ConvGeNBenchmarkResults/data_result/Repeater/folding_hypothyroid.log

///////////////////////////////////////////
// Running Repeater 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: 521, 82
LR fn, tp: 4, 27
LR f1 score: 0.386
LR cohens kappa score: 0.335
LR average precision score: 0.473

-> test with 'GB'
GB tn, fp: 592, 11
GB fn, tp: 3, 28
GB f1 score: 0.800
GB cohens kappa score: 0.788

-> test with 'KNN'
KNN tn, fp: 574, 29
KNN fn, tp: 2, 29
KNN f1 score: 0.652
KNN cohens kappa score: 0.628


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 506, 97
LR fn, tp: 2, 29
LR f1 score: 0.369
LR cohens kappa score: 0.316
LR average precision score: 0.466

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

-> test with 'KNN'
KNN tn, fp: 566, 37
KNN fn, tp: 6, 25
KNN f1 score: 0.538
KNN cohens kappa score: 0.505


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> 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: 0.344

-> test with 'GB'
GB tn, fp: 587, 16
GB fn, tp: 2, 29
GB f1 score: 0.763
GB cohens kappa score: 0.749

-> 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 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 499, 104
LR fn, tp: 3, 28
LR f1 score: 0.344
LR cohens kappa score: 0.287
LR average precision score: 0.418

-> 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: 571, 32
KNN fn, tp: 11, 20
KNN f1 score: 0.482
KNN cohens kappa score: 0.448


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 509, 91
LR fn, tp: 3, 24
LR f1 score: 0.338
LR cohens kappa score: 0.288
LR average precision score: 0.523

-> test with 'GB'
GB tn, fp: 592, 8
GB fn, tp: 3, 24
GB f1 score: 0.814
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 568, 32
KNN fn, tp: 3, 24
KNN f1 score: 0.578
KNN cohens kappa score: 0.552


====== 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: 518, 85
LR fn, tp: 3, 28
LR f1 score: 0.389
LR cohens kappa score: 0.338
LR average precision score: 0.477

-> 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: 580, 23
KNN fn, tp: 6, 25
KNN f1 score: 0.633
KNN cohens kappa score: 0.610


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 520, 83
LR fn, tp: 5, 26
LR f1 score: 0.371
LR cohens kappa score: 0.320
LR average precision score: 0.410

-> test with 'GB'
GB tn, fp: 588, 15
GB fn, tp: 3, 28
GB f1 score: 0.757
GB cohens kappa score: 0.742

-> test with 'KNN'
KNN tn, fp: 573, 30
KNN fn, tp: 4, 27
KNN f1 score: 0.614
KNN cohens kappa score: 0.588


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 503, 100
LR fn, tp: 2, 29
LR f1 score: 0.362
LR cohens kappa score: 0.308
LR average precision score: 0.577

-> test with 'GB'
GB tn, fp: 594, 9
GB fn, tp: 6, 25
GB f1 score: 0.769
GB cohens kappa score: 0.757

-> test with 'KNN'
KNN tn, fp: 569, 34
KNN fn, tp: 7, 24
KNN f1 score: 0.539
KNN cohens kappa score: 0.508


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 507, 96
LR fn, tp: 5, 26
LR f1 score: 0.340
LR cohens kappa score: 0.284
LR average precision score: 0.310

-> 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: 569, 34
KNN fn, tp: 7, 24
KNN f1 score: 0.539
KNN cohens kappa score: 0.508


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 495, 105
LR fn, tp: 1, 26
LR f1 score: 0.329
LR cohens kappa score: 0.278
LR average precision score: 0.427

-> test with 'GB'
GB tn, fp: 591, 9
GB fn, tp: 2, 25
GB f1 score: 0.820
GB cohens kappa score: 0.811

-> test with 'KNN'
KNN tn, fp: 565, 35
KNN fn, tp: 5, 22
KNN f1 score: 0.524
KNN cohens kappa score: 0.494


====== 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: 499, 104
LR fn, tp: 1, 30
LR f1 score: 0.364
LR cohens kappa score: 0.309
LR average precision score: 0.478

-> 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: 7, 24
KNN f1 score: 0.608
KNN cohens kappa score: 0.583


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 525, 78
LR fn, tp: 10, 21
LR f1 score: 0.323
LR cohens kappa score: 0.269
LR average precision score: 0.289

-> test with 'GB'
GB tn, fp: 588, 15
GB fn, tp: 3, 28
GB f1 score: 0.757
GB cohens kappa score: 0.742

-> 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 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 509, 94
LR fn, tp: 1, 30
LR f1 score: 0.387
LR cohens kappa score: 0.335
LR average precision score: 0.548

-> test with 'GB'
GB tn, fp: 585, 18
GB fn, tp: 3, 28
GB f1 score: 0.727
GB cohens kappa score: 0.710

-> test with 'KNN'
KNN tn, fp: 566, 37
KNN fn, tp: 7, 24
KNN f1 score: 0.522
KNN cohens kappa score: 0.489


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 508, 95
LR fn, tp: 2, 29
LR f1 score: 0.374
LR cohens kappa score: 0.321
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: 570, 33
KNN fn, tp: 7, 24
KNN f1 score: 0.545
KNN cohens kappa score: 0.515


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 510, 90
LR fn, tp: 4, 23
LR f1 score: 0.329
LR cohens kappa score: 0.278
LR average precision score: 0.285

-> test with 'GB'
GB tn, fp: 591, 9
GB fn, tp: 1, 26
GB f1 score: 0.839
GB cohens kappa score: 0.830

