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

///////////////////////////////////////////
// Running convGAN-proxymary-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: 123, 15
GAN fn, tp: 0, 9
GAN f1 score: 0.545
GAN cohens kappa score: 0.501

-> test with 'LR'
LR tn, fp: 121, 17
LR fn, tp: 0, 9
LR f1 score: 0.514
LR cohens kappa score: 0.466
LR average precision score: 0.886

-> test with 'GB'
GB tn, fp: 129, 9
GB fn, tp: 7, 2
GB f1 score: 0.200
GB cohens kappa score: 0.142

-> test with 'KNN'
KNN tn, fp: 115, 23
KNN fn, tp: 3, 6
KNN f1 score: 0.316
KNN cohens kappa score: 0.245


------ 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: 129, 9
GAN fn, tp: 4, 5
GAN f1 score: 0.435
GAN cohens kappa score: 0.389

-> test with 'LR'
LR tn, fp: 129, 9
LR fn, tp: 3, 6
LR f1 score: 0.500
LR cohens kappa score: 0.459
LR average precision score: 0.580

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 5, 4
GB f1 score: 0.421
GB cohens kappa score: 0.381

-> test with 'KNN'
KNN tn, fp: 120, 18
KNN fn, tp: 4, 5
KNN f1 score: 0.312
KNN cohens kappa score: 0.246


------ 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: 135, 3
GAN fn, tp: 3, 6
GAN f1 score: 0.667
GAN cohens kappa score: 0.645

-> test with 'LR'
LR tn, fp: 127, 11
LR fn, tp: 1, 8
LR f1 score: 0.571
LR cohens kappa score: 0.533
LR average precision score: 0.782

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 126, 12
KNN fn, tp: 3, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.395


------ 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: 129, 9
GAN fn, tp: 3, 6
GAN f1 score: 0.500
GAN cohens kappa score: 0.459

-> test with 'LR'
LR tn, fp: 129, 9
LR fn, tp: 3, 6
LR f1 score: 0.500
LR cohens kappa score: 0.459
LR average precision score: 0.638

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 5, 4
GB f1 score: 0.471
GB cohens kappa score: 0.438

-> test with 'KNN'
KNN tn, fp: 125, 13
KNN fn, tp: 2, 7
KNN f1 score: 0.483
KNN cohens kappa score: 0.435


------ 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: 127, 10
GAN fn, tp: 3, 3
GAN f1 score: 0.316
GAN cohens kappa score: 0.274

-> 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: 0.488

-> test with 'GB'
GB tn, fp: 130, 7
GB fn, tp: 4, 2
GB f1 score: 0.267
GB cohens kappa score: 0.228

-> test with 'KNN'
KNN tn, fp: 127, 10
KNN fn, tp: 2, 4
KNN f1 score: 0.400
KNN cohens kappa score: 0.363


====== 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: 134, 4
GAN fn, tp: 5, 4
GAN f1 score: 0.471
GAN cohens kappa score: 0.438

-> test with 'LR'
LR tn, fp: 123, 15
LR fn, tp: 1, 8
LR f1 score: 0.500
LR cohens kappa score: 0.452
LR average precision score: 0.628

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 5, 4
GB f1 score: 0.471
GB cohens kappa score: 0.438

-> test with 'KNN'
KNN tn, fp: 122, 16
KNN fn, tp: 3, 6
KNN f1 score: 0.387
KNN cohens kappa score: 0.329


------ 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: 128, 10
GAN fn, tp: 2, 7
GAN f1 score: 0.538
GAN cohens kappa score: 0.498

-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 1, 8
LR f1 score: 0.667
LR cohens kappa score: 0.639
LR average precision score: 0.771

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 6, 3
GB f1 score: 0.333
GB cohens kappa score: 0.290

-> test with 'KNN'
KNN tn, fp: 128, 10
KNN fn, tp: 3, 6
KNN f1 score: 0.480
KNN cohens kappa score: 0.436


------ 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: 2, 7
GAN f1 score: 0.636
GAN cohens kappa score: 0.608

-> 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: 0.731

-> 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: 119, 19
KNN fn, tp: 1, 8
KNN f1 score: 0.444
KNN cohens kappa score: 0.388


------ 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: 130, 8
GAN fn, tp: 3, 6
GAN f1 score: 0.522
GAN cohens kappa score: 0.483

-> test with 'LR'
LR tn, fp: 128, 10
LR fn, tp: 1, 8
LR f1 score: 0.593
LR cohens kappa score: 0.556
LR average precision score: 0.711

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

-> test with 'KNN'
KNN tn, fp: 125, 13
KNN fn, tp: 4, 5
KNN f1 score: 0.370
KNN cohens kappa score: 0.314


------ 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: 123, 14
GAN fn, tp: 1, 5
GAN f1 score: 0.400
GAN cohens kappa score: 0.359

-> test with 'LR'
LR tn, fp: 128, 9
LR fn, tp: 1, 5
LR f1 score: 0.500
LR cohens kappa score: 0.469
LR average precision score: 0.587

-> test with 'GB'
GB tn, fp: 127, 10
GB fn, tp: 3, 3
GB f1 score: 0.316
GB cohens kappa score: 0.274

