ConvGeNBenchmarkResults/data_result/CTGAN/folding_kr-vs-k-zero-one_vs_draw.log

///////////////////////////////////////////
// Running ctGAN on folding_kr-vs-k-zero-one_vs_draw
///////////////////////////////////////////

Load 'data_input/folding_kr-vs-k-zero-one_vs_draw'
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 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 496, 64
LR fn, tp: 0, 21
LR f1 score: 0.396
LR cohens kappa score: 0.359
LR average precision score: 0.889

-> test with 'RF'
RF tn, fp: 541, 19
RF fn, tp: 0, 21
RF f1 score: 0.689
RF cohens kappa score: 0.673

-> test with 'GB'
GB tn, fp: 535, 25
GB fn, tp: 0, 21
GB f1 score: 0.627
GB cohens kappa score: 0.607

-> test with 'KNN'
KNN tn, fp: 537, 23
KNN fn, tp: 0, 21
KNN f1 score: 0.646
KNN cohens kappa score: 0.628


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 500, 60
LR fn, tp: 0, 21
LR f1 score: 0.412
LR cohens kappa score: 0.376
LR average precision score: 0.903

-> test with 'RF'
RF tn, fp: 545, 15
RF fn, tp: 0, 21
RF f1 score: 0.737
RF cohens kappa score: 0.724

-> test with 'GB'
GB tn, fp: 540, 20
GB fn, tp: 0, 21
GB f1 score: 0.677
GB cohens kappa score: 0.661

-> test with 'KNN'
KNN tn, fp: 548, 12
KNN fn, tp: 0, 21
KNN f1 score: 0.778
KNN cohens kappa score: 0.768


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 494, 66
LR fn, tp: 0, 21
LR f1 score: 0.389
LR cohens kappa score: 0.351
LR average precision score: 0.833

-> test with 'RF'
RF tn, fp: 535, 25
RF fn, tp: 0, 21
RF f1 score: 0.627
RF cohens kappa score: 0.607

-> test with 'GB'
GB tn, fp: 531, 29
GB fn, tp: 0, 21
GB f1 score: 0.592
GB cohens kappa score: 0.570

-> test with 'KNN'
KNN tn, fp: 538, 22
KNN fn, tp: 0, 21
KNN f1 score: 0.656
KNN cohens kappa score: 0.639


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 491, 69
LR fn, tp: 0, 21
LR f1 score: 0.378
LR cohens kappa score: 0.340
LR average precision score: 0.800

-> test with 'RF'
RF tn, fp: 529, 31
RF fn, tp: 0, 21
RF f1 score: 0.575
RF cohens kappa score: 0.552

-> test with 'GB'
GB tn, fp: 529, 31
GB fn, tp: 0, 21
GB f1 score: 0.575
GB cohens kappa score: 0.552

-> test with 'KNN'
KNN tn, fp: 532, 28
KNN fn, tp: 0, 21
KNN f1 score: 0.600
KNN cohens kappa score: 0.579


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 495, 61
LR fn, tp: 0, 21
LR f1 score: 0.408
LR cohens kappa score: 0.371
LR average precision score: 0.910

-> test with 'RF'
RF tn, fp: 539, 17
RF fn, tp: 0, 21
RF f1 score: 0.712
RF cohens kappa score: 0.698

-> test with 'GB'
GB tn, fp: 537, 19
GB fn, tp: 0, 21
GB f1 score: 0.689
GB cohens kappa score: 0.673

-> test with 'KNN'
KNN tn, fp: 542, 14
KNN fn, tp: 0, 21
KNN f1 score: 0.750
KNN cohens kappa score: 0.738


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 487, 73
LR fn, tp: 0, 21
LR f1 score: 0.365
LR cohens kappa score: 0.325
LR average precision score: 0.883

-> test with 'RF'
RF tn, fp: 540, 20
RF fn, tp: 0, 21
RF f1 score: 0.677
RF cohens kappa score: 0.661

-> test with 'GB'
GB tn, fp: 531, 29
GB fn, tp: 0, 21
GB f1 score: 0.592
GB cohens kappa score: 0.570

-> test with 'KNN'
KNN tn, fp: 544, 16
KNN fn, tp: 0, 21
KNN f1 score: 0.724
KNN cohens kappa score: 0.711


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 504, 56
LR fn, tp: 0, 21
LR f1 score: 0.429
LR cohens kappa score: 0.394
LR average precision score: 0.819

-> test with 'RF'
RF tn, fp: 542, 18
RF fn, tp: 0, 21
RF f1 score: 0.700
RF cohens kappa score: 0.685

