ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_car-vgood.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_car-vgood
///////////////////////////////////////////

Load 'data_input/folding_car-vgood'
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 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 295, 38
LR fn, tp: 0, 13
LR f1 score: 0.406
LR cohens kappa score: 0.368
LR average precision score: 0.325

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 321, 12
KNN fn, tp: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.668


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

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 47
LR fn, tp: 2, 11
LR f1 score: 0.310
LR cohens kappa score: 0.265
LR average precision score: 0.294

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 314, 19
KNN fn, tp: 0, 13
KNN f1 score: 0.578
KNN cohens kappa score: 0.554


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

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 276, 57
LR fn, tp: 0, 13
LR f1 score: 0.313
LR cohens kappa score: 0.267
LR average precision score: 0.378

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

-> test with 'KNN'
KNN tn, fp: 323, 10
KNN fn, tp: 0, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.708


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

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 49
LR fn, tp: 0, 13
LR f1 score: 0.347
LR cohens kappa score: 0.303
LR average precision score: 0.394

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 315, 18
KNN fn, tp: 1, 12
KNN f1 score: 0.558
KNN cohens kappa score: 0.534


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

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-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 45
LR fn, tp: 0, 13
LR f1 score: 0.366
LR cohens kappa score: 0.324
LR average precision score: 0.388

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 322, 9
KNN fn, tp: 0, 13
KNN f1 score: 0.743
KNN cohens kappa score: 0.730


====== 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 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 316, 17
LR fn, tp: 8, 5
LR f1 score: 0.286
LR cohens kappa score: 0.250
LR average precision score: 0.286

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 309, 24
KNN fn, tp: 0, 13
KNN f1 score: 0.520
KNN cohens kappa score: 0.492


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

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 272, 61
LR fn, tp: 0, 13
LR f1 score: 0.299
LR cohens kappa score: 0.251
LR average precision score: 0.397

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 316, 17
KNN fn, tp: 3, 10
KNN f1 score: 0.500
KNN cohens kappa score: 0.473


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

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 291, 42
LR fn, tp: 1, 12
LR f1 score: 0.358
LR cohens kappa score: 0.317
LR average precision score: 0.355

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 321, 12
KNN fn, tp: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.668


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

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 15
LR fn, tp: 7, 6
LR f1 score: 0.353
LR cohens kappa score: 0.321
LR average precision score: 0.355

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

-> test with 'KNN'
KNN tn, fp: 321, 12
KNN fn, tp: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.668


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

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-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 319, 12
LR fn, tp: 4, 9
LR f1 score: 0.529
LR cohens kappa score: 0.506
LR average precision score: 0.433

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 326, 5
KNN fn, tp: 6, 7
KNN f1 score: 0.560
KNN cohens kappa score: 0.543


====== 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 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 302, 31
LR fn, tp: 1, 12
LR f1 score: 0.429
LR cohens kappa score: 0.394
LR average precision score: 0.291

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 2, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914

-> test with 'KNN'
KNN tn, fp: 321, 12
KNN fn, tp: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.668


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

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 298, 35
LR fn, tp: 0, 13
LR f1 score: 0.426
LR cohens kappa score: 0.390
LR average precision score: 0.446

-> test with 'GB'
GB tn, fp: 331, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 321, 12
KNN fn, tp: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.668


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

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 316, 17
LR fn, tp: 6, 7
LR f1 score: 0.378
LR cohens kappa score: 0.347
LR average precision score: 0.329

-> test with 'GB'
GB tn, fp: 331, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 307, 26
KNN fn, tp: 0, 13
KNN f1 score: 0.500
KNN cohens kappa score: 0.470


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

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 291, 42
LR fn, tp: 0, 13
LR f1 score: 0.382
LR cohens kappa score: 0.342
LR average precision score: 0.390

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 323, 10
KNN fn, tp: 0, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.708


------ 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,  1.23it/s]
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 56
LR fn, tp: 0, 13
LR f1 score: 0.317
LR cohens kappa score: 0.271
LR average precision score: 0.429

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 323, 8
KNN fn, tp: 0, 13
KNN f1 score: 0.765
KNN cohens kappa score: 0.753


====== 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 [00:07<00:00,  1.27it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 15
LR fn, tp: 5, 8
LR f1 score: 0.444
LR cohens kappa score: 0.416
LR average precision score: 0.386

