ConvGeNBenchmarkResults/data_result/convGAN/folding_abalone9-18.log

///////////////////////////////////////////
// Running convGAN 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 '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: 0.892

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


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.575

-> 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: 114, 24
KNN fn, tp: 3, 6
KNN f1 score: 0.308
KNN cohens kappa score: 0.236


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 126, 12
LR fn, tp: 0, 9
LR f1 score: 0.600
LR cohens kappa score: 0.562
LR average precision score: 0.826

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


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 126, 12
LR fn, tp: 2, 7
LR f1 score: 0.500
LR cohens kappa score: 0.455
LR average precision score: 0.542

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 5, 4
GB f1 score: 0.500
GB cohens kappa score: 0.472

-> test with 'KNN'
KNN tn, fp: 124, 14
KNN fn, tp: 3, 6
KNN f1 score: 0.414
KNN cohens kappa score: 0.360


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> 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: 0.489

-> test with 'GB'
GB tn, fp: 132, 5
GB fn, tp: 4, 2
GB f1 score: 0.308
GB cohens kappa score: 0.275

-> 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 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 '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: 0.624

-> 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: 123, 15
KNN fn, tp: 5, 4
KNN f1 score: 0.286
KNN cohens kappa score: 0.221


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.782

-> 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: 131, 7
KNN fn, tp: 3, 6
KNN f1 score: 0.545
KNN cohens kappa score: 0.510


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.728

-> 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: 122, 16
KNN fn, tp: 2, 7
KNN f1 score: 0.438
KNN cohens kappa score: 0.383


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.720

-> 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: 120, 18
KNN fn, tp: 3, 6
KNN f1 score: 0.364
KNN cohens kappa score: 0.301


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 122, 15
LR fn, tp: 1, 5
LR f1 score: 0.385
LR cohens kappa score: 0.342
LR average precision score: 0.571

-> test with 'GB'
GB tn, fp: 128, 9
GB fn, tp: 3, 3
GB f1 score: 0.333
GB cohens kappa score: 0.294

-> test with 'KNN'
KNN tn, fp: 122, 15
KNN fn, tp: 3, 3
KNN f1 score: 0.250
KNN cohens kappa score: 0.200


====== 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 '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: 0.609

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 7, 2
GB f1 score: 0.235
GB cohens kappa score: 0.189

-> test with 'KNN'
KNN tn, fp: 127, 11
KNN fn, tp: 5, 4
KNN f1 score: 0.333
KNN cohens kappa score: 0.278


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.897

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 2, 7
GB f1 score: 0.700
GB cohens kappa score: 0.678

-> test with 'KNN'
KNN tn, fp: 115, 23
KNN fn, tp: 1, 8
KNN f1 score: 0.400
KNN cohens kappa score: 0.337


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.648

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 7, 2
GB f1 score: 0.235
GB cohens kappa score: 0.189

-> test with 'KNN'
KNN tn, fp: 126, 12
KNN fn, tp: 5, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.262


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 117, 21
LR fn, tp: 2, 7
LR f1 score: 0.378
LR cohens kappa score: 0.315
LR average precision score: 0.656

-> test with 'GB'
GB tn, fp: 129, 9
GB fn, tp: 6, 3
GB f1 score: 0.286
GB cohens kappa score: 0.232

-> test with 'KNN'
KNN tn, fp: 117, 21
KNN fn, tp: 4, 5
KNN f1 score: 0.286
KNN cohens kappa score: 0.214


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 127, 10
LR fn, tp: 2, 4
LR f1 score: 0.400
LR cohens kappa score: 0.363
LR average precision score: 0.530

-> test with 'GB'
GB tn, fp: 129, 8
GB fn, tp: 4, 2
GB f1 score: 0.250
GB cohens kappa score: 0.208

-> 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 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 '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: 0.567

-> 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: 121, 17
KNN fn, tp: 6, 3
KNN f1 score: 0.207
KNN cohens kappa score: 0.134


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 124, 14
LR fn, tp: 2, 7
LR f1 score: 0.467
LR cohens kappa score: 0.417
LR average precision score: 0.706

