ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_kr-vs-k-zero-one_vs_draw.log

///////////////////////////////////////////
// Running CTAB-GAN 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

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 553, 7
LR fn, tp: 3, 18
LR f1 score: 0.783
LR cohens kappa score: 0.774
LR average precision score: 0.863

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

-> 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

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100%|██████████| 10/10 [00:10<00:00,  1.08s/it]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 542, 18
LR fn, tp: 2, 19
LR f1 score: 0.655
LR cohens kappa score: 0.639
LR average precision score: 0.899

-> 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: 3, 18
KNN f1 score: 0.766
KNN cohens kappa score: 0.756


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

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100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 551, 9
LR fn, tp: 3, 18
LR f1 score: 0.750
LR cohens kappa score: 0.739
LR average precision score: 0.889

-> 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: 553, 7
KNN fn, tp: 3, 18
KNN f1 score: 0.783
KNN cohens kappa score: 0.774


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 542, 18
LR fn, tp: 2, 19
LR f1 score: 0.655
LR cohens kappa score: 0.639
LR average precision score: 0.857

-> 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: 546, 14
KNN fn, tp: 2, 19
KNN f1 score: 0.704
KNN cohens kappa score: 0.690


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

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-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 0
LR fn, tp: 1, 20
LR f1 score: 0.976
LR cohens kappa score: 0.975
LR average precision score: 0.962

-> 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: 1, 20
KNN f1 score: 0.976
KNN cohens kappa score: 0.975


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

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

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100%|██████████| 10/10 [00:22<00:00,  1.71s/it]
100%|██████████| 10/10 [00:22<00:00,  2.26s/it]
-> 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: 0.943

-> 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: 554, 6
KNN fn, tp: 1, 20
KNN f1 score: 0.851
KNN cohens kappa score: 0.845


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

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 533, 27
LR fn, tp: 2, 19
LR f1 score: 0.567
LR cohens kappa score: 0.545
LR average precision score: 0.885

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

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 4
LR fn, tp: 3, 18
LR f1 score: 0.837
LR cohens kappa score: 0.831
LR average precision score: 0.939

-> 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: 557, 3
KNN fn, tp: 4, 17
KNN f1 score: 0.829
KNN cohens kappa score: 0.823


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 553, 7
LR fn, tp: 4, 17
LR f1 score: 0.756
LR cohens kappa score: 0.746
LR average precision score: 0.850

-> 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: 5, 16
KNN f1 score: 0.762
KNN cohens kappa score: 0.753


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

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-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 2
LR fn, tp: 2, 19
LR f1 score: 0.905
LR cohens kappa score: 0.901
LR average precision score: 0.910

-> 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: 552, 4
KNN fn, tp: 2, 19
KNN f1 score: 0.864
KNN cohens kappa score: 0.858


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

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

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 534, 26
LR fn, tp: 0, 21
LR f1 score: 0.618
LR cohens kappa score: 0.598
LR average precision score: 0.927

-> 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: 1, 20
KNN f1 score: 0.755
KNN cohens kappa score: 0.744


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

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-> 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: 0.887

-> 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: 555, 5
KNN fn, tp: 4, 17
KNN f1 score: 0.791
KNN cohens kappa score: 0.783


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

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 555, 5
LR fn, tp: 4, 17
LR f1 score: 0.791
LR cohens kappa score: 0.783
LR average precision score: 0.851

-> 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: 558, 2
KNN fn, tp: 4, 17
KNN f1 score: 0.850
KNN cohens kappa score: 0.845


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 525, 35
LR fn, tp: 0, 21
LR f1 score: 0.545
LR cohens kappa score: 0.520
LR average precision score: 0.906

-> 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: 547, 13
KNN fn, tp: 0, 21
KNN f1 score: 0.764
KNN cohens kappa score: 0.753


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

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100%|██████████| 10/10 [00:07<00:00,  1.34it/s]
100%|██████████| 10/10 [00:07<00:00,  1.37it/s]
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 2
LR fn, tp: 2, 19
LR f1 score: 0.905
LR cohens kappa score: 0.901
LR average precision score: 0.959

-> 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: 554, 2
KNN fn, tp: 1, 20
KNN f1 score: 0.930
KNN cohens kappa score: 0.928


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
100%|██████████| 10/10 [00:08<00:00,  1.14it/s]
-> 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: 0.910

