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

///////////////////////////////////////////
// Running convGAN-proximary-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: 311, 22
GAN fn, tp: 0, 13
GAN f1 score: 0.542
GAN cohens kappa score: 0.515

-> 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: 0.355

-> 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 1/5: 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: 1, 12
GAN f1 score: 0.774
GAN cohens kappa score: 0.764

-> 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: 0.299

-> 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: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.668


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

-> 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: 0.377

-> 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: 317, 16
KNN fn, tp: 0, 13
KNN f1 score: 0.619
KNN cohens kappa score: 0.598


------ 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: 323, 10
GAN fn, tp: 2, 11
GAN f1 score: 0.647
GAN cohens kappa score: 0.630

-> 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: 0.375

-> 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 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with GAN.predict
GAN tn, fp: 321, 10
GAN fn, tp: 0, 13
GAN f1 score: 0.722
GAN cohens kappa score: 0.708

-> test with 'LR'
LR tn, fp: 297, 34
LR fn, tp: 1, 12
LR f1 score: 0.407
LR cohens kappa score: 0.370
LR average precision score: 0.446

-> 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: 318, 13
KNN fn, tp: 0, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.649


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

-> 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: 0.282

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


------ 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: 317, 16
GAN fn, tp: 1, 12
GAN f1 score: 0.585
GAN cohens kappa score: 0.563

-> 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: 0.369

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

-> 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: 0.337

-> 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: 318, 15
KNN fn, tp: 0, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


------ 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: 311, 22
GAN fn, tp: 1, 12
GAN f1 score: 0.511
GAN cohens kappa score: 0.483

-> 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: 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 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with GAN.predict
GAN tn, fp: 311, 20
GAN fn, tp: 0, 13
GAN f1 score: 0.565
GAN cohens kappa score: 0.540

-> test with 'LR'
LR tn, fp: 292, 39
LR fn, tp: 1, 12
LR f1 score: 0.375
LR cohens kappa score: 0.335
LR average precision score: 0.550

-> 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: 327, 4
KNN fn, tp: 0, 13
KNN f1 score: 0.867
KNN cohens kappa score: 0.861


====== 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: 318, 15
GAN fn, tp: 0, 13
GAN f1 score: 0.634
GAN cohens kappa score: 0.614

-> test with 'LR'
LR tn, fp: 291, 42
LR fn, tp: 1, 12
LR f1 score: 0.358
LR cohens kappa score: 0.317
LR average precision score: 0.308

-> 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: 319, 14
KNN fn, tp: 0, 13
KNN f1 score: 0.650
KNN cohens kappa score: 0.631


------ 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: 319, 14
GAN fn, tp: 2, 11
GAN f1 score: 0.579
GAN cohens kappa score: 0.557

-> 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: 0.435

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


------ 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: 315, 18
GAN fn, tp: 0, 13
GAN f1 score: 0.591
GAN cohens kappa score: 0.568

-> test with 'LR'
LR tn, fp: 281, 52
LR fn, tp: 0, 13
LR f1 score: 0.333
LR cohens kappa score: 0.289
LR average precision score: 0.322

-> 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: 315, 18
KNN fn, tp: 0, 13
KNN f1 score: 0.591
KNN cohens kappa score: 0.568


------ 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: 319, 14
GAN fn, tp: 0, 13
GAN f1 score: 0.650
GAN cohens kappa score: 0.631

-> 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: 0.382

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


------ 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: 326, 5
GAN fn, tp: 0, 13
GAN f1 score: 0.839
GAN cohens kappa score: 0.831

-> test with 'LR'
LR tn, fp: 295, 36
LR fn, tp: 2, 11
LR f1 score: 0.367
LR cohens kappa score: 0.327
LR average precision score: 0.378

-> 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: 0, 13
KNN f1 score: 0.788
KNN cohens kappa score: 0.778


====== 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: 326, 7
GAN fn, tp: 2, 11
GAN f1 score: 0.710
GAN cohens kappa score: 0.696

-> 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: 0.409

-> 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 4/5: 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: 0, 13
GAN f1 score: 0.765
GAN cohens kappa score: 0.753

