ConvGeNBenchmarkResults/data_result/ProWRAS/folding_abalone9-18.log

///////////////////////////////////////////
// Running ProWRAS on folding_abalone9-18
///////////////////////////////////////////

Load '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: 129, 9
LR fn, tp: 1, 8
LR f1 score: 0.615
LR cohens kappa score: 0.582
LR average precision score: 0.910

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

-> 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: 126, 12
KNN fn, tp: 4, 5
KNN f1 score: 0.385
KNN cohens kappa score: 0.331


------ Step 1/5: 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: 4, 5
LR f1 score: 0.500
LR cohens kappa score: 0.464
LR average precision score: 0.569

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

-> 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: 123, 15
KNN fn, tp: 4, 5
KNN f1 score: 0.345
KNN cohens kappa score: 0.284


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 1, 8
LR f1 score: 0.696
LR cohens kappa score: 0.671
LR average precision score: 0.799

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

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

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


------ 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: 0.603

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

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


------ Step 1/5: 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: 2, 4
LR f1 score: 0.421
LR cohens kappa score: 0.386
LR average precision score: 0.447

-> test with 'RF'
RF tn, fp: 137, 0
RF fn, tp: 4, 2
RF f1 score: 0.500
RF cohens kappa score: 0.489

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


====== 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: 129, 9
LR fn, tp: 1, 8
LR f1 score: 0.615
LR cohens kappa score: 0.582
LR average precision score: 0.625

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 5, 4
RF f1 score: 0.444
RF cohens kappa score: 0.408

-> 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: 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: 3, 6
LR f1 score: 0.600
LR cohens kappa score: 0.571
LR average precision score: 0.796

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

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 5, 4
GB f1 score: 0.444
GB cohens kappa score: 0.408

-> 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 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 2, 7
LR f1 score: 0.700
LR cohens kappa score: 0.678
LR average precision score: 0.708

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

-> 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: 129, 9
KNN fn, tp: 2, 7
KNN f1 score: 0.560
KNN cohens kappa score: 0.523


------ Step 2/5: 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: 2, 7
LR f1 score: 0.636
LR cohens kappa score: 0.608
LR average precision score: 0.737

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

-> 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: 124, 14
KNN fn, tp: 5, 4
KNN f1 score: 0.296
KNN cohens kappa score: 0.234


------ 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: 0, 6
LR f1 score: 0.571
LR cohens kappa score: 0.544
LR average precision score: 0.573

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

-> 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: 124, 13
KNN fn, tp: 3, 3
KNN f1 score: 0.273
KNN cohens kappa score: 0.225


====== 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: 132, 6
LR fn, tp: 5, 4
LR f1 score: 0.421
LR cohens kappa score: 0.381
LR average precision score: 0.523

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

-> 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: 130, 8
KNN fn, tp: 8, 1
KNN f1 score: 0.111
KNN cohens kappa score: 0.053


------ 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 'RF'
RF tn, fp: 135, 3
RF fn, tp: 4, 5
RF f1 score: 0.588
RF cohens kappa score: 0.563

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 2, 7
GB f1 score: 0.778
GB cohens kappa score: 0.763

-> test with 'KNN'
KNN tn, fp: 122, 16
KNN fn, tp: 4, 5
KNN f1 score: 0.333
KNN cohens kappa score: 0.271


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

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

-> 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: 130, 8
KNN fn, tp: 5, 4
KNN f1 score: 0.381
KNN cohens kappa score: 0.334


------ 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: 1, 8
LR f1 score: 0.457
LR cohens kappa score: 0.403
LR average precision score: 0.699

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

-> test with 'GB'
GB tn, fp: 129, 9
GB fn, tp: 5, 4
GB f1 score: 0.364
GB cohens kappa score: 0.314

-> 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 3/5: 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: 0.530

-> test with 'RF'
RF tn, fp: 135, 2
RF fn, tp: 4, 2
RF f1 score: 0.400
RF cohens kappa score: 0.379

-> 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: 127, 10
KNN fn, tp: 2, 4
KNN f1 score: 0.400
KNN cohens kappa score: 0.363


