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

///////////////////////////////////////////
// Running SimpleGAN 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 557, 3
LR fn, tp: 4, 17
LR f1 score: 0.829
LR cohens kappa score: 0.823
LR average precision score: 0.793

-> 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: 559, 1
KNN fn, tp: 7, 14
KNN f1 score: 0.778
KNN cohens kappa score: 0.771


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 557, 3
LR fn, tp: 5, 16
LR f1 score: 0.800
LR cohens kappa score: 0.793
LR average precision score: 0.916

-> 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: 4, 17
KNN f1 score: 0.872
KNN cohens kappa score: 0.867


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 545, 15
LR fn, tp: 7, 14
LR f1 score: 0.560
LR cohens kappa score: 0.541
LR average precision score: 0.678

-> 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: 3, 18
KNN f1 score: 0.878
KNN cohens kappa score: 0.874


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 555, 5
LR fn, tp: 3, 18
LR f1 score: 0.818
LR cohens kappa score: 0.811
LR average precision score: 0.888

-> 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: 560, 0
KNN fn, tp: 5, 16
KNN f1 score: 0.865
KNN cohens kappa score: 0.861


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 553, 3
LR fn, tp: 1, 20
LR f1 score: 0.909
LR cohens kappa score: 0.905
LR average precision score: 0.988

-> 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: 556, 0
KNN fn, tp: 4, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> 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: 0.809

-> 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: 560, 0
KNN fn, tp: 3, 18
KNN f1 score: 0.923
KNN cohens kappa score: 0.920


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 559, 1
LR fn, tp: 4, 17
LR f1 score: 0.872
LR cohens kappa score: 0.867
LR average precision score: 0.920

-> 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: 560, 0
KNN fn, tp: 2, 19
KNN f1 score: 0.950
KNN cohens kappa score: 0.948


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 548, 12
LR fn, tp: 5, 16
LR f1 score: 0.653
LR cohens kappa score: 0.638
LR average precision score: 0.808

-> 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: 5, 16
KNN f1 score: 0.842
KNN cohens kappa score: 0.837


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 539, 21
LR fn, tp: 2, 19
LR f1 score: 0.623
LR cohens kappa score: 0.604
LR average precision score: 0.851

-> 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: 556, 4
KNN fn, tp: 6, 15
KNN f1 score: 0.750
KNN cohens kappa score: 0.741


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 551, 5
LR fn, tp: 2, 19
LR f1 score: 0.844
LR cohens kappa score: 0.838
LR average precision score: 0.927

-> 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: 5, 16
KNN f1 score: 0.865
KNN cohens kappa score: 0.860


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 557, 3
LR fn, tp: 2, 19
LR f1 score: 0.884
LR cohens kappa score: 0.879
LR average precision score: 0.935

-> 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: 2, 19
KNN f1 score: 0.905
KNN cohens kappa score: 0.901


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 557, 3
LR fn, tp: 5, 16
LR f1 score: 0.800
LR cohens kappa score: 0.793
LR average precision score: 0.898

-> 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: 6, 15
KNN f1 score: 0.789
KNN cohens kappa score: 0.782


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 6
LR fn, tp: 6, 15
LR f1 score: 0.714
LR cohens kappa score: 0.704
LR average precision score: 0.854

-> 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: 560, 0
KNN fn, tp: 6, 15
KNN f1 score: 0.833
KNN cohens kappa score: 0.828


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 552, 8
LR fn, tp: 2, 19
LR f1 score: 0.792
LR cohens kappa score: 0.783
LR average precision score: 0.911

-> 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: 560, 0
KNN fn, tp: 3, 18
KNN f1 score: 0.923
KNN cohens kappa score: 0.920


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 544, 12
LR fn, tp: 5, 16
LR f1 score: 0.653
LR cohens kappa score: 0.638
LR average precision score: 0.786

-> 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: 3, 18
KNN f1 score: 0.923
KNN cohens kappa score: 0.920


