ConvGeNBenchmarkResults/data_result/ProWRAS/folding_hypothyroid.log

///////////////////////////////////////////
// Running ProWRAS 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: 553, 50
LR fn, tp: 6, 25
LR f1 score: 0.472
LR cohens kappa score: 0.432
LR average precision score: 0.512

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 12, 19
RF f1 score: 0.731
RF cohens kappa score: 0.720

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 7, 24
GB f1 score: 0.787
GB cohens kappa score: 0.776

-> test with 'KNN'
KNN tn, fp: 587, 16
KNN fn, tp: 6, 25
KNN f1 score: 0.694
KNN cohens kappa score: 0.676


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 530, 73
LR fn, tp: 5, 26
LR f1 score: 0.400
LR cohens kappa score: 0.352
LR average precision score: 0.456

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 9, 22
RF f1 score: 0.772
RF cohens kappa score: 0.761

-> test with 'GB'
GB tn, fp: 594, 9
GB fn, tp: 4, 27
GB f1 score: 0.806
GB cohens kappa score: 0.795

-> 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 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 530, 73
LR fn, tp: 7, 24
LR f1 score: 0.375
LR cohens kappa score: 0.325
LR average precision score: 0.363

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 10, 21
RF f1 score: 0.778
RF cohens kappa score: 0.768

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 5, 26
GB f1 score: 0.825
GB cohens kappa score: 0.816

-> test with 'KNN'
KNN tn, fp: 586, 17
KNN fn, tp: 9, 22
KNN f1 score: 0.629
KNN cohens kappa score: 0.607


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> 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: 0.409

-> test with 'RF'
RF tn, fp: 602, 1
RF fn, tp: 14, 17
RF f1 score: 0.694
RF cohens kappa score: 0.682

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 10, 21
GB f1 score: 0.750
GB cohens kappa score: 0.739

-> test with 'KNN'
KNN tn, fp: 580, 23
KNN fn, tp: 12, 19
KNN f1 score: 0.521
KNN cohens kappa score: 0.492


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 542, 58
LR fn, tp: 3, 24
LR f1 score: 0.440
LR cohens kappa score: 0.402
LR average precision score: 0.573

-> test with 'RF'
RF tn, fp: 600, 0
RF fn, tp: 8, 19
RF f1 score: 0.826
RF cohens kappa score: 0.820

-> test with 'GB'
GB tn, fp: 596, 4
GB fn, tp: 5, 22
GB f1 score: 0.830
GB cohens kappa score: 0.823

-> test with 'KNN'
KNN tn, fp: 583, 17
KNN fn, tp: 6, 21
KNN f1 score: 0.646
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: 544, 59
LR fn, tp: 8, 23
LR f1 score: 0.407
LR cohens kappa score: 0.362
LR average precision score: 0.481

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 9, 22
RF f1 score: 0.772
RF cohens kappa score: 0.761

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 9, 22
GB f1 score: 0.733
GB cohens kappa score: 0.720

-> test with 'KNN'
KNN tn, fp: 586, 17
KNN fn, tp: 7, 24
KNN f1 score: 0.667
KNN cohens kappa score: 0.647


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 545, 58
LR fn, tp: 5, 26
LR f1 score: 0.452
LR cohens kappa score: 0.410
LR average precision score: 0.495

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 15, 16
RF f1 score: 0.653
RF cohens kappa score: 0.640

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 3, 28
GB f1 score: 0.848
GB cohens kappa score: 0.840

-> 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 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> 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: 0.581

-> test with 'RF'
RF tn, fp: 600, 3
RF fn, tp: 14, 17
RF f1 score: 0.667
RF cohens kappa score: 0.653

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 10, 21
GB f1 score: 0.737
GB cohens kappa score: 0.725

-> 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 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 526, 77
LR fn, tp: 7, 24
LR f1 score: 0.364
LR cohens kappa score: 0.312
LR average precision score: 0.313

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 10, 21
RF f1 score: 0.750
RF cohens kappa score: 0.739

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 7, 24
GB f1 score: 0.814
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 587, 16
KNN fn, tp: 8, 23
KNN f1 score: 0.657
KNN cohens kappa score: 0.637


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 514, 86
LR fn, tp: 1, 26
LR f1 score: 0.374
LR cohens kappa score: 0.327
LR average precision score: 0.526

-> test with 'RF'
RF tn, fp: 595, 5
RF fn, tp: 4, 23
RF f1 score: 0.836
RF cohens kappa score: 0.829

-> 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: 585, 15
KNN fn, tp: 5, 22
KNN f1 score: 0.688
KNN cohens kappa score: 0.671


====== 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: 530, 73
LR fn, tp: 7, 24
LR f1 score: 0.375
LR cohens kappa score: 0.325
LR average precision score: 0.494

