ConvGeNBenchmarkResults/data_result/convGAN-majority-full/folding_car-vgood.log

///////////////////////////////////////////
// Running convGAN-majority-full 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 GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 7, 6
GAN f1 score: 0.571
GAN cohens kappa score: 0.559

-> test with 'LR'
LR tn, fp: 303, 30
LR fn, tp: 1, 12
LR f1 score: 0.436
LR cohens kappa score: 0.402
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: 333, 0
KNN fn, tp: 0, 13
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 330, 3
GAN fn, tp: 5, 8
GAN f1 score: 0.667
GAN cohens kappa score: 0.655

-> test with 'LR'
LR tn, fp: 297, 36
LR fn, tp: 1, 12
LR f1 score: 0.393
LR cohens kappa score: 0.355
LR average precision score: 0.296

-> 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: 323, 10
KNN fn, tp: 0, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.708


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 330, 3
GAN fn, tp: 8, 5
GAN f1 score: 0.476
GAN cohens kappa score: 0.461

-> 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: 0.392

-> 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: 1, 12
KNN f1 score: 0.727
KNN cohens kappa score: 0.714


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 5, 8
GAN f1 score: 0.727
GAN cohens kappa score: 0.719

-> 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: 0.374

-> 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 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with GAN.predict
GAN tn, fp: 330, 1
GAN fn, tp: 7, 6
GAN f1 score: 0.600
GAN cohens kappa score: 0.589

-> test with 'LR'
LR tn, fp: 306, 25
LR fn, tp: 3, 10
LR f1 score: 0.417
LR cohens kappa score: 0.383
LR average precision score: 0.433

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


====== 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 GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 6, 7
GAN f1 score: 0.636
GAN cohens kappa score: 0.625

-> test with 'LR'
LR tn, fp: 305, 28
LR fn, tp: 5, 8
LR f1 score: 0.327
LR cohens kappa score: 0.287
LR average precision score: 0.284

-> 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 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 8, 5
GAN f1 score: 0.526
GAN cohens kappa score: 0.515

-> 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: 0.329

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


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 9, 4
GAN f1 score: 0.444
GAN cohens kappa score: 0.433

-> test with 'LR'
LR tn, fp: 303, 30
LR fn, tp: 2, 11
LR f1 score: 0.407
LR cohens kappa score: 0.372
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: 331, 2
KNN fn, tp: 3, 10
KNN f1 score: 0.800
KNN cohens kappa score: 0.793


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 327, 6
GAN fn, tp: 5, 8
GAN f1 score: 0.593
GAN cohens kappa score: 0.576

-> test with 'LR'
LR tn, fp: 301, 32
LR fn, tp: 0, 13
LR f1 score: 0.448
LR cohens kappa score: 0.414
LR average precision score: 0.295

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 3, 10
GB f1 score: 0.870
GB cohens kappa score: 0.865

-> 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 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with GAN.predict
GAN tn, fp: 326, 5
GAN fn, tp: 5, 8
GAN f1 score: 0.615
GAN cohens kappa score: 0.600

-> 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: 0.549

-> 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: 331, 0
KNN fn, tp: 0, 13
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


====== 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 GAN.predict
GAN tn, fp: 330, 3
GAN fn, tp: 5, 8
GAN f1 score: 0.667
GAN cohens kappa score: 0.655

-> test with 'LR'
LR tn, fp: 301, 32
LR fn, tp: 2, 11
LR f1 score: 0.393
LR cohens kappa score: 0.356
LR average precision score: 0.309

-> 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 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 333, 0
GAN fn, tp: 5, 8
GAN f1 score: 0.762
GAN cohens kappa score: 0.755

-> test with 'LR'
LR tn, fp: 302, 31
LR fn, tp: 0, 13
LR f1 score: 0.456
LR cohens kappa score: 0.423
LR average precision score: 0.431

-> 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: 0, 13
KNN f1 score: 0.867
KNN cohens kappa score: 0.861


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 5, 8
GAN f1 score: 0.727
GAN cohens kappa score: 0.719

-> 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: 0.322

-> 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 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 3, 10
GAN f1 score: 0.800
GAN cohens kappa score: 0.793

-> 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: 0.382

-> 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 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with GAN.predict
GAN tn, fp: 329, 2
GAN fn, tp: 5, 8
GAN f1 score: 0.696
GAN cohens kappa score: 0.685

-> test with 'LR'
LR tn, fp: 298, 33
LR fn, tp: 3, 10
LR f1 score: 0.357
LR cohens kappa score: 0.318
LR average precision score: 0.380

