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

///////////////////////////////////////////
// Running Repeater 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: 544, 16
LR fn, tp: 2, 19
LR f1 score: 0.679
LR cohens kappa score: 0.663
LR average precision score: 0.877

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

-> test with 'KNN'
KNN tn, fp: 549, 11
KNN fn, tp: 0, 21
KNN f1 score: 0.792
KNN cohens kappa score: 0.783


------ Step 1/5: Slice 2/5 -------
-> 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: 0.918

-> 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: 557, 3
KNN fn, tp: 0, 21
KNN f1 score: 0.933
KNN cohens kappa score: 0.931


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 530, 30
LR fn, tp: 0, 21
LR f1 score: 0.583
LR cohens kappa score: 0.561
LR average precision score: 0.810

-> test with 'GB'
GB tn, fp: 557, 3
GB fn, tp: 0, 21
GB f1 score: 0.933
GB cohens kappa score: 0.931

-> 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 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 532, 28
LR fn, tp: 1, 20
LR f1 score: 0.580
LR cohens kappa score: 0.557
LR average precision score: 0.826

-> test with 'GB'
GB tn, fp: 553, 7
GB fn, tp: 0, 21
GB f1 score: 0.857
GB cohens kappa score: 0.851

-> 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 1/5: 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: 0.968

-> 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: 549, 7
KNN fn, tp: 0, 21
KNN f1 score: 0.857
KNN cohens kappa score: 0.851


====== 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: 539, 21
LR fn, tp: 1, 20
LR f1 score: 0.645
LR cohens kappa score: 0.627
LR average precision score: 0.925

-> test with 'GB'
GB tn, fp: 557, 3
GB fn, tp: 0, 21
GB f1 score: 0.933
GB cohens kappa score: 0.931

-> 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 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 541, 19
LR fn, tp: 2, 19
LR f1 score: 0.644
LR cohens kappa score: 0.627
LR average precision score: 0.917

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

-> test with 'KNN'
KNN tn, fp: 553, 7
KNN fn, tp: 0, 21
KNN f1 score: 0.857
KNN cohens kappa score: 0.851


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 539, 21
LR fn, tp: 0, 21
LR f1 score: 0.667
LR cohens kappa score: 0.650
LR average precision score: 0.895

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 0, 21
GB f1 score: 0.977
GB cohens kappa score: 0.976

-> 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 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 535, 25
LR fn, tp: 0, 21
LR f1 score: 0.627
LR cohens kappa score: 0.607
LR average precision score: 0.860

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


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 535, 21
LR fn, tp: 0, 21
LR f1 score: 0.667
LR cohens kappa score: 0.650
LR average precision score: 0.872

-> test with 'GB'
GB tn, fp: 554, 2
GB fn, tp: 0, 21
GB f1 score: 0.955
GB cohens kappa score: 0.953

-> test with 'KNN'
KNN tn, fp: 551, 5
KNN fn, tp: 0, 21
KNN f1 score: 0.894
KNN cohens kappa score: 0.889


====== 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: 544, 16
LR fn, tp: 0, 21
LR f1 score: 0.724
LR cohens kappa score: 0.711
LR average precision score: 0.940

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


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 546, 14
LR fn, tp: 1, 20
LR f1 score: 0.727
LR cohens kappa score: 0.715
LR average precision score: 0.887

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 0, 21
GB f1 score: 0.977
GB cohens kappa score: 0.976

-> 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 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 534, 26
LR fn, tp: 1, 20
LR f1 score: 0.597
LR cohens kappa score: 0.576
LR average precision score: 0.794

-> 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: 551, 9
KNN fn, tp: 0, 21
KNN f1 score: 0.824
KNN cohens kappa score: 0.816


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 535, 25
LR fn, tp: 0, 21
LR f1 score: 0.627
LR cohens kappa score: 0.607
LR average precision score: 0.909

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 0, 21
GB f1 score: 0.977
GB cohens kappa score: 0.976

-> test with 'KNN'
KNN tn, fp: 554, 6
KNN fn, tp: 0, 21
KNN f1 score: 0.875
KNN cohens kappa score: 0.870


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 524, 32
LR fn, tp: 0, 21
LR f1 score: 0.568
LR cohens kappa score: 0.544
LR average precision score: 0.882

-> test with 'GB'
GB tn, fp: 551, 5
GB fn, tp: 0, 21
GB f1 score: 0.894
GB cohens kappa score: 0.889