-> test with 'RF'
RF tn, fp: 602, 1
RF fn, tp: 14, 17
RF f1 score: 0.694
RF cohens kappa score: 0.682

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 8, 23
GB f1 score: 0.807
GB cohens kappa score: 0.798

-> test with 'KNN'
KNN tn, fp: 591, 12
KNN fn, tp: 12, 19
KNN f1 score: 0.613
KNN cohens kappa score: 0.593


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 546, 57
LR fn, tp: 12, 19
LR f1 score: 0.355
LR cohens kappa score: 0.307
LR average precision score: 0.335

-> test with 'RF'
RF tn, fp: 596, 7
RF fn, tp: 9, 22
RF f1 score: 0.733
RF cohens kappa score: 0.720

-> test with 'GB'
GB tn, fp: 593, 10
GB fn, tp: 4, 27
GB f1 score: 0.794
GB cohens kappa score: 0.783

-> test with 'KNN'
KNN tn, fp: 569, 34
KNN fn, tp: 6, 25
KNN f1 score: 0.556
KNN cohens kappa score: 0.525


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 531, 72
LR fn, tp: 1, 30
LR f1 score: 0.451
LR cohens kappa score: 0.407
LR average precision score: 0.599

-> test with 'RF'
RF tn, fp: 600, 3
RF fn, tp: 8, 23
RF f1 score: 0.807
RF cohens kappa score: 0.798

-> 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: 580, 23
KNN fn, tp: 8, 23
KNN f1 score: 0.597
KNN cohens kappa score: 0.572


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> 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: 0.487

-> test with 'RF'
RF tn, fp: 597, 6
RF fn, tp: 13, 18
RF f1 score: 0.655
RF cohens kappa score: 0.639

-> test with 'GB'
GB tn, fp: 591, 12
GB fn, tp: 10, 21
GB f1 score: 0.656
GB cohens kappa score: 0.638

-> test with 'KNN'
KNN tn, fp: 580, 23
KNN fn, tp: 11, 20
KNN f1 score: 0.541
KNN cohens kappa score: 0.513


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 532, 68
LR fn, tp: 6, 21
LR f1 score: 0.362
LR cohens kappa score: 0.317
LR average precision score: 0.336

-> test with 'RF'
RF tn, fp: 599, 1
RF fn, tp: 7, 20
RF f1 score: 0.833
RF cohens kappa score: 0.827

-> test with 'GB'
GB tn, fp: 597, 3
GB fn, tp: 5, 22
GB f1 score: 0.846
GB cohens kappa score: 0.840

-> test with 'KNN'
KNN tn, fp: 586, 14
KNN fn, tp: 4, 23
KNN f1 score: 0.719
KNN cohens kappa score: 0.704


====== 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: 533, 70
LR fn, tp: 5, 26
LR f1 score: 0.409
LR cohens kappa score: 0.362
LR average precision score: 0.427

-> test with 'RF'
RF tn, fp: 600, 3
RF fn, tp: 9, 22
RF f1 score: 0.786
RF cohens kappa score: 0.776

-> 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: 577, 26
KNN fn, tp: 9, 22
KNN f1 score: 0.557
KNN cohens kappa score: 0.529


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 546, 57
LR fn, tp: 7, 24
LR f1 score: 0.429
LR cohens kappa score: 0.385
LR average precision score: 0.461

-> test with 'RF'
RF tn, fp: 600, 3
RF fn, tp: 12, 19
RF f1 score: 0.717
RF cohens kappa score: 0.705

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 7, 24
GB f1 score: 0.800
GB cohens kappa score: 0.790

-> 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 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 524, 79
LR fn, tp: 2, 29
LR f1 score: 0.417
LR cohens kappa score: 0.369
LR average precision score: 0.590

-> test with 'RF'
RF tn, fp: 603, 0
RF fn, tp: 12, 19
RF f1 score: 0.760
RF cohens kappa score: 0.751

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 8, 23
GB f1 score: 0.807
GB cohens kappa score: 0.798

-> test with 'KNN'
KNN tn, fp: 589, 14
KNN fn, tp: 6, 25
KNN f1 score: 0.714
KNN cohens kappa score: 0.698


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 521, 82
LR fn, tp: 4, 27
LR f1 score: 0.386
LR cohens kappa score: 0.335
LR average precision score: 0.471

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 14, 17
RF f1 score: 0.680
RF cohens kappa score: 0.668

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 6, 25
GB f1 score: 0.833
GB cohens kappa score: 0.825

-> 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 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 540, 60
LR fn, tp: 9, 18
LR f1 score: 0.343
LR cohens kappa score: 0.298
LR average precision score: 0.422