-> 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 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 GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 5, 8
GAN f1 score: 0.727
GAN cohens kappa score: 0.719

-> test with 'LR'
LR tn, fp: 302, 31
LR fn, tp: 1, 12
LR f1 score: 0.429
LR cohens kappa score: 0.394
LR average precision score: 0.419

-> 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 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 5, 8
GAN f1 score: 0.727
GAN cohens kappa score: 0.719

-> 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: 0.510

-> 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 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 330, 3
GAN fn, tp: 2, 11
GAN f1 score: 0.815
GAN cohens kappa score: 0.807

-> 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: 0.321

-> 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 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 9, 4
GAN f1 score: 0.421
GAN cohens kappa score: 0.407

-> test with 'LR'
LR tn, fp: 302, 31
LR fn, tp: 3, 10
LR f1 score: 0.370
LR cohens kappa score: 0.332
LR average precision score: 0.276

-> 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 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with GAN.predict
GAN tn, fp: 329, 2
GAN fn, tp: 5, 8
GAN f1 score: 0.696
GAN cohens kappa score: 0.685

-> 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: 0.327

-> 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 GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 4, 9
GAN f1 score: 0.750
GAN cohens kappa score: 0.741

-> 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: 0.292

-> 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 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 333, 0
GAN fn, tp: 8, 5
GAN f1 score: 0.556
GAN cohens kappa score: 0.546

-> test with 'LR'
LR tn, fp: 312, 21
LR fn, tp: 3, 10
LR f1 score: 0.455
LR cohens kappa score: 0.424
LR average precision score: 0.338

-> 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: 1, 12
KNN f1 score: 0.857
KNN cohens kappa score: 0.851


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 8, 5
GAN f1 score: 0.500
GAN cohens kappa score: 0.486

-> test with 'LR'
LR tn, fp: 312, 21
LR fn, tp: 3, 10
LR f1 score: 0.455
LR cohens kappa score: 0.424
LR average precision score: 0.338

-> 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: 333, 0
KNN fn, tp: 3, 10
KNN f1 score: 0.870
KNN cohens kappa score: 0.865


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 8, 5
GAN f1 score: 0.526
GAN cohens kappa score: 0.515

-> 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: 0.294

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


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with GAN.predict
GAN tn, fp: 330, 1
GAN fn, tp: 8, 5
GAN f1 score: 0.526
GAN cohens kappa score: 0.515

-> test with 'LR'
LR tn, fp: 299, 32
LR fn, tp: 0, 13
LR f1 score: 0.448
LR cohens kappa score: 0.414
LR average precision score: 0.479

-> 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: 2, 11
KNN f1 score: 0.846
KNN cohens kappa score: 0.840

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 312, 44
LR fn, tp: 5, 13
LR f1 score: 0.456
LR cohens kappa score: 0.424
LR average precision score: 0.549


average:
LR tn, fp: 299.52, 33.08
LR fn, tp: 1.28, 11.72
LR f1 score: 0.407
LR cohens kappa score: 0.370
LR average precision score: 0.363


minimum:
LR tn, fp: 289, 21
LR fn, tp: 0, 8
LR f1 score: 0.327
LR cohens kappa score: 0.287
LR average precision score: 0.276


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


average:
GB tn, fp: 332.44, 0.16
GB fn, tp: 0.28, 12.72
GB f1 score: 0.982
GB cohens kappa score: 0.982


minimum:
GB tn, fp: 329, 0
GB fn, tp: 0, 10
GB f1 score: 0.870
GB cohens kappa score: 0.865


-----[ KNN ]-----
maximum:
KNN tn, fp: 333, 10
KNN fn, tp: 3, 13
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


average:
KNN tn, fp: 327.92, 4.68
KNN fn, tp: 0.56, 12.44
KNN f1 score: 0.831
KNN cohens kappa score: 0.824


minimum:
KNN tn, fp: 323, 0
KNN fn, tp: 0, 10
KNN f1 score: 0.722
KNN cohens kappa score: 0.708


-----[ GAN ]-----
maximum:
GAN tn, fp: 333, 6
GAN fn, tp: 9, 11
GAN f1 score: 0.815
GAN cohens kappa score: 0.807


average:
GAN tn, fp: 330.68, 1.92
GAN fn, tp: 6.0, 7.0
GAN f1 score: 0.630
GAN cohens kappa score: 0.619


minimum:
GAN tn, fp: 326, 0
GAN fn, tp: 2, 4
GAN f1 score: 0.421
GAN cohens kappa score: 0.407