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: 0.881

-> test with 'GB'
GB tn, fp: 533, 27
GB fn, tp: 0, 21
GB f1 score: 0.609
GB cohens kappa score: 0.588

-> test with 'KNN'
KNN tn, fp: 547, 13
KNN fn, tp: 1, 20
KNN f1 score: 0.741
KNN cohens kappa score: 0.729


------ Step 1/5: Slice 2/5 -------
-> 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: 0.900

-> 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: 556, 4
KNN fn, tp: 0, 21
KNN f1 score: 0.913
KNN cohens kappa score: 0.909


------ Step 1/5: Slice 3/5 -------
-> 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: 0.832

-> test with 'GB'
GB tn, fp: 534, 26
GB fn, tp: 0, 21
GB f1 score: 0.618
GB cohens kappa score: 0.598

-> 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 1/5: Slice 4/5 -------
-> 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: 0.818

-> 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: 546, 14
KNN fn, tp: 1, 20
KNN f1 score: 0.727
KNN cohens kappa score: 0.715


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 502, 54
LR fn, tp: 0, 21
LR f1 score: 0.438
LR cohens kappa score: 0.404
LR average precision score: 0.927

-> test with 'GB'
GB tn, fp: 538, 18
GB fn, tp: 0, 21
GB f1 score: 0.700
GB cohens kappa score: 0.685

-> test with 'KNN'
KNN tn, fp: 546, 10
KNN fn, tp: 0, 21
KNN f1 score: 0.808
KNN cohens kappa score: 0.799


====== 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: 483, 77
LR fn, tp: 0, 21
LR f1 score: 0.353
LR cohens kappa score: 0.312
LR average precision score: 0.871

-> 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: 547, 13
KNN fn, tp: 0, 21
KNN f1 score: 0.764
KNN cohens kappa score: 0.753


------ 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: 0.852

-> test with 'GB'
GB tn, fp: 533, 27
GB fn, tp: 0, 21
GB f1 score: 0.609
GB cohens kappa score: 0.588

-> 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 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: 0.889

-> 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: 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: 491, 69
LR fn, tp: 0, 21
LR f1 score: 0.378
LR cohens kappa score: 0.340
LR average precision score: 0.852

-> test with 'GB'
GB tn, fp: 534, 26
GB fn, tp: 0, 21
GB f1 score: 0.618
GB cohens kappa score: 0.598

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


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 500, 56
LR fn, tp: 0, 21
LR f1 score: 0.429
LR cohens kappa score: 0.394
LR average precision score: 0.866

-> 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: 548, 8
KNN fn, tp: 0, 21
KNN f1 score: 0.840
KNN cohens kappa score: 0.833


====== 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: 0.920

-> 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: 542, 18
KNN fn, tp: 0, 21
KNN f1 score: 0.700
KNN cohens kappa score: 0.685


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 512, 48
LR fn, tp: 0, 21
LR f1 score: 0.467
LR cohens kappa score: 0.435
LR average precision score: 0.875

-> 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: 552, 8
KNN fn, tp: 0, 21
KNN f1 score: 0.840
KNN cohens kappa score: 0.833


------ Step 3/5: Slice 3/5 -------
-> 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: 0.757

-> test with 'GB'
GB tn, fp: 533, 27
GB fn, tp: 0, 21
GB f1 score: 0.609
GB cohens kappa score: 0.588

-> 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 4/5 -------
-> 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: 0.902

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

-> test with 'KNN'
KNN tn, fp: 556, 4
KNN fn, tp: 1, 20
KNN f1 score: 0.889
KNN cohens kappa score: 0.884


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 481, 75
LR fn, tp: 0, 21
LR f1 score: 0.359
LR cohens kappa score: 0.318
LR average precision score: 0.844

-> test with 'GB'
GB tn, fp: 527, 29
GB fn, tp: 0, 21
GB f1 score: 0.592
GB cohens kappa score: 0.569

-> 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 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: 0.894

-> test with 'GB'
GB tn, fp: 533, 27
GB fn, tp: 0, 21
GB f1 score: 0.609
GB cohens kappa score: 0.588

