ConvGeNBenchmarkResults/data_result/Repeater/folding_abalone9-18.log

///////////////////////////////////////////
// Running Repeater 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: 115, 23
LR fn, tp: 0, 9
LR f1 score: 0.439
LR cohens kappa score: 0.380
LR average precision score: 0.927

-> 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: 132, 6
GB fn, tp: 3, 6
GB f1 score: 0.571
GB cohens kappa score: 0.539

-> 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 1/5: 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: 3, 6
LR f1 score: 0.444
LR cohens kappa score: 0.395
LR average precision score: 0.587

-> 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: 131, 7
GB fn, tp: 6, 3
GB f1 score: 0.316
GB cohens kappa score: 0.269

-> 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 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: 1, 8
LR f1 score: 0.552
LR cohens kappa score: 0.510
LR average precision score: 0.805

-> 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: 135, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.253

-> 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 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: 0.526

-> 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: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 124, 14
KNN fn, tp: 2, 7
KNN f1 score: 0.467
KNN cohens kappa score: 0.417


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 124, 13
LR fn, tp: 1, 5
LR f1 score: 0.417
LR cohens kappa score: 0.377
LR average precision score: 0.482

-> test with 'RF'
RF tn, fp: 136, 1
RF fn, tp: 5, 1
RF f1 score: 0.250
RF cohens kappa score: 0.234

-> test with 'GB'
GB tn, fp: 132, 5
GB fn, tp: 5, 1
GB f1 score: 0.167
GB cohens kappa score: 0.130

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


====== 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: 0.651

-> test with 'RF'
RF tn, fp: 135, 3
RF fn, tp: 8, 1
RF f1 score: 0.154
RF cohens kappa score: 0.121

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


------ Step 2/5: 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: 0.787

-> 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: 135, 3
GB fn, tp: 5, 4
GB f1 score: 0.500
GB cohens kappa score: 0.472

-> 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 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.726

-> 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: 119, 19
KNN fn, tp: 3, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.289


------ Step 2/5: 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: 0, 9
LR f1 score: 0.500
LR cohens kappa score: 0.449
LR average precision score: 0.710

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

-> 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 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 124, 13
LR fn, tp: 1, 5
LR f1 score: 0.417
LR cohens kappa score: 0.377
LR average precision score: 0.661

-> test with 'RF'
RF tn, fp: 137, 0
RF fn, tp: 3, 3
RF f1 score: 0.667
RF cohens kappa score: 0.657

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

-> test with 'KNN'
KNN tn, fp: 124, 13
KNN fn, tp: 2, 4
KNN f1 score: 0.348
KNN cohens kappa score: 0.305


====== 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: 124, 14
LR fn, tp: 2, 7
LR f1 score: 0.467
LR cohens kappa score: 0.417
LR average precision score: 0.548

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


------ Step 3/5: 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: 0, 9
LR f1 score: 0.621
LR cohens kappa score: 0.586
LR average precision score: 0.906

-> 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: 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: 3, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.289


------ Step 3/5: 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: 4, 5
LR f1 score: 0.417
LR cohens kappa score: 0.368
LR average precision score: 0.653

-> 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: 133, 5
GB fn, tp: 7, 2
GB f1 score: 0.250
GB cohens kappa score: 0.208

-> 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 'LR'
LR tn, fp: 114, 24
LR fn, tp: 2, 7
LR f1 score: 0.350
LR cohens kappa score: 0.282
LR average precision score: 0.620

-> test with 'RF'
RF tn, fp: 138, 0
RF fn, tp: 7, 2
RF f1 score: 0.364
RF cohens kappa score: 0.349

-> 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: 122, 16
KNN fn, tp: 4, 5
KNN f1 score: 0.333
KNN cohens kappa score: 0.271


------ Step 3/5: 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: 0.528

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

-> test with 'GB'
GB tn, fp: 132, 5
GB fn, tp: 3, 3
GB f1 score: 0.429
GB cohens kappa score: 0.400

-> test with 'KNN'
KNN tn, fp: 126, 11
KNN fn, tp: 3, 3
KNN f1 score: 0.300
KNN cohens kappa score: 0.256


====== 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: 125, 13
LR fn, tp: 4, 5
LR f1 score: 0.370
LR cohens kappa score: 0.314
LR average precision score: 0.529

-> 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: 132, 6
GB fn, tp: 6, 3
GB f1 score: 0.333
GB cohens kappa score: 0.290

-> 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 '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: 0.650

-> test with 'RF'
RF tn, fp: 136, 2
RF fn, tp: 5, 4
RF f1 score: 0.533
RF cohens kappa score: 0.509

