ConvGeNBenchmarkResults/data_result/CTGAN/folding_kr-vs-k-three_vs_eleven.log

///////////////////////////////////////////
// Running ctGAN on folding_kr-vs-k-three_vs_eleven
///////////////////////////////////////////

Load 'data_input/folding_kr-vs-k-three_vs_eleven'
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 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 547, 24
LR fn, tp: 0, 17
LR f1 score: 0.586
LR cohens kappa score: 0.569
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 559, 12
RF fn, tp: 0, 17
RF f1 score: 0.739
RF cohens kappa score: 0.729

-> test with 'GB'
GB tn, fp: 558, 13
GB fn, tp: 0, 17
GB f1 score: 0.723
GB cohens kappa score: 0.713

-> test with 'KNN'
KNN tn, fp: 551, 20
KNN fn, tp: 0, 17
KNN f1 score: 0.630
KNN cohens kappa score: 0.614


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 553, 18
LR fn, tp: 0, 17
LR f1 score: 0.654
LR cohens kappa score: 0.640
LR average precision score: 0.993

-> test with 'RF'
RF tn, fp: 566, 5
RF fn, tp: 0, 17
RF f1 score: 0.872
RF cohens kappa score: 0.867

-> test with 'GB'
GB tn, fp: 564, 7
GB fn, tp: 0, 17
GB f1 score: 0.829
GB cohens kappa score: 0.823

-> test with 'KNN'
KNN tn, fp: 555, 16
KNN fn, tp: 0, 17
KNN f1 score: 0.680
KNN cohens kappa score: 0.667


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 545, 26
LR fn, tp: 0, 17
LR f1 score: 0.567
LR cohens kappa score: 0.548
LR average precision score: 0.997

-> test with 'RF'
RF tn, fp: 562, 9
RF fn, tp: 0, 17
RF f1 score: 0.791
RF cohens kappa score: 0.783

-> test with 'GB'
GB tn, fp: 561, 10
GB fn, tp: 0, 17
GB f1 score: 0.773
GB cohens kappa score: 0.764

-> test with 'KNN'
KNN tn, fp: 555, 16
KNN fn, tp: 0, 17
KNN f1 score: 0.680
KNN cohens kappa score: 0.667


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 532, 39
LR fn, tp: 0, 17
LR f1 score: 0.466
LR cohens kappa score: 0.441
LR average precision score: 0.994

-> test with 'RF'
RF tn, fp: 558, 13
RF fn, tp: 0, 17
RF f1 score: 0.723
RF cohens kappa score: 0.713

-> test with 'GB'
GB tn, fp: 557, 14
GB fn, tp: 0, 17
GB f1 score: 0.708
GB cohens kappa score: 0.697

-> test with 'KNN'
KNN tn, fp: 547, 24
KNN fn, tp: 0, 17
KNN f1 score: 0.586
KNN cohens kappa score: 0.569


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 551, 19
LR fn, tp: 0, 13
LR f1 score: 0.578
LR cohens kappa score: 0.564
LR average precision score: 0.995

-> test with 'RF'
RF tn, fp: 563, 7
RF fn, tp: 0, 13
RF f1 score: 0.788
RF cohens kappa score: 0.782

-> test with 'GB'
GB tn, fp: 559, 11
GB fn, tp: 0, 13
GB f1 score: 0.703
GB cohens kappa score: 0.694

-> test with 'KNN'
KNN tn, fp: 554, 16
KNN fn, tp: 0, 13
KNN f1 score: 0.619
KNN cohens kappa score: 0.607


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 540, 31
LR fn, tp: 0, 17
LR f1 score: 0.523
LR cohens kappa score: 0.502
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 564, 7
RF fn, tp: 0, 17
RF f1 score: 0.829
RF cohens kappa score: 0.823

-> test with 'GB'
GB tn, fp: 561, 10
GB fn, tp: 0, 17
GB f1 score: 0.773
GB cohens kappa score: 0.764

-> test with 'KNN'
KNN tn, fp: 556, 15
KNN fn, tp: 0, 17
KNN f1 score: 0.694
KNN cohens kappa score: 0.682


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 545, 26
LR fn, tp: 0, 17
LR f1 score: 0.567
LR cohens kappa score: 0.548
LR average precision score: 0.994

-> test with 'RF'
RF tn, fp: 567, 4
RF fn, tp: 0, 17
RF f1 score: 0.895
RF cohens kappa score: 0.891

-> test with 'GB'
GB tn, fp: 560, 11
GB fn, tp: 0, 17
GB f1 score: 0.756
GB cohens kappa score: 0.746

