ConvGeNBenchmarkResults/data_result/convGAN-proximary-full/folding_hypothyroid.log

///////////////////////////////////////////
// Running convGAN-proximary-full 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 GAN.predict
GAN tn, fp: 553, 50
GAN fn, tp: 4, 27
GAN f1 score: 0.500
GAN cohens kappa score: 0.463

-> test with 'LR'
LR tn, fp: 540, 63
LR fn, tp: 5, 26
LR f1 score: 0.433
LR cohens kappa score: 0.389
LR average precision score: 0.475

-> 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: 583, 20
KNN fn, tp: 4, 27
KNN f1 score: 0.692
KNN cohens kappa score: 0.673


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 529, 74
GAN fn, tp: 3, 28
GAN f1 score: 0.421
GAN cohens kappa score: 0.374

-> test with 'LR'
LR tn, fp: 531, 72
LR fn, tp: 4, 27
LR f1 score: 0.415
LR cohens kappa score: 0.368
LR average precision score: 0.459

-> 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: 578, 25
KNN fn, tp: 7, 24
KNN f1 score: 0.600
KNN cohens kappa score: 0.575


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 535, 68
GAN fn, tp: 4, 27
GAN f1 score: 0.429
GAN cohens kappa score: 0.383

-> test with 'LR'
LR tn, fp: 517, 86
LR fn, tp: 7, 24
LR f1 score: 0.340
LR cohens kappa score: 0.286
LR average precision score: 0.330

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


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 535, 68
GAN fn, tp: 3, 28
GAN f1 score: 0.441
GAN cohens kappa score: 0.396

-> test with 'LR'
LR tn, fp: 515, 88
LR fn, tp: 4, 27
LR f1 score: 0.370
LR cohens kappa score: 0.317
LR average precision score: 0.389

-> 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: 582, 21
KNN fn, tp: 11, 20
KNN f1 score: 0.556
KNN cohens kappa score: 0.529


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with GAN.predict
GAN tn, fp: 540, 60
GAN fn, tp: 6, 21
GAN f1 score: 0.389
GAN cohens kappa score: 0.347

-> test with 'LR'
LR tn, fp: 537, 63
LR fn, tp: 3, 24
LR f1 score: 0.421
LR cohens kappa score: 0.380
LR average precision score: 0.559

-> 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: 579, 21
KNN fn, tp: 5, 22
KNN f1 score: 0.629
KNN cohens kappa score: 0.608


====== 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 GAN.predict
GAN tn, fp: 544, 59
GAN fn, tp: 3, 28
GAN f1 score: 0.475
GAN cohens kappa score: 0.434

-> test with 'LR'
LR tn, fp: 535, 68
LR fn, tp: 6, 25
LR f1 score: 0.403
LR cohens kappa score: 0.356
LR average precision score: 0.487

-> 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: 586, 17
KNN fn, tp: 6, 25
KNN f1 score: 0.685
KNN cohens kappa score: 0.666


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 547, 56
GAN fn, tp: 3, 28
GAN f1 score: 0.487
GAN cohens kappa score: 0.447

-> test with 'LR'
LR tn, fp: 543, 60
LR fn, tp: 6, 25
LR f1 score: 0.431
LR cohens kappa score: 0.387
LR average precision score: 0.457

-> 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: 585, 18
KNN fn, tp: 6, 25
KNN f1 score: 0.676
KNN cohens kappa score: 0.656


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 488, 115
GAN fn, tp: 2, 29
GAN f1 score: 0.331
GAN cohens kappa score: 0.273

-> test with 'LR'
LR tn, fp: 538, 65
LR fn, tp: 5, 26
LR f1 score: 0.426
LR cohens kappa score: 0.381
LR average precision score: 0.553

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 5, 26
GB f1 score: 0.812
GB cohens kappa score: 0.803

-> test with 'KNN'
KNN tn, fp: 583, 20
KNN fn, tp: 11, 20
KNN f1 score: 0.563
KNN cohens kappa score: 0.538


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 516, 87
GAN fn, tp: 6, 25
GAN f1 score: 0.350
GAN cohens kappa score: 0.296

-> test with 'LR'
LR tn, fp: 518, 85
LR fn, tp: 6, 25
LR f1 score: 0.355
LR cohens kappa score: 0.301
LR average precision score: 0.288

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 5, 26
GB f1 score: 0.812
GB cohens kappa score: 0.803

-> test with 'KNN'
KNN tn, fp: 584, 19
KNN fn, tp: 7, 24
KNN f1 score: 0.649
KNN cohens kappa score: 0.627


