ConvGeNBenchmarkResults/data_result/convGAN-majority-5/folding_car-vgood.log

///////////////////////////////////////////
// Running convGAN-majority-5 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: 323, 10
GAN fn, tp: 1, 12
GAN f1 score: 0.686
GAN cohens kappa score: 0.670

-> 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: 0.361

-> 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: 327, 6
KNN fn, tp: 0, 13
KNN f1 score: 0.813
KNN cohens kappa score: 0.804


------ 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: 329, 4
GAN fn, tp: 3, 10
GAN f1 score: 0.741
GAN cohens kappa score: 0.730

-> 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: 0.304

-> 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: 314, 19
KNN fn, tp: 1, 12
KNN f1 score: 0.545
KNN cohens kappa score: 0.520


------ 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: 326, 7
GAN fn, tp: 3, 10
GAN f1 score: 0.667
GAN cohens kappa score: 0.652

-> 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: 0.382

-> 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 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: 2, 11
GAN f1 score: 0.733
GAN cohens kappa score: 0.721

-> test with 'LR'
LR tn, fp: 293, 40
LR fn, tp: 0, 13
LR f1 score: 0.394
LR cohens kappa score: 0.355
LR average precision score: 0.372

-> 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 1/5: 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: 2, 11
GAN f1 score: 0.759
GAN cohens kappa score: 0.748

-> test with 'LR'
LR tn, fp: 295, 36
LR fn, tp: 1, 12
LR f1 score: 0.393
LR cohens kappa score: 0.355
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: 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 GAN.predict
GAN tn, fp: 328, 5
GAN fn, tp: 4, 9
GAN f1 score: 0.667
GAN cohens kappa score: 0.653

-> 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: 0.283

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


------ 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: 325, 8
GAN fn, tp: 1, 12
GAN f1 score: 0.727
GAN cohens kappa score: 0.714

-> 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: 0.290

-> 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: 313, 20
KNN fn, tp: 0, 13
KNN f1 score: 0.565
KNN cohens kappa score: 0.540


------ 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: 329, 4
GAN fn, tp: 3, 10
GAN f1 score: 0.741
GAN cohens kappa score: 0.730

-> test with 'LR'
LR tn, fp: 295, 38
LR fn, tp: 2, 11
LR f1 score: 0.355
LR cohens kappa score: 0.314
LR average precision score: 0.337

-> 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: 322, 11
KNN fn, tp: 0, 13
KNN f1 score: 0.703
KNN cohens kappa score: 0.687


------ 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: 323, 10
GAN fn, tp: 1, 12
GAN f1 score: 0.686
GAN cohens kappa score: 0.670

-> 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: 0.284

-> 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: 327, 6
KNN fn, tp: 1, 12
KNN f1 score: 0.774
KNN cohens kappa score: 0.764


------ 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: 324, 7
GAN fn, tp: 0, 13
GAN f1 score: 0.788
GAN cohens kappa score: 0.778

-> test with 'LR'
LR tn, fp: 288, 43
LR fn, tp: 0, 13
LR f1 score: 0.377
LR cohens kappa score: 0.336
LR average precision score: 0.532

-> 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: 322, 9
KNN fn, tp: 0, 13
KNN f1 score: 0.743
KNN cohens kappa score: 0.730


====== 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: 329, 4
GAN fn, tp: 6, 7
GAN f1 score: 0.583
GAN cohens kappa score: 0.568

-> test with 'LR'
LR tn, fp: 296, 37
LR fn, tp: 1, 12
LR f1 score: 0.387
LR cohens kappa score: 0.348
LR average precision score: 0.310

-> 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: 325, 8
KNN fn, tp: 3, 10
KNN f1 score: 0.645
KNN cohens kappa score: 0.629


------ 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: 328, 5
GAN fn, tp: 2, 11
GAN f1 score: 0.759
GAN cohens kappa score: 0.748

-> 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: 0.439

-> 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: 330, 3
KNN fn, tp: 0, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.892


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

-> 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: 0.340

-> 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: 314, 19
KNN fn, tp: 0, 13
KNN f1 score: 0.578
KNN cohens kappa score: 0.554


------ 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: 326, 7
GAN fn, tp: 1, 12
GAN f1 score: 0.750
GAN cohens kappa score: 0.738

-> 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: 0.407

-> 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 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with GAN.predict
GAN tn, fp: 327, 4
GAN fn, tp: 2, 11
GAN f1 score: 0.786
GAN cohens kappa score: 0.777

-> 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: 0.338

-> 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: 324, 7
KNN fn, tp: 2, 11
KNN f1 score: 0.710
KNN cohens kappa score: 0.696


====== 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: 330, 3
GAN fn, tp: 2, 11
GAN f1 score: 0.815
GAN cohens kappa score: 0.807

