ConvGeNBenchmarkResults/data_result/CTGAN/folding_hypothyroid.log

///////////////////////////////////////////
// Running CTGAN on folding_hypothyroid
///////////////////////////////////////////

Load 'folding_hypothyroid'
from pickle file
non empty cut in 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: 503, 100
LR fn, tp: 3, 28
LR f1 score: 0.352
LR cohens kappa score: 0.297
LR average precision score: 0.346

-> 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: 593, 10
GB fn, tp: 2, 29
GB f1 score: 0.829
GB cohens kappa score: 0.819

-> 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: 481, 122
LR fn, tp: 2, 29
LR f1 score: 0.319
LR cohens kappa score: 0.259
LR average precision score: 0.299

-> test with 'RF'
RF tn, fp: 584, 19
RF fn, tp: 1, 30
RF f1 score: 0.750
RF cohens kappa score: 0.734

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

-> test with 'KNN'
KNN tn, fp: 578, 25
KNN fn, tp: 4, 27
KNN f1 score: 0.651
KNN cohens kappa score: 0.628


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 480, 123
LR fn, tp: 6, 25
LR f1 score: 0.279
LR cohens kappa score: 0.216
LR average precision score: 0.215

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

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

-> test with 'KNN'
KNN tn, fp: 580, 23
KNN fn, tp: 10, 21
KNN f1 score: 0.560
KNN cohens kappa score: 0.533


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 489, 114
LR fn, tp: 2, 29
LR f1 score: 0.333
LR cohens kappa score: 0.275
LR average precision score: 0.262

-> test with 'RF'
RF tn, fp: 590, 13
RF fn, tp: 4, 27
RF f1 score: 0.761
RF cohens kappa score: 0.747

-> test with 'GB'
GB tn, fp: 591, 12
GB fn, tp: 3, 28
GB f1 score: 0.789
GB cohens kappa score: 0.776

-> test with 'KNN'
KNN tn, fp: 582, 21
KNN fn, tp: 10, 21
KNN f1 score: 0.575
KNN cohens kappa score: 0.550


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 484, 116
LR fn, tp: 3, 24
LR f1 score: 0.287
LR cohens kappa score: 0.232
LR average precision score: 0.391

-> test with 'RF'
RF tn, fp: 590, 10
RF fn, tp: 4, 23
RF f1 score: 0.767
RF cohens kappa score: 0.755

-> test with 'GB'
GB tn, fp: 588, 12
GB fn, tp: 4, 23
GB f1 score: 0.742
GB cohens kappa score: 0.729

-> test with 'KNN'
KNN tn, fp: 585, 15
KNN fn, tp: 7, 20
KNN f1 score: 0.645
KNN cohens kappa score: 0.627


====== 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: 500, 103
LR fn, tp: 4, 27
LR f1 score: 0.335
LR cohens kappa score: 0.278
LR average precision score: 0.331

-> 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: 591, 12
GB fn, tp: 2, 29
GB f1 score: 0.806
GB cohens kappa score: 0.794

-> test with 'KNN'
KNN tn, fp: 586, 17
KNN fn, tp: 6, 25
KNN f1 score: 0.685
KNN cohens kappa score: 0.666


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 500, 103
LR fn, tp: 7, 24
LR f1 score: 0.304
LR cohens kappa score: 0.244
LR average precision score: 0.328

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

-> 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: 586, 17
KNN fn, tp: 4, 27
KNN f1 score: 0.720
KNN cohens kappa score: 0.703


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 472, 131
LR fn, tp: 0, 31
LR f1 score: 0.321
LR cohens kappa score: 0.261
LR average precision score: 0.436

-> 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: 588, 15
GB fn, tp: 4, 27
GB f1 score: 0.740
GB cohens kappa score: 0.724

-> test with 'KNN'
KNN tn, fp: 579, 24
KNN fn, tp: 9, 22
KNN f1 score: 0.571
KNN cohens kappa score: 0.545


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 489, 114
LR fn, tp: 7, 24
LR f1 score: 0.284
LR cohens kappa score: 0.222
LR average precision score: 0.189

-> test with 'RF'
RF tn, fp: 594, 9
RF fn, tp: 5, 26
RF f1 score: 0.788
RF cohens kappa score: 0.776

-> test with 'GB'
GB tn, fp: 589, 14
GB fn, tp: 5, 26
GB f1 score: 0.732
GB cohens kappa score: 0.717

-> 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 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 477, 123
LR fn, tp: 1, 26
LR f1 score: 0.295
LR cohens kappa score: 0.240
LR average precision score: 0.339

-> test with 'RF'
RF tn, fp: 586, 14
RF fn, tp: 2, 25
RF f1 score: 0.758
RF cohens kappa score: 0.745

