ConvGeNBenchmarkResults/data_result/convGAN-proximary-full/folding_abalone9-18.log

///////////////////////////////////////////
// Running convGAN-proximary-full on folding_abalone9-18
///////////////////////////////////////////

Load 'data_input/folding_abalone9-18'
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 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 135, 3
GAN fn, tp: 4, 5
GAN f1 score: 0.588
GAN cohens kappa score: 0.563

-> test with 'LR'
LR tn, fp: 125, 13
LR fn, tp: 0, 9
LR f1 score: 0.581
LR cohens kappa score: 0.541
LR average precision score: 0.910

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.163

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 8, 1
KNN f1 score: 0.182
KNN cohens kappa score: 0.163


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 136, 2
GAN fn, tp: 5, 4
GAN f1 score: 0.533
GAN cohens kappa score: 0.509

-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 3, 6
LR f1 score: 0.545
LR cohens kappa score: 0.510
LR average precision score: 0.573

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.121

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 8, 1
KNN f1 score: 0.154
KNN cohens kappa score: 0.121


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 136, 2
GAN fn, tp: 4, 5
GAN f1 score: 0.625
GAN cohens kappa score: 0.604

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 1, 8
LR f1 score: 0.640
LR cohens kappa score: 0.609
LR average precision score: 0.795

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 5, 4
GB f1 score: 0.533
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 7, 2
KNN f1 score: 0.333
KNN cohens kappa score: 0.312


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 138, 0
GAN fn, tp: 6, 3
GAN f1 score: 0.500
GAN cohens kappa score: 0.484

-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 4, 5
LR f1 score: 0.556
LR cohens kappa score: 0.527
LR average precision score: 0.619

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 5, 4
GB f1 score: 0.533
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with GAN.predict
GAN tn, fp: 137, 0
GAN fn, tp: 5, 1
GAN f1 score: 0.286
GAN cohens kappa score: 0.277

-> test with 'LR'
LR tn, fp: 134, 3
LR fn, tp: 2, 4
LR f1 score: 0.615
LR cohens kappa score: 0.597
LR average precision score: 0.525

-> test with 'GB'
GB tn, fp: 136, 1
GB fn, tp: 5, 1
GB f1 score: 0.250
GB cohens kappa score: 0.234

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 135, 3
GAN fn, tp: 8, 1
GAN f1 score: 0.154
GAN cohens kappa score: 0.121

-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.577
LR average precision score: 0.612

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.253

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 133, 5
GAN fn, tp: 3, 6
GAN f1 score: 0.600
GAN cohens kappa score: 0.571

-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 2, 7
LR f1 score: 0.636
LR cohens kappa score: 0.608
LR average precision score: 0.796

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 132, 6
GAN fn, tp: 4, 5
GAN f1 score: 0.500
GAN cohens kappa score: 0.464

-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 2, 7
LR f1 score: 0.636
LR cohens kappa score: 0.608
LR average precision score: 0.733

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 6, 3
GB f1 score: 0.375
GB cohens kappa score: 0.340

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 7, 2
KNN f1 score: 0.333
KNN cohens kappa score: 0.312


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 135, 3
GAN fn, tp: 6, 3
GAN f1 score: 0.400
GAN cohens kappa score: 0.369

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 2, 7
LR f1 score: 0.583
LR cohens kappa score: 0.549
LR average precision score: 0.736

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with GAN.predict
GAN tn, fp: 135, 2
GAN fn, tp: 3, 3
GAN f1 score: 0.545
GAN cohens kappa score: 0.527

-> test with 'LR'
LR tn, fp: 130, 7
LR fn, tp: 1, 5
LR f1 score: 0.556
LR cohens kappa score: 0.529
LR average precision score: 0.640

-> test with 'GB'
GB tn, fp: 136, 1
GB fn, tp: 5, 1
GB f1 score: 0.250
GB cohens kappa score: 0.234