-> 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 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: 0.910

-> test with 'GB'
GB tn, fp: 536, 24
GB fn, tp: 0, 21
GB f1 score: 0.636
GB cohens kappa score: 0.618

-> 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 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 493, 67
LR fn, tp: 0, 21
LR f1 score: 0.385
LR cohens kappa score: 0.347
LR average precision score: 0.724

-> test with 'GB'
GB tn, fp: 536, 24
GB fn, tp: 0, 21
GB f1 score: 0.636
GB cohens kappa score: 0.618

-> 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 4/5 -------
-> 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: 0.891

-> 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: 550, 10
KNN fn, tp: 0, 21
KNN f1 score: 0.808
KNN cohens kappa score: 0.799


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 499, 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 'GB'
GB tn, fp: 535, 21
GB fn, tp: 0, 21
GB f1 score: 0.667
GB cohens kappa score: 0.650

-> test with 'KNN'
KNN tn, fp: 545, 11
KNN fn, tp: 0, 21
KNN f1 score: 0.792
KNN cohens kappa score: 0.783


====== 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: 493, 67
LR fn, tp: 0, 21
LR f1 score: 0.385
LR cohens kappa score: 0.347
LR average precision score: 0.926

-> 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: 545, 15
KNN fn, tp: 0, 21
KNN f1 score: 0.737
KNN cohens kappa score: 0.724


------ Step 5/5: 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: 0.844

-> 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: 548, 12
KNN fn, tp: 0, 21
KNN f1 score: 0.778
KNN cohens kappa score: 0.768


------ Step 5/5: Slice 3/5 -------
-> 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: 0.810

-> 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: 547, 13
KNN fn, tp: 0, 21
KNN f1 score: 0.764
KNN cohens kappa score: 0.753


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 484, 76
LR fn, tp: 0, 21
LR f1 score: 0.356
LR cohens kappa score: 0.315
LR average precision score: 0.817

-> test with 'GB'
GB tn, fp: 536, 24
GB fn, tp: 0, 21
GB f1 score: 0.636
GB cohens kappa score: 0.618

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 498, 58
LR fn, tp: 0, 21
LR f1 score: 0.420
LR cohens kappa score: 0.385
LR average precision score: 0.898

-> test with 'GB'
GB tn, fp: 533, 23
GB fn, tp: 0, 21
GB f1 score: 0.646
GB cohens kappa score: 0.628

-> test with 'KNN'
KNN tn, fp: 545, 11
KNN fn, tp: 0, 21
KNN f1 score: 0.792
KNN cohens kappa score: 0.783

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 512, 77
LR fn, tp: 0, 21
LR f1 score: 0.467
LR cohens kappa score: 0.435
LR average precision score: 0.927


average:
LR tn, fp: 495.76, 63.44
LR fn, tp: 0.0, 21.0
LR f1 score: 0.401
LR cohens kappa score: 0.364
LR average precision score: 0.864


minimum:
LR tn, fp: 481, 48
LR fn, tp: 0, 21
LR f1 score: 0.353
LR cohens kappa score: 0.312
LR average precision score: 0.724


-----[ GB ]-----
maximum:
GB tn, fp: 543, 32
GB fn, tp: 0, 21
GB f1 score: 0.712
GB cohens kappa score: 0.698


average:
GB tn, fp: 534.92, 24.28
GB fn, tp: 0.0, 21.0
GB f1 score: 0.636
GB cohens kappa score: 0.617


minimum:
GB tn, fp: 527, 17
GB fn, tp: 0, 21
GB f1 score: 0.568
GB cohens kappa score: 0.544


-----[ KNN ]-----
maximum:
KNN tn, fp: 556, 20
KNN fn, tp: 1, 21
KNN f1 score: 0.913
KNN cohens kappa score: 0.909


average:
KNN tn, fp: 547.6, 11.6
KNN fn, tp: 0.12, 20.88
KNN f1 score: 0.784
KNN cohens kappa score: 0.774


minimum:
KNN tn, fp: 540, 4
KNN fn, tp: 0, 20
KNN f1 score: 0.677
KNN cohens kappa score: 0.661