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

///////////////////////////////////////////
// Running convGAN-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 'LR'
LR tn, fp: 119, 19
LR fn, tp: 0, 9
LR f1 score: 0.486
LR cohens kappa score: 0.434
LR average precision score: 0.879

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


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.581

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 5, 4
GB f1 score: 0.400
GB cohens kappa score: 0.357

-> test with 'KNN'
KNN tn, fp: 120, 18
KNN fn, tp: 1, 8
KNN f1 score: 0.457
KNN cohens kappa score: 0.403


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.809

-> 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: 128, 10
KNN fn, tp: 3, 6
KNN f1 score: 0.480
KNN cohens kappa score: 0.436


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.582

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 6, 3
GB f1 score: 0.429
GB cohens kappa score: 0.402

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


------ Step 1/5: 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: 0.452

-> test with 'GB'
GB tn, fp: 134, 3
GB fn, tp: 4, 2
GB f1 score: 0.364
GB cohens kappa score: 0.338

-> 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 'LR'
LR tn, fp: 122, 16
LR fn, tp: 1, 8
LR f1 score: 0.485
LR cohens kappa score: 0.434
LR average precision score: 0.651

-> 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: 129, 9
KNN fn, tp: 5, 4
KNN f1 score: 0.364
KNN cohens kappa score: 0.314


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.783

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 5, 4
GB f1 score: 0.400
GB cohens kappa score: 0.357

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


------ Step 2/5: 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: 2, 7
LR f1 score: 0.538
LR cohens kappa score: 0.498
LR average precision score: 0.653

-> 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: 123, 15
KNN fn, tp: 2, 7
KNN f1 score: 0.452
KNN cohens kappa score: 0.399


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.719

-> 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 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> 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: 0.660

-> 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: 120, 17
KNN fn, tp: 2, 4
KNN f1 score: 0.296
KNN cohens kappa score: 0.247


====== 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: 128, 10
LR fn, tp: 4, 5
LR f1 score: 0.417
LR cohens kappa score: 0.368
LR average precision score: 0.556

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


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 0, 9
LR f1 score: 0.783
LR cohens kappa score: 0.765
LR average precision score: 0.906

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 2, 7
GB f1 score: 0.667
GB cohens kappa score: 0.642

-> test with 'KNN'
KNN tn, fp: 124, 14
KNN fn, tp: 1, 8
KNN f1 score: 0.516
KNN cohens kappa score: 0.470


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 3, 6
LR f1 score: 0.600
LR cohens kappa score: 0.571
LR average precision score: 0.693

-> 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: 127, 11
KNN fn, tp: 5, 4
KNN f1 score: 0.333
KNN cohens kappa score: 0.278


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 120, 18
LR fn, tp: 2, 7
LR f1 score: 0.412
LR cohens kappa score: 0.354
LR average precision score: 0.630

-> test with 'GB'
GB tn, fp: 130, 8
GB fn, tp: 6, 3
GB f1 score: 0.300
GB cohens kappa score: 0.249

-> test with 'KNN'
KNN tn, fp: 123, 15
KNN fn, tp: 4, 5
KNN f1 score: 0.345
KNN cohens kappa score: 0.284


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> 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: 0.564

-> 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: 119, 18
KNN fn, tp: 2, 4
KNN f1 score: 0.286
KNN cohens kappa score: 0.235


====== 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: 0.547

-> 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: 128, 10
KNN fn, tp: 6, 3
KNN f1 score: 0.273
KNN cohens kappa score: 0.216


------ Step 4/5: 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: 2, 7
LR f1 score: 0.500
LR cohens kappa score: 0.455
LR average precision score: 0.732

-> test with 'GB'
GB tn, fp: 128, 10
GB fn, tp: 4, 5
GB f1 score: 0.417
GB cohens kappa score: 0.368

-> 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 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 125, 13
LR fn, tp: 1, 8
LR f1 score: 0.533
LR cohens kappa score: 0.490
LR average precision score: 0.664

-> 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: 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: 0.906

-> test with 'GB'
GB tn, fp: 130, 8
GB fn, tp: 6, 3
GB f1 score: 0.300
GB cohens kappa score: 0.249

-> test with 'KNN'
KNN tn, fp: 116, 22
KNN fn, tp: 2, 7
KNN f1 score: 0.368
KNN cohens kappa score: 0.303


------ 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: 0.510

-> 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: 125, 12
KNN fn, tp: 1, 5
KNN f1 score: 0.435
KNN cohens kappa score: 0.397


====== 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: 125, 13
LR fn, tp: 2, 7
LR f1 score: 0.483
LR cohens kappa score: 0.435
LR average precision score: 0.693

-> 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: 125, 13
KNN fn, tp: 7, 2
KNN f1 score: 0.167
KNN cohens kappa score: 0.098


------ Step 5/5: Slice 2/5 -------
-> 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: 0.677

-> 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: 114, 24
KNN fn, tp: 5, 4
KNN f1 score: 0.216
KNN cohens kappa score: 0.136


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 129, 9
LR fn, tp: 4, 5
LR f1 score: 0.435
LR cohens kappa score: 0.389
LR average precision score: 0.532

-> 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: 122, 16
KNN fn, tp: 5, 4
KNN f1 score: 0.276
KNN cohens kappa score: 0.209


------ Step 5/5: Slice 4/5 -------
-> 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: 0.841

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


------ Step 5/5: 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: 1, 5
LR f1 score: 0.526
LR cohens kappa score: 0.497
LR average precision score: 0.809

-> test with 'GB'
GB tn, fp: 133, 4
GB fn, tp: 3, 3
GB f1 score: 0.462
GB cohens kappa score: 0.436

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

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 133, 19
LR fn, tp: 4, 9
LR f1 score: 0.783
LR cohens kappa score: 0.765
LR average precision score: 0.906


average:
LR tn, fp: 127.72, 10.08
LR fn, tp: 1.64, 6.76
LR f1 score: 0.539
LR cohens kappa score: 0.501
LR average precision score: 0.681


minimum:
LR tn, fp: 119, 5
LR fn, tp: 0, 4
LR f1 score: 0.400
LR cohens kappa score: 0.354
LR average precision score: 0.452


-----[ GB ]-----
maximum:
GB tn, fp: 136, 10
GB fn, tp: 7, 7
GB f1 score: 0.667
GB cohens kappa score: 0.642


average:
GB tn, fp: 131.64, 6.16
GB fn, tp: 5.04, 3.36
GB f1 score: 0.374
GB cohens kappa score: 0.334


minimum:
GB tn, fp: 128, 2
GB fn, tp: 2, 2
GB f1 score: 0.222
GB cohens kappa score: 0.171


-----[ KNN ]-----
maximum:
KNN tn, fp: 132, 24
KNN fn, tp: 7, 8
KNN f1 score: 0.571
KNN cohens kappa score: 0.539


average:
KNN tn, fp: 123.6, 14.2
KNN fn, tp: 3.36, 5.04
KNN f1 score: 0.367
KNN cohens kappa score: 0.312


minimum:
KNN tn, fp: 114, 6
KNN fn, tp: 1, 2
KNN f1 score: 0.167
KNN cohens kappa score: 0.098