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

///////////////////////////////////////////
// Running convGAN-majority-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: 579, 24
GAN fn, tp: 5, 26
GAN f1 score: 0.642
GAN cohens kappa score: 0.619

-> test with 'LR'
LR tn, fp: 542, 61
LR fn, tp: 5, 26
LR f1 score: 0.441
LR cohens kappa score: 0.397
LR average precision score: 0.462

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


------ 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: 549, 54
GAN fn, tp: 4, 27
GAN f1 score: 0.482
GAN cohens kappa score: 0.443

-> test with 'LR'
LR tn, fp: 515, 88
LR fn, tp: 3, 28
LR f1 score: 0.381
LR cohens kappa score: 0.329
LR average precision score: 0.460

-> test with 'GB'
GB tn, fp: 591, 12
GB fn, tp: 2, 29
GB f1 score: 0.806
GB cohens kappa score: 0.794

-> test with 'KNN'
KNN tn, fp: 571, 32
KNN fn, tp: 7, 24
KNN f1 score: 0.552
KNN cohens kappa score: 0.522


------ 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: 544, 59
GAN fn, tp: 6, 25
GAN f1 score: 0.435
GAN cohens kappa score: 0.391

-> 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: 0.327

-> 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: 574, 29
KNN fn, tp: 6, 25
KNN f1 score: 0.588
KNN cohens kappa score: 0.561


------ 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: 550, 53
GAN fn, tp: 7, 24
GAN f1 score: 0.444
GAN cohens kappa score: 0.403

-> test with 'LR'
LR tn, fp: 505, 98
LR fn, tp: 4, 27
LR f1 score: 0.346
LR cohens kappa score: 0.291
LR average precision score: 0.398

-> 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: 11, 20
KNN f1 score: 0.471
KNN cohens kappa score: 0.436


------ 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: 591, 9
GAN fn, tp: 6, 21
GAN f1 score: 0.737
GAN cohens kappa score: 0.724

-> test with 'LR'
LR tn, fp: 527, 73
LR fn, tp: 2, 25
LR f1 score: 0.400
LR cohens kappa score: 0.357
LR average precision score: 0.552

-> 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: 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 GAN.predict
GAN tn, fp: 581, 22
GAN fn, tp: 5, 26
GAN f1 score: 0.658
GAN cohens kappa score: 0.637

-> 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: 0.418

-> 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: 583, 20
KNN fn, tp: 5, 26
KNN f1 score: 0.675
KNN cohens kappa score: 0.655


------ 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: 584, 19
GAN fn, tp: 9, 22
GAN f1 score: 0.611
GAN cohens kappa score: 0.588

-> 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: 0.437

-> 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: 570, 33
KNN fn, tp: 4, 27
KNN f1 score: 0.593
KNN cohens kappa score: 0.565


------ 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: 579, 24
GAN fn, tp: 8, 23
GAN f1 score: 0.590
GAN cohens kappa score: 0.564

-> test with 'LR'
LR tn, fp: 514, 89
LR fn, tp: 5, 26
LR f1 score: 0.356
LR cohens kappa score: 0.302
LR average precision score: 0.569

-> 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: 578, 25
KNN fn, tp: 8, 23
KNN f1 score: 0.582
KNN cohens kappa score: 0.556


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

-> 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: 0.281

-> test with 'GB'
GB tn, fp: 588, 15
GB fn, tp: 5, 26
GB f1 score: 0.722
GB cohens kappa score: 0.706

-> 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 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2288 synthetic samples
-> test with GAN.predict
GAN tn, fp: 565, 35
GAN fn, tp: 1, 26
GAN f1 score: 0.591
GAN cohens kappa score: 0.565

-> 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: 0.483

-> test with 'GB'
GB tn, fp: 590, 10
GB fn, tp: 2, 25
GB f1 score: 0.806
GB cohens kappa score: 0.797

-> test with 'KNN'
KNN tn, fp: 567, 33
KNN fn, tp: 5, 22
KNN f1 score: 0.537
KNN cohens kappa score: 0.508


====== 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: 576, 27
GAN fn, tp: 5, 26
GAN f1 score: 0.619
GAN cohens kappa score: 0.594

-> 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: 0.477

-> 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: 579, 24
KNN fn, tp: 9, 22
KNN f1 score: 0.571
KNN cohens kappa score: 0.545


