ConvGeNBenchmarkResults/data_result/convGAN-proximary-5/folding_abalone9-18.log

///////////////////////////////////////////
// Running convGAN-proximary-5 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 GAN.predict
GAN tn, fp: 130, 8
GAN fn, tp: 1, 8
GAN f1 score: 0.640
GAN cohens kappa score: 0.609

-> 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: 0.918

-> 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: 117, 21
KNN fn, tp: 1, 8
KNN f1 score: 0.421
KNN cohens kappa score: 0.361


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 123, 15
GAN fn, tp: 4, 5
GAN f1 score: 0.345
GAN cohens kappa score: 0.284

-> 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: 0.566

-> 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: 3, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.344


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 127, 11
GAN fn, tp: 2, 7
GAN f1 score: 0.519
GAN cohens kappa score: 0.476

-> 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: 0.818

-> 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: 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 GAN.predict
GAN tn, fp: 118, 20
GAN fn, tp: 2, 7
GAN f1 score: 0.389
GAN cohens kappa score: 0.327

-> 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: 0.538

-> 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: 122, 16
KNN fn, tp: 3, 6
KNN f1 score: 0.387
KNN cohens kappa score: 0.329


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with GAN.predict
GAN tn, fp: 119, 18
GAN fn, tp: 2, 4
GAN f1 score: 0.286
GAN cohens kappa score: 0.235

-> 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: 0.470

-> 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: 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 GAN.predict
GAN tn, fp: 109, 29
GAN fn, tp: 2, 7
GAN f1 score: 0.311
GAN cohens kappa score: 0.236

-> 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: 0.698

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


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 134, 4
GAN fn, tp: 3, 6
GAN f1 score: 0.632
GAN cohens kappa score: 0.606

-> 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: 0.767

-> 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: 125, 13
KNN fn, tp: 3, 6
KNN f1 score: 0.429
KNN cohens kappa score: 0.377


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 129, 9
GAN fn, tp: 2, 7
GAN f1 score: 0.560
GAN cohens kappa score: 0.523

-> 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: 0.686

-> 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: 123, 15
KNN fn, tp: 3, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.344


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 126, 12
GAN fn, tp: 3, 6
GAN f1 score: 0.444
GAN cohens kappa score: 0.395

-> 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: 0.702

-> 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: 118, 20
KNN fn, tp: 3, 6
KNN f1 score: 0.343
KNN cohens kappa score: 0.277


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with GAN.predict
GAN tn, fp: 115, 22
GAN fn, tp: 1, 5
GAN f1 score: 0.303
GAN cohens kappa score: 0.252

-> 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: 0.590

-> test with 'GB'
GB tn, fp: 128, 9
GB fn, tp: 2, 4
GB f1 score: 0.421
GB cohens kappa score: 0.386

-> test with 'KNN'
KNN tn, fp: 121, 16
KNN fn, tp: 3, 3
KNN f1 score: 0.240
KNN cohens kappa score: 0.188


====== 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 GAN.predict
GAN tn, fp: 124, 14
GAN fn, tp: 5, 4
GAN f1 score: 0.296
GAN cohens kappa score: 0.234

-> 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: 0.552

-> 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: 4, 5
KNN f1 score: 0.345
KNN cohens kappa score: 0.284


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 128, 10
GAN fn, tp: 0, 9
GAN f1 score: 0.643
GAN cohens kappa score: 0.610

-> 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: 0.878

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 3, 6
GB f1 score: 0.545
GB cohens kappa score: 0.510

-> 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 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 123, 15
GAN fn, tp: 4, 5
GAN f1 score: 0.345
GAN cohens kappa score: 0.284

-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 3, 6
LR f1 score: 0.571
LR cohens kappa score: 0.539
LR average precision score: 0.697

-> 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: 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 GAN.predict
GAN tn, fp: 101, 37
GAN fn, tp: 1, 8
GAN f1 score: 0.296
GAN cohens kappa score: 0.216

-> 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: 0.627

-> 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: 121, 17
KNN fn, tp: 4, 5
KNN f1 score: 0.323
KNN cohens kappa score: 0.258


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with GAN.predict
GAN tn, fp: 133, 4
GAN fn, tp: 1, 5
GAN f1 score: 0.667
GAN cohens kappa score: 0.649

-> 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: 0.540

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

-> 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 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 GAN.predict
GAN tn, fp: 126, 12
GAN fn, tp: 5, 4
GAN f1 score: 0.320
GAN cohens kappa score: 0.262

-> 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: 0.521

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


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 118, 20
GAN fn, tp: 2, 7
GAN f1 score: 0.389
GAN cohens kappa score: 0.327

-> test with 'LR'
LR tn, fp: 122, 16
LR fn, tp: 2, 7
LR f1 score: 0.438
LR cohens kappa score: 0.383
LR average precision score: 0.719

-> test with 'GB'
GB tn, fp: 126, 12
GB fn, tp: 4, 5
GB f1 score: 0.385
GB cohens kappa score: 0.331

-> 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 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 128, 10
GAN fn, tp: 4, 5
GAN f1 score: 0.417
GAN cohens kappa score: 0.368

