ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_yeast4.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_yeast4
///////////////////////////////////////////

Load 'data_input/folding_yeast4'
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 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 18
LR fn, tp: 6, 5
LR f1 score: 0.294
LR cohens kappa score: 0.257
LR average precision score: 0.214

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 10, 1
GB f1 score: 0.143
GB cohens kappa score: 0.129

-> test with 'KNN'
KNN tn, fp: 277, 10
KNN fn, tp: 8, 3
KNN f1 score: 0.250
KNN cohens kappa score: 0.219


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 4
LR fn, tp: 8, 3
LR f1 score: 0.333
LR cohens kappa score: 0.314
LR average precision score: 0.369

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 5, 6
GB f1 score: 0.571
GB cohens kappa score: 0.556

-> test with 'KNN'
KNN tn, fp: 269, 18
KNN fn, tp: 7, 4
KNN f1 score: 0.242
KNN cohens kappa score: 0.203


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 17
LR fn, tp: 7, 4
LR f1 score: 0.250
LR cohens kappa score: 0.212
LR average precision score: 0.162

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006

-> test with 'KNN'
KNN tn, fp: 267, 20
KNN fn, tp: 7, 4
KNN f1 score: 0.229
KNN cohens kappa score: 0.187


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 276, 11
LR fn, tp: 10, 1
LR f1 score: 0.087
LR cohens kappa score: 0.050
LR average precision score: 0.163

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 8, 3
GB f1 score: 0.316
GB cohens kappa score: 0.294

-> test with 'KNN'
KNN tn, fp: 267, 20
KNN fn, tp: 7, 4
KNN f1 score: 0.229
KNN cohens kappa score: 0.187


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

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-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 4
LR fn, tp: 6, 1
LR f1 score: 0.167
LR cohens kappa score: 0.150
LR average precision score: 0.262

-> test with 'GB'
GB tn, fp: 283, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.188

-> test with 'KNN'
KNN tn, fp: 275, 10
KNN fn, tp: 6, 1
KNN f1 score: 0.111
KNN cohens kappa score: 0.084


====== 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 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 6
LR fn, tp: 10, 1
LR f1 score: 0.111
LR cohens kappa score: 0.085
LR average precision score: 0.271

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 10, 1
GB f1 score: 0.118
GB cohens kappa score: 0.094

-> test with 'KNN'
KNN tn, fp: 271, 16
KNN fn, tp: 7, 4
KNN f1 score: 0.258
KNN cohens kappa score: 0.221


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 17
LR fn, tp: 4, 7
LR f1 score: 0.400
LR cohens kappa score: 0.368
LR average precision score: 0.395

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 6, 5
GB f1 score: 0.588
GB cohens kappa score: 0.577

-> test with 'KNN'
KNN tn, fp: 263, 24
KNN fn, tp: 3, 8
KNN f1 score: 0.372
KNN cohens kappa score: 0.336


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 262, 25
LR fn, tp: 3, 8
LR f1 score: 0.364
LR cohens kappa score: 0.326
LR average precision score: 0.277

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 8, 3
GB f1 score: 0.400
GB cohens kappa score: 0.388

-> test with 'KNN'
KNN tn, fp: 267, 20
KNN fn, tp: 7, 4
KNN f1 score: 0.229
KNN cohens kappa score: 0.187


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 6
LR fn, tp: 8, 3
LR f1 score: 0.300
LR cohens kappa score: 0.276
LR average precision score: 0.240

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 8, 3
GB f1 score: 0.375
GB cohens kappa score: 0.360

-> test with 'KNN'
KNN tn, fp: 269, 18
KNN fn, tp: 3, 8
KNN f1 score: 0.432
KNN cohens kappa score: 0.401


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

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-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 272, 13
LR fn, tp: 4, 3
LR f1 score: 0.261
LR cohens kappa score: 0.235
LR average precision score: 0.362

-> test with 'GB'
GB tn, fp: 284, 1
GB fn, tp: 6, 1
GB f1 score: 0.222
GB cohens kappa score: 0.214

