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

///////////////////////////////////////////
// Running ProWRAS 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: 550, 10
LR fn, tp: 3, 18
LR f1 score: 0.735
LR cohens kappa score: 0.723
LR average precision score: 0.860

-> test with 'RF'
RF tn, fp: 559, 1
RF fn, tp: 3, 18
RF f1 score: 0.900
RF cohens kappa score: 0.896

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 3, 18
GB f1 score: 0.900
GB cohens kappa score: 0.896

-> test with 'KNN'
KNN tn, fp: 555, 5
KNN fn, tp: 3, 18
KNN f1 score: 0.818
KNN cohens kappa score: 0.811


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 550, 10
LR fn, tp: 0, 21
LR f1 score: 0.808
LR cohens kappa score: 0.799
LR average precision score: 0.940

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 1, 20
RF f1 score: 0.976
RF cohens kappa score: 0.975

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 559, 1
KNN fn, tp: 1, 20
KNN f1 score: 0.952
KNN cohens kappa score: 0.951


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 541, 19
LR fn, tp: 1, 20
LR f1 score: 0.667
LR cohens kappa score: 0.650
LR average precision score: 0.850

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 2, 19
RF f1 score: 0.950
RF cohens kappa score: 0.948

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 555, 5
KNN fn, tp: 1, 20
KNN f1 score: 0.870
KNN cohens kappa score: 0.864


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 547, 13
LR fn, tp: 2, 19
LR f1 score: 0.717
LR cohens kappa score: 0.704
LR average precision score: 0.886

-> test with 'RF'
RF tn, fp: 555, 5
RF fn, tp: 1, 20
RF f1 score: 0.870
RF cohens kappa score: 0.864

-> test with 'GB'
GB tn, fp: 555, 5
GB fn, tp: 0, 21
GB f1 score: 0.894
GB cohens kappa score: 0.889

-> test with 'KNN'
KNN tn, fp: 555, 5
KNN fn, tp: 1, 20
KNN f1 score: 0.870
KNN cohens kappa score: 0.864


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 543, 13
LR fn, tp: 0, 21
LR f1 score: 0.764
LR cohens kappa score: 0.752
LR average precision score: 0.988

-> test with 'RF'
RF tn, fp: 556, 0
RF fn, tp: 1, 20
RF f1 score: 0.976
RF cohens kappa score: 0.975

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 553, 3
KNN fn, tp: 1, 20
KNN f1 score: 0.909
KNN cohens kappa score: 0.905


====== 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: 551, 9
LR fn, tp: 1, 20
LR f1 score: 0.800
LR cohens kappa score: 0.791
LR average precision score: 0.936

-> test with 'RF'
RF tn, fp: 558, 2
RF fn, tp: 0, 21
RF f1 score: 0.955
RF cohens kappa score: 0.953

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 558, 2
KNN fn, tp: 0, 21
KNN f1 score: 0.955
KNN cohens kappa score: 0.953


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 553, 7
LR fn, tp: 2, 19
LR f1 score: 0.809
LR cohens kappa score: 0.801
LR average precision score: 0.919

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 2, 19
RF f1 score: 0.950
RF cohens kappa score: 0.948

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 2, 19
GB f1 score: 0.950
GB cohens kappa score: 0.948

-> test with 'KNN'
KNN tn, fp: 558, 2
KNN fn, tp: 2, 19
KNN f1 score: 0.905
KNN cohens kappa score: 0.901


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 545, 15
LR fn, tp: 0, 21
LR f1 score: 0.737
LR cohens kappa score: 0.724
LR average precision score: 0.910

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 3, 18
RF f1 score: 0.923
RF cohens kappa score: 0.920

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 558, 2
KNN fn, tp: 1, 20
KNN f1 score: 0.930
KNN cohens kappa score: 0.928


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 538, 22
LR fn, tp: 0, 21
LR f1 score: 0.656
LR cohens kappa score: 0.639
LR average precision score: 0.883

-> test with 'RF'
RF tn, fp: 559, 1
RF fn, tp: 2, 19
RF f1 score: 0.927
RF cohens kappa score: 0.924

-> test with 'GB'
GB tn, fp: 558, 2
GB fn, tp: 2, 19
GB f1 score: 0.905
GB cohens kappa score: 0.901

-> test with 'KNN'
KNN tn, fp: 553, 7
KNN fn, tp: 2, 19
KNN f1 score: 0.809
KNN cohens kappa score: 0.801


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 539, 17
LR fn, tp: 2, 19
LR f1 score: 0.667
LR cohens kappa score: 0.651
LR average precision score: 0.907

