ConvGeNBenchmarkResults/data_result/convGAN/folding_car-vgood.log

///////////////////////////////////////////
// Running convGAN 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: 291, 42
LR fn, tp: 0, 13
LR f1 score: 0.382
LR cohens kappa score: 0.342
LR average precision score: 0.363

-> 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 1/5: 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: 3, 10
LR f1 score: 0.323
LR cohens kappa score: 0.280
LR average precision score: 0.305

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 1, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 321, 12
KNN fn, tp: 1, 12
KNN f1 score: 0.649
KNN cohens kappa score: 0.631


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 282, 51
LR fn, tp: 0, 13
LR f1 score: 0.338
LR cohens kappa score: 0.294
LR average precision score: 0.404

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 1, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> 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 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 292, 41
LR fn, tp: 0, 13
LR f1 score: 0.388
LR cohens kappa score: 0.349
LR average precision score: 0.366

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


------ Step 1/5: 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: 2, 11
LR f1 score: 0.379
LR cohens kappa score: 0.341
LR average precision score: 0.449

-> test with 'GB'
GB tn, fp: 326, 5
GB fn, tp: 1, 12
GB f1 score: 0.800
GB cohens kappa score: 0.791

-> test with 'KNN'
KNN tn, fp: 317, 14
KNN fn, tp: 0, 13
KNN f1 score: 0.650
KNN cohens kappa score: 0.631


====== 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: 296, 37
LR fn, tp: 0, 13
LR f1 score: 0.413
LR cohens kappa score: 0.375
LR average precision score: 0.287

-> 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: 318, 15
KNN fn, tp: 0, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 273, 60
LR fn, tp: 0, 13
LR f1 score: 0.302
LR cohens kappa score: 0.255
LR average precision score: 0.360

-> test with 'GB'
GB tn, fp: 327, 6
GB fn, tp: 0, 13
GB f1 score: 0.813
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 306, 27
KNN fn, tp: 0, 13
KNN f1 score: 0.491
KNN cohens kappa score: 0.460


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 293, 40
LR fn, tp: 2, 11
LR f1 score: 0.344
LR cohens kappa score: 0.302
LR average precision score: 0.335

-> 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: 312, 21
KNN fn, tp: 0, 13
KNN f1 score: 0.553
KNN cohens kappa score: 0.528


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.283

-> 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: 2, 11
KNN f1 score: 0.688
KNN cohens kappa score: 0.673


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 291, 40
LR fn, tp: 0, 13
LR f1 score: 0.394
LR cohens kappa score: 0.355
LR average precision score: 0.546

-> 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: 328, 3
KNN fn, tp: 0, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.892


====== 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: 295, 38
LR fn, tp: 1, 12
LR f1 score: 0.381
LR cohens kappa score: 0.342
LR average precision score: 0.295

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 3, 10
GB f1 score: 0.833
GB cohens kappa score: 0.827

-> test with 'KNN'
KNN tn, fp: 321, 12
KNN fn, tp: 2, 11
KNN f1 score: 0.611
KNN cohens kappa score: 0.591


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 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 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 278, 55
LR fn, tp: 0, 13
LR f1 score: 0.321
LR cohens kappa score: 0.275
LR average precision score: 0.341

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 1, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 312, 21
KNN fn, tp: 0, 13
KNN f1 score: 0.553
KNN cohens kappa score: 0.528


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 294, 39
LR fn, tp: 0, 13
LR f1 score: 0.400
LR cohens kappa score: 0.362
LR average precision score: 0.383

-> 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
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 293, 38
LR fn, tp: 2, 11
LR f1 score: 0.355
LR cohens kappa score: 0.314
LR average precision score: 0.336

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.958

-> 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 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: 298, 35
LR fn, tp: 0, 13
LR f1 score: 0.426
LR cohens kappa score: 0.390
LR average precision score: 0.408

-> 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: 1, 12
KNN f1 score: 0.686
KNN cohens kappa score: 0.670


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 44
LR fn, tp: 1, 12
LR f1 score: 0.348
LR cohens kappa score: 0.305
LR average precision score: 0.508

-> 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: 316, 17
KNN fn, tp: 0, 13
KNN f1 score: 0.605
KNN cohens kappa score: 0.583


------ Step 4/5: Slice 3/5 -------
-> 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: 0.313

-> test with 'GB'
GB tn, fp: 329, 4
GB fn, tp: 0, 13
GB f1 score: 0.867
GB cohens kappa score: 0.861

-> 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 4/5: Slice 4/5 -------
-> 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: 0.301

-> 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
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 298, 33
LR fn, tp: 1, 12
LR f1 score: 0.414
LR cohens kappa score: 0.377
LR average precision score: 0.328

-> 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: 318, 13
KNN fn, tp: 0, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.649


====== 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: 270, 63
LR fn, tp: 0, 13
LR f1 score: 0.292
LR cohens kappa score: 0.244
LR average precision score: 0.342

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 2, 11
GB f1 score: 0.880
GB cohens kappa score: 0.876

-> test with 'KNN'
KNN tn, fp: 320, 13
KNN fn, tp: 0, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.649


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 298, 35
LR fn, tp: 1, 12
LR f1 score: 0.400
LR cohens kappa score: 0.362
LR average precision score: 0.361

-> 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: 320, 13
KNN fn, tp: 0, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.649


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 306, 27
LR fn, tp: 1, 12
LR f1 score: 0.462
LR cohens kappa score: 0.429
LR average precision score: 0.336

-> 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: 1, 12
KNN f1 score: 0.800
KNN cohens kappa score: 0.791


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.295

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 1, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 320, 13
KNN fn, tp: 0, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.649


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 42
LR fn, tp: 0, 13
LR f1 score: 0.382
LR cohens kappa score: 0.342
LR average precision score: 0.556

-> test with 'GB'
GB tn, fp: 330, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 318, 13
KNN fn, tp: 0, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.649

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 306, 63
LR fn, tp: 3, 13
LR f1 score: 0.462
LR cohens kappa score: 0.429
LR average precision score: 0.556


average:
LR tn, fp: 291.24, 41.36
LR fn, tp: 0.6, 12.4
LR f1 score: 0.376
LR cohens kappa score: 0.336
LR average precision score: 0.370


minimum:
LR tn, fp: 270, 27
LR fn, tp: 0, 10
LR f1 score: 0.292
LR cohens kappa score: 0.244
LR average precision score: 0.283


-----[ GB ]-----
maximum:
GB tn, fp: 333, 6
GB fn, tp: 3, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 331.56, 1.04
GB fn, tp: 0.52, 12.48
GB f1 score: 0.943
GB cohens kappa score: 0.941


minimum:
GB tn, fp: 326, 0
GB fn, tp: 0, 10
GB f1 score: 0.800
GB cohens kappa score: 0.791


-----[ KNN ]-----
maximum:
KNN tn, fp: 328, 27
KNN fn, tp: 2, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.892


average:
KNN tn, fp: 320.16, 12.44
KNN fn, tp: 0.28, 12.72
KNN f1 score: 0.680
KNN cohens kappa score: 0.663


minimum:
KNN tn, fp: 306, 3
KNN fn, tp: 0, 11
KNN f1 score: 0.491
KNN cohens kappa score: 0.460