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: 551, 52
LR fn, tp: 6, 25
LR f1 score: 0.463
LR cohens kappa score: 0.423
LR average precision score: 0.511

-> 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: 529, 74
LR fn, tp: 5, 26
LR f1 score: 0.397
LR cohens kappa score: 0.348
LR average precision score: 0.469

-> test with 'GB'
GB tn, fp: 594, 9
GB fn, tp: 3, 28
GB f1 score: 0.824
GB cohens kappa score: 0.814

-> 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 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 532, 71
LR fn, tp: 7, 24
LR f1 score: 0.381
LR cohens kappa score: 0.332
LR average precision score: 0.363

-> 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: 521, 82
LR fn, tp: 5, 26
LR f1 score: 0.374
LR cohens kappa score: 0.323
LR average precision score: 0.393

-> 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: 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: 540, 60
LR fn, tp: 3, 24
LR f1 score: 0.432
LR cohens kappa score: 0.393
LR average precision score: 0.559

-> 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: 543, 60
LR fn, tp: 8, 23
LR f1 score: 0.404
LR cohens kappa score: 0.358
LR average precision score: 0.484

-> 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: 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: 547, 56
LR fn, tp: 6, 25
LR f1 score: 0.446
LR cohens kappa score: 0.404
LR average precision score: 0.491

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 2, 29
GB f1 score: 0.879
GB cohens kappa score: 0.872

-> 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 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 526, 77
LR fn, tp: 4, 27
LR f1 score: 0.400
LR cohens kappa score: 0.351
LR average precision score: 0.593

-> 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: 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: 529, 74
LR fn, tp: 7, 24
LR f1 score: 0.372
LR cohens kappa score: 0.322
LR average precision score: 0.312

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

-> 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 'GB'
GB tn, fp: 594, 6
GB fn, tp: 4, 23
GB f1 score: 0.821
GB cohens kappa score: 0.813

-> test with 'KNN'
KNN tn, fp: 584, 16
KNN fn, tp: 5, 22
KNN f1 score: 0.677
KNN cohens kappa score: 0.660


====== 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: 533, 70
LR fn, tp: 6, 25
LR f1 score: 0.397
LR cohens kappa score: 0.349
LR average precision score: 0.499

-> 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: 549, 54
LR fn, tp: 12, 19
LR f1 score: 0.365
LR cohens kappa score: 0.319
LR average precision score: 0.333

-> 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: 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: 532, 71
LR fn, tp: 1, 30
LR f1 score: 0.455
LR cohens kappa score: 0.410
LR average precision score: 0.600

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 6, 25
GB f1 score: 0.781
GB cohens kappa score: 0.770

-> 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: 516, 87
LR fn, tp: 3, 28
LR f1 score: 0.384
LR cohens kappa score: 0.332
LR average precision score: 0.483

-> 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: 0.361

-> test with 'GB'
GB tn, fp: 597, 3
GB fn, tp: 4, 23
GB f1 score: 0.868
GB cohens kappa score: 0.862

-> 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: 532, 71
LR fn, tp: 5, 26
LR f1 score: 0.406
LR cohens kappa score: 0.359
LR average precision score: 0.418

-> 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: 6, 25
LR f1 score: 0.446
LR cohens kappa score: 0.404
LR average precision score: 0.458

-> 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: 523, 80
LR fn, tp: 3, 28
LR f1 score: 0.403
LR cohens kappa score: 0.354
LR average precision score: 0.590

-> test with 'GB'
GB tn, fp: 601, 2
GB fn, tp: 7, 24
GB f1 score: 0.842
GB cohens kappa score: 0.835

-> 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: 0.456

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 7, 24
GB f1 score: 0.828
GB cohens kappa score: 0.819

-> 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: 538, 62
LR fn, tp: 9, 18
LR f1 score: 0.336
LR cohens kappa score: 0.291
LR average precision score: 0.410

-> test with 'GB'
GB tn, fp: 598, 2
GB fn, tp: 9, 18
GB f1 score: 0.766
GB cohens kappa score: 0.757

-> 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: 539, 64
LR fn, tp: 6, 25
LR f1 score: 0.417
LR cohens kappa score: 0.371
LR average precision score: 0.458

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 10, 21
GB f1 score: 0.700
GB cohens kappa score: 0.685

-> test with 'KNN'
KNN tn, fp: 582, 21
KNN fn, tp: 9, 22
KNN f1 score: 0.595
KNN cohens kappa score: 0.570


------ 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: 0.518

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 5, 26
GB f1 score: 0.867
GB cohens kappa score: 0.860

-> 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: 524, 79
LR fn, tp: 4, 27
LR f1 score: 0.394
LR cohens kappa score: 0.345
LR average precision score: 0.517

-> 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: 528, 75
LR fn, tp: 4, 27
LR f1 score: 0.406
LR cohens kappa score: 0.358
LR average precision score: 0.549

-> 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: 533, 67
LR fn, tp: 4, 23
LR f1 score: 0.393
LR cohens kappa score: 0.350
LR average precision score: 0.370

-> test with 'GB'
GB tn, fp: 596, 4
GB fn, tp: 10, 17
GB f1 score: 0.708
GB cohens kappa score: 0.697

-> test with 'KNN'
KNN tn, fp: 582, 18
KNN fn, tp: 10, 17
KNN f1 score: 0.548
KNN cohens kappa score: 0.525

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 551, 87
LR fn, tp: 12, 30
LR f1 score: 0.463
LR cohens kappa score: 0.423
LR average precision score: 0.600


average:
LR tn, fp: 532.48, 69.92
LR fn, tp: 5.24, 24.96
LR f1 score: 0.400
LR cohens kappa score: 0.353
LR average precision score: 0.469


minimum:
LR tn, fp: 514, 52
LR fn, tp: 1, 18
LR f1 score: 0.336
LR cohens kappa score: 0.291
LR average precision score: 0.312


-----[ GB ]-----
maximum:
GB tn, fp: 601, 12
GB fn, tp: 11, 29
GB f1 score: 0.879
GB cohens kappa score: 0.872


average:
GB tn, fp: 596.96, 5.44
GB fn, tp: 6.92, 23.28
GB f1 score: 0.789
GB cohens kappa score: 0.779


minimum:
GB tn, fp: 591, 2
GB fn, tp: 2, 17
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: 582.08, 20.32
KNN fn, tp: 7.84, 22.36
KNN f1 score: 0.616
KNN cohens kappa score: 0.593


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