ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_yeast6.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_yeast6
///////////////////////////////////////////

Load 'data_input/folding_yeast6'
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 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 9
LR fn, tp: 2, 5
LR f1 score: 0.476
LR cohens kappa score: 0.459
LR average precision score: 0.502

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 277, 13
KNN fn, tp: 2, 5
KNN f1 score: 0.400
KNN cohens kappa score: 0.379


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 246, 44
LR fn, tp: 2, 5
LR f1 score: 0.179
LR cohens kappa score: 0.143
LR average precision score: 0.300

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 5, 2
GB f1 score: 0.333
GB cohens kappa score: 0.320

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 3, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.283


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 290, 0
LR fn, tp: 7, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.195

-> test with 'GB'
GB tn, fp: 290, 0
GB fn, tp: 5, 2
GB f1 score: 0.444
GB cohens kappa score: 0.439

-> test with 'KNN'
KNN tn, fp: 284, 6
KNN fn, tp: 4, 3
KNN f1 score: 0.375
KNN cohens kappa score: 0.358


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 5
LR fn, tp: 2, 5
LR f1 score: 0.588
LR cohens kappa score: 0.576
LR average precision score: 0.529

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 4, 3
GB f1 score: 0.400
GB cohens kappa score: 0.385

-> test with 'KNN'
KNN tn, fp: 285, 5
KNN fn, tp: 2, 5
KNN f1 score: 0.588
KNN cohens kappa score: 0.576


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

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-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 246, 43
LR fn, tp: 0, 7
LR f1 score: 0.246
LR cohens kappa score: 0.213
LR average precision score: 0.323

-> test with 'GB'
GB tn, fp: 286, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 276, 13
KNN fn, tp: 1, 6
KNN f1 score: 0.462
KNN cohens kappa score: 0.442


====== 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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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 6
LR fn, tp: 1, 6
LR f1 score: 0.632
LR cohens kappa score: 0.620
LR average precision score: 0.635

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 3, 4
GB f1 score: 0.533
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 277, 13
KNN fn, tp: 2, 5
KNN f1 score: 0.400
KNN cohens kappa score: 0.379


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 282, 8
LR fn, tp: 4, 3
LR f1 score: 0.333
LR cohens kappa score: 0.314
LR average precision score: 0.332

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 284, 6
KNN fn, tp: 0, 7
KNN f1 score: 0.700
KNN cohens kappa score: 0.691


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 4
LR fn, tp: 4, 3
LR f1 score: 0.429
LR cohens kappa score: 0.415
LR average precision score: 0.345

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 283, 7
KNN fn, tp: 3, 4
KNN f1 score: 0.444
KNN cohens kappa score: 0.428


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 5
LR fn, tp: 4, 3
LR f1 score: 0.400
LR cohens kappa score: 0.385
LR average precision score: 0.327

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 5, 2
GB f1 score: 0.308
GB cohens kappa score: 0.292

-> test with 'KNN'
KNN tn, fp: 270, 20
KNN fn, tp: 4, 3
KNN f1 score: 0.200
KNN cohens kappa score: 0.170


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

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-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 5
LR fn, tp: 5, 2
LR f1 score: 0.286
LR cohens kappa score: 0.268
LR average precision score: 0.270

-> test with 'GB'
GB tn, fp: 289, 0
GB fn, tp: 5, 2
GB f1 score: 0.444
GB cohens kappa score: 0.439

-> test with 'KNN'
KNN tn, fp: 283, 6
KNN fn, tp: 3, 4
KNN f1 score: 0.471
KNN cohens kappa score: 0.455


====== 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 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 2
LR fn, tp: 3, 4
LR f1 score: 0.615
LR cohens kappa score: 0.607
LR average precision score: 0.559

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 276, 14
KNN fn, tp: 3, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.296


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 5
LR fn, tp: 1, 6
LR f1 score: 0.667
LR cohens kappa score: 0.657
LR average precision score: 0.551

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 285, 5
KNN fn, tp: 2, 5
KNN f1 score: 0.588
KNN cohens kappa score: 0.576


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 4
LR fn, tp: 6, 1
LR f1 score: 0.167
LR cohens kappa score: 0.150
LR average precision score: 0.252

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.189

-> test with 'KNN'
KNN tn, fp: 284, 6
KNN fn, tp: 2, 5
KNN f1 score: 0.556
KNN cohens kappa score: 0.542


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 15
LR fn, tp: 2, 5
LR f1 score: 0.370
LR cohens kappa score: 0.348
LR average precision score: 0.321

-> test with 'GB'
GB tn, fp: 284, 6
GB fn, tp: 5, 2
GB f1 score: 0.267
GB cohens kappa score: 0.248

-> test with 'KNN'
KNN tn, fp: 274, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.334


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

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100%|██████████| 10/10 [00:09<00:00,  1.14it/s]
100%|██████████| 10/10 [00:09<00:00,  1.08it/s]
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 271, 18
LR fn, tp: 3, 4
LR f1 score: 0.276
LR cohens kappa score: 0.249
LR average precision score: 0.299

-> test with 'GB'
GB tn, fp: 287, 2
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011

-> test with 'KNN'
KNN tn, fp: 280, 9
KNN fn, tp: 3, 4
KNN f1 score: 0.400
KNN cohens kappa score: 0.381