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 135, 3
GAN fn, tp: 4, 5
GAN f1 score: 0.588
GAN cohens kappa score: 0.563

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 5, 4
LR f1 score: 0.381
LR cohens kappa score: 0.334
LR average precision score: 0.526

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.140

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 129, 9
GAN fn, tp: 5, 4
GAN f1 score: 0.364
GAN cohens kappa score: 0.314

-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 0, 9
LR f1 score: 0.818
LR cohens kappa score: 0.804
LR average precision score: 0.832

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.140

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 131, 7
GAN fn, tp: 6, 3
GAN f1 score: 0.316
GAN cohens kappa score: 0.269

-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 4, 5
LR f1 score: 0.526
LR cohens kappa score: 0.494
LR average precision score: 0.693

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.281

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 138, 0
GAN fn, tp: 5, 4
GAN f1 score: 0.615
GAN cohens kappa score: 0.600

-> test with 'LR'
LR tn, fp: 127, 11
LR fn, tp: 2, 7
LR f1 score: 0.519
LR cohens kappa score: 0.476
LR average precision score: 0.650

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 5, 4
GB f1 score: 0.571
GB cohens kappa score: 0.552

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with GAN.predict
GAN tn, fp: 134, 3
GAN fn, tp: 5, 1
GAN f1 score: 0.200
GAN cohens kappa score: 0.172

-> test with 'LR'
LR tn, fp: 129, 8
LR fn, tp: 2, 4
LR f1 score: 0.444
LR cohens kappa score: 0.412
LR average precision score: 0.534

-> test with 'GB'
GB tn, fp: 134, 3
GB fn, tp: 5, 1
GB f1 score: 0.200
GB cohens kappa score: 0.172

-> test with 'KNN'
KNN tn, fp: 136, 1
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 136, 2
GAN fn, tp: 4, 5
GAN f1 score: 0.625
GAN cohens kappa score: 0.604

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 5, 4
LR f1 score: 0.381
LR cohens kappa score: 0.334
LR average precision score: 0.531

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 7, 2
KNN f1 score: 0.333
KNN cohens kappa score: 0.312


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 132, 6
GAN fn, tp: 4, 5
GAN f1 score: 0.500
GAN cohens kappa score: 0.464

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 3, 6
LR f1 score: 0.522
LR cohens kappa score: 0.483
LR average precision score: 0.725

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 4, 5
GB f1 score: 0.588
GB cohens kappa score: 0.563

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 130, 8
GAN fn, tp: 5, 4
GAN f1 score: 0.381
GAN cohens kappa score: 0.334

-> test with 'LR'
LR tn, fp: 129, 9
LR fn, tp: 1, 8
LR f1 score: 0.615
LR cohens kappa score: 0.582
LR average precision score: 0.717

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.253

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 132, 6
GAN fn, tp: 3, 6
GAN f1 score: 0.571
GAN cohens kappa score: 0.539

-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 0, 9
LR f1 score: 0.750
LR cohens kappa score: 0.729
LR average precision score: 0.928

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.140

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.032


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with GAN.predict
GAN tn, fp: 134, 3
GAN fn, tp: 3, 3
GAN f1 score: 0.500
GAN cohens kappa score: 0.478

-> test with 'LR'
LR tn, fp: 132, 5
LR fn, tp: 1, 5
LR f1 score: 0.625
LR cohens kappa score: 0.604
LR average precision score: 0.611

-> test with 'GB'
GB tn, fp: 137, 0
GB fn, tp: 5, 1
GB f1 score: 0.286
GB cohens kappa score: 0.277

-> test with 'KNN'
KNN tn, fp: 136, 1
KNN fn, tp: 5, 1
KNN f1 score: 0.250
KNN cohens kappa score: 0.234


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 127, 11
GAN fn, tp: 2, 7
GAN f1 score: 0.519
GAN cohens kappa score: 0.476

