ConvGeNBenchmarkResults/data_result/ctGAN/folding_yeast5.log

///////////////////////////////////////////
// Running ctGAN on folding_yeast5
///////////////////////////////////////////

Load 'data_input/folding_yeast5'
from pickle file
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 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 278, 10
LR fn, tp: 0, 9
LR f1 score: 0.643
LR cohens kappa score: 0.628
LR average precision score: 0.840

-> test with 'RF'
RF tn, fp: 281, 7
RF fn, tp: 4, 5
RF f1 score: 0.476
RF cohens kappa score: 0.457

-> test with 'GB'
GB tn, fp: 282, 6
GB fn, tp: 4, 5
GB f1 score: 0.500
GB cohens kappa score: 0.483

-> test with 'KNN'
KNN tn, fp: 280, 8
KNN fn, tp: 1, 8
KNN f1 score: 0.640
KNN cohens kappa score: 0.625


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 262, 26
LR fn, tp: 0, 9
LR f1 score: 0.409
LR cohens kappa score: 0.379
LR average precision score: 0.681

-> test with 'RF'
RF tn, fp: 279, 9
RF fn, tp: 0, 9
RF f1 score: 0.667
RF cohens kappa score: 0.653

-> test with 'GB'
GB tn, fp: 279, 9
GB fn, tp: 2, 7
GB f1 score: 0.560
GB cohens kappa score: 0.542

-> test with 'KNN'
KNN tn, fp: 271, 17
KNN fn, tp: 0, 9
KNN f1 score: 0.514
KNN cohens kappa score: 0.491


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 268, 20
LR fn, tp: 1, 8
LR f1 score: 0.432
LR cohens kappa score: 0.405
LR average precision score: 0.454

-> test with 'RF'
RF tn, fp: 284, 4
RF fn, tp: 1, 8
RF f1 score: 0.762
RF cohens kappa score: 0.753

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 1, 8
GB f1 score: 0.800
GB cohens kappa score: 0.793

-> test with 'KNN'
KNN tn, fp: 273, 15
KNN fn, tp: 1, 8
KNN f1 score: 0.500
KNN cohens kappa score: 0.477


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.594
LR average precision score: 0.783

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 0, 9
RF f1 score: 0.947
RF cohens kappa score: 0.946

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 2, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 287, 1
KNN fn, tp: 1, 8
KNN f1 score: 0.889
KNN cohens kappa score: 0.885


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 277, 11
LR fn, tp: 1, 7
LR f1 score: 0.538
LR cohens kappa score: 0.521
LR average precision score: 0.627

-> test with 'RF'
RF tn, fp: 285, 3
RF fn, tp: 2, 6
RF f1 score: 0.706
RF cohens kappa score: 0.697

-> test with 'GB'
GB tn, fp: 281, 7
GB fn, tp: 1, 7
GB f1 score: 0.636
GB cohens kappa score: 0.623

-> test with 'KNN'
KNN tn, fp: 273, 15
KNN fn, tp: 0, 8
KNN f1 score: 0.516
KNN cohens kappa score: 0.496


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 0, 9
LR f1 score: 0.720
LR cohens kappa score: 0.709
LR average precision score: 0.748

-> test with 'RF'
RF tn, fp: 283, 5
RF fn, tp: 0, 9
RF f1 score: 0.783
RF cohens kappa score: 0.774

-> test with 'GB'
GB tn, fp: 281, 7
GB fn, tp: 1, 8
GB f1 score: 0.667
GB cohens kappa score: 0.654

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 0, 9
KNN f1 score: 0.750
KNN cohens kappa score: 0.740


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 4, 5
LR f1 score: 0.345
LR cohens kappa score: 0.316
LR average precision score: 0.334

-> test with 'RF'
RF tn, fp: 280, 8
RF fn, tp: 5, 4
RF f1 score: 0.381
RF cohens kappa score: 0.359

-> test with 'GB'
GB tn, fp: 278, 10
GB fn, tp: 4, 5
GB f1 score: 0.417
GB cohens kappa score: 0.394

-> test with 'KNN'
KNN tn, fp: 274, 14
KNN fn, tp: 2, 7
KNN f1 score: 0.467
KNN cohens kappa score: 0.443


