ConvGeNBenchmarkResults/data_result/convGAN-proximary-5/folding_hypothyroid.log

///////////////////////////////////////////
// Running convGAN-proximary-5 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: 499, 104
GAN fn, tp: 6, 25
GAN f1 score: 0.312
GAN cohens kappa score: 0.254

-> test with 'LR'
LR tn, fp: 523, 80
LR fn, tp: 5, 26
LR f1 score: 0.380
LR cohens kappa score: 0.329
LR average precision score: 0.477

-> 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: 576, 27
KNN fn, tp: 3, 28
KNN f1 score: 0.651
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 GAN.predict
GAN tn, fp: 474, 129
GAN fn, tp: 8, 23
GAN f1 score: 0.251
GAN cohens kappa score: 0.185

-> test with 'LR'
LR tn, fp: 519, 84
LR fn, tp: 3, 28
LR f1 score: 0.392
LR cohens kappa score: 0.341
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: 569, 34
KNN fn, tp: 4, 27
KNN f1 score: 0.587
KNN cohens kappa score: 0.558


------ 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: 520, 83
GAN fn, tp: 11, 20
GAN f1 score: 0.299
GAN cohens kappa score: 0.241

-> test with 'LR'
LR tn, fp: 506, 97
LR fn, tp: 6, 25
LR f1 score: 0.327
LR cohens kappa score: 0.270
LR average precision score: 0.327

-> test with 'GB'
GB tn, fp: 589, 14
GB fn, tp: 2, 29
GB f1 score: 0.784
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 575, 28
KNN fn, tp: 7, 24
KNN f1 score: 0.578
KNN cohens kappa score: 0.551


------ 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: 579, 24
GAN fn, tp: 12, 19
GAN f1 score: 0.514
GAN cohens kappa score: 0.484

-> test with 'LR'
LR tn, fp: 501, 102
LR fn, tp: 3, 28
LR f1 score: 0.348
LR cohens kappa score: 0.292
LR average precision score: 0.429

-> 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: 573, 30
KNN fn, tp: 11, 20
KNN f1 score: 0.494
KNN cohens kappa score: 0.461


------ 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: 585, 15
GAN fn, tp: 11, 16
GAN f1 score: 0.552
GAN cohens kappa score: 0.530

-> test with 'LR'
LR tn, fp: 525, 75
LR fn, tp: 2, 25
LR f1 score: 0.394
LR cohens kappa score: 0.350
LR average precision score: 0.560

-> 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: 569, 31
KNN fn, tp: 2, 25
KNN f1 score: 0.602
KNN cohens kappa score: 0.578


====== 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: 524, 79
GAN fn, tp: 8, 23
GAN f1 score: 0.346
GAN cohens kappa score: 0.293

-> 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: 0.482

-> 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: 581, 22
KNN fn, tp: 6, 25
KNN f1 score: 0.641
KNN cohens kappa score: 0.619


------ 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: 454, 149
GAN fn, tp: 8, 23
GAN f1 score: 0.227
GAN cohens kappa score: 0.157

-> test with 'LR'
LR tn, fp: 536, 67
LR fn, tp: 6, 25
LR f1 score: 0.407
LR cohens kappa score: 0.360
LR average precision score: 0.414

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 4, 27
GB f1 score: 0.831
GB cohens kappa score: 0.822

-> test with 'KNN'
KNN tn, fp: 579, 24
KNN fn, tp: 6, 25
KNN f1 score: 0.625
KNN cohens kappa score: 0.601


------ 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: 569, 34
GAN fn, tp: 10, 21
GAN f1 score: 0.488
GAN cohens kappa score: 0.454

-> test with 'LR'
LR tn, fp: 507, 96
LR fn, tp: 4, 27
LR f1 score: 0.351
LR cohens kappa score: 0.296
LR average precision score: 0.582

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


------ 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: 511, 92
GAN fn, tp: 6, 25
GAN f1 score: 0.338
GAN cohens kappa score: 0.282

-> test with 'LR'
LR tn, fp: 509, 94
LR fn, tp: 6, 25
LR f1 score: 0.333
LR cohens kappa score: 0.277
LR average precision score: 0.277

-> 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: 574, 29
KNN fn, tp: 7, 24
KNN f1 score: 0.571
KNN cohens kappa score: 0.543


------ 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: 582, 18
GAN fn, tp: 8, 19
GAN f1 score: 0.594
GAN cohens kappa score: 0.572

-> test with 'LR'
LR tn, fp: 500, 100
LR fn, tp: 1, 26
LR f1 score: 0.340
LR cohens kappa score: 0.289
LR average precision score: 0.507

