ConvGeNBenchmarkResults/data_result/convGAN-majority-full/folding_hypothyroid.log

///////////////////////////////////////////
// Running convGAN-majority-full 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 GAN.predict
GAN tn, fp: 597, 6
GAN fn, tp: 10, 21
GAN f1 score: 0.724
GAN cohens kappa score: 0.711

-> test with 'LR'
LR tn, fp: 524, 79
LR fn, tp: 5, 26
LR f1 score: 0.382
LR cohens kappa score: 0.332
LR average precision score: 0.435

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

-> test with 'KNN'
KNN tn, fp: 582, 21
KNN fn, tp: 5, 26
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 GAN.predict
GAN tn, fp: 584, 19
GAN fn, tp: 5, 26
GAN f1 score: 0.684
GAN cohens kappa score: 0.665

-> test with 'LR'
LR tn, fp: 514, 89
LR fn, tp: 3, 28
LR f1 score: 0.378
LR cohens kappa score: 0.326
LR average precision score: 0.460

-> 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: 572, 31
KNN fn, tp: 6, 25
KNN f1 score: 0.575
KNN cohens kappa score: 0.546


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 584, 19
GAN fn, tp: 7, 24
GAN f1 score: 0.649
GAN cohens kappa score: 0.627

-> test with 'LR'
LR tn, fp: 510, 93
LR fn, tp: 7, 24
LR f1 score: 0.324
LR cohens kappa score: 0.268
LR average precision score: 0.318

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

-> test with 'KNN'
KNN tn, fp: 576, 27
KNN fn, tp: 6, 25
KNN f1 score: 0.602
KNN cohens kappa score: 0.576


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 591, 12
GAN fn, tp: 10, 21
GAN f1 score: 0.656
GAN cohens kappa score: 0.638

-> test with 'LR'
LR tn, fp: 503, 100
LR fn, tp: 4, 27
LR f1 score: 0.342
LR cohens kappa score: 0.286
LR average precision score: 0.384

-> test with 'GB'
GB tn, fp: 593, 10
GB fn, tp: 5, 26
GB f1 score: 0.776
GB cohens kappa score: 0.764

-> test with 'KNN'
KNN tn, fp: 578, 25
KNN fn, tp: 11, 20
KNN f1 score: 0.526
KNN cohens kappa score: 0.497


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with GAN.predict
GAN tn, fp: 592, 8
GAN fn, tp: 6, 21
GAN f1 score: 0.750
GAN cohens kappa score: 0.738

-> test with 'LR'
LR tn, fp: 524, 76
LR fn, tp: 3, 24
LR f1 score: 0.378
LR cohens kappa score: 0.333
LR average precision score: 0.544

-> test with 'GB'
GB tn, fp: 593, 7
GB fn, tp: 3, 24
GB f1 score: 0.828
GB cohens kappa score: 0.819

-> test with 'KNN'
KNN tn, fp: 572, 28
KNN fn, tp: 4, 23
KNN f1 score: 0.590
KNN cohens kappa score: 0.565


====== 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 GAN.predict
GAN tn, fp: 596, 7
GAN fn, tp: 8, 23
GAN f1 score: 0.754
GAN cohens kappa score: 0.742

-> test with 'LR'
LR tn, fp: 527, 76
LR fn, tp: 5, 26
LR f1 score: 0.391
LR cohens kappa score: 0.342
LR average precision score: 0.485

-> test with 'GB'
GB tn, fp: 592, 11
GB fn, tp: 4, 27
GB f1 score: 0.783
GB cohens kappa score: 0.770

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


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 585, 18
GAN fn, tp: 8, 23
GAN f1 score: 0.639
GAN cohens kappa score: 0.618

-> test with 'LR'
LR tn, fp: 532, 71
LR fn, tp: 6, 25
LR f1 score: 0.394
LR cohens kappa score: 0.345
LR average precision score: 0.438

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

-> test with 'KNN'
KNN tn, fp: 579, 24
KNN fn, tp: 6, 25
KNN f1 score: 0.625
KNN cohens kappa score: 0.601