-> test with 'KNN'
KNN tn, fp: 554, 17
KNN fn, tp: 0, 17
KNN f1 score: 0.667
KNN cohens kappa score: 0.653


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 550, 21
LR fn, tp: 0, 17
LR f1 score: 0.618
LR cohens kappa score: 0.602
LR average precision score: 0.969

-> test with 'RF'
RF tn, fp: 558, 13
RF fn, tp: 0, 17
RF f1 score: 0.723
RF cohens kappa score: 0.713

-> test with 'GB'
GB tn, fp: 557, 14
GB fn, tp: 0, 17
GB f1 score: 0.708
GB cohens kappa score: 0.697

-> test with 'KNN'
KNN tn, fp: 552, 19
KNN fn, tp: 0, 17
KNN f1 score: 0.642
KNN cohens kappa score: 0.627


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 552, 19
LR fn, tp: 0, 17
LR f1 score: 0.642
LR cohens kappa score: 0.627
LR average precision score: 0.991

-> test with 'RF'
RF tn, fp: 563, 8
RF fn, tp: 0, 17
RF f1 score: 0.810
RF cohens kappa score: 0.803

-> test with 'GB'
GB tn, fp: 561, 10
GB fn, tp: 0, 17
GB f1 score: 0.773
GB cohens kappa score: 0.764

-> test with 'KNN'
KNN tn, fp: 557, 14
KNN fn, tp: 0, 17
KNN f1 score: 0.708
KNN cohens kappa score: 0.697


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 536, 34
LR fn, tp: 0, 13
LR f1 score: 0.433
LR cohens kappa score: 0.413
LR average precision score: 0.990

-> test with 'RF'
RF tn, fp: 560, 10
RF fn, tp: 0, 13
RF f1 score: 0.722
RF cohens kappa score: 0.714

-> test with 'GB'
GB tn, fp: 559, 11
GB fn, tp: 0, 13
GB f1 score: 0.703
GB cohens kappa score: 0.694

-> test with 'KNN'
KNN tn, fp: 548, 22
KNN fn, tp: 0, 13
KNN f1 score: 0.542
KNN cohens kappa score: 0.526


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 552, 19
LR fn, tp: 0, 17
LR f1 score: 0.642
LR cohens kappa score: 0.627
LR average precision score: 0.989

-> test with 'RF'
RF tn, fp: 565, 6
RF fn, tp: 0, 17
RF f1 score: 0.850
RF cohens kappa score: 0.845

-> test with 'GB'
GB tn, fp: 561, 10
GB fn, tp: 0, 17
GB f1 score: 0.773
GB cohens kappa score: 0.764

-> test with 'KNN'
KNN tn, fp: 555, 16
KNN fn, tp: 0, 17
KNN f1 score: 0.680
KNN cohens kappa score: 0.667


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 548, 23
LR fn, tp: 0, 17
LR f1 score: 0.596
LR cohens kappa score: 0.579
LR average precision score: 0.997

-> test with 'RF'
RF tn, fp: 564, 7
RF fn, tp: 0, 17
RF f1 score: 0.829
RF cohens kappa score: 0.823

-> test with 'GB'
GB tn, fp: 564, 7
GB fn, tp: 0, 17
GB f1 score: 0.829
GB cohens kappa score: 0.823

-> test with 'KNN'
KNN tn, fp: 557, 14
KNN fn, tp: 0, 17
KNN f1 score: 0.708
KNN cohens kappa score: 0.697


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 542, 29
LR fn, tp: 0, 17
LR f1 score: 0.540
LR cohens kappa score: 0.519
LR average precision score: 0.991

-> test with 'RF'
RF tn, fp: 561, 10
RF fn, tp: 0, 17
RF f1 score: 0.773
RF cohens kappa score: 0.764

-> test with 'GB'
GB tn, fp: 559, 12
GB fn, tp: 0, 17
GB f1 score: 0.739
GB cohens kappa score: 0.729

-> test with 'KNN'
KNN tn, fp: 553, 18
KNN fn, tp: 0, 17
KNN f1 score: 0.654
KNN cohens kappa score: 0.640


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 17
LR fn, tp: 0, 17
LR f1 score: 0.667
LR cohens kappa score: 0.653
LR average precision score: 0.997

-> test with 'RF'
RF tn, fp: 566, 5
RF fn, tp: 0, 17
RF f1 score: 0.872
RF cohens kappa score: 0.867

-> test with 'GB'
GB tn, fp: 561, 10
GB fn, tp: 0, 17
GB f1 score: 0.773
GB cohens kappa score: 0.764

