ConvGeNBenchmarkResults/data_result/ProWRAS/folding_hypothyroid.log

///////////////////////////////////////////
// Running ProWRAS 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: 550, 53
LR fn, tp: 6, 25
LR f1 score: 0.459
LR cohens kappa score: 0.418
LR average precision score: 0.510

-> 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: 598, 5
GB fn, tp: 8, 23
GB f1 score: 0.780
GB cohens kappa score: 0.769

-> 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: 533, 70
LR fn, tp: 5, 26
LR f1 score: 0.409
LR cohens kappa score: 0.362
LR average precision score: 0.457

-> test with 'RF'
RF tn, fp: 595, 8
RF fn, tp: 12, 19
RF f1 score: 0.655
RF cohens kappa score: 0.639

-> 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: 531, 72
LR fn, tp: 8, 23
LR f1 score: 0.365
LR cohens kappa score: 0.315
LR average precision score: 0.367

-> 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: 599, 4
GB fn, tp: 5, 26
GB f1 score: 0.852
GB cohens kappa score: 0.845

-> 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: 523, 80
LR fn, tp: 4, 27
LR f1 score: 0.391
LR cohens kappa score: 0.341
LR average precision score: 0.408

-> test with 'RF'
RF tn, fp: 603, 0
RF fn, tp: 15, 16
RF f1 score: 0.681
RF cohens kappa score: 0.670

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

-> 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: 544, 56
LR fn, tp: 3, 24
LR f1 score: 0.449
LR cohens kappa score: 0.411
LR average precision score: 0.560

-> test with 'RF'
RF tn, fp: 599, 1
RF fn, tp: 8, 19
RF f1 score: 0.809
RF cohens kappa score: 0.801

-> 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: 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: 542, 61
LR fn, tp: 8, 23
LR f1 score: 0.400
LR cohens kappa score: 0.354
LR average precision score: 0.484

-> test with 'RF'
RF tn, fp: 600, 3
RF fn, tp: 11, 20
RF f1 score: 0.741
RF cohens kappa score: 0.729

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 9, 22
GB f1 score: 0.746
GB cohens kappa score: 0.733

-> 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 2/5: 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: 5, 26
LR f1 score: 0.456
LR cohens kappa score: 0.414
LR average precision score: 0.501

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 16, 15
RF f1 score: 0.625
RF cohens kappa score: 0.612

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 3, 28
GB f1 score: 0.862
GB cohens kappa score: 0.854

-> 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: 529, 74
LR fn, tp: 5, 26
LR f1 score: 0.397
LR cohens kappa score: 0.348
LR average precision score: 0.585

-> test with 'RF'
RF tn, fp: 600, 3
RF fn, tp: 16, 15
RF f1 score: 0.612
RF cohens kappa score: 0.598

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 11, 20
GB f1 score: 0.727
GB cohens kappa score: 0.715

-> 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 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 525, 78
LR fn, tp: 7, 24
LR f1 score: 0.361
LR cohens kappa score: 0.309
LR average precision score: 0.313

-> test with 'RF'
RF tn, fp: 598, 5
RF fn, tp: 9, 22
RF f1 score: 0.759
RF cohens kappa score: 0.747

-> 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: 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: 0.513

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

-> test with 'GB'
GB tn, fp: 594, 6
GB fn, tp: 3, 24
GB f1 score: 0.842
GB cohens kappa score: 0.835

-> 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: 0.496

-> test with 'RF'
RF tn, fp: 602, 1
RF fn, tp: 17, 14
RF f1 score: 0.609
RF cohens kappa score: 0.596

-> test with 'GB'
GB tn, fp: 601, 2
GB fn, tp: 8, 23
GB f1 score: 0.821
GB cohens kappa score: 0.813

-> 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: 545, 58
LR fn, tp: 12, 19
LR f1 score: 0.352
LR cohens kappa score: 0.303
LR average precision score: 0.315

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

-> 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: 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: 531, 72
LR fn, tp: 1, 30
LR f1 score: 0.451
LR cohens kappa score: 0.407
LR average precision score: 0.595

-> 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: 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: 515, 88
LR fn, tp: 2, 29
LR f1 score: 0.392
LR cohens kappa score: 0.341
LR average precision score: 0.489

-> test with 'RF'
RF tn, fp: 598, 5
RF fn, tp: 12, 19
RF f1 score: 0.691
RF cohens kappa score: 0.677

-> test with 'GB'
GB tn, fp: 592, 11
GB fn, tp: 8, 23
GB f1 score: 0.708
GB cohens kappa score: 0.692

-> 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: 531, 69
LR fn, tp: 6, 21
LR f1 score: 0.359
LR cohens kappa score: 0.313
LR average precision score: 0.349

-> test with 'RF'
RF tn, fp: 600, 0
RF fn, tp: 6, 21
RF f1 score: 0.875
RF cohens kappa score: 0.870

-> test with 'GB'
GB tn, fp: 598, 2
GB fn, tp: 3, 24
GB f1 score: 0.906
GB cohens kappa score: 0.901

-> test with 'KNN'
KNN tn, fp: 585, 15
KNN fn, tp: 4, 23
KNN f1 score: 0.708
KNN cohens kappa score: 0.692