-> 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: 0.730

-> 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: 3, 6
KNN f1 score: 0.387
KNN cohens kappa score: 0.329


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 125, 13
GAN fn, tp: 3, 6
GAN f1 score: 0.429
GAN cohens kappa score: 0.377

-> test with 'LR'
LR tn, fp: 121, 17
LR fn, tp: 0, 9
LR f1 score: 0.514
LR cohens kappa score: 0.466
LR average precision score: 0.967

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


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with GAN.predict
GAN tn, fp: 131, 6
GAN fn, tp: 2, 4
GAN f1 score: 0.500
GAN cohens kappa score: 0.472

-> 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: 0.543

-> test with 'GB'
GB tn, fp: 128, 9
GB fn, tp: 4, 2
GB f1 score: 0.235
GB cohens kappa score: 0.191

-> 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 GAN.predict
GAN tn, fp: 128, 10
GAN fn, tp: 4, 5
GAN f1 score: 0.417
GAN cohens kappa score: 0.368

-> test with 'LR'
LR tn, fp: 122, 16
LR fn, tp: 2, 7
LR f1 score: 0.438
LR cohens kappa score: 0.383
LR average precision score: 0.696

-> test with 'GB'
GB tn, fp: 130, 8
GB fn, tp: 8, 1
GB f1 score: 0.111
GB cohens kappa score: 0.053

-> test with 'KNN'
KNN tn, fp: 119, 19
KNN fn, tp: 6, 3
KNN f1 score: 0.194
KNN cohens kappa score: 0.117


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 89, 49
GAN fn, tp: 1, 8
GAN f1 score: 0.242
GAN cohens kappa score: 0.153

-> 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: 0.721

-> 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: 115, 23
KNN fn, tp: 5, 4
KNN f1 score: 0.222
KNN cohens kappa score: 0.144


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 133, 5
GAN fn, tp: 4, 5
GAN f1 score: 0.526
GAN cohens kappa score: 0.494

-> test with 'LR'
LR tn, fp: 128, 10
LR fn, tp: 3, 6
LR f1 score: 0.480
LR cohens kappa score: 0.436
LR average precision score: 0.543

-> 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: 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 GAN.predict
GAN tn, fp: 132, 6
GAN fn, tp: 2, 7
GAN f1 score: 0.636
GAN cohens kappa score: 0.608

-> 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 '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: 122, 16
KNN fn, tp: 3, 6
KNN f1 score: 0.387
KNN cohens kappa score: 0.329


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with GAN.predict
GAN tn, fp: 132, 5
GAN fn, tp: 2, 4
GAN f1 score: 0.533
GAN cohens kappa score: 0.509

-> test with 'LR'
LR tn, fp: 131, 6
LR fn, tp: 0, 6
LR f1 score: 0.667
LR cohens kappa score: 0.647
LR average precision score: 0.859

-> test with 'GB'
GB tn, fp: 134, 3
GB fn, tp: 3, 3
GB f1 score: 0.500
GB cohens kappa score: 0.478

-> test with 'KNN'
KNN tn, fp: 128, 9
KNN fn, tp: 2, 4
KNN f1 score: 0.421
KNN cohens kappa score: 0.386

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 133, 24
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: 126.28, 11.52
LR fn, tp: 1.56, 6.84
LR f1 score: 0.518
LR cohens kappa score: 0.478
LR average precision score: 0.690


minimum:
LR tn, fp: 114, 5
LR fn, tp: 0, 4
LR f1 score: 0.350
LR cohens kappa score: 0.282
LR average precision score: 0.470


-----[ GB ]-----
maximum:
GB tn, fp: 136, 12
GB fn, tp: 8, 6
GB f1 score: 0.545
GB cohens kappa score: 0.510


average:
GB tn, fp: 132.08, 5.72
GB fn, tp: 5.16, 3.24
GB f1 score: 0.374
GB cohens kappa score: 0.335


minimum:
GB tn, fp: 126, 2
GB fn, tp: 2, 1
GB f1 score: 0.111
GB cohens kappa score: 0.053


-----[ KNN ]-----
maximum:
KNN tn, fp: 128, 23
KNN fn, tp: 6, 8
KNN f1 score: 0.429
KNN cohens kappa score: 0.386


average:
KNN tn, fp: 121.8, 16.0
KNN fn, tp: 3.36, 5.04
KNN f1 score: 0.342
KNN cohens kappa score: 0.283


minimum:
KNN tn, fp: 115, 9
KNN fn, tp: 1, 3
KNN f1 score: 0.194
KNN cohens kappa score: 0.117


-----[ GAN ]-----
maximum:
GAN tn, fp: 134, 49
GAN fn, tp: 5, 9
GAN f1 score: 0.667
GAN cohens kappa score: 0.649


average:
GAN tn, fp: 123.24, 14.56
GAN fn, tp: 2.48, 5.92
GAN f1 score: 0.443
GAN cohens kappa score: 0.395


minimum:
GAN tn, fp: 89, 4
GAN fn, tp: 0, 4
GAN f1 score: 0.242
GAN cohens kappa score: 0.153