-> 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: 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: 488, 115
LR fn, tp: 5, 26
LR f1 score: 0.302
LR cohens kappa score: 0.242
LR average precision score: 0.345

-> test with 'RF'
RF tn, fp: 596, 7
RF fn, tp: 0, 31
RF f1 score: 0.899
RF cohens kappa score: 0.893

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 1, 30
GB f1 score: 0.923
GB cohens kappa score: 0.919

-> test with 'KNN'
KNN tn, fp: 593, 10
KNN fn, tp: 10, 21
KNN f1 score: 0.677
KNN cohens kappa score: 0.661


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 500, 103
LR fn, tp: 5, 26
LR f1 score: 0.325
LR cohens kappa score: 0.267
LR average precision score: 0.215

-> 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: 584, 19
GB fn, tp: 3, 28
GB f1 score: 0.718
GB cohens kappa score: 0.700

-> 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: 491, 112
LR fn, tp: 3, 28
LR f1 score: 0.327
LR cohens kappa score: 0.269
LR average precision score: 0.410

-> test with 'RF'
RF tn, fp: 584, 19
RF fn, tp: 4, 27
RF f1 score: 0.701
RF cohens kappa score: 0.683

-> test with 'GB'
GB tn, fp: 583, 20
GB fn, tp: 4, 27
GB f1 score: 0.692
GB cohens kappa score: 0.673

-> test with 'KNN'
KNN tn, fp: 578, 25
KNN fn, tp: 10, 21
KNN f1 score: 0.545
KNN cohens kappa score: 0.517


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 490, 113
LR fn, tp: 2, 29
LR f1 score: 0.335
LR cohens kappa score: 0.277
LR average precision score: 0.382

-> 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: 588, 15
GB fn, tp: 2, 29
GB f1 score: 0.773
GB cohens kappa score: 0.760

-> test with 'KNN'
KNN tn, fp: 580, 23
KNN fn, tp: 8, 23
KNN f1 score: 0.597
KNN cohens kappa score: 0.572


------ 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: 2, 25
LR f1 score: 0.311
LR cohens kappa score: 0.257
LR average precision score: 0.210

-> test with 'RF'
RF tn, fp: 587, 13
RF fn, tp: 1, 26
RF f1 score: 0.788
RF cohens kappa score: 0.777

-> test with 'GB'
GB tn, fp: 588, 12
GB fn, tp: 1, 26
GB f1 score: 0.800
GB cohens kappa score: 0.789

-> test with 'KNN'
KNN tn, fp: 581, 19
KNN fn, tp: 3, 24
KNN f1 score: 0.686
KNN cohens kappa score: 0.668


====== 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: 502, 101
LR fn, tp: 6, 25
LR f1 score: 0.318
LR cohens kappa score: 0.260
LR average precision score: 0.273

-> test with 'RF'
RF tn, fp: 585, 18
RF fn, tp: 4, 27
RF f1 score: 0.711
RF cohens kappa score: 0.693

-> 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: 585, 18
KNN fn, tp: 8, 23
KNN f1 score: 0.639
KNN cohens kappa score: 0.618


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 498, 105
LR fn, tp: 4, 27
LR f1 score: 0.331
LR cohens kappa score: 0.274
LR average precision score: 0.321

-> test with 'RF'
RF tn, fp: 594, 9
RF fn, tp: 1, 30
RF f1 score: 0.857
RF cohens kappa score: 0.849

-> test with 'GB'
GB tn, fp: 590, 13
GB fn, tp: 1, 30
GB f1 score: 0.811
GB cohens kappa score: 0.799

-> test with 'KNN'
KNN tn, fp: 590, 13
KNN fn, tp: 6, 25
KNN f1 score: 0.725
KNN cohens kappa score: 0.709


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 480, 123
LR fn, tp: 3, 28
LR f1 score: 0.308
LR cohens kappa score: 0.247
LR average precision score: 0.412

-> 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: 590, 13
GB fn, tp: 4, 27
GB f1 score: 0.761
GB cohens kappa score: 0.747

-> test with 'KNN'
KNN tn, fp: 578, 25
KNN fn, tp: 6, 25
KNN f1 score: 0.617
KNN cohens kappa score: 0.593


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 480, 123
LR fn, tp: 3, 28
LR f1 score: 0.308
LR cohens kappa score: 0.247
LR average precision score: 0.239

-> test with 'RF'
RF tn, fp: 591, 12
RF fn, tp: 0, 31
RF f1 score: 0.838
RF cohens kappa score: 0.828

-> 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: 568, 35
KNN fn, tp: 9, 22
KNN f1 score: 0.500
KNN cohens kappa score: 0.466