====== 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: 531, 72
LR fn, tp: 5, 26
LR f1 score: 0.403
LR cohens kappa score: 0.355
LR average precision score: 0.425

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 11, 20
RF f1 score: 0.727
RF cohens kappa score: 0.715

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 4, 27
GB f1 score: 0.818
GB cohens kappa score: 0.808

-> 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: 547, 56
LR fn, tp: 7, 24
LR f1 score: 0.432
LR cohens kappa score: 0.389
LR average precision score: 0.460

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 11, 20
RF f1 score: 0.727
RF cohens kappa score: 0.715

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 8, 23
GB f1 score: 0.767
GB cohens kappa score: 0.755

-> 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: 3, 28
LR f1 score: 0.406
LR cohens kappa score: 0.357
LR average precision score: 0.585

-> test with 'RF'
RF tn, fp: 602, 1
RF fn, tp: 11, 20
RF f1 score: 0.769
RF cohens kappa score: 0.760

-> test with 'GB'
GB tn, fp: 601, 2
GB fn, tp: 8, 23
GB f1 score: 0.821
GB cohens kappa score: 0.813

-> 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: 520, 83
LR fn, tp: 4, 27
LR f1 score: 0.383
LR cohens kappa score: 0.332
LR average precision score: 0.462

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 13, 18
RF f1 score: 0.706
RF cohens kappa score: 0.694

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 5, 26
GB f1 score: 0.839
GB cohens kappa score: 0.830

-> 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: 539, 61
LR fn, tp: 9, 18
LR f1 score: 0.340
LR cohens kappa score: 0.294
LR average precision score: 0.415

-> test with 'RF'
RF tn, fp: 597, 3
RF fn, tp: 10, 17
RF f1 score: 0.723
RF cohens kappa score: 0.713

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

-> 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: 0.460

-> 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: 596, 7
GB fn, tp: 10, 21
GB f1 score: 0.712
GB cohens kappa score: 0.698

-> 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: 534, 69
LR fn, tp: 6, 25
LR f1 score: 0.400
LR cohens kappa score: 0.352
LR average precision score: 0.522

-> test with 'RF'
RF tn, fp: 602, 1
RF fn, tp: 10, 21
RF f1 score: 0.792
RF cohens kappa score: 0.784

-> test with 'GB'
GB tn, fp: 601, 2
GB fn, tp: 5, 26
GB f1 score: 0.881
GB cohens kappa score: 0.876

-> 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: 522, 81
LR fn, tp: 4, 27
LR f1 score: 0.388
LR cohens kappa score: 0.338
LR average precision score: 0.508

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


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 534, 69
LR fn, tp: 5, 26
LR f1 score: 0.413
LR cohens kappa score: 0.366
LR average precision score: 0.559

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 9, 22
RF f1 score: 0.800
RF cohens kappa score: 0.791

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


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 534, 66
LR fn, tp: 4, 23
LR f1 score: 0.397
LR cohens kappa score: 0.354
LR average precision score: 0.367

-> 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: 550, 88
LR fn, tp: 12, 30
LR f1 score: 0.459
LR cohens kappa score: 0.418
LR average precision score: 0.595


average:
LR tn, fp: 532.56, 69.84
LR fn, tp: 5.32, 24.88
LR f1 score: 0.399
LR cohens kappa score: 0.352
LR average precision score: 0.468


minimum:
LR tn, fp: 514, 53
LR fn, tp: 1, 18
LR f1 score: 0.340
LR cohens kappa score: 0.294
LR average precision score: 0.313


-----[ RF ]-----
maximum:
RF tn, fp: 603, 8
RF fn, tp: 17, 22
RF f1 score: 0.875
RF cohens kappa score: 0.870


average:
RF tn, fp: 599.44, 2.96
RF fn, tp: 11.28, 18.92
RF f1 score: 0.725
RF cohens kappa score: 0.714


minimum:
RF tn, fp: 595, 0
RF fn, tp: 5, 14
RF f1 score: 0.600
RF cohens kappa score: 0.585


-----[ GB ]-----
maximum:
GB tn, fp: 601, 11
GB fn, tp: 12, 28
GB f1 score: 0.906
GB cohens kappa score: 0.901


average:
GB tn, fp: 597.12, 5.28
GB fn, tp: 6.72, 23.48
GB f1 score: 0.795
GB cohens kappa score: 0.785


minimum:
GB tn, fp: 592, 2
GB fn, tp: 3, 16
GB f1 score: 0.681
GB cohens kappa score: 0.669


-----[ KNN ]-----
maximum:
KNN tn, fp: 591, 33
KNN fn, tp: 12, 25
KNN f1 score: 0.714
KNN cohens kappa score: 0.698


average:
KNN tn, fp: 582.0, 20.4
KNN fn, tp: 7.84, 22.36
KNN f1 score: 0.615
KNN cohens kappa score: 0.592


minimum:
KNN tn, fp: 570, 12
KNN fn, tp: 4, 17
KNN f1 score: 0.521
KNN cohens kappa score: 0.492

  wall time: 00:49:20s, process time: 11:53:00s