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 276, 12
LR fn, tp: 1, 8
LR f1 score: 0.552
LR cohens kappa score: 0.532
LR average precision score: 0.561

-> test with 'RF'
RF tn, fp: 284, 4
RF fn, tp: 1, 8
RF f1 score: 0.762
RF cohens kappa score: 0.753

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 1, 8
GB f1 score: 0.762
GB cohens kappa score: 0.753

-> test with 'KNN'
KNN tn, fp: 280, 8
KNN fn, tp: 1, 8
KNN f1 score: 0.640
KNN cohens kappa score: 0.625


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 261, 27
LR fn, tp: 0, 9
LR f1 score: 0.400
LR cohens kappa score: 0.369
LR average precision score: 0.622

-> test with 'RF'
RF tn, fp: 278, 10
RF fn, tp: 1, 8
RF f1 score: 0.593
RF cohens kappa score: 0.575

-> test with 'GB'
GB tn, fp: 280, 8
GB fn, tp: 1, 8
GB f1 score: 0.640
GB cohens kappa score: 0.625

-> test with 'KNN'
KNN tn, fp: 270, 18
KNN fn, tp: 0, 9
KNN f1 score: 0.500
KNN cohens kappa score: 0.476


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 2, 6
LR f1 score: 0.545
LR cohens kappa score: 0.529
LR average precision score: 0.607

-> test with 'RF'
RF tn, fp: 284, 4
RF fn, tp: 2, 6
RF f1 score: 0.667
RF cohens kappa score: 0.656

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 3, 5
GB f1 score: 0.588
GB cohens kappa score: 0.576

-> test with 'KNN'
KNN tn, fp: 281, 7
KNN fn, tp: 0, 8
KNN f1 score: 0.696
KNN cohens kappa score: 0.685


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 2, 7
LR f1 score: 0.583
LR cohens kappa score: 0.567
LR average precision score: 0.664

-> test with 'RF'
RF tn, fp: 282, 6
RF fn, tp: 3, 6
RF f1 score: 0.571
RF cohens kappa score: 0.556

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 3, 6
GB f1 score: 0.600
GB cohens kappa score: 0.586

-> test with 'KNN'
KNN tn, fp: 280, 8
KNN fn, tp: 3, 6
KNN f1 score: 0.522
KNN cohens kappa score: 0.503


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 1, 8
LR f1 score: 0.500
LR cohens kappa score: 0.477
LR average precision score: 0.573

-> test with 'RF'
RF tn, fp: 282, 6
RF fn, tp: 2, 7
RF f1 score: 0.636
RF cohens kappa score: 0.623

-> test with 'GB'
GB tn, fp: 280, 8
GB fn, tp: 2, 7
GB f1 score: 0.583
GB cohens kappa score: 0.567

-> test with 'KNN'
KNN tn, fp: 277, 11
KNN fn, tp: 1, 8
KNN f1 score: 0.571
KNN cohens kappa score: 0.553


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 4
LR fn, tp: 2, 7
LR f1 score: 0.700
LR cohens kappa score: 0.690
LR average precision score: 0.841

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 0, 9
RF f1 score: 0.900
RF cohens kappa score: 0.897

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 2, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 285, 3
KNN fn, tp: 0, 9
KNN f1 score: 0.857
KNN cohens kappa score: 0.852


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 21
LR fn, tp: 1, 8
LR f1 score: 0.421
LR cohens kappa score: 0.393
LR average precision score: 0.366

-> test with 'RF'
RF tn, fp: 279, 9
RF fn, tp: 6, 3
RF f1 score: 0.286
RF cohens kappa score: 0.260

-> test with 'GB'
GB tn, fp: 279, 9
GB fn, tp: 7, 2
GB f1 score: 0.200
GB cohens kappa score: 0.172

-> test with 'KNN'
KNN tn, fp: 268, 20
KNN fn, tp: 3, 6
KNN f1 score: 0.343
KNN cohens kappa score: 0.312


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 257, 31
LR fn, tp: 0, 8
LR f1 score: 0.340
LR cohens kappa score: 0.309
LR average precision score: 0.279