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 580, 23
GAN fn, tp: 5, 26
GAN f1 score: 0.650
GAN cohens kappa score: 0.628

-> test with 'LR'
LR tn, fp: 514, 89
LR fn, tp: 3, 28
LR f1 score: 0.378
LR cohens kappa score: 0.326
LR average precision score: 0.544

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 6, 25
GB f1 score: 0.794
GB cohens kappa score: 0.783

-> test with 'KNN'
KNN tn, fp: 574, 29
KNN fn, tp: 9, 22
KNN f1 score: 0.537
KNN cohens kappa score: 0.507


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 590, 13
GAN fn, tp: 7, 24
GAN f1 score: 0.706
GAN cohens kappa score: 0.689

-> test with 'LR'
LR tn, fp: 514, 89
LR fn, tp: 6, 25
LR f1 score: 0.345
LR cohens kappa score: 0.290
LR average precision score: 0.267

-> test with 'GB'
GB tn, fp: 593, 10
GB fn, tp: 5, 26
GB f1 score: 0.776
GB cohens kappa score: 0.764

-> test with 'KNN'
KNN tn, fp: 576, 27
KNN fn, tp: 7, 24
KNN f1 score: 0.585
KNN cohens kappa score: 0.559


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with GAN.predict
GAN tn, fp: 583, 17
GAN fn, tp: 3, 24
GAN f1 score: 0.706
GAN cohens kappa score: 0.690

-> test with 'LR'
LR tn, fp: 506, 94
LR fn, tp: 1, 26
LR f1 score: 0.354
LR cohens kappa score: 0.305
LR average precision score: 0.439

-> test with 'GB'
GB tn, fp: 590, 10
GB fn, tp: 3, 24
GB f1 score: 0.787
GB cohens kappa score: 0.776

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


====== 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 GAN.predict
GAN tn, fp: 597, 6
GAN fn, tp: 7, 24
GAN f1 score: 0.787
GAN cohens kappa score: 0.776

-> test with 'LR'
LR tn, fp: 505, 98
LR fn, tp: 4, 27
LR f1 score: 0.346
LR cohens kappa score: 0.291
LR average precision score: 0.468

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 4, 27
GB f1 score: 0.857
GB cohens kappa score: 0.850

-> 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 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 588, 15
GAN fn, tp: 5, 26
GAN f1 score: 0.722
GAN cohens kappa score: 0.706

-> test with 'LR'
LR tn, fp: 529, 74
LR fn, tp: 10, 21
LR f1 score: 0.333
LR cohens kappa score: 0.280
LR average precision score: 0.289

-> 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: 575, 28
KNN fn, tp: 8, 23
KNN f1 score: 0.561
KNN cohens kappa score: 0.533


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 588, 15
GAN fn, tp: 7, 24
GAN f1 score: 0.686
GAN cohens kappa score: 0.668

-> test with 'LR'
LR tn, fp: 516, 87
LR fn, tp: 1, 30
LR f1 score: 0.405
LR cohens kappa score: 0.356
LR average precision score: 0.547

-> 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: 572, 31
KNN fn, tp: 9, 22
KNN f1 score: 0.524
KNN cohens kappa score: 0.492


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 590, 13
GAN fn, tp: 9, 22
GAN f1 score: 0.667
GAN cohens kappa score: 0.648

-> test with 'LR'
LR tn, fp: 521, 82
LR fn, tp: 2, 29
LR f1 score: 0.408
LR cohens kappa score: 0.359
LR average precision score: 0.483

-> test with 'GB'
GB tn, fp: 593, 10
GB fn, tp: 5, 26
GB f1 score: 0.776
GB cohens kappa score: 0.764

-> test with 'KNN'
KNN tn, fp: 578, 25
KNN fn, tp: 7, 24
KNN f1 score: 0.600
KNN cohens kappa score: 0.575


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with GAN.predict
GAN tn, fp: 594, 6
GAN fn, tp: 6, 21
GAN f1 score: 0.778
GAN cohens kappa score: 0.768

-> test with 'LR'
LR tn, fp: 515, 85
LR fn, tp: 5, 22
LR f1 score: 0.328
LR cohens kappa score: 0.279
LR average precision score: 0.345