-> test with 'GB'
GB tn, fp: 589, 11
GB fn, tp: 1, 26
GB f1 score: 0.812
GB cohens kappa score: 0.803

-> test with 'KNN'
KNN tn, fp: 575, 25
KNN fn, tp: 4, 23
KNN f1 score: 0.613
KNN cohens kappa score: 0.591


====== 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: 564, 39
GAN fn, tp: 13, 18
GAN f1 score: 0.409
GAN cohens kappa score: 0.369

-> test with 'LR'
LR tn, fp: 501, 102
LR fn, tp: 5, 26
LR f1 score: 0.327
LR cohens kappa score: 0.270
LR average precision score: 0.474

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 6, 25
GB f1 score: 0.833
GB cohens kappa score: 0.825

-> test with 'KNN'
KNN tn, fp: 577, 26
KNN fn, tp: 10, 21
KNN f1 score: 0.538
KNN cohens kappa score: 0.510


------ 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: 558, 45
GAN fn, tp: 10, 21
GAN f1 score: 0.433
GAN cohens kappa score: 0.393

-> test with 'LR'
LR tn, fp: 527, 76
LR fn, tp: 10, 21
LR f1 score: 0.328
LR cohens kappa score: 0.274
LR average precision score: 0.288

-> 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: 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 GAN.predict
GAN tn, fp: 586, 17
GAN fn, tp: 7, 24
GAN f1 score: 0.667
GAN cohens kappa score: 0.647

-> test with 'LR'
LR tn, fp: 512, 91
LR fn, tp: 1, 30
LR f1 score: 0.395
LR cohens kappa score: 0.344
LR average precision score: 0.515

-> 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: 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 GAN.predict
GAN tn, fp: 552, 51
GAN fn, tp: 8, 23
GAN f1 score: 0.438
GAN cohens kappa score: 0.397

-> test with 'LR'
LR tn, fp: 514, 89
LR fn, tp: 2, 29
LR f1 score: 0.389
LR cohens kappa score: 0.338
LR average precision score: 0.487

-> test with 'GB'
GB tn, fp: 586, 17
GB fn, tp: 5, 26
GB f1 score: 0.703
GB cohens kappa score: 0.685

-> 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 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with GAN.predict
GAN tn, fp: 516, 84
GAN fn, tp: 7, 20
GAN f1 score: 0.305
GAN cohens kappa score: 0.254

-> test with 'LR'
LR tn, fp: 508, 92
LR fn, tp: 4, 23
LR f1 score: 0.324
LR cohens kappa score: 0.273
LR average precision score: 0.301

-> 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: 575, 25
KNN fn, tp: 2, 25
KNN f1 score: 0.649
KNN cohens kappa score: 0.629


====== 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: 503, 100
GAN fn, tp: 5, 26
GAN f1 score: 0.331
GAN cohens kappa score: 0.274

-> test with 'LR'
LR tn, fp: 527, 76
LR fn, tp: 5, 26
LR f1 score: 0.391
LR cohens kappa score: 0.342
LR average precision score: 0.397

-> 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: 572, 31
KNN fn, tp: 7, 24
KNN f1 score: 0.558
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 GAN.predict
GAN tn, fp: 472, 131
GAN fn, tp: 9, 22
GAN f1 score: 0.239
GAN cohens kappa score: 0.172

-> 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: 0.429

-> 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: 572, 31
KNN fn, tp: 4, 27
KNN f1 score: 0.607
KNN cohens kappa score: 0.580


------ 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: 570, 33
GAN fn, tp: 13, 18
GAN f1 score: 0.439
GAN cohens kappa score: 0.403

-> 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: 0.585

-> 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: 576, 27
KNN fn, tp: 4, 27
KNN f1 score: 0.635
KNN cohens kappa score: 0.611


------ 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: 483, 120
GAN fn, tp: 8, 23
GAN f1 score: 0.264
GAN cohens kappa score: 0.200

-> test with 'LR'
LR tn, fp: 500, 103
LR fn, tp: 3, 28
LR f1 score: 0.346
LR cohens kappa score: 0.289
LR average precision score: 0.428

-> test with 'GB'
GB tn, fp: 590, 13
GB fn, tp: 0, 31
GB f1 score: 0.827
GB cohens kappa score: 0.816

-> test with 'KNN'
KNN tn, fp: 576, 27
KNN fn, tp: 7, 24
KNN f1 score: 0.585
KNN cohens kappa score: 0.559


------ 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: 514, 86
GAN fn, tp: 7, 20
GAN f1 score: 0.301
GAN cohens kappa score: 0.249

-> test with 'LR'
LR tn, fp: 510, 90
LR fn, tp: 6, 21
LR f1 score: 0.304
LR cohens kappa score: 0.253
LR average precision score: 0.415

