ConvGeNBenchmarkResults/data_result/SimpleGAN/folding_hypothyroid.log

///////////////////////////////////////////
// Running SimpleGAN 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 595, 8
LR fn, tp: 20, 11
LR f1 score: 0.440
LR cohens kappa score: 0.418
LR average precision score: 0.440

-> 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: 600, 3
KNN fn, tp: 15, 16
KNN f1 score: 0.640
KNN cohens kappa score: 0.626


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 592, 11
LR fn, tp: 19, 12
LR f1 score: 0.444
LR cohens kappa score: 0.420
LR average precision score: 0.501

-> 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: 596, 7
KNN fn, tp: 15, 16
KNN f1 score: 0.593
KNN cohens kappa score: 0.575


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 594, 9
LR fn, tp: 24, 7
LR f1 score: 0.298
LR cohens kappa score: 0.274
LR average precision score: 0.402

-> 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: 599, 4
KNN fn, tp: 16, 15
KNN f1 score: 0.600
KNN cohens kappa score: 0.585


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 594, 9
LR fn, tp: 21, 10
LR f1 score: 0.400
LR cohens kappa score: 0.377
LR average precision score: 0.436

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 10, 21
GB f1 score: 0.764
GB cohens kappa score: 0.753

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


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 595, 5
LR fn, tp: 17, 10
LR f1 score: 0.476
LR cohens kappa score: 0.460
LR average precision score: 0.578

-> 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: 597, 3
KNN fn, tp: 16, 11
KNN f1 score: 0.537
KNN cohens kappa score: 0.523


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 596, 7
LR fn, tp: 20, 11
LR f1 score: 0.449
LR cohens kappa score: 0.428
LR average precision score: 0.521

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 12, 19
GB f1 score: 0.667
GB cohens kappa score: 0.651

-> test with 'KNN'
KNN tn, fp: 598, 5
KNN fn, tp: 22, 9
KNN f1 score: 0.400
KNN cohens kappa score: 0.381


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 595, 8
LR fn, tp: 23, 8
LR f1 score: 0.340
LR cohens kappa score: 0.318
LR average precision score: 0.507

-> 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: 600, 3
KNN fn, tp: 15, 16
KNN f1 score: 0.640
KNN cohens kappa score: 0.626


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 596, 7
LR fn, tp: 17, 14
LR f1 score: 0.538
LR cohens kappa score: 0.519
LR average precision score: 0.495

-> test with 'GB'
GB tn, fp: 601, 2
GB fn, tp: 11, 20
GB f1 score: 0.755
GB cohens kappa score: 0.744

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


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 595, 8
LR fn, tp: 26, 5
LR f1 score: 0.227
LR cohens kappa score: 0.204
LR average precision score: 0.369

-> 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: 596, 7
KNN fn, tp: 16, 15
KNN f1 score: 0.566
KNN cohens kappa score: 0.548


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 592, 8
LR fn, tp: 17, 10
LR f1 score: 0.444
LR cohens kappa score: 0.425
LR average precision score: 0.563

-> 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: 595, 5
KNN fn, tp: 12, 15
KNN f1 score: 0.638
KNN cohens kappa score: 0.625


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 598, 5
LR fn, tp: 22, 9
LR f1 score: 0.400
LR cohens kappa score: 0.381
LR average precision score: 0.533

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

-> test with 'KNN'
KNN tn, fp: 602, 1
KNN fn, tp: 19, 12
KNN f1 score: 0.545
KNN cohens kappa score: 0.532


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 594, 9
LR fn, tp: 23, 8
LR f1 score: 0.333
LR cohens kappa score: 0.309
LR average precision score: 0.351

-> 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: 598, 5
KNN fn, tp: 16, 15
KNN f1 score: 0.588
KNN cohens kappa score: 0.572


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 595, 8
LR fn, tp: 16, 15
LR f1 score: 0.556
LR cohens kappa score: 0.536
LR average precision score: 0.659

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 6, 25
GB f1 score: 0.820
GB cohens kappa score: 0.811

-> test with 'KNN'
KNN tn, fp: 596, 7
KNN fn, tp: 15, 16
KNN f1 score: 0.593
KNN cohens kappa score: 0.575


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 596, 7
LR fn, tp: 20, 11
LR f1 score: 0.449
LR cohens kappa score: 0.428
LR average precision score: 0.511

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 7, 24
GB f1 score: 0.774
GB cohens kappa score: 0.763

-> test with 'KNN'
KNN tn, fp: 599, 4
KNN fn, tp: 17, 14
KNN f1 score: 0.571
KNN cohens kappa score: 0.555


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 595, 5
LR fn, tp: 21, 6
LR f1 score: 0.316
LR cohens kappa score: 0.298
LR average precision score: 0.394

-> test with 'GB'
GB tn, fp: 597, 3
GB fn, tp: 8, 19
GB f1 score: 0.776
GB cohens kappa score: 0.766

-> test with 'KNN'
KNN tn, fp: 599, 1
KNN fn, tp: 14, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.623


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 592, 11
LR fn, tp: 22, 9
LR f1 score: 0.353
LR cohens kappa score: 0.327
LR average precision score: 0.382