-> test with 'GB'
GB tn, fp: 594, 6
GB fn, tp: 1, 26
GB f1 score: 0.881
GB cohens kappa score: 0.876

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


====== 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 GAN.predict
GAN tn, fp: 590, 13
GAN fn, tp: 7, 24
GAN f1 score: 0.706
GAN cohens kappa score: 0.689

-> test with 'LR'
LR tn, fp: 522, 81
LR fn, tp: 4, 27
LR f1 score: 0.388
LR cohens kappa score: 0.338
LR average precision score: 0.382

-> 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: 579, 24
KNN fn, tp: 5, 26
KNN f1 score: 0.642
KNN cohens kappa score: 0.619


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 593, 10
GAN fn, tp: 8, 23
GAN f1 score: 0.719
GAN cohens kappa score: 0.704

-> test with 'LR'
LR tn, fp: 532, 71
LR fn, tp: 5, 26
LR f1 score: 0.406
LR cohens kappa score: 0.359
LR average precision score: 0.431

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 3, 28
GB f1 score: 0.836
GB cohens kappa score: 0.827

-> test with 'KNN'
KNN tn, fp: 579, 24
KNN fn, tp: 6, 25
KNN f1 score: 0.625
KNN cohens kappa score: 0.601


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 594, 9
GAN fn, tp: 5, 26
GAN f1 score: 0.788
GAN cohens kappa score: 0.776

-> test with 'LR'
LR tn, fp: 511, 92
LR fn, tp: 3, 28
LR f1 score: 0.371
LR cohens kappa score: 0.318
LR average precision score: 0.565

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 5, 26
GB f1 score: 0.812
GB cohens kappa score: 0.803

-> test with 'KNN'
KNN tn, fp: 579, 24
KNN fn, tp: 6, 25
KNN f1 score: 0.625
KNN cohens kappa score: 0.601


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 591, 12
GAN fn, tp: 7, 24
GAN f1 score: 0.716
GAN cohens kappa score: 0.701

-> test with 'LR'
LR tn, fp: 501, 102
LR fn, tp: 3, 28
LR f1 score: 0.348
LR cohens kappa score: 0.292
LR average precision score: 0.437

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

-> test with 'KNN'
KNN tn, fp: 578, 25
KNN fn, tp: 9, 22
KNN f1 score: 0.564
KNN cohens kappa score: 0.537


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with GAN.predict
GAN tn, fp: 592, 8
GAN fn, tp: 8, 19
GAN f1 score: 0.704
GAN cohens kappa score: 0.690

-> test with 'LR'
LR tn, fp: 509, 91
LR fn, tp: 5, 22
LR f1 score: 0.314
LR cohens kappa score: 0.263
LR average precision score: 0.393

-> test with 'GB'
GB tn, fp: 589, 11
GB fn, tp: 4, 23
GB f1 score: 0.754
GB cohens kappa score: 0.742

-> test with 'KNN'
KNN tn, fp: 568, 32
KNN fn, tp: 6, 21
KNN f1 score: 0.525
KNN cohens kappa score: 0.496


====== 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 GAN.predict
GAN tn, fp: 591, 12
GAN fn, tp: 9, 22
GAN f1 score: 0.677
GAN cohens kappa score: 0.660

-> test with 'LR'
LR tn, fp: 518, 85
LR fn, tp: 4, 27
LR f1 score: 0.378
LR cohens kappa score: 0.326
LR average precision score: 0.375

-> test with 'GB'
GB tn, fp: 592, 11
GB fn, tp: 4, 27
GB f1 score: 0.783
GB cohens kappa score: 0.770

-> test with 'KNN'
KNN tn, fp: 581, 22
KNN fn, tp: 6, 25
KNN f1 score: 0.641
KNN cohens kappa score: 0.619


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 598, 5
GAN fn, tp: 9, 22
GAN f1 score: 0.759
GAN cohens kappa score: 0.747

-> test with 'LR'
LR tn, fp: 526, 77
LR fn, tp: 5, 26
LR f1 score: 0.388
LR cohens kappa score: 0.338
LR average precision score: 0.503