====== 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,  1.16it/s]
100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 9
LR fn, tp: 0, 7
LR f1 score: 0.609
LR cohens kappa score: 0.595
LR average precision score: 0.571

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 282, 8
KNN fn, tp: 1, 6
KNN f1 score: 0.571
KNN cohens kappa score: 0.558


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

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100%|██████████| 10/10 [00:09<00:00,  1.10it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 9
LR fn, tp: 4, 3
LR f1 score: 0.316
LR cohens kappa score: 0.295
LR average precision score: 0.290

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 5, 2
GB f1 score: 0.286
GB cohens kappa score: 0.268

-> test with 'KNN'
KNN tn, fp: 277, 13
KNN fn, tp: 1, 6
KNN f1 score: 0.462
KNN cohens kappa score: 0.442


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

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100%|██████████| 10/10 [00:11<00:00,  1.20s/it]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 228, 62
LR fn, tp: 1, 6
LR f1 score: 0.160
LR cohens kappa score: 0.122
LR average precision score: 0.266

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 272, 18
KNN fn, tp: 2, 5
KNN f1 score: 0.333
KNN cohens kappa score: 0.308


------ 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,  1.03it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 4
LR fn, tp: 2, 5
LR f1 score: 0.625
LR cohens kappa score: 0.615
LR average precision score: 0.658

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 281, 9
KNN fn, tp: 3, 4
KNN f1 score: 0.400
KNN cohens kappa score: 0.381


------ 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,  1.13it/s]
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 0
LR fn, tp: 4, 3
LR f1 score: 0.600
LR cohens kappa score: 0.594
LR average precision score: 0.542

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.537

-> test with 'KNN'
KNN tn, fp: 279, 10
KNN fn, tp: 4, 3
KNN f1 score: 0.300
KNN cohens kappa score: 0.278


====== 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 [00:09<00:00,  1.08it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 23
LR fn, tp: 0, 7
LR f1 score: 0.378
LR cohens kappa score: 0.354
LR average precision score: 0.496

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 3, 4
GB f1 score: 0.571
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 274, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.334


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 2
LR fn, tp: 7, 0
LR f1 score: 0.000
LR cohens kappa score: -0.011
LR average precision score: 0.202

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.189

-> test with 'KNN'
KNN tn, fp: 280, 10
KNN fn, tp: 4, 3
KNN f1 score: 0.300
KNN cohens kappa score: 0.278


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

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100%|██████████| 10/10 [00:10<00:00,  1.01s/it]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 5
LR fn, tp: 2, 5
LR f1 score: 0.588
LR cohens kappa score: 0.576
LR average precision score: 0.768

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 1, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795

-> test with 'KNN'
KNN tn, fp: 282, 8
KNN fn, tp: 0, 7
KNN f1 score: 0.636
KNN cohens kappa score: 0.624


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

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100%|██████████| 10/10 [00:08<00:00,  1.13it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 4
LR fn, tp: 3, 4
LR f1 score: 0.533
LR cohens kappa score: 0.521
LR average precision score: 0.403

-> test with 'GB'
GB tn, fp: 290, 0
GB fn, tp: 5, 2
GB f1 score: 0.444
GB cohens kappa score: 0.439

-> test with 'KNN'
KNN tn, fp: 281, 9
KNN fn, tp: 2, 5
KNN f1 score: 0.476
KNN cohens kappa score: 0.459


------ 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,  1.14it/s]
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 278, 11
LR fn, tp: 1, 6
LR f1 score: 0.500
LR cohens kappa score: 0.483
LR average precision score: 0.336

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

-> test with 'KNN'
KNN tn, fp: 285, 4
KNN fn, tp: 4, 3
KNN f1 score: 0.429
KNN cohens kappa score: 0.415

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 290, 62
LR fn, tp: 7, 7
LR f1 score: 0.667
LR cohens kappa score: 0.657
LR average precision score: 0.768


average:
LR tn, fp: 277.72, 12.08
LR fn, tp: 2.8, 4.2
LR f1 score: 0.399
LR cohens kappa score: 0.382
LR average precision score: 0.411


minimum:
LR tn, fp: 228, 0
LR fn, tp: 0, 0
LR f1 score: 0.000
LR cohens kappa score: -0.011
LR average precision score: 0.195


-----[ GB ]-----
maximum:
GB tn, fp: 290, 6
GB fn, tp: 7, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795


average:
GB tn, fp: 287.48, 2.32
GB fn, tp: 4.4, 2.6
GB f1 score: 0.428
GB cohens kappa score: 0.417


minimum:
GB tn, fp: 284, 0
GB fn, tp: 1, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011


-----[ KNN ]-----
maximum:
KNN tn, fp: 285, 20
KNN fn, tp: 4, 7
KNN f1 score: 0.700
KNN cohens kappa score: 0.691


average:
KNN tn, fp: 279.44, 10.36
KNN fn, tp: 2.36, 4.64
KNN f1 score: 0.433
KNN cohens kappa score: 0.415


minimum:
KNN tn, fp: 270, 4
KNN fn, tp: 0, 3
KNN f1 score: 0.200
KNN cohens kappa score: 0.170