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

///////////////////////////////////////////
// Running convGAN-full 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: 2, 19
LR f1 score: 0.760
LR cohens kappa score: 0.749
LR average precision score: 0.855

-> 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: 552, 8
KNN fn, tp: 2, 19
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: 548, 12
LR fn, tp: 0, 21
LR f1 score: 0.778
LR cohens kappa score: 0.768
LR average precision score: 0.924

-> 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: 0.841

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


------ 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: 0.883

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


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 538, 18
LR fn, tp: 0, 21
LR f1 score: 0.700
LR cohens kappa score: 0.685
LR average precision score: 0.980

-> test with 'GB'
GB tn, fp: 555, 1
GB fn, tp: 1, 20
GB f1 score: 0.952
GB cohens kappa score: 0.951

-> 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: 550, 10
LR fn, tp: 1, 20
LR f1 score: 0.784
LR cohens kappa score: 0.775
LR average precision score: 0.931

-> 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: 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: 551, 9
LR fn, tp: 2, 19
LR f1 score: 0.776
LR cohens kappa score: 0.766
LR average precision score: 0.923

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

-> 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 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: 0.909

-> 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: 539, 21
LR fn, tp: 0, 21
LR f1 score: 0.667
LR cohens kappa score: 0.650
LR average precision score: 0.883

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

-> 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 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: 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: 0, 21
KNN f1 score: 0.933
KNN cohens kappa score: 0.931


====== 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: 551, 9
LR fn, tp: 0, 21
LR f1 score: 0.824
LR cohens kappa score: 0.816
LR average precision score: 0.957

-> 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 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: 0.902

-> 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: 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: 0.873

-> 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: 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: 541, 19
LR fn, tp: 1, 20
LR f1 score: 0.667
LR cohens kappa score: 0.650
LR average precision score: 0.931

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


------ 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: 0.940

-> 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: 545, 11
KNN fn, tp: 0, 21
KNN f1 score: 0.792
KNN cohens kappa score: 0.783


====== 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: 548, 12
LR fn, tp: 1, 20
LR f1 score: 0.755
LR cohens kappa score: 0.744
LR average precision score: 0.931

-> 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: 536, 24
LR fn, tp: 0, 21
LR f1 score: 0.636
LR cohens kappa score: 0.618
LR average precision score: 0.946

-> 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: 5, 16
LR f1 score: 0.640
LR cohens kappa score: 0.624
LR average precision score: 0.794

-> 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: 1, 20
KNN f1 score: 0.833
KNN cohens kappa score: 0.826


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 541, 19
LR fn, tp: 0, 21
LR f1 score: 0.689
LR cohens kappa score: 0.673
LR average precision score: 0.910

-> 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: 0, 21
KNN f1 score: 0.875
KNN cohens kappa score: 0.870


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 548, 8
LR fn, tp: 1, 20
LR f1 score: 0.816
LR cohens kappa score: 0.808
LR average precision score: 0.930

-> 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 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: 544, 16
LR fn, tp: 0, 21
LR f1 score: 0.724
LR cohens kappa score: 0.711
LR average precision score: 0.970

-> 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: 549, 11
LR fn, tp: 1, 20
LR f1 score: 0.769
LR cohens kappa score: 0.759
LR average precision score: 0.911

-> 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: 1, 20
KNN f1 score: 0.889
KNN cohens kappa score: 0.884


------ Step 5/5: Slice 3/5 -------
-> 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: 0.878

-> 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 5/5: 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: 0.908

-> 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: 1, 20
KNN f1 score: 0.769
KNN cohens kappa score: 0.759


------ Step 5/5: 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: 1, 20
LR f1 score: 0.741
LR cohens kappa score: 0.729
LR average precision score: 0.914

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

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 555, 24
LR fn, tp: 5, 21
LR f1 score: 0.870
LR cohens kappa score: 0.864
LR average precision score: 0.980


average:
LR tn, fp: 544.2, 15.0
LR fn, tp: 0.8, 20.2
LR f1 score: 0.724
LR cohens kappa score: 0.711
LR average precision score: 0.908


minimum:
LR tn, fp: 535, 5
LR fn, tp: 0, 16
LR f1 score: 0.636
LR cohens kappa score: 0.618
LR average precision score: 0.794


-----[ GB ]-----
maximum:
GB tn, fp: 560, 3
GB fn, tp: 4, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 558.52, 0.68
GB fn, tp: 1.2, 19.8
GB f1 score: 0.954
GB cohens kappa score: 0.953


minimum:
GB tn, fp: 554, 0
GB fn, tp: 0, 17
GB f1 score: 0.895
GB cohens kappa score: 0.891


-----[ KNN ]-----
maximum:
KNN tn, fp: 558, 11
KNN fn, tp: 2, 21
KNN f1 score: 0.955
KNN cohens kappa score: 0.953


average:
KNN tn, fp: 552.72, 6.48
KNN fn, tp: 0.28, 20.72
KNN f1 score: 0.862
KNN cohens kappa score: 0.856


minimum:
KNN tn, fp: 545, 2
KNN fn, tp: 0, 19
KNN f1 score: 0.769
KNN cohens kappa score: 0.759