-> test with 'RF'
RF tn, fp: 556, 0
RF fn, tp: 2, 19
RF f1 score: 0.950
RF cohens kappa score: 0.948

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 0, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 556, 0
KNN fn, tp: 0, 21
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


====== 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: 550, 10
LR fn, tp: 0, 21
LR f1 score: 0.808
LR cohens kappa score: 0.799
LR average precision score: 0.962

-> test with 'RF'
RF tn, fp: 559, 1
RF fn, tp: 2, 19
RF f1 score: 0.927
RF cohens kappa score: 0.924

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 1, 20
GB f1 score: 0.952
GB cohens kappa score: 0.951

-> test with 'KNN'
KNN tn, fp: 554, 6
KNN fn, tp: 2, 19
KNN f1 score: 0.826
KNN cohens kappa score: 0.819


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 555, 5
LR fn, tp: 2, 19
LR f1 score: 0.844
LR cohens kappa score: 0.838
LR average precision score: 0.908

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 2, 19
RF f1 score: 0.950
RF cohens kappa score: 0.948

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 2, 19
GB f1 score: 0.950
GB cohens kappa score: 0.948

-> test with 'KNN'
KNN tn, fp: 557, 3
KNN fn, tp: 2, 19
KNN f1 score: 0.884
KNN cohens kappa score: 0.879


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 542, 18
LR fn, tp: 1, 20
LR f1 score: 0.678
LR cohens kappa score: 0.662
LR average precision score: 0.869

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 3, 18
RF f1 score: 0.923
RF cohens kappa score: 0.920

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 555, 5
KNN fn, tp: 2, 19
KNN f1 score: 0.844
KNN cohens kappa score: 0.838


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 544, 16
LR fn, tp: 1, 20
LR f1 score: 0.702
LR cohens kappa score: 0.687
LR average precision score: 0.948

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 1, 20
RF f1 score: 0.976
RF cohens kappa score: 0.975

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 558, 2
KNN fn, tp: 0, 21
KNN f1 score: 0.955
KNN cohens kappa score: 0.953


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 536, 20
LR fn, tp: 0, 21
LR f1 score: 0.677
LR cohens kappa score: 0.661
LR average precision score: 0.926

-> test with 'RF'
RF tn, fp: 556, 0
RF fn, tp: 0, 21
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 555, 1
GB fn, tp: 0, 21
GB f1 score: 0.977
GB cohens kappa score: 0.976

-> test with 'KNN'
KNN tn, fp: 550, 6
KNN fn, tp: 0, 21
KNN f1 score: 0.875
KNN cohens kappa score: 0.870


====== 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: 551, 9
LR fn, tp: 1, 20
LR f1 score: 0.800
LR cohens kappa score: 0.791
LR average precision score: 0.937

-> test with 'RF'
RF tn, fp: 558, 2
RF fn, tp: 0, 21
RF f1 score: 0.955
RF cohens kappa score: 0.953

-> test with 'GB'
GB tn, fp: 558, 2
GB fn, tp: 0, 21
GB f1 score: 0.955
GB cohens kappa score: 0.953

-> test with 'KNN'
KNN tn, fp: 555, 5
KNN fn, tp: 0, 21
KNN f1 score: 0.894
KNN cohens kappa score: 0.889


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 542, 18
LR fn, tp: 0, 21
LR f1 score: 0.700
LR cohens kappa score: 0.685
LR average precision score: 0.955

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 1, 20
RF f1 score: 0.976
RF cohens kappa score: 0.975

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> 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 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 552, 8
LR fn, tp: 6, 15
LR f1 score: 0.682
LR cohens kappa score: 0.669
LR average precision score: 0.752

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 6, 15
RF f1 score: 0.833
RF cohens kappa score: 0.828

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 555, 5
KNN fn, tp: 4, 17
KNN f1 score: 0.791
KNN cohens kappa score: 0.783


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 543, 17
LR fn, tp: 0, 21
LR f1 score: 0.712
LR cohens kappa score: 0.698
LR average precision score: 0.927

-> test with 'RF'
RF tn, fp: 559, 1
RF fn, tp: 1, 20
RF f1 score: 0.952
RF cohens kappa score: 0.951

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 1, 20
GB f1 score: 0.952
GB cohens kappa score: 0.951

-> test with 'KNN'
KNN tn, fp: 557, 3
KNN fn, tp: 1, 20
KNN f1 score: 0.909
KNN cohens kappa score: 0.906


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 549, 7
LR fn, tp: 1, 20
LR f1 score: 0.833
LR cohens kappa score: 0.826
LR average precision score: 0.937