-> test with 'KNN'
KNN tn, fp: 267, 18
KNN fn, tp: 4, 3
KNN f1 score: 0.214
KNN cohens kappa score: 0.185


====== 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 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 259, 28
LR fn, tp: 5, 6
LR f1 score: 0.267
LR cohens kappa score: 0.223
LR average precision score: 0.244

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 9, 2
GB f1 score: 0.250
GB cohens kappa score: 0.232

-> test with 'KNN'
KNN tn, fp: 267, 20
KNN fn, tp: 4, 7
KNN f1 score: 0.368
KNN cohens kappa score: 0.333


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 8
LR fn, tp: 7, 4
LR f1 score: 0.348
LR cohens kappa score: 0.322
LR average precision score: 0.302

-> test with 'GB'
GB tn, fp: 287, 0
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 279, 8
KNN fn, tp: 10, 1
KNN f1 score: 0.100
KNN cohens kappa score: 0.069


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 7
LR fn, tp: 9, 2
LR f1 score: 0.200
LR cohens kappa score: 0.173
LR average precision score: 0.272

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 9, 2
GB f1 score: 0.250
GB cohens kappa score: 0.232

-> test with 'KNN'
KNN tn, fp: 272, 15
KNN fn, tp: 8, 3
KNN f1 score: 0.207
KNN cohens kappa score: 0.169


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 229, 58
LR fn, tp: 3, 8
LR f1 score: 0.208
LR cohens kappa score: 0.154
LR average precision score: 0.177

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 7, 4
GB f1 score: 0.444
GB cohens kappa score: 0.428

-> test with 'KNN'
KNN tn, fp: 272, 15
KNN fn, tp: 4, 7
KNN f1 score: 0.424
KNN cohens kappa score: 0.394


------ 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,  1.09it/s]
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 277, 8
LR fn, tp: 5, 2
LR f1 score: 0.235
LR cohens kappa score: 0.213
LR average precision score: 0.226

-> test with 'GB'
GB tn, fp: 284, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.537

-> test with 'KNN'
KNN tn, fp: 270, 15
KNN fn, tp: 1, 6
KNN f1 score: 0.429
KNN cohens kappa score: 0.407


====== 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 [00:12<00:00,  1.23s/it]
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 278, 9
LR fn, tp: 9, 2
LR f1 score: 0.182
LR cohens kappa score: 0.150
LR average precision score: 0.300

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 8, 3
GB f1 score: 0.400
GB cohens kappa score: 0.388

-> test with 'KNN'
KNN tn, fp: 267, 20
KNN fn, tp: 9, 2
KNN f1 score: 0.121
KNN cohens kappa score: 0.076


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 256, 31
LR fn, tp: 2, 9
LR f1 score: 0.353
LR cohens kappa score: 0.313
LR average precision score: 0.226

-> test with 'GB'
GB tn, fp: 287, 0
GB fn, tp: 8, 3
GB f1 score: 0.429
GB cohens kappa score: 0.419

-> test with 'KNN'
KNN tn, fp: 267, 20
KNN fn, tp: 7, 4
KNN f1 score: 0.229
KNN cohens kappa score: 0.187


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 17
LR fn, tp: 6, 5
LR f1 score: 0.303
LR cohens kappa score: 0.267
LR average precision score: 0.134

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 8, 3
GB f1 score: 0.316
GB cohens kappa score: 0.294

-> test with 'KNN'
KNN tn, fp: 277, 10
KNN fn, tp: 7, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.291


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 244, 43
LR fn, tp: 2, 9
LR f1 score: 0.286
LR cohens kappa score: 0.239
LR average precision score: 0.226

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 10, 1
GB f1 score: 0.133
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 254, 33
KNN fn, tp: 6, 5
KNN f1 score: 0.204
KNN cohens kappa score: 0.156


------ 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,  1.22it/s]
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 282, 3
LR fn, tp: 5, 2
LR f1 score: 0.333
LR cohens kappa score: 0.320
LR average precision score: 0.410

