ConvGeNBenchmarkResults/data_result/ProWRAS/folding_car-vgood.log

///////////////////////////////////////////
// Running ProWRAS 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
-> 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: 0.356

-> 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: 330, 3
KNN fn, tp: 0, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.892


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 303, 30
LR fn, tp: 3, 10
LR f1 score: 0.377
LR cohens kappa score: 0.340
LR average precision score: 0.302

-> 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: 326, 7
KNN fn, tp: 3, 10
KNN f1 score: 0.667
KNN cohens kappa score: 0.652


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 290, 43
LR fn, tp: 0, 13
LR f1 score: 0.377
LR cohens kappa score: 0.336
LR average precision score: 0.389

-> 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: 325, 8
KNN fn, tp: 0, 13
KNN f1 score: 0.765
KNN cohens kappa score: 0.753


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 296, 37
LR fn, tp: 1, 12
LR f1 score: 0.387
LR cohens kappa score: 0.348
LR average precision score: 0.364

-> 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: 331, 2
KNN fn, tp: 1, 12
KNN f1 score: 0.889
KNN cohens kappa score: 0.884


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 304, 27
LR fn, tp: 2, 11
LR f1 score: 0.431
LR cohens kappa score: 0.397
LR average precision score: 0.441

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 1, 12
GB f1 score: 0.889
GB cohens kappa score: 0.884

-> test with 'KNN'
KNN tn, fp: 329, 2
KNN fn, tp: 2, 11
KNN f1 score: 0.846
KNN cohens kappa score: 0.840


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 306, 27
LR fn, tp: 5, 8
LR f1 score: 0.333
LR cohens kappa score: 0.295
LR average precision score: 0.282

-> 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: 326, 7
KNN fn, tp: 0, 13
KNN f1 score: 0.788
KNN cohens kappa score: 0.778


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 50
LR fn, tp: 0, 13
LR f1 score: 0.342
LR cohens kappa score: 0.298
LR average precision score: 0.370

-> 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 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 298, 35
LR fn, tp: 2, 11
LR f1 score: 0.373
LR cohens kappa score: 0.334
LR average precision score: 0.333

-> 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: 328, 5
KNN fn, tp: 3, 10
KNN f1 score: 0.714
KNN cohens kappa score: 0.702


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 299, 34
LR fn, tp: 0, 13
LR f1 score: 0.433
LR cohens kappa score: 0.398
LR average precision score: 0.285

-> 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: 327, 6
KNN fn, tp: 2, 11
KNN f1 score: 0.733
KNN cohens kappa score: 0.721


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 295, 36
LR fn, tp: 1, 12
LR f1 score: 0.393
LR cohens kappa score: 0.355
LR average precision score: 0.555

-> 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: 329, 2
KNN fn, tp: 0, 13
KNN f1 score: 0.929
KNN cohens kappa score: 0.926


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 299, 34
LR fn, tp: 1, 12
LR f1 score: 0.407
LR cohens kappa score: 0.370
LR average precision score: 0.307

-> 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: 330, 3
KNN fn, tp: 4, 9
KNN f1 score: 0.720
KNN cohens kappa score: 0.710


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 303, 30
LR fn, tp: 0, 13
LR f1 score: 0.464
LR cohens kappa score: 0.431
LR average precision score: 0.437

-> 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: 331, 2
KNN fn, tp: 1, 12
KNN f1 score: 0.889
KNN cohens kappa score: 0.884


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 44
LR fn, tp: 0, 13
LR f1 score: 0.371
LR cohens kappa score: 0.330
LR average precision score: 0.321

-> 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: 327, 6
KNN fn, tp: 1, 12
KNN f1 score: 0.774
KNN cohens kappa score: 0.764


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 297, 36
LR fn, tp: 0, 13
LR f1 score: 0.419
LR cohens kappa score: 0.383
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: 329, 4
KNN fn, tp: 0, 13
KNN f1 score: 0.867
KNN cohens kappa score: 0.861


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 299, 32
LR fn, tp: 3, 10
LR f1 score: 0.364
LR cohens kappa score: 0.325
LR average precision score: 0.366

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 2, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914