-> test with 'LR'
LR tn, fp: 291, 42
LR fn, tp: 1, 12
LR f1 score: 0.358
LR cohens kappa score: 0.317
LR average precision score: 0.487

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


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

-> 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: 0.323

-> 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: 319, 14
KNN fn, tp: 0, 13
KNN f1 score: 0.650
KNN cohens kappa score: 0.631


------ 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: 315, 18
GAN fn, tp: 0, 13
GAN f1 score: 0.591
GAN cohens kappa score: 0.568

-> 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: 0.272

-> 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: 315, 18
KNN fn, tp: 0, 13
KNN f1 score: 0.591
KNN cohens kappa score: 0.568


------ 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: 325, 6
GAN fn, tp: 0, 13
GAN f1 score: 0.813
GAN cohens kappa score: 0.804

-> test with 'LR'
LR tn, fp: 299, 32
LR fn, tp: 1, 12
LR f1 score: 0.421
LR cohens kappa score: 0.385
LR average precision score: 0.325

-> 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: 321, 10
KNN fn, tp: 0, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.708


====== 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: 316, 17
GAN fn, tp: 1, 12
GAN f1 score: 0.571
GAN cohens kappa score: 0.548

-> test with 'LR'
LR tn, fp: 281, 52
LR fn, tp: 0, 13
LR f1 score: 0.333
LR cohens kappa score: 0.289
LR average precision score: 0.289

-> 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: 318, 15
KNN fn, tp: 0, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


------ 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: 321, 12
GAN fn, tp: 2, 11
GAN f1 score: 0.611
GAN cohens kappa score: 0.591

-> test with 'LR'
LR tn, fp: 300, 33
LR fn, tp: 3, 10
LR f1 score: 0.357
LR cohens kappa score: 0.318
LR average precision score: 0.358

-> 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 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 316, 17
GAN fn, tp: 2, 11
GAN f1 score: 0.537
GAN cohens kappa score: 0.512

-> 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: 0.334

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


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

-> 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: 0.292

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


------ 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: 316, 15
GAN fn, tp: 1, 12
GAN f1 score: 0.600
GAN cohens kappa score: 0.578

-> 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: 0.474

-> 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: 319, 12
KNN fn, tp: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.668

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 302, 55
LR fn, tp: 3, 13
LR f1 score: 0.448
LR cohens kappa score: 0.414
LR average precision score: 0.550


average:
LR tn, fp: 292.0, 40.6
LR fn, tp: 0.56, 12.44
LR f1 score: 0.380
LR cohens kappa score: 0.340
LR average precision score: 0.362


minimum:
LR tn, fp: 278, 31
LR fn, tp: 0, 10
LR f1 score: 0.321
LR cohens kappa score: 0.275
LR average precision score: 0.272


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


average:
GB tn, fp: 332.24, 0.36
GB fn, tp: 0.24, 12.76
GB f1 score: 0.977
GB cohens kappa score: 0.976


minimum:
GB tn, fp: 330, 0
GB fn, tp: 0, 10
GB f1 score: 0.870
GB cohens kappa score: 0.865


-----[ KNN ]-----
maximum:
KNN tn, fp: 328, 20
KNN fn, tp: 0, 13
KNN f1 score: 0.867
KNN cohens kappa score: 0.861


average:
KNN tn, fp: 321.24, 11.36
KNN fn, tp: 0.0, 13.0
KNN f1 score: 0.705
KNN cohens kappa score: 0.689


minimum:
KNN tn, fp: 313, 4
KNN fn, tp: 0, 13
KNN f1 score: 0.565
KNN cohens kappa score: 0.540


-----[ GAN ]-----
maximum:
GAN tn, fp: 327, 22
GAN fn, tp: 2, 13
GAN f1 score: 0.839
GAN cohens kappa score: 0.831


average:
GAN tn, fp: 318.68, 13.92
GAN fn, tp: 0.64, 12.36
GAN f1 score: 0.639
GAN cohens kappa score: 0.620


minimum:
GAN tn, fp: 311, 5
GAN fn, tp: 0, 11
GAN f1 score: 0.511
GAN cohens kappa score: 0.483