------ 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: 549, 54
GAN fn, tp: 3, 28
GAN f1 score: 0.496
GAN cohens kappa score: 0.457

-> test with 'LR'
LR tn, fp: 528, 75
LR fn, tp: 10, 21
LR f1 score: 0.331
LR cohens kappa score: 0.277
LR average precision score: 0.288

-> test with 'GB'
GB tn, fp: 586, 17
GB fn, tp: 3, 28
GB f1 score: 0.737
GB cohens kappa score: 0.721

-> test with 'KNN'
KNN tn, fp: 571, 32
KNN fn, tp: 6, 25
KNN f1 score: 0.568
KNN cohens kappa score: 0.539


------ 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: 583, 20
GAN fn, tp: 6, 25
GAN f1 score: 0.658
GAN cohens kappa score: 0.637

-> test with 'LR'
LR tn, fp: 517, 86
LR fn, tp: 1, 30
LR f1 score: 0.408
LR cohens kappa score: 0.359
LR average precision score: 0.546

-> test with 'GB'
GB tn, fp: 588, 15
GB fn, tp: 2, 29
GB f1 score: 0.773
GB cohens kappa score: 0.760

-> 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 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 572, 31
GAN fn, tp: 8, 23
GAN f1 score: 0.541
GAN cohens kappa score: 0.511

-> 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: 0.452

-> 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: 569, 34
KNN fn, tp: 6, 25
KNN f1 score: 0.556
KNN cohens kappa score: 0.525


------ 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: 587, 13
GAN fn, tp: 5, 22
GAN f1 score: 0.710
GAN cohens kappa score: 0.695

-> test with 'LR'
LR tn, fp: 512, 88
LR fn, tp: 5, 22
LR f1 score: 0.321
LR cohens kappa score: 0.271
LR average precision score: 0.308

-> test with 'GB'
GB tn, fp: 594, 6
GB fn, tp: 1, 26
GB f1 score: 0.881
GB cohens kappa score: 0.876

-> test with 'KNN'
KNN tn, fp: 578, 22
KNN fn, tp: 2, 25
KNN f1 score: 0.676
KNN cohens kappa score: 0.657


====== 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: 587, 16
GAN fn, tp: 6, 25
GAN f1 score: 0.694
GAN cohens kappa score: 0.676

-> test with 'LR'
LR tn, fp: 522, 81
LR fn, tp: 4, 27
LR f1 score: 0.388
LR cohens kappa score: 0.338
LR average precision score: 0.395

-> 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: 571, 32
KNN fn, tp: 4, 27
KNN f1 score: 0.600
KNN cohens kappa score: 0.573


------ 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: 547, 56
GAN fn, tp: 5, 26
GAN f1 score: 0.460
GAN cohens kappa score: 0.419

-> 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: 0.436

-> 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: 571, 32
KNN fn, tp: 3, 28
KNN f1 score: 0.615
KNN cohens kappa score: 0.589


------ 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: 583, 20
GAN fn, tp: 6, 25
GAN f1 score: 0.658
GAN cohens kappa score: 0.637

-> test with 'LR'
LR tn, fp: 501, 102
LR fn, tp: 2, 29
LR f1 score: 0.358
LR cohens kappa score: 0.303
LR average precision score: 0.576

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 6, 25
GB f1 score: 0.781
GB cohens kappa score: 0.770

-> test with 'KNN'
KNN tn, fp: 579, 24
KNN fn, tp: 5, 26
KNN f1 score: 0.642
KNN cohens kappa score: 0.619


------ 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: 558, 45
GAN fn, tp: 6, 25
GAN f1 score: 0.495
GAN cohens kappa score: 0.458

-> test with 'LR'
LR tn, fp: 497, 106
LR fn, tp: 2, 29
LR f1 score: 0.349
LR cohens kappa score: 0.293
LR average precision score: 0.434

-> 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: 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: 569, 31
GAN fn, tp: 6, 21
GAN f1 score: 0.532
GAN cohens kappa score: 0.503

-> test with 'LR'
LR tn, fp: 512, 88
LR fn, tp: 6, 21
LR f1 score: 0.309
LR cohens kappa score: 0.258
LR average precision score: 0.410

-> test with 'GB'
GB tn, fp: 588, 12
GB fn, tp: 5, 22
GB f1 score: 0.721
GB cohens kappa score: 0.707

