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

///////////////////////////////////////////
// Running convGAN-majority-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: 121, 17
GAN fn, tp: 0, 9
GAN f1 score: 0.514
GAN cohens kappa score: 0.466

-> 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: 0.888

-> 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: 119, 19
KNN fn, tp: 1, 8
KNN f1 score: 0.444
KNN cohens kappa score: 0.388


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

-> 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: 0.581

-> 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: 117, 21
KNN fn, tp: 2, 7
KNN f1 score: 0.378
KNN cohens kappa score: 0.315


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

-> 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: 0.815

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


------ 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: 124, 14
GAN fn, tp: 2, 7
GAN f1 score: 0.467
GAN cohens kappa score: 0.417

-> 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: 0.600

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


------ 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: 126, 11
GAN fn, tp: 2, 4
GAN f1 score: 0.381
GAN cohens kappa score: 0.341

-> 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: 0.477

-> 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: 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 GAN.predict
GAN tn, fp: 129, 9
GAN fn, tp: 3, 6
GAN f1 score: 0.500
GAN cohens kappa score: 0.459

-> test with 'LR'
LR tn, fp: 119, 19
LR fn, tp: 2, 7
LR f1 score: 0.400
LR cohens kappa score: 0.340
LR average precision score: 0.625

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


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

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 1, 8
LR f1 score: 0.640
LR cohens kappa score: 0.609
LR average precision score: 0.784

-> 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: 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 GAN.predict
GAN tn, fp: 126, 12
GAN fn, tp: 2, 7
GAN f1 score: 0.500
GAN cohens kappa score: 0.455

-> 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: 0.731

-> 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: 125, 13
KNN fn, tp: 2, 7
KNN f1 score: 0.483
KNN cohens kappa score: 0.435


------ 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: 124, 14
GAN fn, tp: 1, 8
GAN f1 score: 0.516
GAN cohens kappa score: 0.470

-> 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: 0.715

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


------ 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: 122, 15
GAN fn, tp: 2, 4
GAN f1 score: 0.320
GAN cohens kappa score: 0.274

-> test with 'LR'
LR tn, fp: 125, 12
LR fn, tp: 1, 5
LR f1 score: 0.435
LR cohens kappa score: 0.397
LR average precision score: 0.579

-> 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: 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 GAN.predict
GAN tn, fp: 121, 17
GAN fn, tp: 3, 6
GAN f1 score: 0.375
GAN cohens kappa score: 0.315

-> 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: 0.631

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 8, 1
GB f1 score: 0.118
GB cohens kappa score: 0.064

-> 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 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with GAN.predict
GAN tn, fp: 125, 13
GAN fn, tp: 0, 9
GAN f1 score: 0.581
GAN cohens kappa score: 0.541

-> 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: 0.869

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


------ 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: 127, 11
GAN fn, tp: 4, 5
GAN f1 score: 0.400
GAN cohens kappa score: 0.349

-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 4, 5
LR f1 score: 0.526
LR cohens kappa score: 0.494
LR average precision score: 0.695

-> 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: 127, 11
KNN fn, tp: 4, 5
KNN f1 score: 0.400
KNN cohens kappa score: 0.349


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

-> test with 'LR'
LR tn, fp: 119, 19
LR fn, tp: 2, 7
LR f1 score: 0.400
LR cohens kappa score: 0.340
LR average precision score: 0.624

-> 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: 119, 19
KNN fn, tp: 5, 4
KNN f1 score: 0.250
KNN cohens kappa score: 0.178


------ 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: 118, 19
GAN fn, tp: 1, 5
GAN f1 score: 0.333
GAN cohens kappa score: 0.285

-> test with 'LR'
LR tn, fp: 126, 11
LR fn, tp: 1, 5
LR f1 score: 0.455
LR cohens kappa score: 0.419
LR average precision score: 0.549

