ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_kr-vs-k-three_vs_eleven.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_kr-vs-k-three_vs_eleven
///////////////////////////////////////////

Load 'data_input/folding_kr-vs-k-three_vs_eleven'
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 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 555, 16
LR fn, tp: 0, 17
LR f1 score: 0.680
LR cohens kappa score: 0.667
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 557, 14
KNN fn, tp: 0, 17
KNN f1 score: 0.708
KNN cohens kappa score: 0.697


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

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-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 565, 6
LR fn, tp: 0, 17
LR f1 score: 0.850
LR cohens kappa score: 0.845
LR average precision score: 0.987

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 567, 4
KNN fn, tp: 0, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


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

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-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 559, 12
LR fn, tp: 0, 17
LR f1 score: 0.739
LR cohens kappa score: 0.729
LR average precision score: 0.994

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 566, 5
KNN fn, tp: 0, 17
KNN f1 score: 0.872
KNN cohens kappa score: 0.867


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

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-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 17
LR fn, tp: 0, 17
LR f1 score: 0.667
LR cohens kappa score: 0.653
LR average precision score: 0.991

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 566, 5
KNN fn, tp: 1, 16
KNN f1 score: 0.842
KNN cohens kappa score: 0.837


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

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-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 563, 7
LR fn, tp: 0, 13
LR f1 score: 0.788
LR cohens kappa score: 0.782
LR average precision score: 0.980

-> test with 'GB'
GB tn, fp: 568, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.927

-> test with 'KNN'
KNN tn, fp: 565, 5
KNN fn, tp: 0, 13
KNN f1 score: 0.839
KNN cohens kappa score: 0.834


====== 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 [00:09<00:00,  1.08it/s]
100%|██████████| 10/10 [00:09<00:00,  1.02it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 555, 16
LR fn, tp: 0, 17
LR f1 score: 0.680
LR cohens kappa score: 0.667
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 564, 7
KNN fn, tp: 1, 16
KNN f1 score: 0.800
KNN cohens kappa score: 0.793


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

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-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 551, 20
LR fn, tp: 0, 17
LR f1 score: 0.630
LR cohens kappa score: 0.614
LR average precision score: 0.994

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 562, 9
KNN fn, tp: 0, 17
KNN f1 score: 0.791
KNN cohens kappa score: 0.783


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

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-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 551, 20
LR fn, tp: 0, 17
LR f1 score: 0.630
LR cohens kappa score: 0.614
LR average precision score: 0.984

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 563, 8
KNN fn, tp: 0, 17
KNN f1 score: 0.810
KNN cohens kappa score: 0.803


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

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-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 563, 8
LR fn, tp: 1, 16
LR f1 score: 0.780
LR cohens kappa score: 0.773
LR average precision score: 0.980

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 567, 4
KNN fn, tp: 3, 14
KNN f1 score: 0.800
KNN cohens kappa score: 0.794


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

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-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 555, 15
LR fn, tp: 0, 13
LR f1 score: 0.634
LR cohens kappa score: 0.623
LR average precision score: 0.995

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 563, 7
KNN fn, tp: 0, 13
KNN f1 score: 0.788
KNN cohens kappa score: 0.782


====== 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 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 17
LR fn, tp: 0, 17
LR f1 score: 0.667
LR cohens kappa score: 0.653
LR average precision score: 0.990

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 563, 8
KNN fn, tp: 0, 17
KNN f1 score: 0.810
KNN cohens kappa score: 0.803


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

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-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 550, 21
LR fn, tp: 0, 17
LR f1 score: 0.618
LR cohens kappa score: 0.602
LR average precision score: 0.997

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 561, 10
KNN fn, tp: 0, 17
KNN f1 score: 0.773
KNN cohens kappa score: 0.764


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

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-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 558, 13
LR fn, tp: 0, 17
LR f1 score: 0.723
LR cohens kappa score: 0.713
LR average precision score: 0.985

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 568, 3
KNN fn, tp: 0, 17
KNN f1 score: 0.919
KNN cohens kappa score: 0.916


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

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-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 562, 9
LR fn, tp: 0, 17
LR f1 score: 0.791
LR cohens kappa score: 0.783
LR average precision score: 0.997

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 568, 3
KNN fn, tp: 1, 16
KNN f1 score: 0.889
KNN cohens kappa score: 0.885


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

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100%|██████████| 10/10 [00:08<00:00,  1.19it/s]
100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 558, 12
LR fn, tp: 0, 13
LR f1 score: 0.684
LR cohens kappa score: 0.675
LR average precision score: 0.990

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 562, 8
KNN fn, tp: 0, 13
KNN f1 score: 0.765
KNN cohens kappa score: 0.758