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 6, 25
GB f1 score: 0.820
GB cohens kappa score: 0.811

-> test with 'KNN'
KNN tn, fp: 600, 3
KNN fn, tp: 21, 10
KNN f1 score: 0.455
KNN cohens kappa score: 0.438


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 593, 10
LR fn, tp: 21, 10
LR f1 score: 0.392
LR cohens kappa score: 0.368
LR average precision score: 0.494

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

-> test with 'KNN'
KNN tn, fp: 599, 4
KNN fn, tp: 18, 13
KNN f1 score: 0.542
KNN cohens kappa score: 0.525


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 600, 3
LR fn, tp: 21, 10
LR f1 score: 0.455
LR cohens kappa score: 0.438
LR average precision score: 0.611

-> 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: 600, 3
KNN fn, tp: 17, 14
KNN f1 score: 0.583
KNN cohens kappa score: 0.568


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 594, 9
LR fn, tp: 18, 13
LR f1 score: 0.491
LR cohens kappa score: 0.469
LR average precision score: 0.520

-> 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: 597, 6
KNN fn, tp: 17, 14
KNN f1 score: 0.549
KNN cohens kappa score: 0.531


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 592, 8
LR fn, tp: 19, 8
LR f1 score: 0.372
LR cohens kappa score: 0.351
LR average precision score: 0.442

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

-> test with 'KNN'
KNN tn, fp: 593, 7
KNN fn, tp: 12, 15
KNN f1 score: 0.612
KNN cohens kappa score: 0.597


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 588, 15
LR fn, tp: 19, 12
LR f1 score: 0.414
LR cohens kappa score: 0.386
LR average precision score: 0.484

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 8, 23
GB f1 score: 0.793
GB cohens kappa score: 0.783

-> test with 'KNN'
KNN tn, fp: 599, 4
KNN fn, tp: 17, 14
KNN f1 score: 0.571
KNN cohens kappa score: 0.555


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 600, 3
LR fn, tp: 20, 11
LR f1 score: 0.489
LR cohens kappa score: 0.473
LR average precision score: 0.543

-> test with 'GB'
GB tn, fp: 602, 1
GB fn, tp: 8, 23
GB f1 score: 0.836
GB cohens kappa score: 0.829

-> test with 'KNN'
KNN tn, fp: 599, 4
KNN fn, tp: 18, 13
KNN f1 score: 0.542
KNN cohens kappa score: 0.525


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 594, 9
LR fn, tp: 18, 13
LR f1 score: 0.491
LR cohens kappa score: 0.469
LR average precision score: 0.546

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 14, 17
GB f1 score: 0.642
GB cohens kappa score: 0.626

-> test with 'KNN'
KNN tn, fp: 598, 5
KNN fn, tp: 18, 13
KNN f1 score: 0.531
KNN cohens kappa score: 0.513


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 600, 3
LR fn, tp: 22, 9
LR f1 score: 0.419
LR cohens kappa score: 0.402
LR average precision score: 0.497

-> 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: 601, 2
KNN fn, tp: 14, 17
KNN f1 score: 0.680
KNN cohens kappa score: 0.668


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 592, 8
LR fn, tp: 19, 8
LR f1 score: 0.372
LR cohens kappa score: 0.351
LR average precision score: 0.382

-> test with 'GB'
GB tn, fp: 594, 6
GB fn, tp: 10, 17
GB f1 score: 0.680
GB cohens kappa score: 0.667

-> test with 'KNN'
KNN tn, fp: 597, 3
KNN fn, tp: 17, 10
KNN f1 score: 0.500
KNN cohens kappa score: 0.486

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 600, 15
LR fn, tp: 26, 15
LR f1 score: 0.556
LR cohens kappa score: 0.536
LR average precision score: 0.659


average:
LR tn, fp: 594.68, 7.72
LR fn, tp: 20.2, 10.0
LR f1 score: 0.414
LR cohens kappa score: 0.393
LR average precision score: 0.486


minimum:
LR tn, fp: 588, 3
LR fn, tp: 16, 5
LR f1 score: 0.227
LR cohens kappa score: 0.204
LR average precision score: 0.351


-----[ GB ]-----
maximum:
GB tn, fp: 603, 9
GB fn, tp: 14, 26
GB f1 score: 0.867
GB cohens kappa score: 0.860


average:
GB tn, fp: 598.24, 4.16
GB fn, tp: 8.04, 22.16
GB f1 score: 0.783
GB cohens kappa score: 0.773


minimum:
GB tn, fp: 594, 0
GB fn, tp: 5, 17
GB f1 score: 0.642
GB cohens kappa score: 0.626


-----[ KNN ]-----
maximum:
KNN tn, fp: 602, 7
KNN fn, tp: 22, 17
KNN f1 score: 0.680
KNN cohens kappa score: 0.668


average:
KNN tn, fp: 598.4, 4.0
KNN fn, tp: 16.36, 13.84
KNN f1 score: 0.574
KNN cohens kappa score: 0.559


minimum:
KNN tn, fp: 593, 1
KNN fn, tp: 12, 9
KNN f1 score: 0.400
KNN cohens kappa score: 0.381