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with GAN.predict
GAN tn, fp: 494, 106
GAN fn, tp: 2, 25
GAN f1 score: 0.316
GAN cohens kappa score: 0.264

-> test with 'LR'
LR tn, fp: 518, 82
LR fn, tp: 1, 26
LR f1 score: 0.385
LR cohens kappa score: 0.340
LR average precision score: 0.490

-> test with 'GB'
GB tn, fp: 593, 7
GB fn, tp: 3, 24
GB f1 score: 0.828
GB cohens kappa score: 0.819

-> test with 'KNN'
KNN tn, fp: 578, 22
KNN fn, tp: 6, 21
KNN f1 score: 0.600
KNN cohens kappa score: 0.578


====== 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 GAN.predict
GAN tn, fp: 501, 102
GAN fn, tp: 3, 28
GAN f1 score: 0.348
GAN cohens kappa score: 0.292

-> test with 'LR'
LR tn, fp: 517, 86
LR fn, tp: 4, 27
LR f1 score: 0.375
LR cohens kappa score: 0.323
LR average precision score: 0.488

-> 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: 7, 24
KNN f1 score: 0.667
KNN cohens kappa score: 0.647


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 520, 83
GAN fn, tp: 4, 27
GAN f1 score: 0.383
GAN cohens kappa score: 0.332

-> test with 'LR'
LR tn, fp: 539, 64
LR fn, tp: 11, 20
LR f1 score: 0.348
LR cohens kappa score: 0.298
LR average precision score: 0.299

-> 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: 575, 28
KNN fn, tp: 8, 23
KNN f1 score: 0.561
KNN cohens kappa score: 0.533


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 544, 59
GAN fn, tp: 4, 27
GAN f1 score: 0.462
GAN cohens kappa score: 0.420

-> test with 'LR'
LR tn, fp: 528, 75
LR fn, tp: 1, 30
LR f1 score: 0.441
LR cohens kappa score: 0.396
LR average precision score: 0.568

-> 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: 569, 34
KNN fn, tp: 8, 23
KNN f1 score: 0.523
KNN cohens kappa score: 0.490


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 528, 75
GAN fn, tp: 4, 27
GAN f1 score: 0.406
GAN cohens kappa score: 0.358

-> test with 'LR'
LR tn, fp: 525, 78
LR fn, tp: 2, 29
LR f1 score: 0.420
LR cohens kappa score: 0.373
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: 581, 22
KNN fn, tp: 8, 23
KNN f1 score: 0.605
KNN cohens kappa score: 0.581


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with GAN.predict
GAN tn, fp: 549, 51
GAN fn, tp: 2, 25
GAN f1 score: 0.485
GAN cohens kappa score: 0.451

-> test with 'LR'
LR tn, fp: 536, 64
LR fn, tp: 5, 22
LR f1 score: 0.389
LR cohens kappa score: 0.347
LR average precision score: 0.381

-> test with 'GB'
GB tn, fp: 596, 4
GB fn, tp: 1, 26
GB f1 score: 0.912
GB cohens kappa score: 0.908

-> test with 'KNN'
KNN tn, fp: 592, 8
KNN fn, tp: 4, 23
KNN f1 score: 0.793
KNN cohens kappa score: 0.783


====== 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 GAN.predict
GAN tn, fp: 545, 58
GAN fn, tp: 8, 23
GAN f1 score: 0.411
GAN cohens kappa score: 0.366

-> 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: 0.359

-> 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: 578, 25
KNN fn, tp: 5, 26
KNN f1 score: 0.634
KNN cohens kappa score: 0.610


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 509, 94
GAN fn, tp: 2, 29
GAN f1 score: 0.377
GAN cohens kappa score: 0.324

-> 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: 0.457

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 4, 27
GB f1 score: 0.844
GB cohens kappa score: 0.835

-> 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 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 507, 96
GAN fn, tp: 3, 28
GAN f1 score: 0.361
GAN cohens kappa score: 0.307

-> test with 'LR'
LR tn, fp: 538, 65
LR fn, tp: 3, 28
LR f1 score: 0.452
LR cohens kappa score: 0.408
LR average precision score: 0.591

-> 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: 587, 16
KNN fn, tp: 7, 24
KNN f1 score: 0.676
KNN cohens kappa score: 0.657


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 541, 62
GAN fn, tp: 3, 28
GAN f1 score: 0.463
GAN cohens kappa score: 0.421

-> test with 'LR'
LR tn, fp: 519, 84
LR fn, tp: 2, 29
LR f1 score: 0.403
LR cohens kappa score: 0.353
LR average precision score: 0.504