-> 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: 553, 7
KNN fn, tp: 1, 20
KNN f1 score: 0.833
KNN cohens kappa score: 0.826


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:08<00:00,  1.21it/s]
100%|██████████| 10/10 [00:08<00:00,  1.14it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 522, 38
LR fn, tp: 0, 21
LR f1 score: 0.525
LR cohens kappa score: 0.498
LR average precision score: 0.899

-> 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: 540, 20
KNN fn, tp: 1, 20
KNN f1 score: 0.656
KNN cohens kappa score: 0.639


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:10<00:00,  1.16it/s]
100%|██████████| 10/10 [00:10<00:00,  1.03s/it]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 558, 2
LR fn, tp: 7, 14
LR f1 score: 0.757
LR cohens kappa score: 0.749
LR average precision score: 0.715

-> 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: 555, 5
KNN fn, tp: 6, 15
KNN f1 score: 0.732
KNN cohens kappa score: 0.722


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:08<00:00,  1.25it/s]
-> 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: 0.953

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


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:08<00:00,  1.25it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 555, 1
LR fn, tp: 4, 17
LR f1 score: 0.872
LR cohens kappa score: 0.867
LR average precision score: 0.909

-> 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: 552, 4
KNN fn, tp: 2, 19
KNN f1 score: 0.864
KNN cohens kappa score: 0.858


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

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

  0%|          | 0/10 [00:00<?, ?it/s]
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100%|██████████| 10/10 [00:07<00:00,  1.16it/s]
100%|██████████| 10/10 [00:07<00:00,  1.26it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 522, 38
LR fn, tp: 0, 21
LR f1 score: 0.525
LR cohens kappa score: 0.498
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: 541, 19
KNN fn, tp: 1, 20
KNN f1 score: 0.667
KNN cohens kappa score: 0.650


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

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100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 540, 20
LR fn, tp: 4, 17
LR f1 score: 0.586
LR cohens kappa score: 0.566
LR average precision score: 0.836

-> 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: 550, 10
KNN fn, tp: 5, 16
KNN f1 score: 0.681
KNN cohens kappa score: 0.668


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

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100%|██████████| 10/10 [00:07<00:00,  1.23it/s]
100%|██████████| 10/10 [00:07<00:00,  1.27it/s]
-> 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: 0.881

-> 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: 4, 17
KNN f1 score: 0.829
KNN cohens kappa score: 0.823


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:07<00:00,  1.33it/s]
100%|██████████| 10/10 [00:07<00:00,  1.26it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 543, 17
LR fn, tp: 3, 18
LR f1 score: 0.643
LR cohens kappa score: 0.626
LR average precision score: 0.850

-> 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: 2, 19
KNN f1 score: 0.776
KNN cohens kappa score: 0.766


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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 20%|██        | 2/10 [00:02<00:11,  1.46s/it]
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100%|██████████| 10/10 [00:09<00:00,  1.07it/s]
100%|██████████| 10/10 [00:09<00:00,  1.02it/s]
-> 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: 0.948

-> 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: 1, 20
KNN f1 score: 0.851
KNN cohens kappa score: 0.845

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 558, 38
LR fn, tp: 7, 21
LR f1 score: 0.976
LR cohens kappa score: 0.975
LR average precision score: 0.962


average:
LR tn, fp: 546.36, 12.84
LR fn, tp: 2.36, 18.64
LR f1 score: 0.739
LR cohens kappa score: 0.727
LR average precision score: 0.895


minimum:
LR tn, fp: 522, 0
LR fn, tp: 0, 14
LR f1 score: 0.525
LR cohens kappa score: 0.498
LR average precision score: 0.715


-----[ 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: 558.72, 0.48
GB fn, tp: 1.56, 19.44
GB f1 score: 0.949
GB cohens kappa score: 0.948


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, 20
KNN fn, tp: 6, 21
KNN f1 score: 0.976
KNN cohens kappa score: 0.975


average:
KNN tn, fp: 551.56, 7.64
KNN fn, tp: 2.32, 18.68
KNN f1 score: 0.795
KNN cohens kappa score: 0.787


minimum:
KNN tn, fp: 540, 0
KNN fn, tp: 0, 15
KNN f1 score: 0.656
KNN cohens kappa score: 0.639