ConvGeNBenchmarkResults/data_result/ProWRAS/folding_abalone9-18.log

///////////////////////////////////////////
// Running ProWRAS 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: 128, 10
LR fn, tp: 1, 8
LR f1 score: 0.593
LR cohens kappa score: 0.556
LR average precision score: 0.908

-> 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 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 '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: 0.815

-> 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: 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: 0.599

-> 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: 0.442

-> 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: 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: 0.610

-> 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: 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 '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: 133, 5
KNN fn, tp: 3, 6
KNN f1 score: 0.600
KNN cohens kappa score: 0.571


------ 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: 0.728

-> test with 'GB'
GB tn, fp: 130, 8
GB fn, tp: 7, 2
GB f1 score: 0.211
GB cohens kappa score: 0.156

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


------ Step 2/5: 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: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.577
LR average precision score: 0.726

-> 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: 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 '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: 123, 14
KNN fn, tp: 3, 3
KNN f1 score: 0.261
KNN cohens kappa score: 0.212


====== 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: 0.515

-> 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: 7, 2
KNN f1 score: 0.211
KNN cohens kappa score: 0.156


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

-> 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: 0.649

-> 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: 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 '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: 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: 129, 8
LR fn, tp: 2, 4
LR f1 score: 0.444
LR cohens kappa score: 0.412
LR average precision score: 0.532

-> 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 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: 0.500

-> 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: 5, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.247


------ 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: 0.668

-> test with 'GB'
GB tn, fp: 127, 11
GB fn, tp: 4, 5
GB f1 score: 0.400
GB cohens kappa score: 0.349

-> 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: 133, 5
LR fn, tp: 2, 7
LR f1 score: 0.667
LR cohens kappa score: 0.642
LR average precision score: 0.654

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


------ Step 4/5: 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: 0, 9
LR f1 score: 0.720
LR cohens kappa score: 0.696
LR average precision score: 0.912

-> 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: 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: 0.602

-> 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: 0.676

-> 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: 127, 11
LR fn, tp: 1, 8
LR f1 score: 0.571
LR cohens kappa score: 0.533
LR average precision score: 0.710

-> 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: 130, 8
KNN fn, tp: 4, 5
KNN f1 score: 0.455
KNN cohens kappa score: 0.412


------ 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: 0.528

-> 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: 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: 2, 7
LR f1 score: 0.737
LR cohens kappa score: 0.719
LR average precision score: 0.925

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


------ 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: 0.802

-> 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: 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: 0.783
LR cohens kappa score: 0.765
LR average precision score: 0.925


average:
LR tn, fp: 130.52, 7.28
LR fn, tp: 2.16, 6.24
LR f1 score: 0.568
LR cohens kappa score: 0.535
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: 0.442


-----[ GB ]-----
maximum:
GB tn, fp: 135, 11
GB fn, tp: 7, 6
GB f1 score: 0.667
GB cohens kappa score: 0.645


average:
GB tn, fp: 132.12, 5.68
GB fn, tp: 4.88, 3.52
GB f1 score: 0.399
GB cohens kappa score: 0.361


minimum:
GB tn, fp: 127, 3
GB fn, tp: 3, 2
GB f1 score: 0.211
GB cohens kappa score: 0.156


-----[ KNN ]-----
maximum:
KNN tn, fp: 133, 16
KNN fn, tp: 7, 8
KNN f1 score: 0.615
KNN cohens kappa score: 0.582


average:
KNN tn, fp: 127.12, 10.68
KNN fn, tp: 3.76, 4.64
KNN f1 score: 0.395
KNN cohens kappa score: 0.347


minimum:
KNN tn, fp: 122, 5
KNN fn, tp: 1, 2
KNN f1 score: 0.211
KNN cohens kappa score: 0.156