-> test with 'GB'
GB tn, fp: 282, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.449

-> test with 'KNN'
KNN tn, fp: 272, 13
KNN fn, tp: 4, 3
KNN f1 score: 0.261
KNN cohens kappa score: 0.235


====== 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 [00:07<00:00,  1.29it/s]
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 3
LR fn, tp: 7, 4
LR f1 score: 0.444
LR cohens kappa score: 0.428
LR average precision score: 0.304

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.016

-> test with 'KNN'
KNN tn, fp: 277, 10
KNN fn, tp: 6, 5
KNN f1 score: 0.385
KNN cohens kappa score: 0.357


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 18
LR fn, tp: 4, 7
LR f1 score: 0.389
LR cohens kappa score: 0.356
LR average precision score: 0.227

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 8, 3
GB f1 score: 0.375
GB cohens kappa score: 0.360

-> test with 'KNN'
KNN tn, fp: 261, 26
KNN fn, tp: 4, 7
KNN f1 score: 0.318
KNN cohens kappa score: 0.278


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 4
LR fn, tp: 8, 3
LR f1 score: 0.333
LR cohens kappa score: 0.314
LR average precision score: 0.340

-> test with 'GB'
GB tn, fp: 287, 0
GB fn, tp: 10, 1
GB f1 score: 0.167
GB cohens kappa score: 0.162

-> test with 'KNN'
KNN tn, fp: 277, 10
KNN fn, tp: 8, 3
KNN f1 score: 0.250
KNN cohens kappa score: 0.219


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

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-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 13
LR fn, tp: 7, 4
LR f1 score: 0.286
LR cohens kappa score: 0.252
LR average precision score: 0.461

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 10, 1
GB f1 score: 0.118
GB cohens kappa score: 0.094

-> test with 'KNN'
KNN tn, fp: 263, 24
KNN fn, tp: 8, 3
KNN f1 score: 0.158
KNN cohens kappa score: 0.111


------ 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,  1.16it/s]
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 4
LR fn, tp: 5, 2
LR f1 score: 0.308
LR cohens kappa score: 0.292
LR average precision score: 0.234

-> test with 'GB'
GB tn, fp: 281, 4
GB fn, tp: 5, 2
GB f1 score: 0.308
GB cohens kappa score: 0.292

-> test with 'KNN'
KNN tn, fp: 271, 14
KNN fn, tp: 2, 5
KNN f1 score: 0.385
KNN cohens kappa score: 0.362

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 284, 58
LR fn, tp: 10, 9
LR f1 score: 0.444
LR cohens kappa score: 0.428
LR average precision score: 0.461


average:
LR tn, fp: 271.6, 15.0
LR fn, tp: 6.0, 4.2
LR f1 score: 0.282
LR cohens kappa score: 0.252
LR average precision score: 0.272


minimum:
LR tn, fp: 229, 3
LR fn, tp: 2, 1
LR f1 score: 0.087
LR cohens kappa score: 0.050
LR average precision score: 0.134


-----[ GB ]-----
maximum:
GB tn, fp: 287, 5
GB fn, tp: 11, 6
GB f1 score: 0.588
GB cohens kappa score: 0.577


average:
GB tn, fp: 284.2, 2.4
GB fn, tp: 8.0, 2.2
GB f1 score: 0.285
GB cohens kappa score: 0.271


minimum:
GB tn, fp: 281, 0
GB fn, tp: 4, 0
GB f1 score: 0.000
GB cohens kappa score: -0.016


-----[ KNN ]-----
maximum:
KNN tn, fp: 279, 33
KNN fn, tp: 10, 8
KNN f1 score: 0.432
KNN cohens kappa score: 0.407


average:
KNN tn, fp: 269.52, 17.08
KNN fn, tp: 5.88, 4.32
KNN f1 score: 0.269
KNN cohens kappa score: 0.234


minimum:
KNN tn, fp: 254, 8
KNN fn, tp: 1, 1
KNN f1 score: 0.100
KNN cohens kappa score: 0.069