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

///////////////////////////////////////////
// Running convGAN-proxymary-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: 319, 14
GAN fn, tp: 1, 12
GAN f1 score: 0.615
GAN cohens kappa score: 0.595

-> 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: 0.360

-> test with 'GB'
GB tn, fp: 331, 2
GB fn, tp: 2, 11
GB f1 score: 0.846
GB cohens kappa score: 0.840

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


------ 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: 1, 12
GAN f1 score: 0.857
GAN cohens kappa score: 0.851

-> 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: 0.304

-> 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 1/5: Slice 3/5 -------
-> 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: 283, 50
LR fn, tp: 0, 13
LR f1 score: 0.342
LR cohens kappa score: 0.298
LR average precision score: 0.402

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


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

-> test with 'LR'
LR tn, fp: 293, 40
LR fn, tp: 1, 12
LR f1 score: 0.369
LR cohens kappa score: 0.329
LR average precision score: 0.373

-> 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 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: 3, 10
GAN f1 score: 0.800
GAN cohens kappa score: 0.792

-> test with 'LR'
LR tn, fp: 299, 32
LR fn, tp: 2, 11
LR f1 score: 0.393
LR cohens kappa score: 0.355
LR average precision score: 0.442

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


====== 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: 300, 33
LR fn, tp: 2, 11
LR f1 score: 0.386
LR cohens kappa score: 0.348
LR average precision score: 0.285

-> 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 2/5: 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: 2, 11
GAN f1 score: 0.786
GAN cohens kappa score: 0.777

-> 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: 0.360

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


------ 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: 0, 13
GAN f1 score: 0.867
GAN cohens kappa score: 0.861

-> test with 'LR'
LR tn, fp: 293, 40
LR fn, tp: 1, 12
LR f1 score: 0.369
LR cohens kappa score: 0.329
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: 319, 14
KNN fn, tp: 0, 13
KNN f1 score: 0.650
KNN cohens kappa score: 0.631


------ 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: 324, 9
GAN fn, tp: 5, 8
GAN f1 score: 0.533
GAN cohens kappa score: 0.513

-> 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: 0.280

-> 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: 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: 323, 8
GAN fn, tp: 1, 12
GAN f1 score: 0.727
GAN cohens kappa score: 0.714

-> test with 'LR'
LR tn, fp: 293, 38
LR fn, tp: 1, 12
LR f1 score: 0.381
LR cohens kappa score: 0.341
LR average precision score: 0.536

-> 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: 0, 13
KNN f1 score: 0.929
KNN cohens kappa score: 0.926


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

-> test with 'LR'
LR tn, fp: 290, 43
LR fn, tp: 1, 12
LR f1 score: 0.353
LR cohens kappa score: 0.311
LR average precision score: 0.309

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


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

-> 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: 0.446

-> 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: 324, 9
KNN fn, tp: 0, 13
KNN f1 score: 0.743
KNN cohens kappa score: 0.730


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

-> 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: 0.329

-> 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: 310, 23
KNN fn, tp: 0, 13
KNN f1 score: 0.531
KNN cohens kappa score: 0.503


------ 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: 297, 36
LR fn, tp: 0, 13
LR f1 score: 0.419
LR cohens kappa score: 0.383
LR average precision score: 0.386

-> 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 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> test with GAN.predict
GAN tn, fp: 320, 11
GAN fn, tp: 2, 11
GAN f1 score: 0.629
GAN cohens kappa score: 0.610

-> test with 'LR'
LR tn, fp: 298, 33
LR fn, tp: 2, 11
LR f1 score: 0.386
LR cohens kappa score: 0.348
LR average precision score: 0.372

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 2, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914

-> 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: 330, 3
GAN fn, tp: 1, 12
GAN f1 score: 0.857
GAN cohens kappa score: 0.851

-> 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: 0.368

-> 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 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: 3, 10
GAN f1 score: 0.769
GAN cohens kappa score: 0.760

-> test with 'LR'
LR tn, fp: 290, 43
LR fn, tp: 1, 12
LR f1 score: 0.353
LR cohens kappa score: 0.311
LR average precision score: 0.506