-> test with 'RF'
RF tn, fp: 280, 8
RF fn, tp: 1, 7
RF f1 score: 0.609
RF cohens kappa score: 0.594

-> test with 'GB'
GB tn, fp: 277, 11
GB fn, tp: 1, 7
GB f1 score: 0.538
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 263, 25
KNN fn, tp: 0, 8
KNN f1 score: 0.390
KNN cohens kappa score: 0.363


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 19
LR fn, tp: 0, 9
LR f1 score: 0.486
LR cohens kappa score: 0.462
LR average precision score: 0.551

-> test with 'RF'
RF tn, fp: 283, 5
RF fn, tp: 1, 8
RF f1 score: 0.727
RF cohens kappa score: 0.717

-> test with 'GB'
GB tn, fp: 282, 6
GB fn, tp: 2, 7
GB f1 score: 0.636
GB cohens kappa score: 0.623

-> test with 'KNN'
KNN tn, fp: 280, 8
KNN fn, tp: 0, 9
KNN f1 score: 0.692
KNN cohens kappa score: 0.680


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 14
LR fn, tp: 3, 6
LR f1 score: 0.414
LR cohens kappa score: 0.388
LR average precision score: 0.525

-> test with 'RF'
RF tn, fp: 281, 7
RF fn, tp: 2, 7
RF f1 score: 0.609
RF cohens kappa score: 0.594

-> test with 'GB'
GB tn, fp: 281, 7
GB fn, tp: 2, 7
GB f1 score: 0.609
GB cohens kappa score: 0.594

-> test with 'KNN'
KNN tn, fp: 278, 10
KNN fn, tp: 2, 7
KNN f1 score: 0.538
KNN cohens kappa score: 0.519


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 1, 8
LR f1 score: 0.667
LR cohens kappa score: 0.654
LR average precision score: 0.635

-> test with 'RF'
RF tn, fp: 281, 7
RF fn, tp: 1, 8
RF f1 score: 0.667
RF cohens kappa score: 0.654

-> test with 'GB'
GB tn, fp: 281, 7
GB fn, tp: 2, 7
GB f1 score: 0.609
GB cohens kappa score: 0.594

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 1, 8
KNN f1 score: 0.696
KNN cohens kappa score: 0.684


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 19
LR fn, tp: 0, 9
LR f1 score: 0.486
LR cohens kappa score: 0.462
LR average precision score: 0.473

-> test with 'RF'
RF tn, fp: 282, 6
RF fn, tp: 3, 6
RF f1 score: 0.571
RF cohens kappa score: 0.556

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 2, 7
GB f1 score: 0.700
GB cohens kappa score: 0.690

-> test with 'KNN'
KNN tn, fp: 270, 18
KNN fn, tp: 0, 9
KNN f1 score: 0.500
KNN cohens kappa score: 0.476


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 18
LR fn, tp: 0, 8
LR f1 score: 0.471
LR cohens kappa score: 0.448
LR average precision score: 0.408

-> test with 'RF'
RF tn, fp: 277, 11
RF fn, tp: 1, 7
RF f1 score: 0.538
RF cohens kappa score: 0.521

-> test with 'GB'
GB tn, fp: 275, 13
GB fn, tp: 1, 7
GB f1 score: 0.500
GB cohens kappa score: 0.480

-> test with 'KNN'
KNN tn, fp: 275, 13
KNN fn, tp: 0, 8
KNN f1 score: 0.552
KNN cohens kappa score: 0.533


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 21
LR fn, tp: 0, 9
LR f1 score: 0.462
LR cohens kappa score: 0.435
LR average precision score: 0.586

-> test with 'RF'
RF tn, fp: 276, 12
RF fn, tp: 0, 9
RF f1 score: 0.600
RF cohens kappa score: 0.582

-> test with 'GB'
GB tn, fp: 278, 10
GB fn, tp: 1, 8
GB f1 score: 0.593
GB cohens kappa score: 0.575

-> test with 'KNN'
KNN tn, fp: 268, 20
KNN fn, tp: 0, 9
KNN f1 score: 0.474
KNN cohens kappa score: 0.448