====== 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: 130, 8
LR fn, tp: 5, 4
LR f1 score: 0.381
LR cohens kappa score: 0.334
LR average precision score: 0.499

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

-> 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: 126, 12
KNN fn, tp: 5, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.262


------ Step 4/5: 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: 0.671

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

-> 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: 124, 14
KNN fn, tp: 3, 6
KNN f1 score: 0.414
KNN cohens kappa score: 0.360


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.656

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

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 5, 4
GB f1 score: 0.444
GB cohens kappa score: 0.408

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


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 0, 9
LR f1 score: 0.692
LR cohens kappa score: 0.666
LR average precision score: 0.912

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

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


------ 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: 0.603

-> test with 'RF'
RF tn, fp: 135, 2
RF fn, tp: 4, 2
RF f1 score: 0.400
RF cohens kappa score: 0.379

-> 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: 127, 10
KNN fn, tp: 2, 4
KNN f1 score: 0.400
KNN cohens kappa score: 0.363


====== 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: 129, 9
LR fn, tp: 2, 7
LR f1 score: 0.560
LR cohens kappa score: 0.523
LR average precision score: 0.677

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

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 7, 2
GB f1 score: 0.250
GB cohens kappa score: 0.208

-> test with 'KNN'
KNN tn, fp: 125, 13
KNN fn, tp: 6, 3
KNN f1 score: 0.240
KNN cohens kappa score: 0.175


------ Step 5/5: Slice 2/5 -------
-> 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: 0.706

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

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


------ Step 5/5: Slice 3/5 -------
-> 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: 0.526

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

-> 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: 126, 12
KNN fn, tp: 6, 3
KNN f1 score: 0.250
KNN cohens kappa score: 0.188


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 3
LR fn, tp: 1, 8
LR f1 score: 0.800
LR cohens kappa score: 0.786
LR average precision score: 0.925

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

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

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


------ Step 5/5: 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: 2, 4
LR f1 score: 0.500
LR cohens kappa score: 0.472
LR average precision score: 0.803

-> test with 'RF'
RF tn, fp: 135, 2
RF fn, tp: 5, 1
RF f1 score: 0.222
RF cohens kappa score: 0.200

-> 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: 131, 6
KNN fn, tp: 2, 4
KNN f1 score: 0.500
KNN cohens kappa score: 0.472

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 135, 18
LR fn, tp: 5, 9
LR f1 score: 0.800
LR cohens kappa score: 0.786
LR average precision score: 0.925


average:
LR tn, fp: 130.52, 7.28
LR fn, tp: 2.12, 6.28
LR f1 score: 0.570
LR cohens kappa score: 0.538
LR average precision score: 0.682


minimum:
LR tn, fp: 120, 3
LR fn, tp: 0, 4
LR f1 score: 0.381
LR cohens kappa score: 0.334
LR average precision score: 0.447


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


average:
RF tn, fp: 135.28, 2.52
RF fn, tp: 5.88, 2.52
RF f1 score: 0.367
RF cohens kappa score: 0.340


minimum:
RF tn, fp: 133, 0
RF fn, tp: 3, 1
RF f1 score: 0.167
RF cohens kappa score: 0.140


-----[ GB ]-----
maximum:
GB tn, fp: 136, 10
GB fn, tp: 7, 7
GB f1 score: 0.778
GB cohens kappa score: 0.763


average:
GB tn, fp: 132.28, 5.52
GB fn, tp: 4.88, 3.52
GB f1 score: 0.399
GB cohens kappa score: 0.362


minimum:
GB tn, fp: 128, 2
GB fn, tp: 2, 1
GB f1 score: 0.200
GB cohens kappa score: 0.142


-----[ KNN ]-----
maximum:
KNN tn, fp: 132, 16
KNN fn, tp: 8, 7
KNN f1 score: 0.571
KNN cohens kappa score: 0.539


average:
KNN tn, fp: 127.24, 10.56
KNN fn, tp: 3.88, 4.52
KNN f1 score: 0.386
KNN cohens kappa score: 0.338


minimum:
KNN tn, fp: 122, 6
KNN fn, tp: 2, 1
KNN f1 score: 0.111
KNN cohens kappa score: 0.053

  wall time: 00:02:30s, process time: 00:02:59s