-> 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: 564, 36
KNN fn, tp: 6, 21
KNN f1 score: 0.500
KNN cohens kappa score: 0.469


====== 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: 524, 79
GAN fn, tp: 8, 23
GAN f1 score: 0.346
GAN cohens kappa score: 0.293

-> test with 'LR'
LR tn, fp: 523, 80
LR fn, tp: 5, 26
LR f1 score: 0.380
LR cohens kappa score: 0.329
LR average precision score: 0.408

-> test with 'GB'
GB tn, fp: 591, 12
GB fn, tp: 3, 28
GB f1 score: 0.789
GB cohens kappa score: 0.776

-> 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 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 415, 188
GAN fn, tp: 14, 17
GAN f1 score: 0.144
GAN cohens kappa score: 0.065

-> 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: 0.501

-> 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: 575, 28
KNN fn, tp: 7, 24
KNN f1 score: 0.578
KNN cohens kappa score: 0.551


------ 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: 564, 39
GAN fn, tp: 9, 22
GAN f1 score: 0.478
GAN cohens kappa score: 0.442

-> test with 'LR'
LR tn, fp: 502, 101
LR fn, tp: 2, 29
LR f1 score: 0.360
LR cohens kappa score: 0.305
LR average precision score: 0.502

-> test with 'GB'
GB tn, fp: 593, 10
GB fn, tp: 8, 23
GB f1 score: 0.719
GB cohens kappa score: 0.704

-> test with 'KNN'
KNN tn, fp: 572, 31
KNN fn, tp: 8, 23
KNN f1 score: 0.541
KNN cohens kappa score: 0.511


------ 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: 509, 94
GAN fn, tp: 4, 27
GAN f1 score: 0.355
GAN cohens kappa score: 0.301

-> test with 'LR'
LR tn, fp: 509, 94
LR fn, tp: 3, 28
LR f1 score: 0.366
LR cohens kappa score: 0.312
LR average precision score: 0.501

-> 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: 573, 30
KNN fn, tp: 6, 25
KNN f1 score: 0.581
KNN cohens kappa score: 0.553


------ 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: 553, 47
GAN fn, tp: 6, 21
GAN f1 score: 0.442
GAN cohens kappa score: 0.405

-> test with 'LR'
LR tn, fp: 520, 80
LR fn, tp: 3, 24
LR f1 score: 0.366
LR cohens kappa score: 0.320
LR average precision score: 0.301

-> 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: 572, 28
KNN fn, tp: 4, 23
KNN f1 score: 0.590
KNN cohens kappa score: 0.565

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 536, 103
LR fn, tp: 10, 30
LR f1 score: 0.407
LR cohens kappa score: 0.360
LR average precision score: 0.585


average:
LR tn, fp: 514.52, 87.88
LR fn, tp: 4.08, 26.12
LR f1 score: 0.363
LR cohens kappa score: 0.311
LR average precision score: 0.442


minimum:
LR tn, fp: 500, 67
LR fn, tp: 1, 21
LR f1 score: 0.304
LR cohens kappa score: 0.253
LR average precision score: 0.277


-----[ GB ]-----
maximum:
GB tn, fp: 599, 17
GB fn, tp: 8, 31
GB f1 score: 0.870
GB cohens kappa score: 0.862


average:
GB tn, fp: 591.88, 10.52
GB fn, tp: 3.52, 26.68
GB f1 score: 0.793
GB cohens kappa score: 0.781


minimum:
GB tn, fp: 586, 4
GB fn, tp: 0, 22
GB f1 score: 0.703
GB cohens kappa score: 0.685


-----[ KNN ]-----
maximum:
KNN tn, fp: 581, 37
KNN fn, tp: 11, 28
KNN f1 score: 0.651
KNN cohens kappa score: 0.629


average:
KNN tn, fp: 573.52, 28.88
KNN fn, tp: 6.0, 24.2
KNN f1 score: 0.582
KNN cohens kappa score: 0.555


minimum:
KNN tn, fp: 564, 22
KNN fn, tp: 2, 20
KNN f1 score: 0.494
KNN cohens kappa score: 0.461


-----[ GAN ]-----
maximum:
GAN tn, fp: 586, 188
GAN fn, tp: 14, 27
GAN f1 score: 0.667
GAN cohens kappa score: 0.647


average:
GAN tn, fp: 527.2, 75.2
GAN fn, tp: 8.64, 21.56
GAN f1 score: 0.380
GAN cohens kappa score: 0.333


minimum:
GAN tn, fp: 415, 15
GAN fn, tp: 4, 16
GAN f1 score: 0.144
GAN cohens kappa score: 0.065