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

-> test with 'KNN'
KNN tn, fp: 121, 17
KNN fn, tp: 2, 7
KNN f1 score: 0.424
KNN cohens kappa score: 0.368


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.673

-> 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: 121, 17
KNN fn, tp: 3, 6
KNN f1 score: 0.375
KNN cohens kappa score: 0.315


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 123, 15
LR fn, tp: 0, 9
LR f1 score: 0.545
LR cohens kappa score: 0.501
LR average precision score: 0.967

-> 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: 119, 19
KNN fn, tp: 2, 7
KNN f1 score: 0.400
KNN cohens kappa score: 0.340


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> 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: 0.517

-> test with 'GB'
GB tn, fp: 131, 6
GB fn, tp: 4, 2
GB f1 score: 0.286
GB cohens kappa score: 0.250

-> test with 'KNN'
KNN tn, fp: 124, 13
KNN fn, tp: 1, 5
KNN f1 score: 0.417
KNN cohens kappa score: 0.377


====== 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 'LR'
LR tn, fp: 123, 15
LR fn, tp: 2, 7
LR f1 score: 0.452
LR cohens kappa score: 0.399
LR average precision score: 0.692

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 8, 1
GB f1 score: 0.125
GB cohens kappa score: 0.075

-> test with 'KNN'
KNN tn, fp: 119, 19
KNN fn, tp: 5, 4
KNN f1 score: 0.250
KNN cohens kappa score: 0.178


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 126, 12
LR fn, tp: 1, 8
LR f1 score: 0.552
LR cohens kappa score: 0.510
LR average precision score: 0.707

-> 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: 118, 20
KNN fn, tp: 4, 5
KNN f1 score: 0.294
KNN cohens kappa score: 0.224


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 124, 14
LR fn, tp: 3, 6
LR f1 score: 0.414
LR cohens kappa score: 0.360
LR average precision score: 0.447

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 5, 4
GB f1 score: 0.500
GB cohens kappa score: 0.472

-> test with 'KNN'
KNN tn, fp: 121, 17
KNN fn, tp: 6, 3
KNN f1 score: 0.207
KNN cohens kappa score: 0.134


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.849

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

-> test with 'KNN'
KNN tn, fp: 124, 14
KNN fn, tp: 4, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.299


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> 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: 0.850

-> test with 'GB'
GB tn, fp: 135, 2
GB fn, tp: 3, 3
GB f1 score: 0.545
GB cohens kappa score: 0.527

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

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 132, 21
LR fn, tp: 4, 9
LR f1 score: 0.750
LR cohens kappa score: 0.729
LR average precision score: 0.967


average:
LR tn, fp: 126.44, 11.36
LR fn, tp: 1.48, 6.92
LR f1 score: 0.523
LR cohens kappa score: 0.482
LR average precision score: 0.683


minimum:
LR tn, fp: 117, 6
LR fn, tp: 0, 4
LR f1 score: 0.378
LR cohens kappa score: 0.315
LR average precision score: 0.447


-----[ GB ]-----
maximum:
GB tn, fp: 136, 9
GB fn, tp: 8, 7
GB f1 score: 0.700
GB cohens kappa score: 0.678


average:
GB tn, fp: 132.52, 5.28
GB fn, tp: 5.0, 3.4
GB f1 score: 0.397
GB cohens kappa score: 0.361


minimum:
GB tn, fp: 128, 2
GB fn, tp: 2, 1
GB f1 score: 0.125
GB cohens kappa score: 0.075


-----[ KNN ]-----
maximum:
KNN tn, fp: 131, 24
KNN fn, tp: 6, 8
KNN f1 score: 0.545
KNN cohens kappa score: 0.510


average:
KNN tn, fp: 122.2, 15.6
KNN fn, tp: 3.36, 5.04
KNN f1 score: 0.350
KNN cohens kappa score: 0.293


minimum:
KNN tn, fp: 114, 7
KNN fn, tp: 1, 3
KNN f1 score: 0.207
KNN cohens kappa score: 0.134