-> test with 'GB'
GB tn, fp: 539, 21
GB fn, tp: 0, 21
GB f1 score: 0.667
GB cohens kappa score: 0.650

-> test with 'KNN'
KNN tn, fp: 546, 14
KNN fn, tp: 0, 21
KNN f1 score: 0.750
KNN cohens kappa score: 0.738


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 503, 57
LR fn, tp: 0, 21
LR f1 score: 0.424
LR cohens kappa score: 0.389
LR average precision score: 0.906

-> test with 'RF'
RF tn, fp: 546, 14
RF fn, tp: 0, 21
RF f1 score: 0.750
RF cohens kappa score: 0.738

-> test with 'GB'
GB tn, fp: 540, 20
GB fn, tp: 0, 21
GB f1 score: 0.677
GB cohens kappa score: 0.661

-> test with 'KNN'
KNN tn, fp: 551, 9
KNN fn, tp: 0, 21
KNN f1 score: 0.824
KNN cohens kappa score: 0.816


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 481, 79
LR fn, tp: 0, 21
LR f1 score: 0.347
LR cohens kappa score: 0.306
LR average precision score: 0.845

-> test with 'RF'
RF tn, fp: 539, 21
RF fn, tp: 0, 21
RF f1 score: 0.667
RF cohens kappa score: 0.650

-> test with 'GB'
GB tn, fp: 531, 29
GB fn, tp: 0, 21
GB f1 score: 0.592
GB cohens kappa score: 0.570

-> test with 'KNN'
KNN tn, fp: 538, 22
KNN fn, tp: 0, 21
KNN f1 score: 0.656
KNN cohens kappa score: 0.639


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 487, 69
LR fn, tp: 0, 21
LR f1 score: 0.378
LR cohens kappa score: 0.339
LR average precision score: 0.841

-> test with 'RF'
RF tn, fp: 537, 19
RF fn, tp: 0, 21
RF f1 score: 0.689
RF cohens kappa score: 0.673

-> test with 'GB'
GB tn, fp: 534, 22
GB fn, tp: 0, 21
GB f1 score: 0.656
GB cohens kappa score: 0.639

-> test with 'KNN'
KNN tn, fp: 538, 18
KNN fn, tp: 0, 21
KNN f1 score: 0.700
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 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 506, 54
LR fn, tp: 0, 21
LR f1 score: 0.438
LR cohens kappa score: 0.404
LR average precision score: 0.914

-> test with 'RF'
RF tn, fp: 539, 21
RF fn, tp: 0, 21
RF f1 score: 0.667
RF cohens kappa score: 0.650

-> test with 'GB'
GB tn, fp: 538, 22
GB fn, tp: 0, 21
GB f1 score: 0.656
GB cohens kappa score: 0.639

-> test with 'KNN'
KNN tn, fp: 539, 21
KNN fn, tp: 0, 21
KNN f1 score: 0.667
KNN cohens kappa score: 0.650


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 511, 49
LR fn, tp: 0, 21
LR f1 score: 0.462
LR cohens kappa score: 0.430
LR average precision score: 0.876

-> test with 'RF'
RF tn, fp: 547, 13
RF fn, tp: 0, 21
RF f1 score: 0.764
RF cohens kappa score: 0.753

-> test with 'GB'
GB tn, fp: 540, 20
GB fn, tp: 0, 21
GB f1 score: 0.677
GB cohens kappa score: 0.661

-> test with 'KNN'
KNN tn, fp: 548, 12
KNN fn, tp: 0, 21
KNN f1 score: 0.778
KNN cohens kappa score: 0.768


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 498, 62
LR fn, tp: 0, 21
LR f1 score: 0.404
LR cohens kappa score: 0.367
LR average precision score: 0.781

-> test with 'RF'
RF tn, fp: 540, 20
RF fn, tp: 0, 21
RF f1 score: 0.677
RF cohens kappa score: 0.661

-> test with 'GB'
GB tn, fp: 530, 30
GB fn, tp: 0, 21
GB f1 score: 0.583
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 541, 19
KNN fn, tp: 0, 21
KNN f1 score: 0.689
KNN cohens kappa score: 0.673


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 489, 71
LR fn, tp: 0, 21
LR f1 score: 0.372
LR cohens kappa score: 0.332
LR average precision score: 0.890

-> test with 'RF'
RF tn, fp: 546, 14
RF fn, tp: 0, 21
RF f1 score: 0.750
RF cohens kappa score: 0.738

-> test with 'GB'
GB tn, fp: 539, 21
GB fn, tp: 0, 21
GB f1 score: 0.667
GB cohens kappa score: 0.650

-> test with 'KNN'
KNN tn, fp: 547, 13
KNN fn, tp: 0, 21
KNN f1 score: 0.764
KNN cohens kappa score: 0.753


