ConvGeNBenchmarkResults/data_result/ctGAN/folding_car-vgood.log

///////////////////////////////////////////
// Running ctGAN 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: 284, 49
LR fn, tp: 0, 13
LR f1 score: 0.347
LR cohens kappa score: 0.303
LR average precision score: 0.335

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

-> 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: 273, 60
KNN fn, tp: 0, 13
KNN f1 score: 0.302
KNN cohens kappa score: 0.255


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 64
LR fn, tp: 0, 13
LR f1 score: 0.289
LR cohens kappa score: 0.240
LR average precision score: 0.276

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

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

-> test with 'KNN'
KNN tn, fp: 268, 65
KNN fn, tp: 0, 13
KNN f1 score: 0.286
KNN cohens kappa score: 0.237


------ Step 1/5: Slice 3/5 -------
-> 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: 0.389

-> test with 'RF'
RF tn, fp: 325, 8
RF fn, tp: 0, 13
RF f1 score: 0.765
RF cohens kappa score: 0.753

-> test with 'GB'
GB tn, fp: 325, 8
GB fn, tp: 0, 13
GB f1 score: 0.765
GB cohens kappa score: 0.753

-> test with 'KNN'
KNN tn, fp: 264, 69
KNN fn, tp: 0, 13
KNN f1 score: 0.274
KNN cohens kappa score: 0.223


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.431

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

-> 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: 274, 59
KNN fn, tp: 0, 13
KNN f1 score: 0.306
KNN cohens kappa score: 0.259


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 57
LR fn, tp: 0, 13
LR f1 score: 0.313
LR cohens kappa score: 0.266
LR average precision score: 0.414

-> test with 'RF'
RF tn, fp: 324, 7
RF fn, tp: 0, 13
RF f1 score: 0.788
RF cohens kappa score: 0.778

-> test with 'GB'
GB tn, fp: 324, 7
GB fn, tp: 0, 13
GB f1 score: 0.788
GB cohens kappa score: 0.778

-> test with 'KNN'
KNN tn, fp: 281, 50
KNN fn, tp: 0, 13
KNN f1 score: 0.342
KNN cohens kappa score: 0.298


====== 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: 286, 47
LR fn, tp: 0, 13
LR f1 score: 0.356
LR cohens kappa score: 0.314
LR average precision score: 0.274

-> test with 'RF'
RF tn, fp: 325, 8
RF fn, tp: 0, 13
RF f1 score: 0.765
RF cohens kappa score: 0.753

-> test with 'GB'
GB tn, fp: 325, 8
GB fn, tp: 0, 13
GB f1 score: 0.765
GB cohens kappa score: 0.753

-> test with 'KNN'
KNN tn, fp: 276, 57
KNN fn, tp: 0, 13
KNN f1 score: 0.313
KNN cohens kappa score: 0.267


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 66
LR fn, tp: 0, 13
LR f1 score: 0.283
LR cohens kappa score: 0.233
LR average precision score: 0.310

-> test with 'RF'
RF tn, fp: 322, 11
RF fn, tp: 0, 13
RF f1 score: 0.703
RF cohens kappa score: 0.687

-> test with 'GB'
GB tn, fp: 322, 11
GB fn, tp: 0, 13
GB f1 score: 0.703
GB cohens kappa score: 0.687

-> test with 'KNN'
KNN tn, fp: 261, 72
KNN fn, tp: 0, 13
KNN f1 score: 0.265
KNN cohens kappa score: 0.214


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 287, 46
LR fn, tp: 0, 13
LR f1 score: 0.361
LR cohens kappa score: 0.319
LR average precision score: 0.334

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

-> 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: 269, 64
KNN fn, tp: 0, 13
KNN f1 score: 0.289
KNN cohens kappa score: 0.240


------ Step 2/5: Slice 4/5 -------
-> 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: 0.347

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

-> 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: 271, 62
KNN fn, tp: 0, 13
KNN f1 score: 0.295
KNN cohens kappa score: 0.247


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 278, 53
LR fn, tp: 0, 13
LR f1 score: 0.329
LR cohens kappa score: 0.284
LR average precision score: 0.527

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

-> 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: 274, 57
KNN fn, tp: 0, 13
KNN f1 score: 0.313
KNN cohens kappa score: 0.266


====== 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: 287, 46
LR fn, tp: 0, 13
LR f1 score: 0.361
LR cohens kappa score: 0.319
LR average precision score: 0.294

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

-> 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: 268, 65
KNN fn, tp: 0, 13
KNN f1 score: 0.286
KNN cohens kappa score: 0.237


------ Step 3/5: Slice 2/5 -------
-> 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: 0.429

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

-> 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: 273, 60
KNN fn, tp: 0, 13
KNN f1 score: 0.302
KNN cohens kappa score: 0.255


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 67
LR fn, tp: 0, 13
LR f1 score: 0.280
LR cohens kappa score: 0.230
LR average precision score: 0.312

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

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

-> test with 'KNN'
KNN tn, fp: 266, 67
KNN fn, tp: 0, 13
KNN f1 score: 0.280
KNN cohens kappa score: 0.230


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.434

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

-> 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: 269, 64
KNN fn, tp: 0, 13
KNN f1 score: 0.289
KNN cohens kappa score: 0.240


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 276, 55
LR fn, tp: 1, 12
LR f1 score: 0.300
LR cohens kappa score: 0.253
LR average precision score: 0.365

-> test with 'RF'
RF tn, fp: 327, 4
RF fn, tp: 0, 13
RF f1 score: 0.867
RF cohens kappa score: 0.861

-> test with 'GB'
GB tn, fp: 327, 4
GB fn, tp: 0, 13
GB f1 score: 0.867
GB cohens kappa score: 0.861