-> 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 GAN.predict
GAN tn, fp: 574, 29
GAN fn, tp: 6, 25
GAN f1 score: 0.588
GAN cohens kappa score: 0.561

-> test with 'LR'
LR tn, fp: 514, 89
LR fn, tp: 4, 27
LR f1 score: 0.367
LR cohens kappa score: 0.314
LR average precision score: 0.431

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


------ 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: 586, 17
GAN fn, tp: 10, 21
GAN f1 score: 0.609
GAN cohens kappa score: 0.586

-> test with 'LR'
LR tn, fp: 516, 87
LR fn, tp: 5, 26
LR f1 score: 0.361
LR cohens kappa score: 0.308
LR average precision score: 0.512

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 2, 29
GB f1 score: 0.853
GB cohens kappa score: 0.845

-> 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 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2289 synthetic samples
-> test with GAN.predict
GAN tn, fp: 582, 21
GAN fn, tp: 12, 19
GAN f1 score: 0.535
GAN cohens kappa score: 0.508

-> 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: 0.499

-> test with 'GB'
GB tn, fp: 594, 9
GB fn, tp: 8, 23
GB f1 score: 0.730
GB cohens kappa score: 0.716

-> 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: 580, 23
GAN fn, tp: 5, 26
GAN f1 score: 0.650
GAN cohens kappa score: 0.628

-> 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: 0.570

-> test with 'GB'
GB tn, fp: 593, 10
GB fn, tp: 2, 29
GB f1 score: 0.829
GB cohens kappa score: 0.819

-> 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 GAN.predict
GAN tn, fp: 594, 6
GAN fn, tp: 12, 15
GAN f1 score: 0.625
GAN cohens kappa score: 0.610

-> test with 'LR'
LR tn, fp: 523, 77
LR fn, tp: 3, 24
LR f1 score: 0.375
LR cohens kappa score: 0.329
LR average precision score: 0.309

-> test with 'GB'
GB tn, fp: 593, 7
GB fn, tp: 6, 21
GB f1 score: 0.764
GB cohens kappa score: 0.753

-> test with 'KNN'
KNN tn, fp: 574, 26
KNN fn, tp: 5, 22
KNN f1 score: 0.587
KNN cohens kappa score: 0.563

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 542, 106
LR fn, tp: 10, 30
LR f1 score: 0.441
LR cohens kappa score: 0.397
LR average precision score: 0.576


average:
LR tn, fp: 515.56, 86.84
LR fn, tp: 4.08, 26.12
LR f1 score: 0.366
LR cohens kappa score: 0.314
LR average precision score: 0.441


minimum:
LR tn, fp: 497, 61
LR fn, tp: 1, 21
LR f1 score: 0.309
LR cohens kappa score: 0.258
LR average precision score: 0.281


-----[ GB ]-----
maximum:
GB tn, fp: 600, 17
GB fn, tp: 8, 29
GB f1 score: 0.881
GB cohens kappa score: 0.876


average:
GB tn, fp: 592.36, 10.04
GB fn, tp: 4.0, 26.2
GB f1 score: 0.789
GB cohens kappa score: 0.778


minimum:
GB tn, fp: 586, 3
GB fn, tp: 1, 21
GB f1 score: 0.721
GB cohens kappa score: 0.706


-----[ KNN ]-----
maximum:
KNN tn, fp: 583, 37
KNN fn, tp: 11, 28
KNN f1 score: 0.676
KNN cohens kappa score: 0.657


average:
KNN tn, fp: 573.12, 29.28
KNN fn, tp: 6.0, 24.2
KNN f1 score: 0.580
KNN cohens kappa score: 0.552


minimum:
KNN tn, fp: 565, 20
KNN fn, tp: 2, 20
KNN f1 score: 0.471
KNN cohens kappa score: 0.436


-----[ GAN ]-----
maximum:
GAN tn, fp: 594, 59
GAN fn, tp: 12, 28
GAN f1 score: 0.737
GAN cohens kappa score: 0.724


average:
GAN tn, fp: 573.16, 29.24
GAN fn, tp: 6.48, 23.72
GAN f1 score: 0.585
GAN cohens kappa score: 0.558


minimum:
GAN tn, fp: 544, 6
GAN fn, tp: 1, 15
GAN f1 score: 0.435
GAN cohens kappa score: 0.391