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 2, 7
LR f1 score: 0.583
LR cohens kappa score: 0.549
LR average precision score: 0.674

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.163

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.032


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 136, 2
GAN fn, tp: 5, 4
GAN f1 score: 0.533
GAN cohens kappa score: 0.509

-> test with 'LR'
LR tn, fp: 129, 9
LR fn, tp: 2, 7
LR f1 score: 0.560
LR cohens kappa score: 0.523
LR average precision score: 0.674

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 7, 2
GB f1 score: 0.267
GB cohens kappa score: 0.229

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 7, 2
KNN f1 score: 0.308
KNN cohens kappa score: 0.281


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 132, 6
GAN fn, tp: 6, 3
GAN f1 score: 0.333
GAN cohens kappa score: 0.290

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 4, 5
LR f1 score: 0.455
LR cohens kappa score: 0.412
LR average precision score: 0.557

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.281

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 7, 2
KNN f1 score: 0.286
KNN cohens kappa score: 0.253


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 136, 2
GAN fn, tp: 4, 5
GAN f1 score: 0.625
GAN cohens kappa score: 0.604

-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 1, 8
LR f1 score: 0.727
LR cohens kappa score: 0.706
LR average precision score: 0.886

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 7, 2
KNN f1 score: 0.308
KNN cohens kappa score: 0.281


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with GAN.predict
GAN tn, fp: 131, 6
GAN fn, tp: 1, 5
GAN f1 score: 0.588
GAN cohens kappa score: 0.565

-> test with 'LR'
LR tn, fp: 130, 7
LR fn, tp: 0, 6
LR f1 score: 0.632
LR cohens kappa score: 0.609
LR average precision score: 0.827

-> test with 'GB'
GB tn, fp: 137, 0
GB fn, tp: 5, 1
GB f1 score: 0.286
GB cohens kappa score: 0.277

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 3, 3
KNN f1 score: 0.667
KNN cohens kappa score: 0.657

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 134, 13
LR fn, tp: 5, 9
LR f1 score: 0.818
LR cohens kappa score: 0.804
LR average precision score: 0.928


average:
LR tn, fp: 130.68, 7.12
LR fn, tp: 2.04, 6.36
LR f1 score: 0.580
LR cohens kappa score: 0.548
LR average precision score: 0.692


minimum:
LR tn, fp: 125, 3
LR fn, tp: 0, 4
LR f1 score: 0.381
LR cohens kappa score: 0.334
LR average precision score: 0.525


-----[ GB ]-----
maximum:
GB tn, fp: 137, 4
GB fn, tp: 8, 5
GB f1 score: 0.588
GB cohens kappa score: 0.563


average:
GB tn, fp: 135.96, 1.84
GB fn, tp: 6.28, 2.12
GB f1 score: 0.328
GB cohens kappa score: 0.304


minimum:
GB tn, fp: 134, 0
GB fn, tp: 4, 1
GB f1 score: 0.154
GB cohens kappa score: 0.121


-----[ KNN ]-----
maximum:
KNN tn, fp: 138, 3
KNN fn, tp: 9, 3
KNN f1 score: 0.667
KNN cohens kappa score: 0.657


average:
KNN tn, fp: 136.56, 1.24
KNN fn, tp: 7.36, 1.04
KNN f1 score: 0.194
KNN cohens kappa score: 0.175


minimum:
KNN tn, fp: 135, 0
KNN fn, tp: 3, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.032


-----[ GAN ]-----
maximum:
GAN tn, fp: 138, 11
GAN fn, tp: 8, 7
GAN f1 score: 0.625
GAN cohens kappa score: 0.604


average:
GAN tn, fp: 133.8, 4.0
GAN fn, tp: 4.4, 4.0
GAN f1 score: 0.480
GAN cohens kappa score: 0.451


minimum:
GAN tn, fp: 127, 0
GAN fn, tp: 1, 1
GAN f1 score: 0.154
GAN cohens kappa score: 0.121