-> 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: 0.359

-> 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 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 327, 6
GAN fn, tp: 3, 10
GAN f1 score: 0.690
GAN cohens kappa score: 0.676

-> test with 'LR'
LR tn, fp: 285, 48
LR fn, tp: 1, 12
LR f1 score: 0.329
LR cohens kappa score: 0.285
LR average precision score: 0.533

-> 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: 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 GAN.predict
GAN tn, fp: 324, 9
GAN fn, tp: 1, 12
GAN f1 score: 0.706
GAN cohens kappa score: 0.692

-> 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: 0.276

-> 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: 321, 12
KNN fn, tp: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.668


------ 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: 327, 6
GAN fn, tp: 7, 6
GAN f1 score: 0.480
GAN cohens kappa score: 0.461

-> 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: 0.270

-> 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: 322, 11
KNN fn, tp: 0, 13
KNN f1 score: 0.703
KNN cohens kappa score: 0.687


------ 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: 328, 3
GAN fn, tp: 4, 9
GAN f1 score: 0.720
GAN cohens kappa score: 0.709

-> test with 'LR'
LR tn, fp: 292, 39
LR fn, tp: 0, 13
LR f1 score: 0.400
LR cohens kappa score: 0.361
LR average precision score: 0.359

-> 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: 317, 14
KNN fn, tp: 0, 13
KNN f1 score: 0.650
KNN cohens kappa score: 0.631


====== 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: 324, 9
GAN fn, tp: 1, 12
GAN f1 score: 0.706
GAN cohens kappa score: 0.692

-> 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: 0.328

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


------ 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: 327, 6
GAN fn, tp: 3, 10
GAN f1 score: 0.690
GAN cohens kappa score: 0.676

-> test with 'LR'
LR tn, fp: 296, 37
LR fn, tp: 3, 10
LR f1 score: 0.333
LR cohens kappa score: 0.292
LR average precision score: 0.340

-> 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: 1, 12
KNN f1 score: 0.632
KNN cohens kappa score: 0.612


------ 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: 327, 6
GAN fn, tp: 4, 9
GAN f1 score: 0.643
GAN cohens kappa score: 0.628

-> 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: 0.356

-> 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: 321, 12
KNN fn, tp: 1, 12
KNN f1 score: 0.649
KNN cohens kappa score: 0.631


------ 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: 324, 9
GAN fn, tp: 2, 11
GAN f1 score: 0.667
GAN cohens kappa score: 0.651

-> 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: 0.274

-> 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 5/5: 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: 1, 12
GAN f1 score: 0.800
GAN cohens kappa score: 0.791

-> test with 'LR'
LR tn, fp: 292, 39
LR fn, tp: 0, 13
LR f1 score: 0.400
LR cohens kappa score: 0.361
LR average precision score: 0.471

-> 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: 326, 5
KNN fn, tp: 1, 12
KNN f1 score: 0.800
KNN cohens kappa score: 0.791

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 303, 58
LR fn, tp: 3, 13
LR f1 score: 0.448
LR cohens kappa score: 0.414
LR average precision score: 0.533


average:
LR tn, fp: 291.56, 41.04
LR fn, tp: 0.52, 12.48
LR f1 score: 0.379
LR cohens kappa score: 0.339
LR average precision score: 0.359


minimum:
LR tn, fp: 275, 30
LR fn, tp: 0, 10
LR f1 score: 0.310
LR cohens kappa score: 0.263
LR average precision score: 0.270


-----[ GB ]-----
maximum:
GB tn, fp: 333, 4
GB fn, tp: 3, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 331.88, 0.72
GB fn, tp: 0.6, 12.4
GB f1 score: 0.950
GB cohens kappa score: 0.948


minimum:
GB tn, fp: 329, 0
GB fn, tp: 0, 10
GB f1 score: 0.867
GB cohens kappa score: 0.861


-----[ KNN ]-----
maximum:
KNN tn, fp: 330, 20
KNN fn, tp: 3, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.892


average:
KNN tn, fp: 321.16, 11.44
KNN fn, tp: 0.44, 12.56
KNN f1 score: 0.689
KNN cohens kappa score: 0.673


minimum:
KNN tn, fp: 313, 3
KNN fn, tp: 0, 10
KNN f1 score: 0.545
KNN cohens kappa score: 0.520


-----[ GAN ]-----
maximum:
GAN tn, fp: 330, 13
GAN fn, tp: 7, 13
GAN f1 score: 0.815
GAN cohens kappa score: 0.807


average:
GAN tn, fp: 326.16, 6.44
GAN fn, tp: 2.36, 10.64
GAN f1 score: 0.706
GAN cohens kappa score: 0.693


minimum:
GAN tn, fp: 320, 3
GAN fn, tp: 0, 6
GAN f1 score: 0.480
GAN cohens kappa score: 0.461