-> 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: 9, 22
KNN f1 score: 0.571
KNN cohens kappa score: 0.545


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with GAN.predict
GAN tn, fp: 492, 108
GAN fn, tp: 5, 22
GAN f1 score: 0.280
GAN cohens kappa score: 0.225

-> test with 'LR'
LR tn, fp: 521, 79
LR fn, tp: 6, 21
LR f1 score: 0.331
LR cohens kappa score: 0.282
LR average precision score: 0.405

-> 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: 575, 25
KNN fn, tp: 6, 21
KNN f1 score: 0.575
KNN cohens kappa score: 0.551


====== 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 GAN.predict
GAN tn, fp: 465, 138
GAN fn, tp: 4, 27
GAN f1 score: 0.276
GAN cohens kappa score: 0.211

-> test with 'LR'
LR tn, fp: 524, 79
LR fn, tp: 5, 26
LR f1 score: 0.382
LR cohens kappa score: 0.332
LR average precision score: 0.383

-> 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: 582, 21
KNN fn, tp: 6, 25
KNN f1 score: 0.649
KNN cohens kappa score: 0.628


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 541, 62
GAN fn, tp: 6, 25
GAN f1 score: 0.424
GAN cohens kappa score: 0.379

-> 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: 0.513

-> 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: 583, 20
KNN fn, tp: 9, 22
KNN f1 score: 0.603
KNN cohens kappa score: 0.579


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 477, 126
GAN fn, tp: 6, 25
GAN f1 score: 0.275
GAN cohens kappa score: 0.211

-> 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: 0.513

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

-> 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 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 503, 100
GAN fn, tp: 4, 27
GAN f1 score: 0.342
GAN cohens kappa score: 0.286

-> 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: 0.556

-> 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: 577, 26
KNN fn, tp: 7, 24
KNN f1 score: 0.593
KNN cohens kappa score: 0.566


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with GAN.predict
GAN tn, fp: 482, 118
GAN fn, tp: 3, 24
GAN f1 score: 0.284
GAN cohens kappa score: 0.228

-> test with 'LR'
LR tn, fp: 537, 63
LR fn, tp: 4, 23
LR f1 score: 0.407
LR cohens kappa score: 0.365
LR average precision score: 0.336

-> test with 'GB'
GB tn, fp: 592, 8
GB fn, tp: 6, 21
GB f1 score: 0.750
GB cohens kappa score: 0.738

-> test with 'KNN'
KNN tn, fp: 587, 13
KNN fn, tp: 7, 20
KNN f1 score: 0.667
KNN cohens kappa score: 0.650

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 543, 88
LR fn, tp: 11, 30
LR f1 score: 0.452
LR cohens kappa score: 0.408
LR average precision score: 0.591


average:
LR tn, fp: 529.16, 73.24
LR fn, tp: 4.52, 25.68
LR f1 score: 0.399
LR cohens kappa score: 0.351
LR average precision score: 0.453


minimum:
LR tn, fp: 515, 60
LR fn, tp: 1, 20
LR f1 score: 0.331
LR cohens kappa score: 0.282
LR average precision score: 0.288


-----[ GB ]-----
maximum:
GB tn, fp: 599, 13
GB fn, tp: 12, 29
GB f1 score: 0.912
GB cohens kappa score: 0.908


average:
GB tn, fp: 594.52, 7.88
GB fn, tp: 4.36, 25.84
GB f1 score: 0.809
GB cohens kappa score: 0.798


minimum:
GB tn, fp: 590, 4
GB fn, tp: 1, 19
GB f1 score: 0.655
GB cohens kappa score: 0.639


-----[ KNN ]-----
maximum:
KNN tn, fp: 592, 34
KNN fn, tp: 11, 27
KNN f1 score: 0.793
KNN cohens kappa score: 0.783


average:
KNN tn, fp: 581.32, 21.08
KNN fn, tp: 7.0, 23.2
KNN f1 score: 0.626
KNN cohens kappa score: 0.603


minimum:
KNN tn, fp: 569, 8
KNN fn, tp: 4, 20
KNN f1 score: 0.523
KNN cohens kappa score: 0.490


-----[ GAN ]-----
maximum:
GAN tn, fp: 553, 138
GAN fn, tp: 8, 29
GAN f1 score: 0.500
GAN cohens kappa score: 0.463


average:
GAN tn, fp: 519.4, 83.0
GAN fn, tp: 3.88, 26.32
GAN f1 score: 0.389
GAN cohens kappa score: 0.339


minimum:
GAN tn, fp: 465, 50
GAN fn, tp: 2, 21
GAN f1 score: 0.275
GAN cohens kappa score: 0.211