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 487, 69
LR fn, tp: 0, 21
LR f1 score: 0.378
LR cohens kappa score: 0.339
LR average precision score: 0.877

-> test with 'RF'
RF tn, fp: 535, 21
RF fn, tp: 0, 21
RF f1 score: 0.667
RF cohens kappa score: 0.650

-> test with 'GB'
GB tn, fp: 528, 28
GB fn, tp: 0, 21
GB f1 score: 0.600
GB cohens kappa score: 0.579

-> test with 'KNN'
KNN tn, fp: 535, 21
KNN fn, tp: 0, 21
KNN f1 score: 0.667
KNN cohens kappa score: 0.650


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 499, 61
LR fn, tp: 0, 21
LR f1 score: 0.408
LR cohens kappa score: 0.372
LR average precision score: 0.897

-> test with 'RF'
RF tn, fp: 543, 17
RF fn, tp: 0, 21
RF f1 score: 0.712
RF cohens kappa score: 0.698

-> test with 'GB'
GB tn, fp: 532, 28
GB fn, tp: 0, 21
GB f1 score: 0.600
GB cohens kappa score: 0.579

-> test with 'KNN'
KNN tn, fp: 544, 16
KNN fn, tp: 0, 21
KNN f1 score: 0.724
KNN cohens kappa score: 0.711


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 486, 74
LR fn, tp: 0, 21
LR f1 score: 0.362
LR cohens kappa score: 0.322
LR average precision score: 0.913

-> test with 'RF'
RF tn, fp: 544, 16
RF fn, tp: 0, 21
RF f1 score: 0.724
RF cohens kappa score: 0.711

-> test with 'GB'
GB tn, fp: 537, 23
GB fn, tp: 0, 21
GB f1 score: 0.646
GB cohens kappa score: 0.628

-> test with 'KNN'
KNN tn, fp: 547, 13
KNN fn, tp: 0, 21
KNN f1 score: 0.764
KNN cohens kappa score: 0.753


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 497, 63
LR fn, tp: 0, 21
LR f1 score: 0.400
LR cohens kappa score: 0.363
LR average precision score: 0.748

-> test with 'RF'
RF tn, fp: 547, 13
RF fn, tp: 0, 21
RF f1 score: 0.764
RF cohens kappa score: 0.753

-> test with 'GB'
GB tn, fp: 538, 22
GB fn, tp: 0, 21
GB f1 score: 0.656
GB cohens kappa score: 0.639

-> test with 'KNN'
KNN tn, fp: 545, 15
KNN fn, tp: 0, 21
KNN f1 score: 0.737
KNN cohens kappa score: 0.724


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 501, 59
LR fn, tp: 0, 21
LR f1 score: 0.416
LR cohens kappa score: 0.380
LR average precision score: 0.889

-> test with 'RF'
RF tn, fp: 552, 8
RF fn, tp: 0, 21
RF f1 score: 0.840
RF cohens kappa score: 0.833

-> test with 'GB'
GB tn, fp: 538, 22
GB fn, tp: 0, 21
GB f1 score: 0.656
GB cohens kappa score: 0.639

-> test with 'KNN'
KNN tn, fp: 552, 8
KNN fn, tp: 0, 21
KNN f1 score: 0.840
KNN cohens kappa score: 0.833


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 497, 59
LR fn, tp: 0, 21
LR f1 score: 0.416
LR cohens kappa score: 0.380
LR average precision score: 0.900

-> test with 'RF'
RF tn, fp: 535, 21
RF fn, tp: 0, 21
RF f1 score: 0.667
RF cohens kappa score: 0.650

-> test with 'GB'
GB tn, fp: 530, 26
GB fn, tp: 0, 21
GB f1 score: 0.618
GB cohens kappa score: 0.597

-> test with 'KNN'
KNN tn, fp: 536, 20
KNN fn, tp: 0, 21
KNN f1 score: 0.677
KNN cohens kappa score: 0.661


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 495, 65
LR fn, tp: 0, 21
LR f1 score: 0.393
LR cohens kappa score: 0.355
LR average precision score: 0.927

-> test with 'RF'
RF tn, fp: 535, 25
RF fn, tp: 0, 21
RF f1 score: 0.627
RF cohens kappa score: 0.607

-> test with 'GB'
GB tn, fp: 528, 32
GB fn, tp: 0, 21
GB f1 score: 0.568
GB cohens kappa score: 0.544

-> test with 'KNN'
KNN tn, fp: 539, 21
KNN fn, tp: 0, 21
KNN f1 score: 0.667
KNN cohens kappa score: 0.650