====== 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 [00:09<00:00,  1.18it/s]
100%|██████████| 10/10 [00:09<00:00,  1.11it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 555, 16
LR fn, tp: 0, 17
LR f1 score: 0.680
LR cohens kappa score: 0.667
LR average precision score: 0.994

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 562, 9
KNN fn, tp: 0, 17
KNN f1 score: 0.791
KNN cohens kappa score: 0.783


------ 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,  1.15it/s]
100%|██████████| 10/10 [00:08<00:00,  1.13it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 17
LR fn, tp: 0, 17
LR f1 score: 0.667
LR cohens kappa score: 0.653
LR average precision score: 0.991

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 562, 9
KNN fn, tp: 1, 16
KNN f1 score: 0.762
KNN cohens kappa score: 0.753


------ Step 4/5: Slice 3/5 -------
-> 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,  1.32it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 17
LR fn, tp: 0, 17
LR f1 score: 0.667
LR cohens kappa score: 0.653
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 564, 7
KNN fn, tp: 0, 17
KNN f1 score: 0.829
KNN cohens kappa score: 0.823


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

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100%|██████████| 10/10 [00:09<00:00,  1.11it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 561, 10
LR fn, tp: 0, 17
LR f1 score: 0.773
LR cohens kappa score: 0.764
LR average precision score: 0.990

-> test with 'GB'
GB tn, fp: 569, 2
GB fn, tp: 0, 17
GB f1 score: 0.944
GB cohens kappa score: 0.943

-> test with 'KNN'
KNN tn, fp: 567, 4
KNN fn, tp: 0, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ 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,  1.24it/s]
100%|██████████| 10/10 [00:08<00:00,  1.19it/s]
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 558, 12
LR fn, tp: 0, 13
LR f1 score: 0.684
LR cohens kappa score: 0.675
LR average precision score: 0.995

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 567, 3
KNN fn, tp: 0, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.894


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

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

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100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
100%|██████████| 10/10 [00:08<00:00,  1.19it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 15
LR fn, tp: 0, 17
LR f1 score: 0.694
LR cohens kappa score: 0.682
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 560, 11
KNN fn, tp: 1, 16
KNN f1 score: 0.727
KNN cohens kappa score: 0.717


------ 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,  1.19it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 566, 5
LR fn, tp: 0, 17
LR f1 score: 0.872
LR cohens kappa score: 0.867
LR average precision score: 0.975

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 564, 7
KNN fn, tp: 1, 16
KNN f1 score: 0.800
KNN cohens kappa score: 0.793


------ 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,  1.35it/s]
100%|██████████| 10/10 [00:07<00:00,  1.28it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 550, 21
LR fn, tp: 0, 17
LR f1 score: 0.618
LR cohens kappa score: 0.602
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 562, 9
KNN fn, tp: 0, 17
KNN f1 score: 0.791
KNN cohens kappa score: 0.783


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

  0%|          | 0/10 [00:00<?, ?it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
100%|██████████| 10/10 [00:08<00:00,  1.15it/s]
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 15
LR fn, tp: 0, 17
LR f1 score: 0.694
LR cohens kappa score: 0.682
LR average precision score: 0.990

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 563, 8
KNN fn, tp: 0, 17
KNN f1 score: 0.810
KNN cohens kappa score: 0.803


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

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100%|██████████| 10/10 [00:08<00:00,  1.26it/s]
100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 14
LR fn, tp: 0, 13
LR f1 score: 0.650
LR cohens kappa score: 0.639
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 564, 6
KNN fn, tp: 0, 13
KNN f1 score: 0.813
KNN cohens kappa score: 0.807

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 566, 21
LR fn, tp: 1, 17
LR f1 score: 0.872
LR cohens kappa score: 0.867
LR average precision score: 1.000


average:
LR tn, fp: 556.76, 14.04
LR fn, tp: 0.04, 16.16
LR f1 score: 0.702
LR cohens kappa score: 0.691
LR average precision score: 0.992


minimum:
LR tn, fp: 550, 5
LR fn, tp: 0, 13
LR f1 score: 0.618
LR cohens kappa score: 0.602
LR average precision score: 0.975


-----[ GB ]-----
maximum:
GB tn, fp: 571, 2
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 570.64, 0.16
GB fn, tp: 0.0, 16.2
GB f1 score: 0.995
GB cohens kappa score: 0.995


minimum:
GB tn, fp: 568, 0
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.927


-----[ KNN ]-----
maximum:
KNN tn, fp: 568, 14
KNN fn, tp: 3, 17
KNN f1 score: 0.919
KNN cohens kappa score: 0.916


average:
KNN tn, fp: 563.88, 6.92
KNN fn, tp: 0.36, 15.84
KNN f1 score: 0.816
KNN cohens kappa score: 0.810


minimum:
KNN tn, fp: 557, 3
KNN fn, tp: 0, 13
KNN f1 score: 0.708
KNN cohens kappa score: 0.697