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 488, 112
LR fn, tp: 6, 21
LR f1 score: 0.263
LR cohens kappa score: 0.206
LR average precision score: 0.259

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

-> test with 'GB'
GB tn, fp: 581, 19
GB fn, tp: 3, 24
GB f1 score: 0.686
GB cohens kappa score: 0.668

-> test with 'KNN'
KNN tn, fp: 569, 31
KNN fn, tp: 2, 25
KNN f1 score: 0.602
KNN cohens kappa score: 0.578


====== 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: 489, 114
LR fn, tp: 4, 27
LR f1 score: 0.314
LR cohens kappa score: 0.254
LR average precision score: 0.263

-> 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: 592, 11
GB fn, tp: 2, 29
GB f1 score: 0.817
GB cohens kappa score: 0.806

-> test with 'KNN'
KNN tn, fp: 584, 19
KNN fn, tp: 7, 24
KNN f1 score: 0.649
KNN cohens kappa score: 0.627


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 493, 110
LR fn, tp: 5, 26
LR f1 score: 0.311
LR cohens kappa score: 0.252
LR average precision score: 0.397

-> 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: 591, 12
GB fn, tp: 2, 29
GB f1 score: 0.806
GB cohens kappa score: 0.794

-> test with 'KNN'
KNN tn, fp: 581, 22
KNN fn, tp: 8, 23
KNN f1 score: 0.605
KNN cohens kappa score: 0.581


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 474, 129
LR fn, tp: 3, 28
LR f1 score: 0.298
LR cohens kappa score: 0.235
LR average precision score: 0.339

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

-> 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: 577, 26
KNN fn, tp: 7, 24
KNN f1 score: 0.593
KNN cohens kappa score: 0.566


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 482, 121
LR fn, tp: 2, 29
LR f1 score: 0.320
LR cohens kappa score: 0.261
LR average precision score: 0.420

-> test with 'RF'
RF tn, fp: 591, 12
RF fn, tp: 0, 31
RF f1 score: 0.838
RF cohens kappa score: 0.828

-> test with 'GB'
GB tn, fp: 590, 13
GB fn, tp: 1, 30
GB f1 score: 0.811
GB cohens kappa score: 0.799

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 508, 92
LR fn, tp: 7, 20
LR f1 score: 0.288
LR cohens kappa score: 0.235
LR average precision score: 0.190

-> 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: 583, 17
GB fn, tp: 4, 23
GB f1 score: 0.687
GB cohens kappa score: 0.670

-> test with 'KNN'
KNN tn, fp: 581, 19
KNN fn, tp: 6, 21
KNN f1 score: 0.627
KNN cohens kappa score: 0.607

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 508, 131
LR fn, tp: 7, 31
LR f1 score: 0.352
LR cohens kappa score: 0.297
LR average precision score: 0.436


average:
LR tn, fp: 489.16, 113.24
LR fn, tp: 3.8, 26.4
LR f1 score: 0.311
LR cohens kappa score: 0.252
LR average precision score: 0.312


minimum:
LR tn, fp: 472, 92
LR fn, tp: 0, 20
LR f1 score: 0.263
LR cohens kappa score: 0.206
LR average precision score: 0.189


-----[ RF ]-----
maximum:
RF tn, fp: 596, 23
RF fn, tp: 5, 31
RF f1 score: 0.899
RF cohens kappa score: 0.893


average:
RF tn, fp: 588.24, 14.16
RF fn, tp: 2.28, 27.92
RF f1 score: 0.774
RF cohens kappa score: 0.760


minimum:
RF tn, fp: 577, 7
RF fn, tp: 0, 23
RF f1 score: 0.630
RF cohens kappa score: 0.609


-----[ GB ]-----
maximum:
GB tn, fp: 599, 22
GB fn, tp: 5, 31
GB f1 score: 0.923
GB cohens kappa score: 0.919


average:
GB tn, fp: 588.44, 13.96
GB fn, tp: 2.4, 27.8
GB f1 score: 0.774
GB cohens kappa score: 0.760


minimum:
GB tn, fp: 581, 4
GB fn, tp: 0, 23
GB f1 score: 0.686
GB cohens kappa score: 0.668


-----[ KNN ]-----
maximum:
KNN tn, fp: 593, 35
KNN fn, tp: 10, 27
KNN f1 score: 0.725
KNN cohens kappa score: 0.709


average:
KNN tn, fp: 580.44, 21.96
KNN fn, tp: 7.0, 23.2
KNN f1 score: 0.618
KNN cohens kappa score: 0.595


minimum:
KNN tn, fp: 568, 10
KNN fn, tp: 2, 20
KNN f1 score: 0.500
KNN cohens kappa score: 0.466

  wall time: 00:16:05s, process time: 01:54:28s