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 319, 14
KNN fn, tp: 0, 13
KNN f1 score: 0.650
KNN cohens kappa score: 0.631


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

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100%|██████████| 10/10 [00:07<00:00,  1.31it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 45
LR fn, tp: 0, 13
LR f1 score: 0.366
LR cohens kappa score: 0.325
LR average precision score: 0.386

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

-> test with 'KNN'
KNN tn, fp: 320, 13
KNN fn, tp: 3, 10
KNN f1 score: 0.556
KNN cohens kappa score: 0.533


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

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 58
LR fn, tp: 0, 13
LR f1 score: 0.310
LR cohens kappa score: 0.263
LR average precision score: 0.316

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 326, 7
KNN fn, tp: 0, 13
KNN f1 score: 0.788
KNN cohens kappa score: 0.778


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

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 16
LR fn, tp: 7, 6
LR f1 score: 0.343
LR cohens kappa score: 0.310
LR average precision score: 0.288

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 317, 16
KNN fn, tp: 0, 13
KNN f1 score: 0.619
KNN cohens kappa score: 0.598


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

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100%|██████████| 10/10 [00:07<00:00,  1.35it/s]
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 47
LR fn, tp: 0, 13
LR f1 score: 0.356
LR cohens kappa score: 0.314
LR average precision score: 0.337

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 316, 15
KNN fn, tp: 0, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


====== 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,  1.31it/s]
100%|██████████| 10/10 [00:07<00:00,  1.33it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 277, 56
LR fn, tp: 0, 13
LR f1 score: 0.317
LR cohens kappa score: 0.271
LR average precision score: 0.265

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 15
KNN fn, tp: 1, 12
KNN f1 score: 0.600
KNN cohens kappa score: 0.579


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

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100%|██████████| 10/10 [00:07<00:00,  1.26it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 54
LR fn, tp: 0, 13
LR f1 score: 0.325
LR cohens kappa score: 0.280
LR average precision score: 0.341

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 319, 14
KNN fn, tp: 0, 13
KNN f1 score: 0.650
KNN cohens kappa score: 0.631


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

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100%|██████████| 10/10 [00:09<00:00,  1.11it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 15
LR fn, tp: 4, 9
LR f1 score: 0.486
LR cohens kappa score: 0.460
LR average precision score: 0.379

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

-> test with 'KNN'
KNN tn, fp: 315, 18
KNN fn, tp: 0, 13
KNN f1 score: 0.591
KNN cohens kappa score: 0.568


------ 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,  1.25it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 277, 56
LR fn, tp: 0, 13
LR f1 score: 0.317
LR cohens kappa score: 0.271
LR average precision score: 0.285

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 301, 32
KNN fn, tp: 0, 13
KNN f1 score: 0.448
KNN cohens kappa score: 0.414


------ 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,  1.29it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 297, 34
LR fn, tp: 1, 12
LR f1 score: 0.407
LR cohens kappa score: 0.370
LR average precision score: 0.463

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 321, 10
KNN fn, tp: 0, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.708

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 319, 61
LR fn, tp: 8, 13
LR f1 score: 0.529
LR cohens kappa score: 0.506
LR average precision score: 0.463


average:
LR tn, fp: 294.2, 38.4
LR fn, tp: 1.84, 11.16
LR f1 score: 0.367
LR cohens kappa score: 0.328
LR average precision score: 0.357


minimum:
LR tn, fp: 272, 12
LR fn, tp: 0, 5
LR f1 score: 0.286
LR cohens kappa score: 0.250
LR average precision score: 0.265


-----[ GB ]-----
maximum:
GB tn, fp: 333, 2
GB fn, tp: 2, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 332.16, 0.44
GB fn, tp: 0.24, 12.76
GB f1 score: 0.974
GB cohens kappa score: 0.973


minimum:
GB tn, fp: 329, 0
GB fn, tp: 0, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914


-----[ KNN ]-----
maximum:
KNN tn, fp: 326, 32
KNN fn, tp: 6, 13
KNN f1 score: 0.788
KNN cohens kappa score: 0.778


average:
KNN tn, fp: 318.2, 14.4
KNN fn, tp: 0.56, 12.44
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


minimum:
KNN tn, fp: 301, 5
KNN fn, tp: 0, 7
KNN f1 score: 0.448
KNN cohens kappa score: 0.414