-> test with 'KNN'
KNN tn, fp: 118, 19
KNN fn, tp: 2, 4
KNN f1 score: 0.276
KNN cohens kappa score: 0.224


====== 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: 130, 8
GAN fn, tp: 5, 4
GAN f1 score: 0.381
GAN cohens kappa score: 0.334

-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 4, 5
LR f1 score: 0.500
LR cohens kappa score: 0.464
LR average precision score: 0.538

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 8, 1
GB f1 score: 0.118
GB cohens kappa score: 0.064

-> test with 'KNN'
KNN tn, fp: 125, 13
KNN fn, tp: 5, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.247


------ 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: 128, 10
GAN fn, tp: 1, 8
GAN f1 score: 0.593
GAN cohens kappa score: 0.556

-> 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: 0.906

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 2, 7
GB f1 score: 0.737
GB cohens kappa score: 0.719

-> test with 'KNN'
KNN tn, fp: 117, 21
KNN fn, tp: 0, 9
KNN f1 score: 0.462
KNN cohens kappa score: 0.406


------ 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: 134, 4
GAN fn, tp: 6, 3
GAN f1 score: 0.375
GAN cohens kappa score: 0.340

-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 3, 6
LR f1 score: 0.571
LR cohens kappa score: 0.539
LR average precision score: 0.660

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 7, 2
GB f1 score: 0.222
GB cohens kappa score: 0.171

-> test with 'KNN'
KNN tn, fp: 126, 12
KNN fn, tp: 6, 3
KNN f1 score: 0.250
KNN cohens kappa score: 0.188


------ 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: 130, 8
GAN fn, tp: 3, 6
GAN f1 score: 0.522
GAN cohens kappa score: 0.483

-> test with 'LR'
LR tn, fp: 119, 19
LR fn, tp: 1, 8
LR f1 score: 0.444
LR cohens kappa score: 0.388
LR average precision score: 0.668

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 7, 2
GB f1 score: 0.222
GB cohens kappa score: 0.171

-> test with 'KNN'
KNN tn, fp: 118, 20
KNN fn, tp: 4, 5
KNN f1 score: 0.294
KNN cohens kappa score: 0.224


------ 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: 126, 11
GAN fn, tp: 0, 6
GAN f1 score: 0.522
GAN cohens kappa score: 0.490

-> test with 'LR'
LR tn, fp: 125, 12
LR fn, tp: 1, 5
LR f1 score: 0.435
LR cohens kappa score: 0.397
LR average precision score: 0.599

-> test with 'GB'
GB tn, fp: 130, 7
GB fn, tp: 4, 2
GB f1 score: 0.267
GB cohens kappa score: 0.228

-> test with 'KNN'
KNN tn, fp: 116, 21
KNN fn, tp: 3, 3
KNN f1 score: 0.200
KNN cohens kappa score: 0.142


====== 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: 129, 9
GAN fn, tp: 4, 5
GAN f1 score: 0.435
GAN cohens kappa score: 0.389

-> 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: 0.529

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 124, 14
KNN fn, tp: 5, 4
KNN f1 score: 0.296
KNN cohens kappa score: 0.234


------ 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: 124, 14
GAN fn, tp: 2, 7
GAN f1 score: 0.467
GAN cohens kappa score: 0.417

-> 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: 0.721

-> test with 'GB'
GB tn, fp: 126, 12
GB fn, tp: 4, 5
GB f1 score: 0.385
GB cohens kappa score: 0.331

-> test with 'KNN'
KNN tn, fp: 118, 20
KNN fn, tp: 2, 7
KNN f1 score: 0.389
KNN cohens kappa score: 0.327


------ 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: 127, 11
GAN fn, tp: 4, 5
GAN f1 score: 0.400
GAN cohens kappa score: 0.349

-> test with 'LR'
LR tn, fp: 128, 10
LR fn, tp: 1, 8
LR f1 score: 0.593
LR cohens kappa score: 0.556
LR average precision score: 0.684

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 122, 16
KNN fn, tp: 3, 6
KNN f1 score: 0.387
KNN cohens kappa score: 0.329


------ 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: 130, 8
GAN fn, tp: 3, 6
GAN f1 score: 0.522
GAN cohens kappa score: 0.483

-> test with 'LR'
LR tn, fp: 124, 14
LR fn, tp: 0, 9
LR f1 score: 0.562
LR cohens kappa score: 0.520
LR average precision score: 0.928

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

-> test with 'KNN'
KNN tn, fp: 122, 16
KNN fn, tp: 1, 8
KNN f1 score: 0.485
KNN cohens kappa score: 0.434


------ 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: 132, 5
GAN fn, tp: 2, 4
GAN f1 score: 0.533
GAN cohens kappa score: 0.509

-> test with 'LR'
LR tn, fp: 131, 6
LR fn, tp: 1, 5
LR f1 score: 0.588
LR cohens kappa score: 0.565
LR average precision score: 0.609

-> test with 'GB'
GB tn, fp: 131, 6
GB fn, tp: 3, 3
GB f1 score: 0.400
GB cohens kappa score: 0.368