-> test with 'KNN'
KNN tn, fp: 329, 2
KNN fn, tp: 4, 9
KNN f1 score: 0.750
KNN cohens kappa score: 0.741


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 301, 32
LR fn, tp: 1, 12
LR f1 score: 0.421
LR cohens kappa score: 0.385
LR average precision score: 0.418

-> 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: 328, 5
KNN fn, tp: 2, 11
KNN f1 score: 0.759
KNN cohens kappa score: 0.748


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 294, 39
LR fn, tp: 1, 12
LR f1 score: 0.375
LR cohens kappa score: 0.335
LR average precision score: 0.515

-> 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: 329, 4
KNN fn, tp: 1, 12
KNN f1 score: 0.828
KNN cohens kappa score: 0.820


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.303

-> 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: 329, 4
KNN fn, tp: 1, 12
KNN f1 score: 0.828
KNN cohens kappa score: 0.820


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 299, 34
LR fn, tp: 2, 11
LR f1 score: 0.379
LR cohens kappa score: 0.341
LR average precision score: 0.268

-> 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: 328, 5
KNN fn, tp: 1, 12
KNN f1 score: 0.800
KNN cohens kappa score: 0.791


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 301, 30
LR fn, tp: 1, 12
LR f1 score: 0.436
LR cohens kappa score: 0.402
LR average precision score: 0.320

-> 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: 324, 7
KNN fn, tp: 0, 13
KNN f1 score: 0.788
KNN cohens kappa score: 0.778


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 47
LR fn, tp: 0, 13
LR f1 score: 0.356
LR cohens kappa score: 0.314
LR average precision score: 0.291

-> 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: 328, 5
KNN fn, tp: 0, 13
KNN f1 score: 0.839
KNN cohens kappa score: 0.831


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 305, 28
LR fn, tp: 3, 10
LR f1 score: 0.392
LR cohens kappa score: 0.356
LR average precision score: 0.361

-> 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: 329, 4
KNN fn, tp: 1, 12
KNN f1 score: 0.828
KNN cohens kappa score: 0.820


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 307, 26
LR fn, tp: 2, 11
LR f1 score: 0.440
LR cohens kappa score: 0.407
LR average precision score: 0.334

-> 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: 330, 3
KNN fn, tp: 2, 11
KNN f1 score: 0.815
KNN cohens kappa score: 0.807


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 290, 43
LR fn, tp: 0, 13
LR f1 score: 0.377
LR cohens kappa score: 0.336
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: 327, 6
KNN fn, tp: 0, 13
KNN f1 score: 0.813
KNN cohens kappa score: 0.804


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 297, 34
LR fn, tp: 0, 13
LR f1 score: 0.433
LR cohens kappa score: 0.398
LR average precision score: 0.539

-> 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: 330, 1
KNN fn, tp: 1, 12
KNN f1 score: 0.923
KNN cohens kappa score: 0.920

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 307, 50
LR fn, tp: 5, 13
LR f1 score: 0.464
LR cohens kappa score: 0.431
LR average precision score: 0.555


average:
LR tn, fp: 297.08, 35.52
LR fn, tp: 1.12, 11.88
LR f1 score: 0.395
LR cohens kappa score: 0.357
LR average precision score: 0.366


minimum:
LR tn, fp: 283, 26
LR fn, tp: 0, 8
LR f1 score: 0.333
LR cohens kappa score: 0.295
LR average precision score: 0.268


-----[ 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: 332.4, 0.2
GB fn, tp: 0.48, 12.52
GB f1 score: 0.973
GB cohens kappa score: 0.972


minimum:
GB tn, fp: 329, 0
GB fn, tp: 0, 11
GB f1 score: 0.889
GB cohens kappa score: 0.884


-----[ KNN ]-----
maximum:
KNN tn, fp: 331, 12
KNN fn, tp: 4, 13
KNN f1 score: 0.929
KNN cohens kappa score: 0.926


average:
KNN tn, fp: 328.0, 4.6
KNN fn, tp: 1.2, 11.8
KNN f1 score: 0.805
KNN cohens kappa score: 0.797


minimum:
KNN tn, fp: 321, 1
KNN fn, tp: 0, 9
KNN f1 score: 0.667
KNN cohens kappa score: 0.652