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

-> test with 'KNN'
KNN tn, fp: 573, 30
KNN fn, tp: 9, 22
KNN f1 score: 0.530
KNN cohens kappa score: 0.499


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 594, 9
GAN fn, tp: 9, 22
GAN f1 score: 0.710
GAN cohens kappa score: 0.695

-> test with 'LR'
LR tn, fp: 507, 96
LR fn, tp: 1, 30
LR f1 score: 0.382
LR cohens kappa score: 0.330
LR average precision score: 0.504

-> test with 'GB'
GB tn, fp: 593, 10
GB fn, tp: 7, 24
GB f1 score: 0.738
GB cohens kappa score: 0.724

-> test with 'KNN'
KNN tn, fp: 578, 25
KNN fn, tp: 7, 24
KNN f1 score: 0.600
KNN cohens kappa score: 0.575


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 586, 17
GAN fn, tp: 5, 26
GAN f1 score: 0.703
GAN cohens kappa score: 0.685

-> test with 'LR'
LR tn, fp: 513, 90
LR fn, tp: 3, 28
LR f1 score: 0.376
LR cohens kappa score: 0.323
LR average precision score: 0.580

-> 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: 572, 31
KNN fn, tp: 6, 25
KNN f1 score: 0.575
KNN cohens kappa score: 0.546


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with GAN.predict
GAN tn, fp: 591, 9
GAN fn, tp: 7, 20
GAN f1 score: 0.714
GAN cohens kappa score: 0.701

-> test with 'LR'
LR tn, fp: 525, 75
LR fn, tp: 3, 24
LR f1 score: 0.381
LR cohens kappa score: 0.336
LR average precision score: 0.297

-> test with 'GB'
GB tn, fp: 591, 9
GB fn, tp: 4, 23
GB f1 score: 0.780
GB cohens kappa score: 0.769

-> test with 'KNN'
KNN tn, fp: 574, 26
KNN fn, tp: 5, 22
KNN f1 score: 0.587
KNN cohens kappa score: 0.563

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 532, 102
LR fn, tp: 10, 30
LR f1 score: 0.408
LR cohens kappa score: 0.359
LR average precision score: 0.580


average:
LR tn, fp: 516.72, 85.68
LR fn, tp: 4.04, 26.16
LR f1 score: 0.369
LR cohens kappa score: 0.318
LR average precision score: 0.437


minimum:
LR tn, fp: 501, 71
LR fn, tp: 1, 21
LR f1 score: 0.314
LR cohens kappa score: 0.263
LR average precision score: 0.267


-----[ GB ]-----
maximum:
GB tn, fp: 598, 14
GB fn, tp: 7, 30
GB f1 score: 0.881
GB cohens kappa score: 0.876


average:
GB tn, fp: 592.56, 9.84
GB fn, tp: 3.68, 26.52
GB f1 score: 0.797
GB cohens kappa score: 0.786


minimum:
GB tn, fp: 589, 5
GB fn, tp: 1, 23
GB f1 score: 0.738
GB cohens kappa score: 0.724


-----[ KNN ]-----
maximum:
KNN tn, fp: 585, 32
KNN fn, tp: 11, 26
KNN f1 score: 0.706
KNN cohens kappa score: 0.690


average:
KNN tn, fp: 576.72, 25.68
KNN fn, tp: 6.56, 23.64
KNN f1 score: 0.596
KNN cohens kappa score: 0.570


minimum:
KNN tn, fp: 568, 17
KNN fn, tp: 3, 20
KNN f1 score: 0.524
KNN cohens kappa score: 0.492


-----[ GAN ]-----
maximum:
GAN tn, fp: 598, 23
GAN fn, tp: 10, 26
GAN f1 score: 0.788
GAN cohens kappa score: 0.776


average:
GAN tn, fp: 590.36, 12.04
GAN fn, tp: 7.08, 23.12
GAN f1 score: 0.710
GAN cohens kappa score: 0.694


minimum:
GAN tn, fp: 580, 5
GAN fn, tp: 3, 19
GAN f1 score: 0.639
GAN cohens kappa score: 0.618