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

///////////////////////////////////////////
// Running convGAN 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: 548, 12
LR fn, tp: 2, 19
LR f1 score: 0.731
LR cohens kappa score: 0.719
LR average precision score: 0.872

-> 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: 1, 20
KNN f1 score: 0.800
KNN cohens kappa score: 0.791


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 548, 12
LR fn, tp: 0, 21
LR f1 score: 0.778
LR cohens kappa score: 0.768
LR average precision score: 0.934

-> 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 1/5: Slice 3/5 -------
-> 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: 0.818

-> 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: 552, 8
KNN fn, tp: 1, 20
KNN f1 score: 0.816
KNN cohens kappa score: 0.808


------ Step 1/5: 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: 1, 20
LR f1 score: 0.690
LR cohens kappa score: 0.675
LR average precision score: 0.882

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


------ 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: 0.964

-> 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: 550, 6
KNN fn, tp: 0, 21
KNN f1 score: 0.875
KNN cohens kappa score: 0.870


====== 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: 546, 14
LR fn, tp: 1, 20
LR f1 score: 0.727
LR cohens kappa score: 0.715
LR average precision score: 0.926

-> 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 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: 0.924

-> 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: 555, 5
KNN fn, tp: 0, 21
KNN f1 score: 0.894
KNN cohens kappa score: 0.889


------ Step 2/5: Slice 3/5 -------
-> 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: 0.902

-> 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: 537, 23
LR fn, tp: 0, 21
LR f1 score: 0.646
LR cohens kappa score: 0.628
LR average precision score: 0.868

-> 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: 550, 10
KNN fn, tp: 1, 20
KNN f1 score: 0.784
KNN cohens kappa score: 0.775


------ Step 2/5: 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: 0.941

-> 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: 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: 546, 14
LR fn, tp: 0, 21
LR f1 score: 0.750
LR cohens kappa score: 0.738
LR average precision score: 0.935

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


------ 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: 1, 20
LR f1 score: 0.870
LR cohens kappa score: 0.864
LR average precision score: 0.889

-> 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: 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: 542, 18
LR fn, tp: 1, 20
LR f1 score: 0.678
LR cohens kappa score: 0.662
LR average precision score: 0.886

-> 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: 551, 9
KNN fn, tp: 1, 20
KNN f1 score: 0.800
KNN cohens kappa score: 0.791


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 540, 20
LR fn, tp: 0, 21
LR f1 score: 0.677
LR cohens kappa score: 0.661
LR average precision score: 0.920

-> 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 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 533, 23
LR fn, tp: 0, 21
LR f1 score: 0.646
LR cohens kappa score: 0.628
LR average precision score: 0.905

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


====== 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: 544, 16
LR fn, tp: 1, 20
LR f1 score: 0.702
LR cohens kappa score: 0.687
LR average precision score: 0.915

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


------ Step 4/5: Slice 2/5 -------
-> 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: 0.940

-> 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: 553, 7
KNN fn, tp: 0, 21
KNN f1 score: 0.857
KNN cohens kappa score: 0.851


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 547, 13
LR fn, tp: 4, 17
LR f1 score: 0.667
LR cohens kappa score: 0.652
LR average precision score: 0.797

-> 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: 553, 7
KNN fn, tp: 0, 21
KNN f1 score: 0.857
KNN cohens kappa score: 0.851


------ Step 4/5: Slice 4/5 -------
-> 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: 0.894

-> 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: 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: 546, 10
LR fn, tp: 1, 20
LR f1 score: 0.784
LR cohens kappa score: 0.775
LR average precision score: 0.924

-> 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: 551, 5
KNN fn, tp: 1, 20
KNN f1 score: 0.870
KNN cohens kappa score: 0.864


====== 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: 540, 20
LR fn, tp: 0, 21
LR f1 score: 0.677
LR cohens kappa score: 0.661
LR average precision score: 0.954

-> 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: 555, 5
KNN fn, tp: 0, 21
KNN f1 score: 0.894
KNN cohens kappa score: 0.889


------ Step 5/5: Slice 2/5 -------
-> 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: 0.909

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 5, 16
GB f1 score: 0.865
GB cohens kappa score: 0.861

-> 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: 550, 10
LR fn, tp: 1, 20
LR f1 score: 0.784
LR cohens kappa score: 0.775
LR average precision score: 0.887

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


------ Step 5/5: Slice 4/5 -------
-> 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: 0.875

-> 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: 541, 15
LR fn, tp: 1, 20
LR f1 score: 0.714
LR cohens kappa score: 0.701
LR average precision score: 0.908

-> 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: 547, 9
KNN fn, tp: 0, 21
KNN f1 score: 0.824
KNN cohens kappa score: 0.816

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 555, 23
LR fn, tp: 4, 21
LR f1 score: 0.870
LR cohens kappa score: 0.864
LR average precision score: 0.964


average:
LR tn, fp: 543.24, 15.96
LR fn, tp: 0.76, 20.24
LR f1 score: 0.713
LR cohens kappa score: 0.699
LR average precision score: 0.903


minimum:
LR tn, fp: 533, 5
LR fn, tp: 0, 17
LR f1 score: 0.645
LR cohens kappa score: 0.627
LR average precision score: 0.797


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


average:
GB tn, fp: 558.52, 0.68
GB fn, tp: 1.04, 19.96
GB f1 score: 0.958
GB cohens kappa score: 0.957


minimum:
GB tn, fp: 555, 0
GB fn, tp: 0, 16
GB f1 score: 0.865
GB cohens kappa score: 0.861


-----[ KNN ]-----
maximum:
KNN tn, fp: 558, 10
KNN fn, tp: 1, 21
KNN f1 score: 0.955
KNN cohens kappa score: 0.953


average:
KNN tn, fp: 552.52, 6.68
KNN fn, tp: 0.32, 20.68
KNN f1 score: 0.857
KNN cohens kappa score: 0.851


minimum:
KNN tn, fp: 547, 2
KNN fn, tp: 0, 20
KNN f1 score: 0.784
KNN cohens kappa score: 0.775