-> test with 'KNN'
KNN tn, fp: 126, 11
KNN fn, tp: 2, 4
KNN f1 score: 0.381
KNN cohens kappa score: 0.341


====== 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: 126, 12
GAN fn, tp: 4, 5
GAN f1 score: 0.385
GAN cohens kappa score: 0.331

-> test with 'LR'
LR tn, fp: 128, 10
LR fn, tp: 1, 8
LR f1 score: 0.593
LR cohens kappa score: 0.556
LR average precision score: 0.768

-> test with 'GB'
GB tn, fp: 130, 8
GB fn, tp: 8, 1
GB f1 score: 0.111
GB cohens kappa score: 0.053

-> test with 'KNN'
KNN tn, fp: 122, 16
KNN fn, tp: 8, 1
KNN f1 score: 0.077
KNN cohens kappa score: -0.003


------ 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: 126, 12
GAN fn, tp: 2, 7
GAN f1 score: 0.500
GAN cohens kappa score: 0.455

-> test with 'LR'
LR tn, fp: 128, 10
LR fn, tp: 0, 9
LR f1 score: 0.643
LR cohens kappa score: 0.610
LR average precision score: 0.731

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 6, 3
GB f1 score: 0.333
GB cohens kappa score: 0.290

-> test with 'KNN'
KNN tn, fp: 115, 23
KNN fn, tp: 5, 4
KNN f1 score: 0.222
KNN cohens kappa score: 0.144


------ 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: 129, 9
GAN fn, tp: 4, 5
GAN f1 score: 0.435
GAN cohens kappa score: 0.389

-> test with 'LR'
LR tn, fp: 127, 11
LR fn, tp: 3, 6
LR f1 score: 0.462
LR cohens kappa score: 0.415
LR average precision score: 0.548

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 6, 3
GB f1 score: 0.316
GB cohens kappa score: 0.269

-> test with 'KNN'
KNN tn, fp: 123, 15
KNN fn, tp: 5, 4
KNN f1 score: 0.286
KNN cohens kappa score: 0.221


------ 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: 127, 11
GAN fn, tp: 1, 8
GAN f1 score: 0.571
GAN cohens kappa score: 0.533

-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 1, 8
LR f1 score: 0.667
LR cohens kappa score: 0.639
LR average precision score: 0.892

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 4, 5
GB f1 score: 0.476
GB cohens kappa score: 0.437

-> test with 'KNN'
KNN tn, fp: 125, 13
KNN fn, tp: 2, 7
KNN f1 score: 0.483
KNN cohens kappa score: 0.435


------ 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: 130, 7
GAN fn, tp: 2, 4
GAN f1 score: 0.471
GAN cohens kappa score: 0.440

-> test with 'LR'
LR tn, fp: 129, 8
LR fn, tp: 0, 6
LR f1 score: 0.600
LR cohens kappa score: 0.575
LR average precision score: 0.808

-> test with 'GB'
GB tn, fp: 130, 7
GB fn, tp: 3, 3
GB f1 score: 0.375
GB cohens kappa score: 0.340

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

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 132, 19
LR fn, tp: 4, 9
LR f1 score: 0.750
LR cohens kappa score: 0.729
LR average precision score: 0.928


average:
LR tn, fp: 127.8, 10.0
LR fn, tp: 1.48, 6.92
LR f1 score: 0.548
LR cohens kappa score: 0.510
LR average precision score: 0.696


minimum:
LR tn, fp: 119, 6
LR fn, tp: 0, 4
LR f1 score: 0.435
LR cohens kappa score: 0.388
LR average precision score: 0.488


-----[ GB ]-----
maximum:
GB tn, fp: 135, 12
GB fn, tp: 8, 7
GB f1 score: 0.737
GB cohens kappa score: 0.719


average:
GB tn, fp: 131.36, 6.44
GB fn, tp: 5.2, 3.2
GB f1 score: 0.352
GB cohens kappa score: 0.311


minimum:
GB tn, fp: 126, 3
GB fn, tp: 2, 1
GB f1 score: 0.111
GB cohens kappa score: 0.053


-----[ KNN ]-----
maximum:
KNN tn, fp: 129, 23
KNN fn, tp: 8, 9
KNN f1 score: 0.485
KNN cohens kappa score: 0.436


average:
KNN tn, fp: 122.12, 15.68
KNN fn, tp: 3.2, 5.2
KNN f1 score: 0.355
KNN cohens kappa score: 0.298


minimum:
KNN tn, fp: 115, 8
KNN fn, tp: 0, 1
KNN f1 score: 0.077
KNN cohens kappa score: -0.003


-----[ GAN ]-----
maximum:
GAN tn, fp: 135, 15
GAN fn, tp: 6, 9
GAN f1 score: 0.667
GAN cohens kappa score: 0.645


average:
GAN tn, fp: 128.72, 9.08
GAN fn, tp: 2.76, 5.64
GAN f1 score: 0.486
GAN cohens kappa score: 0.446


minimum:
GAN tn, fp: 123, 3
GAN fn, tp: 0, 3
GAN f1 score: 0.316
GAN cohens kappa score: 0.274