3, 24
GB f1 score: 0.667
GB cohens kappa score: 0.648

-> test with 'KNN'
KNN tn, fp: 577, 23
KNN fn, tp: 4, 23
KNN f1 score: 0.630
KNN cohens kappa score: 0.609


====== 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: 501, 102
LR fn, tp: 8, 23
LR f1 score: 0.295
LR cohens kappa score: 0.235
LR average precision score: 0.222

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 0, 31
GB f1 score: 0.886
GB cohens kappa score: 0.879

-> test with 'KNN'
KNN tn, fp: 594, 9
KNN fn, tp: 9, 22
KNN f1 score: 0.710
KNN cohens kappa score: 0.695


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 513, 90
LR fn, tp: 9, 22
LR f1 score: 0.308
LR cohens kappa score: 0.250
LR average precision score: 0.206

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

-> test with 'KNN'
KNN tn, fp: 564, 39
KNN fn, tp: 6, 25
KNN f1 score: 0.526
KNN cohens kappa score: 0.493


------ Step 3/5: Slice 3/5 -------
-> 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: 0.265

-> test with 'GB'
GB tn, fp: 583, 20
GB fn, tp: 5, 26
GB f1 score: 0.675
GB cohens kappa score: 0.655

-> test with 'KNN'
KNN tn, fp: 576, 27
KNN fn, tp: 10, 21
KNN f1 score: 0.532
KNN cohens kappa score: 0.502


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 492, 111
LR fn, tp: 4, 27
LR f1 score: 0.320
LR cohens kappa score: 0.260
LR average precision score: 0.284

-> test with 'GB'
GB tn, fp: 583, 20
GB fn, tp: 3, 28
GB f1 score: 0.709
GB cohens kappa score: 0.690

-> test with 'KNN'
KNN tn, fp: 574, 29
KNN fn, tp: 7, 24
KNN f1 score: 0.571
KNN cohens kappa score: 0.543


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 496, 104
LR fn, tp: 6, 21
LR f1 score: 0.276
LR cohens kappa score: 0.221
LR average precision score: 0.189

-> test with 'GB'
GB tn, fp: 589, 11
GB fn, tp: 1, 26
GB f1 score: 0.812
GB cohens kappa score: 0.803

-> test with 'KNN'
KNN tn, fp: 582, 18
KNN fn, tp: 3, 24
KNN f1 score: 0.696
KNN cohens kappa score: 0.679


====== 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: 492, 111
LR fn, tp: 10, 21
LR f1 score: 0.258
LR cohens kappa score: 0.194
LR average precision score: 0.189

-> test with 'GB'
GB tn, fp: 584, 19
GB fn, tp: 3, 28
GB f1 score: 0.718
GB cohens kappa score: 0.700

-> 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: 481, 122
LR fn, tp: 8, 23
LR f1 score: 0.261
LR cohens kappa score: 0.197
LR average precision score: 0.200

-> 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: 589, 14
KNN fn, tp: 7, 24
KNN f1 score: 0.696
KNN cohens kappa score: 0.678


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 483, 120
LR fn, tp: 3, 28
LR f1 score: 0.313
LR cohens kappa score: 0.252
LR average precision score: 0.295

-> test with 'GB'
GB tn, fp: 589, 14
GB fn, tp: 4, 27
GB f1 score: 0.750
GB cohens kappa score: 0.735

-> test with 'KNN'
KNN tn, fp: 583, 20
KNN fn, tp: 6, 25
KNN f1 score: 0.658
KNN cohens kappa score: 0.637


------ 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: 8, 23
LR f1 score: 0.258
LR cohens kappa score: 0.193
LR average precision score: 0.188

-> test with 'GB'
GB tn, fp: 586, 17
GB fn, tp: 2, 29
GB f1 score: 0.753
GB cohens kappa score: 0.738

-> test with 'KNN'
KNN tn, fp: 569, 34
KNN fn, tp: 10, 21
KNN f1 score: 0.488
KNN cohens kappa score: 0.454


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 510, 90
LR fn, tp: 8, 19
LR f1 score: 0.279
LR cohens kappa score: 0.226
LR average precision score: 0.226

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

-> test with 'KNN'
KNN tn, fp: 575, 25
KNN fn, tp: 6, 21
KNN f1 score: 0.575
KNN cohens kappa score: 0.551


====== 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: 487, 116
LR fn, tp: 6, 25
LR f1 score: 0.291
LR cohens kappa score: 0.229
LR average precision score: 0.180

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

-> test with 'KNN'
KNN tn, fp: 580, 23
KNN fn, tp: 6, 25
KNN f1 score: 0.633
KNN cohens kappa score: 0.610


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 509, 94
LR fn, tp: 8, 23
LR f1 score: 0.311
LR cohens kappa score: 0.253
LR average precision score: 0.252

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

-> test with 'KNN'
KNN tn, fp: 581, 22
KNN fn, tp: 9, 22
KNN f1 score: 0.587
KNN cohens kappa score: 0.562


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 467, 136
LR fn, tp: 4, 27
LR f1 score: 0.278
LR cohens kappa score: 0.214
LR average precision score: 0.240

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

-> 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: 0.254

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

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 519, 81
LR fn, tp: 10, 17
LR f1 score: 0.272
LR cohens kappa score: 0.219
LR average precision score: 0.169

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

-> test with 'KNN'
KNN tn, fp: 575, 25
KNN fn, tp: 5, 22
KNN f1 score: 0.595
KNN cohens kappa score: 0.571

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 519, 136
LR fn, tp: 12, 28
LR f1 score: 0.346
LR cohens kappa score: 0.289
LR average precision score: 0.295


average:
LR tn, fp: 494.56, 107.84
LR fn, tp: 6.92, 23.28
LR f1 score: 0.289
LR cohens kappa score: 0.229
LR average precision score: 0.225


minimum:
LR tn, fp: 467, 81
LR fn, tp: 3, 17
LR f1 score: 0.258
LR cohens kappa score: 0.193
LR average precision score: 0.169


-----[ GB ]-----
maximum:
GB tn, fp: 595, 23
GB fn, tp: 5, 31
GB f1 score: 0.886
GB cohens kappa score: 0.879


average:
GB tn, fp: 585.6, 16.8
GB fn, tp: 2.52, 27.68
GB f1 score: 0.742
GB cohens kappa score: 0.727


minimum:
GB tn, fp: 579, 8
GB fn, tp: 0, 22
GB f1 score: 0.638
GB cohens kappa score: 0.618


-----[ KNN ]-----
maximum:
KNN tn, fp: 594, 39
KNN fn, tp: 11, 27
KNN f1 score: 0.730
KNN cohens kappa score: 0.713


average:
KNN tn, fp: 580.12, 22.28
KNN fn, tp: 7.32, 22.88
KNN f1 score: 0.610
KNN cohens kappa score: 0.586


minimum:
KNN tn, fp: 564, 9
KNN fn, tp: 3, 19
KNN f1 score: 0.488
KNN cohens kappa score: 0.454