ConvGeNBenchmarkResults/data_result/convGAN/folding_hypothyroid.log

///////////////////////////////////////////
// Running convGAN 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: 0.466

-> 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: 577, 26
KNN fn, tp: 5, 26
KNN f1 score: 0.627
KNN cohens kappa score: 0.602


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 507, 96
LR fn, tp: 2, 29
LR f1 score: 0.372
LR cohens kappa score: 0.318
LR average precision score: 0.459

-> test with 'GB'
GB tn, fp: 583, 20
GB fn, tp: 2, 29
GB f1 score: 0.725
GB cohens kappa score: 0.707

-> test with 'KNN'
KNN tn, fp: 566, 37
KNN fn, tp: 4, 27
KNN f1 score: 0.568
KNN cohens kappa score: 0.538


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 513, 90
LR fn, tp: 6, 25
LR f1 score: 0.342
LR cohens kappa score: 0.288
LR average precision score: 0.330

-> test with 'GB'
GB tn, fp: 591, 12
GB fn, tp: 4, 27
GB f1 score: 0.771
GB cohens kappa score: 0.758

-> 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: 503, 100
LR fn, tp: 4, 27
LR f1 score: 0.342
LR cohens kappa score: 0.286
LR average precision score: 0.405

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 6, 25
GB f1 score: 0.806
GB cohens kappa score: 0.797

-> 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 'LR'
LR tn, fp: 528, 72
LR fn, tp: 2, 25
LR f1 score: 0.403
LR cohens kappa score: 0.360
LR average precision score: 0.550

-> 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: 562, 38
KNN fn, tp: 5, 22
KNN f1 score: 0.506
KNN cohens kappa score: 0.475


====== 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: 529, 74
LR fn, tp: 6, 25
LR f1 score: 0.385
LR cohens kappa score: 0.335
LR average precision score: 0.488

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

-> test with 'KNN'
KNN tn, fp: 581, 22
KNN fn, tp: 5, 26
KNN f1 score: 0.658
KNN cohens kappa score: 0.637


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 531, 72
LR fn, tp: 6, 25
LR f1 score: 0.391
LR cohens kappa score: 0.342
LR average precision score: 0.411

-> 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: 577, 26
KNN fn, tp: 6, 25
KNN f1 score: 0.610
KNN cohens kappa score: 0.584


------ Step 2/5: Slice 3/5 -------
-> 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: 0.571

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


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 510, 93
LR fn, tp: 6, 25
LR f1 score: 0.336
LR cohens kappa score: 0.280
LR average precision score: 0.279

-> test with 'GB'
GB tn, fp: 591, 12
GB fn, tp: 5, 26
GB f1 score: 0.754
GB cohens kappa score: 0.740

-> test with 'KNN'
KNN tn, fp: 572, 31
KNN fn, tp: 7, 24
KNN f1 score: 0.558
KNN cohens kappa score: 0.529


------ 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: 0.426

-> 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: 571, 29
KNN fn, tp: 4, 23
KNN f1 score: 0.582
KNN cohens kappa score: 0.557


====== 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: 513, 90
LR fn, tp: 6, 25
LR f1 score: 0.342
LR cohens kappa score: 0.288
LR average precision score: 0.488

-> 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: 580, 23
KNN fn, tp: 10, 21
KNN f1 score: 0.560
KNN cohens kappa score: 0.533


------ Step 3/5: 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: 11, 20
LR f1 score: 0.333
LR cohens kappa score: 0.281
LR average precision score: 0.295

-> 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: 569, 34
KNN fn, tp: 5, 26
KNN f1 score: 0.571
KNN cohens kappa score: 0.542


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 511, 92
LR fn, tp: 1, 30
LR f1 score: 0.392
LR cohens kappa score: 0.341
LR average precision score: 0.513

-> 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: 571, 32
KNN fn, tp: 8, 23
KNN f1 score: 0.535
KNN cohens kappa score: 0.504


------ Step 3/5: Slice 4/5 -------
-> 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: 0.484

-> test with 'GB'
GB tn, fp: 592, 11
GB fn, tp: 5, 26
GB f1 score: 0.765
GB cohens kappa score: 0.751

-> 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 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 512, 88
LR fn, tp: 4, 23
LR f1 score: 0.333
LR cohens kappa score: 0.284
LR average precision score: 0.311

-> test with 'GB'
GB tn, fp: 593, 7
GB fn, tp: 1, 26
GB f1 score: 0.867
GB cohens kappa score: 0.860

-> test with 'KNN'
KNN tn, fp: 578, 22
KNN fn, tp: 4, 23
KNN f1 score: 0.639
KNN cohens kappa score: 0.618


====== 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: 524, 79
LR fn, tp: 4, 27
LR f1 score: 0.394
LR cohens kappa score: 0.345
LR average precision score: 0.357

-> 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: 573, 30
KNN fn, tp: 7, 24
KNN f1 score: 0.565
KNN cohens kappa score: 0.536