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 549, 11
LR fn, tp: 2, 19
LR f1 score: 0.745
LR cohens kappa score: 0.734
LR average precision score: 0.864

-> 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: 558, 2
KNN fn, tp: 2, 19
KNN f1 score: 0.905
KNN cohens kappa score: 0.901


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 6
LR fn, tp: 1, 20
LR f1 score: 0.851
LR cohens kappa score: 0.845
LR average precision score: 0.975

-> 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: 560, 0
KNN fn, tp: 3, 18
KNN f1 score: 0.923
KNN cohens kappa score: 0.920


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 6
LR fn, tp: 7, 14
LR f1 score: 0.683
LR cohens kappa score: 0.671
LR average precision score: 0.806

-> 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: 8, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.714


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 552, 8
LR fn, tp: 2, 19
LR f1 score: 0.792
LR cohens kappa score: 0.783
LR average precision score: 0.887

-> 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: 3, 18
KNN f1 score: 0.900
KNN cohens kappa score: 0.896


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 543, 13
LR fn, tp: 3, 18
LR f1 score: 0.692
LR cohens kappa score: 0.678
LR average precision score: 0.823

-> 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: 556, 0
KNN fn, tp: 5, 16
KNN f1 score: 0.865
KNN cohens kappa score: 0.860


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 551, 9
LR fn, tp: 4, 17
LR f1 score: 0.723
LR cohens kappa score: 0.712
LR average precision score: 0.887

-> 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: 560, 0
KNN fn, tp: 4, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 4
LR fn, tp: 4, 17
LR f1 score: 0.810
LR cohens kappa score: 0.802
LR average precision score: 0.880

-> 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: 559, 1
KNN fn, tp: 5, 16
KNN f1 score: 0.842
KNN cohens kappa score: 0.837


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 545, 15
LR fn, tp: 5, 16
LR f1 score: 0.615
LR cohens kappa score: 0.598
LR average precision score: 0.702

-> 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: 4, 17
KNN f1 score: 0.850
KNN cohens kappa score: 0.845


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 6
LR fn, tp: 4, 17
LR f1 score: 0.773
LR cohens kappa score: 0.764
LR average precision score: 0.854

-> 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: 5, 16
KNN f1 score: 0.842
KNN cohens kappa score: 0.837


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 550, 6
LR fn, tp: 1, 20
LR f1 score: 0.851
LR cohens kappa score: 0.845
LR average precision score: 0.936

-> 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: 555, 1
KNN fn, tp: 2, 19
KNN f1 score: 0.927
KNN cohens kappa score: 0.924

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 559, 21
LR fn, tp: 7, 20
LR f1 score: 0.909
LR cohens kappa score: 0.905
LR average precision score: 0.988


average:
LR tn, fp: 551.32, 7.88
LR fn, tp: 3.6, 17.4
LR f1 score: 0.758
LR cohens kappa score: 0.748
LR average precision score: 0.863


minimum:
LR tn, fp: 539, 1
LR fn, tp: 1, 14
LR f1 score: 0.560
LR cohens kappa score: 0.541
LR average precision score: 0.678


-----[ GB ]-----
maximum:
GB tn, fp: 560, 1
GB fn, tp: 4, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 559.08, 0.12
GB fn, tp: 1.0, 20.0
GB f1 score: 0.972
GB cohens kappa score: 0.971


minimum:
GB tn, fp: 556, 0
GB fn, tp: 0, 17
GB f1 score: 0.895
GB cohens kappa score: 0.891


-----[ KNN ]-----
maximum:
KNN tn, fp: 560, 4
KNN fn, tp: 8, 19
KNN f1 score: 0.950
KNN cohens kappa score: 0.948


average:
KNN tn, fp: 558.28, 0.92
KNN fn, tp: 4.2, 16.8
KNN f1 score: 0.866
KNN cohens kappa score: 0.862


minimum:
KNN tn, fp: 555, 0
KNN fn, tp: 2, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.714