-> test with 'RF'
RF tn, fp: 556, 0
RF fn, tp: 3, 18
RF f1 score: 0.923
RF cohens kappa score: 0.920

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 2, 19
GB f1 score: 0.950
GB cohens kappa score: 0.948

-> test with 'KNN'
KNN tn, fp: 553, 3
KNN fn, tp: 1, 20
KNN f1 score: 0.909
KNN cohens kappa score: 0.905


====== 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: 546, 14
LR fn, tp: 0, 21
LR f1 score: 0.750
LR cohens kappa score: 0.738
LR average precision score: 0.964

-> test with 'RF'
RF tn, fp: 559, 1
RF fn, tp: 2, 19
RF f1 score: 0.927
RF cohens kappa score: 0.924

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 1, 20
GB f1 score: 0.952
GB cohens kappa score: 0.951

-> test with 'KNN'
KNN tn, fp: 557, 3
KNN fn, tp: 3, 18
KNN f1 score: 0.857
KNN cohens kappa score: 0.852


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 549, 11
LR fn, tp: 1, 20
LR f1 score: 0.769
LR cohens kappa score: 0.759
LR average precision score: 0.895

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 4, 17
RF f1 score: 0.895
RF cohens kappa score: 0.891

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 4, 17
GB f1 score: 0.895
GB cohens kappa score: 0.891

-> test with 'KNN'
KNN tn, fp: 556, 4
KNN fn, tp: 2, 19
KNN f1 score: 0.864
KNN cohens kappa score: 0.858


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 550, 10
LR fn, tp: 1, 20
LR f1 score: 0.784
LR cohens kappa score: 0.775
LR average precision score: 0.894

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 2, 19
RF f1 score: 0.950
RF cohens kappa score: 0.948

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 555, 5
KNN fn, tp: 0, 21
KNN f1 score: 0.894
KNN cohens kappa score: 0.889


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 547, 13
LR fn, tp: 1, 20
LR f1 score: 0.741
LR cohens kappa score: 0.729
LR average precision score: 0.920

-> test with 'RF'
RF tn, fp: 559, 1
RF fn, tp: 1, 20
RF f1 score: 0.952
RF cohens kappa score: 0.951

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 0, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 554, 6
KNN fn, tp: 1, 20
KNN f1 score: 0.851
KNN cohens kappa score: 0.845


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 544, 12
LR fn, tp: 1, 20
LR f1 score: 0.755
LR cohens kappa score: 0.743
LR average precision score: 0.922

-> test with 'RF'
RF tn, fp: 556, 0
RF fn, tp: 2, 19
RF f1 score: 0.950
RF cohens kappa score: 0.948

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 0, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 550, 6
KNN fn, tp: 0, 21
KNN f1 score: 0.875
KNN cohens kappa score: 0.870

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 555, 22
LR fn, tp: 6, 21
LR f1 score: 0.844
LR cohens kappa score: 0.838
LR average precision score: 0.988


average:
LR tn, fp: 546.28, 12.92
LR fn, tp: 1.08, 19.92
LR f1 score: 0.744
LR cohens kappa score: 0.732
LR average precision score: 0.912


minimum:
LR tn, fp: 536, 5
LR fn, tp: 0, 15
LR f1 score: 0.656
LR cohens kappa score: 0.639
LR average precision score: 0.752


-----[ RF ]-----
maximum:
RF tn, fp: 560, 5
RF fn, tp: 6, 21
RF f1 score: 1.000
RF cohens kappa score: 1.000


average:
RF tn, fp: 558.6, 0.6
RF fn, tp: 1.88, 19.12
RF f1 score: 0.939
RF cohens kappa score: 0.936


minimum:
RF tn, fp: 555, 0
RF fn, tp: 0, 15
RF f1 score: 0.833
RF cohens kappa score: 0.828


-----[ GB ]-----
maximum:
GB tn, fp: 560, 5
GB fn, tp: 4, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 558.64, 0.56
GB fn, tp: 1.12, 19.88
GB f1 score: 0.960
GB cohens kappa score: 0.958


minimum:
GB tn, fp: 555, 0
GB fn, tp: 0, 17
GB f1 score: 0.894
GB cohens kappa score: 0.889


-----[ KNN ]-----
maximum:
KNN tn, fp: 559, 7
KNN fn, tp: 4, 21
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


average:
KNN tn, fp: 555.28, 3.92
KNN fn, tp: 1.24, 19.76
KNN f1 score: 0.885
KNN cohens kappa score: 0.881


minimum:
KNN tn, fp: 550, 0
KNN fn, tp: 0, 17
KNN f1 score: 0.791
KNN cohens kappa score: 0.783