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 528, 75
LR fn, tp: 6, 25
LR f1 score: 0.382
LR cohens kappa score: 0.332
LR average precision score: 0.440

-> 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: 578, 25
KNN fn, tp: 6, 25
KNN f1 score: 0.617
KNN cohens kappa score: 0.593


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 517, 86
LR fn, tp: 3, 28
LR f1 score: 0.386
LR cohens kappa score: 0.335
LR average precision score: 0.584

-> 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: 582, 21
KNN fn, tp: 5, 26
KNN f1 score: 0.667
KNN cohens kappa score: 0.646


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 496, 107
LR fn, tp: 2, 29
LR f1 score: 0.347
LR cohens kappa score: 0.291
LR average precision score: 0.441

-> 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: 8, 23
KNN f1 score: 0.548
KNN cohens kappa score: 0.518


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 520, 80
LR fn, tp: 6, 21
LR f1 score: 0.328
LR cohens kappa score: 0.279
LR average precision score: 0.400

-> test with 'GB'
GB tn, fp: 589, 11
GB fn, tp: 5, 22
GB f1 score: 0.733
GB cohens kappa score: 0.720

-> test with 'KNN'
KNN tn, fp: 557, 43
KNN fn, tp: 6, 21
KNN f1 score: 0.462
KNN cohens kappa score: 0.427


====== 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: 520, 83
LR fn, tp: 4, 27
LR f1 score: 0.383
LR cohens kappa score: 0.332
LR average precision score: 0.440

-> test with 'GB'
GB tn, fp: 591, 12
GB fn, tp: 5, 26
GB f1 score: 0.754
GB cohens kappa score: 0.740

-> test with 'KNN'
KNN tn, fp: 577, 26
KNN fn, tp: 6, 25
KNN f1 score: 0.610
KNN cohens kappa score: 0.584


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 518, 85
LR fn, tp: 5, 26
LR f1 score: 0.366
LR cohens kappa score: 0.314
LR average precision score: 0.465

-> test with 'GB'
GB tn, fp: 592, 11
GB fn, tp: 1, 30
GB f1 score: 0.833
GB cohens kappa score: 0.824

-> test with 'KNN'
KNN tn, fp: 571, 32
KNN fn, tp: 9, 22
KNN f1 score: 0.518
KNN cohens kappa score: 0.486


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 504, 99
LR fn, tp: 2, 29
LR f1 score: 0.365
LR cohens kappa score: 0.310
LR average precision score: 0.501

-> test with 'GB'
GB tn, fp: 593, 10
GB fn, tp: 6, 25
GB f1 score: 0.758
GB cohens kappa score: 0.744

-> test with 'KNN'
KNN tn, fp: 578, 25
KNN fn, tp: 8, 23
KNN f1 score: 0.582
KNN cohens kappa score: 0.556


------ Step 5/5: Slice 4/5 -------
-> 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: 0.579

-> 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: 569, 34
KNN fn, tp: 5, 26
KNN f1 score: 0.571
KNN cohens kappa score: 0.542


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> 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: 0.318

-> test with 'GB'
GB tn, fp: 590, 10
GB fn, tp: 4, 23
GB f1 score: 0.767
GB cohens kappa score: 0.755

-> test with 'KNN'
KNN tn, fp: 568, 32
KNN fn, tp: 5, 22
KNN f1 score: 0.543
KNN cohens kappa score: 0.515

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 534, 107
LR fn, tp: 11, 30
LR f1 score: 0.403
LR cohens kappa score: 0.360
LR average precision score: 0.584


average:
LR tn, fp: 515.24, 87.16
LR fn, tp: 4.08, 26.12
LR f1 score: 0.365
LR cohens kappa score: 0.313
LR average precision score: 0.440


minimum:
LR tn, fp: 495, 69
LR fn, tp: 1, 20
LR f1 score: 0.328
LR cohens kappa score: 0.278
LR average precision score: 0.279


-----[ GB ]-----
maximum:
GB tn, fp: 600, 20
GB fn, tp: 7, 30
GB f1 score: 0.867
GB cohens kappa score: 0.860


average:
GB tn, fp: 592.24, 10.16
GB fn, tp: 3.88, 26.32
GB f1 score: 0.791
GB cohens kappa score: 0.779


minimum:
GB tn, fp: 583, 3
GB fn, tp: 1, 22
GB f1 score: 0.714
GB cohens kappa score: 0.698


-----[ KNN ]-----
maximum:
KNN tn, fp: 582, 43
KNN fn, tp: 11, 27
KNN f1 score: 0.667
KNN cohens kappa score: 0.646


average:
KNN tn, fp: 572.8, 29.6
KNN fn, tp: 6.36, 23.84
KNN f1 score: 0.572
KNN cohens kappa score: 0.544


minimum:
KNN tn, fp: 557, 21
KNN fn, tp: 4, 20
KNN f1 score: 0.462
KNN cohens kappa score: 0.427