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 '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: 554, 17
LR fn, tp: 0, 17
LR f1 score: 0.667
LR cohens kappa score: 0.653
LR average precision score: 1.000

-> 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 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 '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 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: 0.987

-> 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: 546, 25
KNN fn, tp: 0, 17
KNN f1 score: 0.576
KNN cohens kappa score: 0.558


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 552, 18
LR fn, tp: 0, 13
LR f1 score: 0.591
LR cohens kappa score: 0.578
LR average precision score: 0.982

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

-> 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 '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: 548, 23
LR fn, tp: 0, 17
LR f1 score: 0.596
LR cohens kappa score: 0.579
LR average precision score: 0.991

-> 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: 550, 21
KNN fn, tp: 0, 17
KNN f1 score: 0.618
KNN cohens kappa score: 0.602


------ Step 2/5: Slice 3/5 -------
-> 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: 0.977

-> 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 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: 0.989

-> 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: 558, 13
KNN fn, tp: 0, 17
KNN f1 score: 0.723
KNN cohens kappa score: 0.713


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 547, 23
LR fn, tp: 0, 13
LR f1 score: 0.531
LR cohens kappa score: 0.515
LR average precision score: 0.990

-> 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: 551, 20
LR fn, tp: 0, 17
LR f1 score: 0.630
LR cohens kappa score: 0.614
LR average precision score: 0.994

-> 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: 553, 18
KNN fn, tp: 0, 17
KNN f1 score: 0.654
KNN cohens kappa score: 0.640


------ 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: 0.994

-> 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: 552, 19
KNN fn, tp: 0, 17
KNN f1 score: 0.642
KNN cohens kappa score: 0.627


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 543, 28
LR fn, tp: 0, 17
LR f1 score: 0.548
LR cohens kappa score: 0.529
LR average precision score: 0.997

-> 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: 554, 17
KNN fn, tp: 0, 17
KNN f1 score: 0.667
KNN cohens kappa score: 0.653


------ Step 3/5: Slice 4/5 -------
-> 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: 0.997

-> 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: 551, 20
KNN fn, tp: 0, 17
KNN f1 score: 0.630
KNN cohens kappa score: 0.614


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 540, 30
LR fn, tp: 0, 13
LR f1 score: 0.464
LR cohens kappa score: 0.445
LR average precision score: 0.990

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

-> test with 'KNN'
KNN tn, fp: 547, 23
KNN fn, tp: 0, 13
KNN f1 score: 0.531
KNN cohens kappa score: 0.515


====== 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: 536, 35
LR fn, tp: 0, 17
LR f1 score: 0.493
LR cohens kappa score: 0.470
LR average precision score: 0.997

-> 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: 549, 22
KNN fn, tp: 0, 17
KNN f1 score: 0.607
KNN cohens kappa score: 0.591


------ Step 4/5: Slice 2/5 -------
-> 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 '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 3/5 -------
-> 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: 0.989

-> 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 4/5 -------
-> 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: 0.991

-> 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 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 555, 15
LR fn, tp: 0, 13
LR f1 score: 0.634
LR cohens kappa score: 0.623
LR average precision score: 0.995

-> test with 'GB'
GB tn, fp: 565, 5
GB fn, tp: 0, 13
GB f1 score: 0.839
GB cohens kappa score: 0.834

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


====== 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 '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: 551, 20
KNN fn, tp: 0, 17
KNN f1 score: 0.630
KNN cohens kappa score: 0.614


------ Step 5/5: 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: 0.973

-> 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: 545, 26
KNN fn, tp: 0, 17
KNN f1 score: 0.567
KNN cohens kappa score: 0.548


------ Step 5/5: Slice 3/5 -------
-> 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: 0.991

-> 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: 555, 16
KNN fn, tp: 0, 17
KNN f1 score: 0.680
KNN cohens kappa score: 0.667


------ 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: 0.987

-> 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: 548, 23
KNN fn, tp: 0, 17
KNN f1 score: 0.596
KNN cohens kappa score: 0.579


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 553, 17
LR fn, tp: 0, 13
LR f1 score: 0.605
LR cohens kappa score: 0.592
LR average precision score: 1.000

-> 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: 558, 12
KNN fn, tp: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.675

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 555, 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: 546.32, 24.48
LR fn, tp: 0.0, 16.2
LR f1 score: 0.575
LR cohens kappa score: 0.558
LR average precision score: 0.992


minimum:
LR tn, fp: 532, 15
LR fn, tp: 0, 13
LR f1 score: 0.464
LR cohens kappa score: 0.441
LR average precision score: 0.973


-----[ GB ]-----
maximum:
GB tn, fp: 565, 14
GB fn, tp: 0, 17
GB f1 score: 0.839
GB cohens kappa score: 0.834


average:
GB tn, fp: 559.8, 11.0
GB fn, tp: 0.0, 16.2
GB f1 score: 0.748
GB cohens kappa score: 0.739


minimum:
GB tn, fp: 557, 5
GB fn, tp: 0, 13
GB f1 score: 0.684
GB cohens kappa score: 0.675


-----[ KNN ]-----
maximum:
KNN tn, fp: 559, 26
KNN fn, tp: 0, 17
KNN f1 score: 0.723
KNN cohens kappa score: 0.713


average:
KNN tn, fp: 552.2, 18.6
KNN fn, tp: 0.0, 16.2
KNN f1 score: 0.638
KNN cohens kappa score: 0.624


minimum:
KNN tn, fp: 545, 11
KNN fn, tp: 0, 13
KNN f1 score: 0.531
KNN cohens kappa score: 0.515