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

-> test with 'KNN'
KNN tn, fp: 124, 14
KNN fn, tp: 2, 7
KNN f1 score: 0.467
KNN cohens kappa score: 0.417


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> 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: 0.743

-> 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: 5, 4
GB f1 score: 0.400
GB cohens kappa score: 0.357

-> test with 'KNN'
KNN tn, fp: 128, 10
KNN fn, tp: 5, 4
KNN f1 score: 0.348
KNN cohens kappa score: 0.295


------ Step 4/5: 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: 0, 9
LR f1 score: 0.500
LR cohens kappa score: 0.449
LR average precision score: 0.947

-> 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: 132, 6
GB fn, tp: 4, 5
GB f1 score: 0.500
GB cohens kappa score: 0.464

-> 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 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 124, 13
LR fn, tp: 1, 5
LR f1 score: 0.417
LR cohens kappa score: 0.377
LR average precision score: 0.589

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

-> test with 'GB'
GB tn, fp: 133, 4
GB fn, tp: 4, 2
GB f1 score: 0.333
GB cohens kappa score: 0.304

-> test with 'KNN'
KNN tn, fp: 121, 16
KNN fn, tp: 2, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.260


====== 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: 116, 22
LR fn, tp: 2, 7
LR f1 score: 0.368
LR cohens kappa score: 0.303
LR average precision score: 0.690

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


------ Step 5/5: Slice 2/5 -------
-> 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: 0.680

-> 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: 133, 5
GB fn, tp: 4, 5
GB f1 score: 0.526
GB cohens kappa score: 0.494

-> 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 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 123, 15
LR fn, tp: 3, 6
LR f1 score: 0.400
LR cohens kappa score: 0.344
LR average precision score: 0.521

-> 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: 132, 6
GB fn, tp: 7, 2
GB f1 score: 0.235
GB cohens kappa score: 0.189

-> 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 5/5: 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: 1, 8
LR f1 score: 0.500
LR cohens kappa score: 0.452
LR average precision score: 0.878

-> 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: 3, 6
GB f1 score: 0.600
GB cohens kappa score: 0.571

-> 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 5/5: 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: 0, 6
LR f1 score: 0.545
LR cohens kappa score: 0.516
LR average precision score: 0.819

-> test with 'RF'
RF tn, fp: 136, 1
RF fn, tp: 5, 1
RF f1 score: 0.250
RF cohens kappa score: 0.234

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

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 128, 24
LR fn, tp: 4, 9
LR f1 score: 0.621
LR cohens kappa score: 0.586
LR average precision score: 0.947


average:
LR tn, fp: 122.64, 15.16
LR fn, tp: 1.44, 6.96
LR f1 score: 0.458
LR cohens kappa score: 0.409
LR average precision score: 0.687


minimum:
LR tn, fp: 114, 10
LR fn, tp: 0, 5
LR f1 score: 0.350
LR cohens kappa score: 0.282
LR average precision score: 0.482


-----[ RF ]-----
maximum:
RF tn, fp: 138, 3
RF fn, tp: 8, 4
RF f1 score: 0.667
RF cohens kappa score: 0.657


average:
RF tn, fp: 135.92, 1.88
RF fn, tp: 6.4, 2.0
RF f1 score: 0.322
RF cohens kappa score: 0.297


minimum:
RF tn, fp: 134, 0
RF fn, tp: 3, 1
RF f1 score: 0.154
RF cohens kappa score: 0.121


-----[ GB ]-----
maximum:
GB tn, fp: 137, 12
GB fn, tp: 7, 6
GB f1 score: 0.600
GB cohens kappa score: 0.571


average:
GB tn, fp: 132.32, 5.48
GB fn, tp: 5.0, 3.4
GB f1 score: 0.383
GB cohens kappa score: 0.346


minimum:
GB tn, fp: 126, 1
GB fn, tp: 2, 1
GB f1 score: 0.167
GB cohens kappa score: 0.130


-----[ KNN ]-----
maximum:
KNN tn, fp: 130, 19
KNN fn, tp: 6, 7
KNN f1 score: 0.467
KNN cohens kappa score: 0.417


average:
KNN tn, fp: 123.88, 13.92
KNN fn, tp: 3.76, 4.64
KNN f1 score: 0.342
KNN cohens kappa score: 0.286


minimum:
KNN tn, fp: 119, 8
KNN fn, tp: 2, 3
KNN f1 score: 0.250
KNN cohens kappa score: 0.188

  wall time: 00:00:13s, process time: 00:00:42s