ConvGeNBenchmarkResults/data_result/Repeater/folding_car-vgood.log

///////////////////////////////////////////
// Running Repeater 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: 286, 47
LR fn, tp: 0, 13
LR f1 score: 0.356
LR cohens kappa score: 0.314
LR average precision score: 0.363

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

-> 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: 303, 30
KNN fn, tp: 0, 13
KNN f1 score: 0.464
KNN cohens kappa score: 0.431


------ Step 1/5: Slice 2/5 -------
-> 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: 0.304

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

-> 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: 294, 39
KNN fn, tp: 0, 13
KNN f1 score: 0.400
KNN cohens kappa score: 0.362


------ Step 1/5: 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: 0.392

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

-> 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: 286, 47
KNN fn, tp: 0, 13
KNN f1 score: 0.356
KNN cohens kappa score: 0.314


------ Step 1/5: 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: 0.367

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

-> 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: 294, 39
KNN fn, tp: 0, 13
KNN f1 score: 0.400
KNN cohens kappa score: 0.362


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 294, 37
LR fn, tp: 1, 12
LR f1 score: 0.387
LR cohens kappa score: 0.348
LR average precision score: 0.447

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

-> test with 'GB'
GB tn, fp: 326, 5
GB fn, tp: 0, 13
GB f1 score: 0.839
GB cohens kappa score: 0.831

-> test with 'KNN'
KNN tn, fp: 300, 31
KNN fn, tp: 0, 13
KNN f1 score: 0.456
KNN cohens kappa score: 0.422


====== 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: 290, 43
LR fn, tp: 0, 13
LR f1 score: 0.377
LR cohens kappa score: 0.336
LR average precision score: 0.289

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

-> test with 'GB'
GB tn, fp: 330, 3
GB fn, tp: 0, 13
GB f1 score: 0.897
GB cohens kappa score: 0.892

-> test with 'KNN'
KNN tn, fp: 297, 36
KNN fn, tp: 0, 13
KNN f1 score: 0.419
KNN cohens kappa score: 0.383


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 59
LR fn, tp: 0, 13
LR f1 score: 0.306
LR cohens kappa score: 0.259
LR average precision score: 0.366

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

-> 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: 287, 46
KNN fn, tp: 0, 13
KNN f1 score: 0.361
KNN cohens kappa score: 0.319


------ 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: 1, 12
LR f1 score: 0.369
LR cohens kappa score: 0.329
LR average precision score: 0.345

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

-> 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: 298, 35
KNN fn, tp: 0, 13
KNN f1 score: 0.426
KNN cohens kappa score: 0.390


------ Step 2/5: Slice 4/5 -------
-> 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: 0.286

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

-> 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: 293, 40
KNN fn, tp: 0, 13
KNN f1 score: 0.394
KNN cohens kappa score: 0.355


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.555

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

-> 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: 303, 28
KNN fn, tp: 0, 13
KNN f1 score: 0.481
KNN cohens kappa score: 0.450


====== 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: 288, 45
LR fn, tp: 0, 13
LR f1 score: 0.366
LR cohens kappa score: 0.325
LR average precision score: 0.308

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

-> 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: 293, 40
KNN fn, tp: 0, 13
KNN f1 score: 0.394
KNN cohens kappa score: 0.355


------ Step 3/5: 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: 0, 13
LR f1 score: 0.400
LR cohens kappa score: 0.362
LR average precision score: 0.436

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

-> 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: 298, 35
KNN fn, tp: 0, 13
KNN f1 score: 0.426
KNN cohens kappa score: 0.390


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 53
LR fn, tp: 0, 13
LR f1 score: 0.329
LR cohens kappa score: 0.284
LR average precision score: 0.314

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

-> test with 'GB'
GB tn, fp: 328, 5
GB fn, tp: 0, 13
GB f1 score: 0.839
GB cohens kappa score: 0.831

-> test with 'KNN'
KNN tn, fp: 295, 38
KNN fn, tp: 0, 13
KNN f1 score: 0.406
KNN cohens kappa score: 0.368


------ Step 3/5: Slice 4/5 -------
-> 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: 0.385

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

-> 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: 288, 45
KNN fn, tp: 0, 13
KNN f1 score: 0.366
KNN cohens kappa score: 0.325


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 45
LR fn, tp: 1, 12
LR f1 score: 0.343
LR cohens kappa score: 0.300
LR average precision score: 0.376

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

-> 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: 297, 34
KNN fn, tp: 0, 13
KNN f1 score: 0.433
KNN cohens kappa score: 0.398