-> test with 'KNN'
KNN tn, fp: 574, 26
KNN fn, tp: 2, 25
KNN f1 score: 0.641
KNN cohens kappa score: 0.620


====== 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: 518, 85
LR fn, tp: 3, 28
LR f1 score: 0.389
LR cohens kappa score: 0.338
LR average precision score: 0.403

-> 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: 570, 33
KNN fn, tp: 5, 26
KNN f1 score: 0.578
KNN cohens kappa score: 0.549


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 517, 86
LR fn, tp: 5, 26
LR f1 score: 0.364
LR cohens kappa score: 0.311
LR average precision score: 0.423

-> 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: 574, 29
KNN fn, tp: 4, 27
KNN f1 score: 0.621
KNN cohens kappa score: 0.595


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 505, 98
LR fn, tp: 3, 28
LR f1 score: 0.357
LR cohens kappa score: 0.302
LR average precision score: 0.582

-> 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: 575, 28
KNN fn, tp: 5, 26
KNN f1 score: 0.612
KNN cohens kappa score: 0.586


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 492, 111
LR fn, tp: 2, 29
LR f1 score: 0.339
LR cohens kappa score: 0.282
LR average precision score: 0.429

-> 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: 570, 33
KNN fn, tp: 7, 24
KNN f1 score: 0.545
KNN cohens kappa score: 0.515


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 506, 94
LR fn, tp: 3, 24
LR f1 score: 0.331
LR cohens kappa score: 0.281
LR average precision score: 0.420

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

-> test with 'KNN'
KNN tn, fp: 565, 35
KNN fn, tp: 6, 21
KNN f1 score: 0.506
KNN cohens kappa score: 0.476


====== 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: 507, 96
LR fn, tp: 3, 28
LR f1 score: 0.361
LR cohens kappa score: 0.307
LR average precision score: 0.437

-> test with 'GB'
GB tn, fp: 587, 16
GB fn, tp: 3, 28
GB f1 score: 0.747
GB cohens kappa score: 0.731

-> test with 'KNN'
KNN tn, fp: 575, 28
KNN fn, tp: 6, 25
KNN f1 score: 0.595
KNN cohens kappa score: 0.569


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 511, 92
LR fn, tp: 4, 27
LR f1 score: 0.360
LR cohens kappa score: 0.306
LR average precision score: 0.466

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 1, 30
GB f1 score: 0.870
GB cohens kappa score: 0.862

-> test with 'KNN'
KNN tn, fp: 570, 33
KNN fn, tp: 8, 23
KNN f1 score: 0.529
KNN cohens kappa score: 0.497


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 500, 103
LR fn, tp: 2, 29
LR f1 score: 0.356
LR cohens kappa score: 0.300
LR average precision score: 0.497

-> test with 'GB'
GB tn, fp: 588, 15
GB fn, tp: 7, 24
GB f1 score: 0.686
GB cohens kappa score: 0.668

-> test with 'KNN'
KNN tn, fp: 569, 34
KNN fn, tp: 7, 24
KNN f1 score: 0.539
KNN cohens kappa score: 0.508


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 504, 99
LR fn, tp: 3, 28
LR f1 score: 0.354
LR cohens kappa score: 0.299
LR average precision score: 0.521

-> 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: 570, 33
KNN fn, tp: 6, 25
KNN f1 score: 0.562
KNN cohens kappa score: 0.532


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 519, 81
LR fn, tp: 2, 25
LR f1 score: 0.376
LR cohens kappa score: 0.330
LR average precision score: 0.309

-> test with 'GB'
GB tn, fp: 590, 10
GB fn, tp: 5, 22
GB f1 score: 0.746
GB cohens kappa score: 0.733

-> test with 'KNN'
KNN tn, fp: 569, 31
KNN fn, tp: 5, 22
KNN f1 score: 0.550
KNN cohens kappa score: 0.523

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 525, 111
LR fn, tp: 10, 30
LR f1 score: 0.389
LR cohens kappa score: 0.338
LR average precision score: 0.582


average:
LR tn, fp: 508.44, 93.96
LR fn, tp: 3.2, 27.0
LR f1 score: 0.357
LR cohens kappa score: 0.304
LR average precision score: 0.440


minimum:
LR tn, fp: 492, 78
LR fn, tp: 1, 21
LR f1 score: 0.323
LR cohens kappa score: 0.269
LR average precision score: 0.285


-----[ GB ]-----
maximum:
GB tn, fp: 597, 18
GB fn, tp: 7, 30
GB f1 score: 0.870
GB cohens kappa score: 0.862


average:
GB tn, fp: 590.44, 11.96
GB fn, tp: 3.28, 26.92
GB f1 score: 0.781
GB cohens kappa score: 0.768


minimum:
GB tn, fp: 585, 6
GB fn, tp: 1, 22
GB f1 score: 0.686
GB cohens kappa score: 0.668


-----[ KNN ]-----
maximum:
KNN tn, fp: 580, 37
KNN fn, tp: 11, 29
KNN f1 score: 0.652
KNN cohens kappa score: 0.628


average:
KNN tn, fp: 571.0, 31.4
KNN fn, tp: 5.8, 24.4
KNN f1 score: 0.568
KNN cohens kappa score: 0.540


minimum:
KNN tn, fp: 565, 23
KNN fn, tp: 2, 20
KNN f1 score: 0.482
KNN cohens kappa score: 0.448