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 0, 9
LR f1 score: 0.581
LR cohens kappa score: 0.562
LR average precision score: 0.775

-> test with 'RF'
RF tn, fp: 284, 4
RF fn, tp: 2, 7
RF f1 score: 0.700
RF cohens kappa score: 0.690

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 2, 7
GB f1 score: 0.700
GB cohens kappa score: 0.690

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 0, 9
KNN f1 score: 0.750
KNN cohens kappa score: 0.740


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 276, 12
LR fn, tp: 1, 8
LR f1 score: 0.552
LR cohens kappa score: 0.532
LR average precision score: 0.740

-> test with 'RF'
RF tn, fp: 285, 3
RF fn, tp: 1, 8
RF f1 score: 0.800
RF cohens kappa score: 0.793

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 1, 8
GB f1 score: 0.727
GB cohens kappa score: 0.717

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 2, 7
KNN f1 score: 0.636
KNN cohens kappa score: 0.623


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 4, 5
LR f1 score: 0.455
LR cohens kappa score: 0.434
LR average precision score: 0.503

-> test with 'RF'
RF tn, fp: 285, 3
RF fn, tp: 3, 6
RF f1 score: 0.667
RF cohens kappa score: 0.656

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 3, 6
GB f1 score: 0.706
GB cohens kappa score: 0.697

-> test with 'KNN'
KNN tn, fp: 280, 8
KNN fn, tp: 4, 5
KNN f1 score: 0.455
KNN cohens kappa score: 0.434


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 272, 16
LR fn, tp: 1, 7
LR f1 score: 0.452
LR cohens kappa score: 0.429
LR average precision score: 0.439

-> test with 'RF'
RF tn, fp: 279, 9
RF fn, tp: 0, 8
RF f1 score: 0.640
RF cohens kappa score: 0.626

-> test with 'GB'
GB tn, fp: 279, 9
GB fn, tp: 2, 6
GB f1 score: 0.522
GB cohens kappa score: 0.504

-> test with 'KNN'
KNN tn, fp: 274, 14
KNN fn, tp: 1, 7
KNN f1 score: 0.483
KNN cohens kappa score: 0.462

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 284, 31
LR fn, tp: 4, 9
LR f1 score: 0.720
LR cohens kappa score: 0.709
LR average precision score: 0.841


average:
LR tn, fp: 273.24, 14.76
LR fn, tp: 1.08, 7.72
LR f1 score: 0.510
LR cohens kappa score: 0.489
LR average precision score: 0.585


minimum:
LR tn, fp: 257, 4
LR fn, tp: 0, 5
LR f1 score: 0.340
LR cohens kappa score: 0.309
LR average precision score: 0.279


-----[ RF ]-----
maximum:
RF tn, fp: 287, 12
RF fn, tp: 6, 9
RF f1 score: 0.947
RF cohens kappa score: 0.946


average:
RF tn, fp: 281.88, 6.12
RF fn, tp: 1.68, 7.12
RF f1 score: 0.651
RF cohens kappa score: 0.638


minimum:
RF tn, fp: 276, 1
RF fn, tp: 0, 3
RF f1 score: 0.286
RF cohens kappa score: 0.260


-----[ GB ]-----
maximum:
GB tn, fp: 287, 13
GB fn, tp: 7, 8
GB f1 score: 0.824
GB cohens kappa score: 0.818


average:
GB tn, fp: 281.6, 6.4
GB fn, tp: 2.12, 6.68
GB f1 score: 0.618
GB cohens kappa score: 0.604


minimum:
GB tn, fp: 275, 1
GB fn, tp: 1, 2
GB f1 score: 0.200
GB cohens kappa score: 0.172


-----[ KNN ]-----
maximum:
KNN tn, fp: 287, 25
KNN fn, tp: 4, 9
KNN f1 score: 0.889
KNN cohens kappa score: 0.885


average:
KNN tn, fp: 276.6, 11.4
KNN fn, tp: 0.92, 7.88
KNN f1 score: 0.583
KNN cohens kappa score: 0.565


minimum:
KNN tn, fp: 263, 1
KNN fn, tp: 0, 5
KNN f1 score: 0.343
KNN cohens kappa score: 0.312