====== 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: 291, 42
LR fn, tp: 0, 13
LR f1 score: 0.382
LR cohens kappa score: 0.342
LR average precision score: 0.419

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

-> 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: 296, 37
KNN fn, tp: 0, 13
KNN f1 score: 0.413
KNN cohens kappa score: 0.375


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.511

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

-> 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: 299, 34
KNN fn, tp: 0, 13
KNN f1 score: 0.433
KNN cohens kappa score: 0.398


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 53
LR fn, tp: 0, 13
LR f1 score: 0.329
LR cohens kappa score: 0.284
LR average precision score: 0.320

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

-> 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: 291, 42
KNN fn, tp: 0, 13
KNN f1 score: 0.382
KNN cohens kappa score: 0.342


------ Step 4/5: 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: 1, 12
LR f1 score: 0.353
LR cohens kappa score: 0.311
LR average precision score: 0.275

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

-> 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: 293, 40
KNN fn, tp: 0, 13
KNN f1 score: 0.394
KNN cohens kappa score: 0.355


------ Step 4/5: 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: 0.321

-> test with 'RF'
RF tn, fp: 328, 3
RF fn, tp: 0, 13
RF f1 score: 0.897
RF cohens kappa score: 0.892

-> 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: 294, 37
KNN fn, tp: 0, 13
KNN f1 score: 0.413
KNN cohens kappa score: 0.375


====== 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: 279, 54
LR fn, tp: 0, 13
LR f1 score: 0.325
LR cohens kappa score: 0.280
LR average precision score: 0.292

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

-> 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: 293, 40
KNN fn, tp: 0, 13
KNN f1 score: 0.394
KNN cohens kappa score: 0.355


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 293, 40
LR fn, tp: 1, 12
LR f1 score: 0.369
LR cohens kappa score: 0.329
LR average precision score: 0.359

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

-> 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: 303, 30
KNN fn, tp: 0, 13
KNN f1 score: 0.464
KNN cohens kappa score: 0.431


------ Step 5/5: 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: 0, 13
LR f1 score: 0.426
LR cohens kappa score: 0.390
LR average precision score: 0.336

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

-> 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: 298, 35
KNN fn, tp: 0, 13
KNN f1 score: 0.426
KNN cohens kappa score: 0.390


------ Step 5/5: Slice 4/5 -------
-> 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: 0.295

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

-> 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: 288, 45
KNN fn, tp: 0, 13
KNN f1 score: 0.366
KNN cohens kappa score: 0.325


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 287, 44
LR fn, tp: 0, 13
LR f1 score: 0.371
LR cohens kappa score: 0.330
LR average precision score: 0.488

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

-> 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: 299, 32
KNN fn, tp: 0, 13
KNN f1 score: 0.448
KNN cohens kappa score: 0.414

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 298, 59
LR fn, tp: 1, 13
LR f1 score: 0.426
LR cohens kappa score: 0.390
LR average precision score: 0.555


average:
LR tn, fp: 287.28, 45.32
LR fn, tp: 0.2, 12.8
LR f1 score: 0.362
LR cohens kappa score: 0.320
LR average precision score: 0.366


minimum:
LR tn, fp: 274, 35
LR fn, tp: 0, 12
LR f1 score: 0.306
LR cohens kappa score: 0.259
LR average precision score: 0.275


-----[ RF ]-----
maximum:
RF tn, fp: 333, 3
RF fn, tp: 2, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000


average:
RF tn, fp: 332.24, 0.36
RF fn, tp: 0.12, 12.88
RF f1 score: 0.982
RF cohens kappa score: 0.982


minimum:
RF tn, fp: 328, 0
RF fn, tp: 0, 11
RF f1 score: 0.897
RF cohens kappa score: 0.892


-----[ GB ]-----
maximum:
GB tn, fp: 333, 6
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 330.8, 1.8
GB fn, tp: 0.0, 13.0
GB f1 score: 0.938
GB cohens kappa score: 0.935


minimum:
GB tn, fp: 326, 0
GB fn, tp: 0, 13
GB f1 score: 0.813
GB cohens kappa score: 0.804


-----[ KNN ]-----
maximum:
KNN tn, fp: 303, 47
KNN fn, tp: 0, 13
KNN f1 score: 0.481
KNN cohens kappa score: 0.450


average:
KNN tn, fp: 295.2, 37.4
KNN fn, tp: 0.0, 13.0
KNN f1 score: 0.413
KNN cohens kappa score: 0.375


minimum:
KNN tn, fp: 286, 28
KNN fn, tp: 0, 13
KNN f1 score: 0.356
KNN cohens kappa score: 0.314