-> test with 'KNN'
KNN tn, fp: 555, 16
KNN fn, tp: 0, 17
KNN f1 score: 0.680
KNN cohens kappa score: 0.667


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 543, 27
LR fn, tp: 0, 13
LR f1 score: 0.491
LR cohens kappa score: 0.473
LR average precision score: 0.990

-> test with 'RF'
RF tn, fp: 562, 8
RF fn, tp: 0, 13
RF f1 score: 0.765
RF cohens kappa score: 0.758

-> test with 'GB'
GB tn, fp: 560, 10
GB fn, tp: 0, 13
GB f1 score: 0.722
GB cohens kappa score: 0.714

-> test with 'KNN'
KNN tn, fp: 549, 21
KNN fn, tp: 0, 13
KNN f1 score: 0.553
KNN cohens kappa score: 0.538


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 538, 33
LR fn, tp: 0, 17
LR f1 score: 0.507
LR cohens kappa score: 0.485
LR average precision score: 0.997

-> test with 'RF'
RF tn, fp: 558, 13
RF fn, tp: 0, 17
RF f1 score: 0.723
RF cohens kappa score: 0.713

-> test with 'GB'
GB tn, fp: 558, 13
GB fn, tp: 0, 17
GB f1 score: 0.723
GB cohens kappa score: 0.713

-> test with 'KNN'
KNN tn, fp: 548, 23
KNN fn, tp: 0, 17
KNN f1 score: 0.596
KNN cohens kappa score: 0.579


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 540, 31
LR fn, tp: 0, 17
LR f1 score: 0.523
LR cohens kappa score: 0.502
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 559, 12
RF fn, tp: 0, 17
RF f1 score: 0.739
RF cohens kappa score: 0.729

-> test with 'GB'
GB tn, fp: 558, 13
GB fn, tp: 0, 17
GB f1 score: 0.723
GB cohens kappa score: 0.713

-> test with 'KNN'
KNN tn, fp: 552, 19
KNN fn, tp: 0, 17
KNN f1 score: 0.642
KNN cohens kappa score: 0.627


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 548, 23
LR fn, tp: 0, 17
LR f1 score: 0.596
LR cohens kappa score: 0.579
LR average precision score: 0.985

-> test with 'RF'
RF tn, fp: 559, 12
RF fn, tp: 0, 17
RF f1 score: 0.739
RF cohens kappa score: 0.729

-> test with 'GB'
GB tn, fp: 558, 13
GB fn, tp: 0, 17
GB f1 score: 0.723
GB cohens kappa score: 0.713

-> test with 'KNN'
KNN tn, fp: 550, 21
KNN fn, tp: 0, 17
KNN f1 score: 0.618
KNN cohens kappa score: 0.602


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 551, 20
LR fn, tp: 0, 17
LR f1 score: 0.630
LR cohens kappa score: 0.614
LR average precision score: 0.988

-> test with 'RF'
RF tn, fp: 566, 5
RF fn, tp: 0, 17
RF f1 score: 0.872
RF cohens kappa score: 0.867

-> test with 'GB'
GB tn, fp: 562, 9
GB fn, tp: 0, 17
GB f1 score: 0.791
GB cohens kappa score: 0.783

-> test with 'KNN'
KNN tn, fp: 553, 18
KNN fn, tp: 0, 17
KNN f1 score: 0.654
KNN cohens kappa score: 0.640


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 16
LR fn, tp: 0, 13
LR f1 score: 0.619
LR cohens kappa score: 0.607
LR average precision score: 0.990

-> test with 'RF'
RF tn, fp: 562, 8
RF fn, tp: 0, 13
RF f1 score: 0.765
RF cohens kappa score: 0.758

-> test with 'GB'
GB tn, fp: 564, 6
GB fn, tp: 0, 13
GB f1 score: 0.813
GB cohens kappa score: 0.807

-> test with 'KNN'
KNN tn, fp: 558, 12
KNN fn, tp: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.675


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 544, 27
LR fn, tp: 0, 17
LR f1 score: 0.557
LR cohens kappa score: 0.538
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 563, 8
RF fn, tp: 0, 17
RF f1 score: 0.810
RF cohens kappa score: 0.803

-> test with 'GB'
GB tn, fp: 562, 9
GB fn, tp: 0, 17
GB f1 score: 0.791
GB cohens kappa score: 0.783

-> test with 'KNN'
KNN tn, fp: 555, 16
KNN fn, tp: 0, 17
KNN f1 score: 0.680
KNN cohens kappa score: 0.667