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 491, 69
LR fn, tp: 0, 21
LR f1 score: 0.378
LR cohens kappa score: 0.340
LR average precision score: 0.844

-> test with 'RF'
RF tn, fp: 542, 18
RF fn, tp: 0, 21
RF f1 score: 0.700
RF cohens kappa score: 0.685

-> test with 'GB'
GB tn, fp: 535, 25
GB fn, tp: 0, 21
GB f1 score: 0.627
GB cohens kappa score: 0.607

-> test with 'KNN'
KNN tn, fp: 545, 15
KNN fn, tp: 0, 21
KNN f1 score: 0.737
KNN cohens kappa score: 0.724


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 496, 64
LR fn, tp: 0, 21
LR f1 score: 0.396
LR cohens kappa score: 0.359
LR average precision score: 0.838

-> test with 'RF'
RF tn, fp: 543, 17
RF fn, tp: 0, 21
RF f1 score: 0.712
RF cohens kappa score: 0.698

-> test with 'GB'
GB tn, fp: 541, 19
GB fn, tp: 0, 21
GB f1 score: 0.689
GB cohens kappa score: 0.673

-> test with 'KNN'
KNN tn, fp: 545, 15
KNN fn, tp: 0, 21
KNN f1 score: 0.737
KNN cohens kappa score: 0.724


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 501, 59
LR fn, tp: 0, 21
LR f1 score: 0.416
LR cohens kappa score: 0.380
LR average precision score: 0.845

-> test with 'RF'
RF tn, fp: 546, 14
RF fn, tp: 0, 21
RF f1 score: 0.750
RF cohens kappa score: 0.738

-> test with 'GB'
GB tn, fp: 544, 16
GB fn, tp: 0, 21
GB f1 score: 0.724
GB cohens kappa score: 0.711

-> test with 'KNN'
KNN tn, fp: 546, 14
KNN fn, tp: 0, 21
KNN f1 score: 0.750
KNN cohens kappa score: 0.738


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 489, 67
LR fn, tp: 0, 21
LR f1 score: 0.385
LR cohens kappa score: 0.347
LR average precision score: 0.884

-> test with 'RF'
RF tn, fp: 541, 15
RF fn, tp: 0, 21
RF f1 score: 0.737
RF cohens kappa score: 0.724

-> test with 'GB'
GB tn, fp: 532, 24
GB fn, tp: 0, 21
GB f1 score: 0.636
GB cohens kappa score: 0.617

-> test with 'KNN'
KNN tn, fp: 544, 12
KNN fn, tp: 1, 20
KNN f1 score: 0.755
KNN cohens kappa score: 0.743

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 511, 79
LR fn, tp: 0, 21
LR f1 score: 0.462
LR cohens kappa score: 0.430
LR average precision score: 0.927


average:
LR tn, fp: 495.24, 63.96
LR fn, tp: 0.0, 21.0
LR f1 score: 0.398
LR cohens kappa score: 0.361
LR average precision score: 0.866


minimum:
LR tn, fp: 481, 49
LR fn, tp: 0, 21
LR f1 score: 0.347
LR cohens kappa score: 0.306
LR average precision score: 0.748


-----[ RF ]-----
maximum:
RF tn, fp: 552, 31
RF fn, tp: 0, 21
RF f1 score: 0.840
RF cohens kappa score: 0.833


average:
RF tn, fp: 541.12, 18.08
RF fn, tp: 0.0, 21.0
RF f1 score: 0.703
RF cohens kappa score: 0.688


minimum:
RF tn, fp: 529, 8
RF fn, tp: 0, 21
RF f1 score: 0.575
RF cohens kappa score: 0.552


-----[ GB ]-----
maximum:
GB tn, fp: 544, 32
GB fn, tp: 0, 21
GB f1 score: 0.724
GB cohens kappa score: 0.711


average:
GB tn, fp: 535.08, 24.12
GB fn, tp: 0.0, 21.0
GB f1 score: 0.638
GB cohens kappa score: 0.619


minimum:
GB tn, fp: 528, 16
GB fn, tp: 0, 21
GB f1 score: 0.568
GB cohens kappa score: 0.544


-----[ KNN ]-----
maximum:
KNN tn, fp: 552, 28
KNN fn, tp: 1, 21
KNN f1 score: 0.840
KNN cohens kappa score: 0.833


average:
KNN tn, fp: 542.68, 16.52
KNN fn, tp: 0.04, 20.96
KNN f1 score: 0.721
KNN cohens kappa score: 0.708


minimum:
KNN tn, fp: 532, 8
KNN fn, tp: 0, 20
KNN f1 score: 0.600
KNN cohens kappa score: 0.579