-> 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 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: 540, 20
LR fn, tp: 1, 20
LR f1 score: 0.656
LR cohens kappa score: 0.639
LR average precision score: 0.926

-> 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: 551, 9
KNN fn, tp: 0, 21
KNN f1 score: 0.824
KNN cohens kappa score: 0.816


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 532, 28
LR fn, tp: 0, 21
LR f1 score: 0.600
LR cohens kappa score: 0.579
LR average precision score: 0.936

-> test with 'GB'
GB tn, fp: 557, 3
GB fn, tp: 0, 21
GB f1 score: 0.933
GB cohens kappa score: 0.931

-> 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 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 532, 28
LR fn, tp: 0, 21
LR f1 score: 0.600
LR cohens kappa score: 0.579
LR average precision score: 0.762

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

-> test with 'KNN'
KNN tn, fp: 551, 9
KNN fn, tp: 1, 20
KNN f1 score: 0.800
KNN cohens kappa score: 0.791


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 537, 23
LR fn, tp: 0, 21
LR f1 score: 0.646
LR cohens kappa score: 0.628
LR average precision score: 0.889

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 0, 21
GB f1 score: 0.977
GB cohens kappa score: 0.976

-> 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 4/5: 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: 0.906

-> test with 'GB'
GB tn, fp: 555, 1
GB fn, tp: 2, 19
GB f1 score: 0.927
GB cohens kappa score: 0.924

-> test with 'KNN'
KNN tn, fp: 547, 9
KNN fn, tp: 0, 21
KNN f1 score: 0.824
KNN cohens kappa score: 0.816


====== 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: 539, 21
LR fn, tp: 0, 21
LR f1 score: 0.667
LR cohens kappa score: 0.650
LR average precision score: 0.948

-> 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: 557, 3
KNN fn, tp: 1, 20
KNN f1 score: 0.909
KNN cohens kappa score: 0.906


------ Step 5/5: 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: 0.901

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

-> 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 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 536, 24
LR fn, tp: 0, 21
LR f1 score: 0.636
LR cohens kappa score: 0.618
LR average precision score: 0.833

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 0, 21
GB f1 score: 0.977
GB cohens kappa score: 0.976

-> 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 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 536, 24
LR fn, tp: 1, 20
LR f1 score: 0.615
LR cohens kappa score: 0.596
LR average precision score: 0.851

-> 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: 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: 532, 24
LR fn, tp: 1, 20
LR f1 score: 0.615
LR cohens kappa score: 0.595
LR average precision score: 0.872

-> test with 'GB'
GB tn, fp: 554, 2
GB fn, tp: 0, 21
GB f1 score: 0.955
GB cohens kappa score: 0.953

-> test with 'KNN'
KNN tn, fp: 548, 8
KNN fn, tp: 0, 21
KNN f1 score: 0.840
KNN cohens kappa score: 0.833

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 546, 32
LR fn, tp: 2, 21
LR f1 score: 0.755
LR cohens kappa score: 0.743
LR average precision score: 0.968


average:
LR tn, fp: 537.2, 22.0
LR fn, tp: 0.48, 20.52
LR f1 score: 0.650
LR cohens kappa score: 0.632
LR average precision score: 0.884


minimum:
LR tn, fp: 524, 12
LR fn, tp: 0, 19
LR f1 score: 0.568
LR cohens kappa score: 0.544
LR average precision score: 0.762


-----[ GB ]-----
maximum:
GB tn, fp: 560, 7
GB fn, tp: 2, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 557.12, 2.08
GB fn, tp: 0.4, 20.6
GB f1 score: 0.944
GB cohens kappa score: 0.942


minimum:
GB tn, fp: 551, 0
GB fn, tp: 0, 19
GB f1 score: 0.857
GB cohens kappa score: 0.851


-----[ KNN ]-----
maximum:
KNN tn, fp: 557, 12
KNN fn, tp: 1, 21
KNN f1 score: 0.933
KNN cohens kappa score: 0.931


average:
KNN tn, fp: 551.36, 7.84
KNN fn, tp: 0.2, 20.8
KNN f1 score: 0.840
KNN cohens kappa score: 0.833


minimum:
KNN tn, fp: 546, 3
KNN fn, tp: 0, 20
KNN f1 score: 0.778
KNN cohens kappa score: 0.768