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 '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: 278, 55
KNN fn, tp: 0, 13
KNN f1 score: 0.321
KNN cohens kappa score: 0.275


------ Step 1/5: 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: 1, 12
LR f1 score: 0.279
LR cohens kappa score: 0.230
LR average precision score: 0.281

-> 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: 275, 58
KNN fn, tp: 0, 13
KNN f1 score: 0.310
KNN cohens kappa score: 0.263


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 263, 70
LR fn, tp: 0, 13
LR f1 score: 0.271
LR cohens kappa score: 0.220
LR average precision score: 0.359

-> 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: 263, 70
KNN fn, tp: 0, 13
KNN f1 score: 0.271
KNN cohens kappa score: 0.220


------ Step 1/5: Slice 4/5 -------
-> 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: 0.412

-> 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: 279, 54
KNN fn, tp: 0, 13
KNN f1 score: 0.325
KNN cohens kappa score: 0.280


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 50
LR fn, tp: 0, 13
LR f1 score: 0.342
LR cohens kappa score: 0.298
LR average precision score: 0.410

-> 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: 277, 54
KNN fn, tp: 0, 13
KNN f1 score: 0.325
KNN cohens kappa score: 0.279


====== 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: 273, 60
LR fn, tp: 0, 13
LR f1 score: 0.302
LR cohens kappa score: 0.255
LR average precision score: 0.274

-> 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: 272, 61
KNN fn, tp: 0, 13
KNN f1 score: 0.299
KNN cohens kappa score: 0.251


------ Step 2/5: Slice 2/5 -------
-> 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: 0.309

-> 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: 266, 67
KNN fn, tp: 0, 13
KNN f1 score: 0.280
KNN cohens kappa score: 0.230


------ Step 2/5: 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: 0.352

-> 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 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: 0.345

-> 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: 275, 58
KNN fn, tp: 0, 13
KNN f1 score: 0.310
KNN cohens kappa score: 0.263


------ Step 2/5: 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: 0.552

-> 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: 281, 50
KNN fn, tp: 0, 13
KNN f1 score: 0.342
KNN cohens kappa score: 0.298


====== 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: 285, 48
LR fn, tp: 0, 13
LR f1 score: 0.351
LR cohens kappa score: 0.309
LR average precision score: 0.285

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


------ Step 3/5: Slice 2/5 -------
-> 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: 0.426

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


------ Step 3/5: Slice 3/5 -------
-> 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: 0.330

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


------ Step 3/5: Slice 4/5 -------
-> 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: 0.450

-> 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 3/5: 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: 0.384

-> 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: 282, 49
KNN fn, tp: 0, 13
KNN f1 score: 0.347
KNN cohens kappa score: 0.303


====== 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: 282, 51
LR fn, tp: 0, 13
LR f1 score: 0.338
LR cohens kappa score: 0.294
LR average precision score: 0.431

-> 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 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.477

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


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.287

-> 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 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: 0.276

-> 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: 275, 58
KNN fn, tp: 0, 13
KNN f1 score: 0.310
KNN cohens kappa score: 0.263


------ Step 4/5: 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: 0.348

-> 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: 265, 66
KNN fn, tp: 0, 13
KNN f1 score: 0.283
KNN cohens kappa score: 0.233


====== 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: 265, 68
LR fn, tp: 0, 13
LR f1 score: 0.277
LR cohens kappa score: 0.227
LR average precision score: 0.337

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


------ Step 5/5: 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: 0.364

-> 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: 283, 50
KNN fn, tp: 0, 13
KNN f1 score: 0.342
KNN cohens kappa score: 0.298


------ Step 5/5: Slice 3/5 -------
-> 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: 0.361

-> 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: 275, 58
LR fn, tp: 0, 13
LR f1 score: 0.310
LR cohens kappa score: 0.263
LR average precision score: 0.308

-> 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: 275, 58
KNN fn, tp: 0, 13
KNN f1 score: 0.310
KNN cohens kappa score: 0.263


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 52
LR fn, tp: 0, 13
LR f1 score: 0.333
LR cohens kappa score: 0.289
LR average precision score: 0.498

-> 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: 277, 54
KNN fn, tp: 0, 13
KNN f1 score: 0.325
KNN cohens kappa score: 0.279

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 286, 70
LR fn, tp: 1, 13
LR f1 score: 0.356
LR cohens kappa score: 0.314
LR average precision score: 0.552


average:
LR tn, fp: 277.4, 55.2
LR fn, tp: 0.04, 12.96
LR f1 score: 0.322
LR cohens kappa score: 0.276
LR average precision score: 0.368


minimum:
LR tn, fp: 263, 47
LR fn, tp: 0, 12
LR f1 score: 0.271
LR cohens kappa score: 0.220
LR average precision score: 0.274


-----[ 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: 283, 70
KNN fn, tp: 0, 13
KNN f1 score: 0.347
KNN cohens kappa score: 0.303


average:
KNN tn, fp: 273.72, 58.88
KNN fn, tp: 0.0, 13.0
KNN f1 score: 0.308
KNN cohens kappa score: 0.260


minimum:
KNN tn, fp: 263, 49
KNN fn, tp: 0, 13
KNN f1 score: 0.271
KNN cohens kappa score: 0.220