-> test with 'KNN'
KNN tn, fp: 277, 54
KNN fn, tp: 0, 13
KNN f1 score: 0.325
KNN cohens kappa score: 0.279


====== 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: 279, 54
LR fn, tp: 0, 13
LR f1 score: 0.325
LR cohens kappa score: 0.280
LR average precision score: 0.402

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

-> 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: 276, 57
KNN fn, tp: 0, 13
KNN f1 score: 0.313
KNN cohens kappa score: 0.267


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.433

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

-> 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: 268, 65
KNN fn, tp: 0, 13
KNN f1 score: 0.286
KNN cohens kappa score: 0.237


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.267

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

-> 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: 270, 63
KNN fn, tp: 0, 13
KNN f1 score: 0.292
KNN cohens kappa score: 0.244


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 48
LR fn, tp: 0, 13
LR f1 score: 0.351
LR cohens kappa score: 0.309
LR average precision score: 0.273

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

-> 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: 277, 56
KNN fn, tp: 0, 13
KNN f1 score: 0.317
KNN cohens kappa score: 0.271


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 277, 54
LR fn, tp: 0, 13
LR f1 score: 0.325
LR cohens kappa score: 0.279
LR average precision score: 0.363

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

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

-> test with 'KNN'
KNN tn, fp: 261, 70
KNN fn, tp: 0, 13
KNN f1 score: 0.271
KNN cohens kappa score: 0.220


====== 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: 264, 69
LR fn, tp: 0, 13
LR f1 score: 0.274
LR cohens kappa score: 0.223
LR average precision score: 0.341

-> test with 'RF'
RF tn, fp: 324, 9
RF fn, tp: 0, 13
RF f1 score: 0.743
RF cohens kappa score: 0.730

-> test with 'GB'
GB tn, fp: 324, 9
GB fn, tp: 0, 13
GB f1 score: 0.743
GB cohens kappa score: 0.730

-> test with 'KNN'
KNN tn, fp: 257, 76
KNN fn, tp: 0, 13
KNN f1 score: 0.255
KNN cohens kappa score: 0.203


------ Step 5/5: 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: 0.366

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

-> 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: 282, 51
KNN fn, tp: 0, 13
KNN f1 score: 0.338
KNN cohens kappa score: 0.294


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 48
LR fn, tp: 0, 13
LR f1 score: 0.351
LR cohens kappa score: 0.309
LR average precision score: 0.362

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

-> 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: 270, 63
KNN fn, tp: 0, 13
KNN f1 score: 0.292
KNN cohens kappa score: 0.244


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 271, 62
LR fn, tp: 0, 13
LR f1 score: 0.295
LR cohens kappa score: 0.247
LR average precision score: 0.298

-> test with 'RF'
RF tn, fp: 324, 9
RF fn, tp: 0, 13
RF f1 score: 0.743
RF cohens kappa score: 0.730

-> test with 'GB'
GB tn, fp: 324, 9
GB fn, tp: 0, 13
GB f1 score: 0.743
GB cohens kappa score: 0.730

-> test with 'KNN'
KNN tn, fp: 267, 66
KNN fn, tp: 0, 13
KNN f1 score: 0.283
KNN cohens kappa score: 0.233


------ Step 5/5: 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: 0.463

-> test with 'RF'
RF tn, fp: 325, 6
RF fn, tp: 0, 13
RF f1 score: 0.813
RF cohens kappa score: 0.804

-> test with 'GB'
GB tn, fp: 325, 6
GB fn, tp: 0, 13
GB f1 score: 0.813
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 274, 57
KNN fn, tp: 0, 13
KNN f1 score: 0.313
KNN cohens kappa score: 0.266

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 292, 69
LR fn, tp: 1, 13
LR f1 score: 0.388
LR cohens kappa score: 0.349
LR average precision score: 0.527


average:
LR tn, fp: 278.0, 54.6
LR fn, tp: 0.04, 12.96
LR f1 score: 0.325
LR cohens kappa score: 0.279
LR average precision score: 0.362


minimum:
LR tn, fp: 264, 41
LR fn, tp: 0, 12
LR f1 score: 0.274
LR cohens kappa score: 0.223
LR average precision score: 0.267


-----[ RF ]-----
maximum:
RF tn, fp: 330, 12
RF fn, tp: 0, 13
RF f1 score: 0.929
RF cohens kappa score: 0.926


average:
RF tn, fp: 326.4, 6.2
RF fn, tp: 0.0, 13.0
RF f1 score: 0.812
RF cohens kappa score: 0.803


minimum:
RF tn, fp: 321, 2
RF fn, tp: 0, 13
RF f1 score: 0.684
RF cohens kappa score: 0.668


-----[ GB ]-----
maximum:
GB tn, fp: 330, 12
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926


average:
GB tn, fp: 326.4, 6.2
GB fn, tp: 0.0, 13.0
GB f1 score: 0.812
GB cohens kappa score: 0.803


minimum:
GB tn, fp: 321, 2
GB fn, tp: 0, 13
GB f1 score: 0.684
GB cohens kappa score: 0.668


-----[ KNN ]-----
maximum:
KNN tn, fp: 282, 76
KNN fn, tp: 0, 13
KNN f1 score: 0.342
KNN cohens kappa score: 0.298


average:
KNN tn, fp: 270.64, 61.96
KNN fn, tp: 0.0, 13.0
KNN f1 score: 0.297
KNN cohens kappa score: 0.249


minimum:
KNN tn, fp: 257, 50
KNN fn, tp: 0, 13
KNN f1 score: 0.255
KNN cohens kappa score: 0.203