-> test with 'RF'
RF tn, fp: 596, 4
RF fn, tp: 8, 19
RF f1 score: 0.760
RF cohens kappa score: 0.750

-> test with 'GB'
GB tn, fp: 598, 2
GB fn, tp: 8, 19
GB f1 score: 0.792
GB cohens kappa score: 0.784

-> 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: 540, 63
LR fn, tp: 6, 25
LR f1 score: 0.420
LR cohens kappa score: 0.375
LR average precision score: 0.455

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 10, 21
RF f1 score: 0.750
RF cohens kappa score: 0.739

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 9, 22
GB f1 score: 0.721
GB cohens kappa score: 0.707

-> 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 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 533, 70
LR fn, tp: 6, 25
LR f1 score: 0.397
LR cohens kappa score: 0.349
LR average precision score: 0.522

-> test with 'RF'
RF tn, fp: 603, 0
RF fn, tp: 13, 18
RF f1 score: 0.735
RF cohens kappa score: 0.725

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 7, 24
GB f1 score: 0.800
GB cohens kappa score: 0.790

-> test with 'KNN'
KNN tn, fp: 586, 17
KNN fn, tp: 7, 24
KNN f1 score: 0.667
KNN cohens kappa score: 0.647


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 525, 78
LR fn, tp: 4, 27
LR f1 score: 0.397
LR cohens kappa score: 0.348
LR average precision score: 0.517

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 16, 15
RF f1 score: 0.600
RF cohens kappa score: 0.585

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 11, 20
GB f1 score: 0.714
GB cohens kappa score: 0.701

-> test with 'KNN'
KNN tn, fp: 576, 27
KNN fn, tp: 9, 22
KNN f1 score: 0.550
KNN cohens kappa score: 0.521


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 531, 72
LR fn, tp: 4, 27
LR f1 score: 0.415
LR cohens kappa score: 0.368
LR average precision score: 0.549

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 10, 21
RF f1 score: 0.778
RF cohens kappa score: 0.768

-> 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: 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: 533, 67
LR fn, tp: 4, 23
LR f1 score: 0.393
LR cohens kappa score: 0.350
LR average precision score: 0.376

-> test with 'RF'
RF tn, fp: 597, 3
RF fn, tp: 12, 15
RF f1 score: 0.667
RF cohens kappa score: 0.655

-> test with 'GB'
GB tn, fp: 596, 4
GB fn, tp: 11, 16
GB f1 score: 0.681
GB cohens kappa score: 0.669

-> test with 'KNN'
KNN tn, fp: 581, 19
KNN fn, tp: 10, 17
KNN f1 score: 0.540
KNN cohens kappa score: 0.516

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 553, 90
LR fn, tp: 12, 30
LR f1 score: 0.472
LR cohens kappa score: 0.432
LR average precision score: 0.599


average:
LR tn, fp: 532.44, 69.96
LR fn, tp: 5.24, 24.96
LR f1 score: 0.400
LR cohens kappa score: 0.353
LR average precision score: 0.470


minimum:
LR tn, fp: 513, 50
LR fn, tp: 1, 18
LR f1 score: 0.343
LR cohens kappa score: 0.298
LR average precision score: 0.313


-----[ RF ]-----
maximum:
RF tn, fp: 603, 7
RF fn, tp: 16, 23
RF f1 score: 0.836
RF cohens kappa score: 0.829


average:
RF tn, fp: 599.6, 2.8
RF fn, tp: 10.88, 19.32
RF f1 score: 0.737
RF cohens kappa score: 0.726


minimum:
RF tn, fp: 595, 0
RF fn, tp: 4, 15
RF f1 score: 0.600
RF cohens kappa score: 0.585


-----[ GB ]-----
maximum:
GB tn, fp: 600, 12
GB fn, tp: 11, 28
GB f1 score: 0.857
GB cohens kappa score: 0.850


average:
GB tn, fp: 596.6, 5.8
GB fn, tp: 6.76, 23.44
GB f1 score: 0.788
GB cohens kappa score: 0.777


minimum:
GB tn, fp: 591, 2
GB fn, tp: 3, 16
GB f1 score: 0.656
GB cohens kappa score: 0.638


-----[ KNN ]-----
maximum:
KNN tn, fp: 591, 34
KNN fn, tp: 12, 25
KNN f1 score: 0.719
KNN cohens kappa score: 0.704


average:
KNN tn, fp: 581.92, 20.48
KNN fn, tp: 7.8, 22.4
KNN f1 score: 0.615
KNN cohens kappa score: 0.592


minimum:
KNN tn, fp: 569, 12
KNN fn, tp: 4, 17
KNN f1 score: 0.521
KNN cohens kappa score: 0.492