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


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

-> 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: 0.313

-> 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: 323, 10
GAN fn, tp: 5, 8
GAN f1 score: 0.516
GAN cohens kappa score: 0.494

-> test with 'LR'
LR tn, fp: 299, 34
LR fn, tp: 2, 11
LR f1 score: 0.379
LR cohens kappa score: 0.341
LR average precision score: 0.271

-> 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 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: 3, 10
GAN f1 score: 0.714
GAN cohens kappa score: 0.702

-> 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: 0.323

-> 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: 315, 16
KNN fn, tp: 0, 13
KNN f1 score: 0.619
KNN cohens kappa score: 0.598


====== 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: 323, 10
GAN fn, tp: 3, 10
GAN f1 score: 0.606
GAN cohens kappa score: 0.587

-> 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: 0.299

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


------ 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: 324, 9
GAN fn, tp: 3, 10
GAN f1 score: 0.625
GAN cohens kappa score: 0.607

-> test with 'LR'
LR tn, fp: 299, 34
LR fn, tp: 3, 10
LR f1 score: 0.351
LR cohens kappa score: 0.311
LR average precision score: 0.357

-> 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 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 328, 5
GAN fn, tp: 3, 10
GAN f1 score: 0.714
GAN cohens kappa score: 0.702

-> test with 'LR'
LR tn, fp: 306, 27
LR fn, tp: 2, 11
LR f1 score: 0.431
LR cohens kappa score: 0.398
LR average precision score: 0.336

-> 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 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> test with GAN.predict
GAN tn, fp: 328, 5
GAN fn, tp: 1, 12
GAN f1 score: 0.800
GAN cohens kappa score: 0.791

-> 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: 0.273

-> 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 5/5: 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: 2, 11
GAN f1 score: 0.846
GAN cohens kappa score: 0.840

-> test with 'LR'
LR tn, fp: 296, 35
LR fn, tp: 0, 13
LR f1 score: 0.426
LR cohens kappa score: 0.390
LR average precision score: 0.536

-> 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: 327, 4
KNN fn, tp: 1, 12
KNN f1 score: 0.828
KNN cohens kappa score: 0.820

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 306, 56
LR fn, tp: 3, 13
LR f1 score: 0.431
LR cohens kappa score: 0.398
LR average precision score: 0.536


average:
LR tn, fp: 292.56, 40.04
LR fn, tp: 0.84, 12.16
LR f1 score: 0.376
LR cohens kappa score: 0.336
LR average precision score: 0.364


minimum:
LR tn, fp: 277, 27
LR fn, tp: 0, 10
LR f1 score: 0.317
LR cohens kappa score: 0.271
LR average precision score: 0.271


-----[ GB ]-----
maximum:
GB tn, fp: 333, 4
GB fn, tp: 2, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 331.76, 0.84
GB fn, tp: 0.52, 12.48
GB f1 score: 0.949
GB cohens kappa score: 0.947


minimum:
GB tn, fp: 329, 0
GB fn, tp: 0, 11
GB f1 score: 0.846
GB cohens kappa score: 0.840


-----[ KNN ]-----
maximum:
KNN tn, fp: 329, 23
KNN fn, tp: 1, 13
KNN f1 score: 0.929
KNN cohens kappa score: 0.926


average:
KNN tn, fp: 322.52, 10.08
KNN fn, tp: 0.08, 12.92
KNN f1 score: 0.730
KNN cohens kappa score: 0.716


minimum:
KNN tn, fp: 310, 2
KNN fn, tp: 0, 12
KNN f1 score: 0.531
KNN cohens kappa score: 0.503


-----[ GAN ]-----
maximum:
GAN tn, fp: 331, 14
GAN fn, tp: 6, 13
GAN f1 score: 0.867
GAN cohens kappa score: 0.861


average:
GAN tn, fp: 325.84, 6.76
GAN fn, tp: 2.16, 10.84
GAN f1 score: 0.713
GAN cohens kappa score: 0.700


minimum:
GAN tn, fp: 319, 2
GAN fn, tp: 0, 7
GAN f1 score: 0.516
GAN cohens kappa score: 0.494