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 549, 22
LR fn, tp: 0, 17
LR f1 score: 0.607
LR cohens kappa score: 0.591
LR average precision score: 0.973

-> test with 'RF'
RF tn, fp: 563, 8
RF fn, tp: 0, 17
RF f1 score: 0.810
RF cohens kappa score: 0.803

-> test with 'GB'
GB tn, fp: 557, 14
GB fn, tp: 0, 17
GB f1 score: 0.708
GB cohens kappa score: 0.697

-> test with 'KNN'
KNN tn, fp: 550, 21
KNN fn, tp: 0, 17
KNN f1 score: 0.618
KNN cohens kappa score: 0.602


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 550, 21
LR fn, tp: 0, 17
LR f1 score: 0.618
LR cohens kappa score: 0.602
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 561, 10
RF fn, tp: 0, 17
RF f1 score: 0.773
RF cohens kappa score: 0.764

-> test with 'GB'
GB tn, fp: 559, 12
GB fn, tp: 0, 17
GB f1 score: 0.739
GB cohens kappa score: 0.729

-> test with 'KNN'
KNN tn, fp: 553, 18
KNN fn, tp: 0, 17
KNN f1 score: 0.654
KNN cohens kappa score: 0.640


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 540, 31
LR fn, tp: 0, 17
LR f1 score: 0.523
LR cohens kappa score: 0.502
LR average precision score: 0.993

-> test with 'RF'
RF tn, fp: 561, 10
RF fn, tp: 0, 17
RF f1 score: 0.773
RF cohens kappa score: 0.764

-> test with 'GB'
GB tn, fp: 559, 12
GB fn, tp: 0, 17
GB f1 score: 0.739
GB cohens kappa score: 0.729

-> test with 'KNN'
KNN tn, fp: 550, 21
KNN fn, tp: 0, 17
KNN f1 score: 0.618
KNN cohens kappa score: 0.602


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 14
LR fn, tp: 0, 13
LR f1 score: 0.650
LR cohens kappa score: 0.639
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 563, 7
RF fn, tp: 0, 13
RF f1 score: 0.788
RF cohens kappa score: 0.782

-> test with 'GB'
GB tn, fp: 561, 9
GB fn, tp: 0, 13
GB f1 score: 0.743
GB cohens kappa score: 0.735

-> test with 'KNN'
KNN tn, fp: 559, 11
KNN fn, tp: 0, 13
KNN f1 score: 0.703
KNN cohens kappa score: 0.694

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 556, 39
LR fn, tp: 0, 17
LR f1 score: 0.667
LR cohens kappa score: 0.653
LR average precision score: 1.000


average:
LR tn, fp: 546.4, 24.4
LR fn, tp: 0.0, 16.2
LR f1 score: 0.576
LR cohens kappa score: 0.559
LR average precision score: 0.992


minimum:
LR tn, fp: 532, 14
LR fn, tp: 0, 13
LR f1 score: 0.433
LR cohens kappa score: 0.413
LR average precision score: 0.969


-----[ RF ]-----
maximum:
RF tn, fp: 567, 13
RF fn, tp: 0, 17
RF f1 score: 0.895
RF cohens kappa score: 0.891


average:
RF tn, fp: 562.12, 8.68
RF fn, tp: 0.0, 16.2
RF f1 score: 0.791
RF cohens kappa score: 0.784


minimum:
RF tn, fp: 558, 4
RF fn, tp: 0, 13
RF f1 score: 0.722
RF cohens kappa score: 0.713


-----[ GB ]-----
maximum:
GB tn, fp: 564, 14
GB fn, tp: 0, 17
GB f1 score: 0.829
GB cohens kappa score: 0.823


average:
GB tn, fp: 560.0, 10.8
GB fn, tp: 0.0, 16.2
GB f1 score: 0.751
GB cohens kappa score: 0.742


minimum:
GB tn, fp: 557, 6
GB fn, tp: 0, 13
GB f1 score: 0.703
GB cohens kappa score: 0.694


-----[ KNN ]-----
maximum:
KNN tn, fp: 559, 24
KNN fn, tp: 0, 17
KNN f1 score: 0.708
KNN cohens kappa score: 0.697


average:
KNN tn, fp: 553.04, 17.76
KNN fn, tp: 0.0, 16.2
KNN f1 score: 0.648
KNN cohens kappa score: 0.634


minimum:
KNN tn, fp: 547, 11
KNN fn, tp: 0, 13
KNN f1 score: 0.542
KNN cohens kappa score: 0.526