-> 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: 117, 20
KNN fn, tp: 2, 4
KNN f1 score: 0.267
KNN cohens kappa score: 0.214


====== 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: 3, 6
GAN f1 score: 0.444
GAN cohens kappa score: 0.395

-> 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: 0.557

-> 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: 127, 11
KNN fn, tp: 4, 5
KNN f1 score: 0.400
KNN cohens kappa score: 0.349


------ 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: 120, 18
GAN fn, tp: 2, 7
GAN f1 score: 0.412
GAN cohens kappa score: 0.354

-> 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: 0.702

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


------ 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: 120, 18
GAN fn, tp: 2, 7
GAN f1 score: 0.412
GAN cohens kappa score: 0.354

-> test with 'LR'
LR tn, fp: 124, 14
LR fn, tp: 1, 8
LR f1 score: 0.516
LR cohens kappa score: 0.470
LR average precision score: 0.728

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


------ 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: 118, 20
GAN fn, tp: 0, 9
GAN f1 score: 0.474
GAN cohens kappa score: 0.419

-> test with 'LR'
LR tn, fp: 122, 16
LR fn, tp: 0, 9
LR f1 score: 0.529
LR cohens kappa score: 0.483
LR average precision score: 0.967

-> 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: 120, 18
KNN fn, tp: 2, 7
KNN f1 score: 0.412
KNN cohens kappa score: 0.354


------ 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: 130, 7
LR fn, tp: 1, 5
LR f1 score: 0.556
LR cohens kappa score: 0.529
LR average precision score: 0.518

-> 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: 122, 15
KNN fn, tp: 2, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.274


====== 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: 123, 15
GAN fn, tp: 3, 6
GAN f1 score: 0.400
GAN cohens kappa score: 0.344

-> 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: 0.698

-> 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: 117, 21
KNN fn, tp: 5, 4
KNN f1 score: 0.235
KNN cohens kappa score: 0.160


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

-> test with 'LR'
LR tn, fp: 126, 12
LR fn, tp: 0, 9
LR f1 score: 0.600
LR cohens kappa score: 0.562
LR average precision score: 0.731

-> 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: 4, 5
KNN f1 score: 0.303
KNN cohens kappa score: 0.235


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

-> 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: 0.542

-> 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: 6, 3
KNN f1 score: 0.222
KNN cohens kappa score: 0.153


------ 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: 126, 12
GAN fn, tp: 1, 8
GAN f1 score: 0.552
GAN cohens kappa score: 0.510

-> 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: 0.932

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 4, 5
GB f1 score: 0.588
GB cohens kappa score: 0.563

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with GAN.predict
GAN tn, fp: 128, 9
GAN fn, tp: 1, 5
GAN f1 score: 0.500
GAN cohens kappa score: 0.469

-> 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: 0.802

-> 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: 126, 11
KNN fn, tp: 2, 4
KNN f1 score: 0.381
KNN cohens kappa score: 0.341

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 133, 19
LR fn, tp: 4, 9
LR f1 score: 0.720
LR cohens kappa score: 0.696
LR average precision score: 0.967


average:
LR tn, fp: 126.76, 11.04
LR fn, tp: 1.6, 6.8
LR f1 score: 0.523
LR cohens kappa score: 0.483
LR average precision score: 0.694


minimum:
LR tn, fp: 119, 5
LR fn, tp: 0, 4
LR f1 score: 0.400
LR cohens kappa score: 0.340
LR average precision score: 0.477


-----[ GB ]-----
maximum:
GB tn, fp: 135, 11
GB fn, tp: 8, 6
GB f1 score: 0.588
GB cohens kappa score: 0.563


average:
GB tn, fp: 131.48, 6.32
GB fn, tp: 5.28, 3.12
GB f1 score: 0.348
GB cohens kappa score: 0.307


minimum:
GB tn, fp: 127, 3
GB fn, tp: 3, 1
GB f1 score: 0.118
GB cohens kappa score: 0.064


-----[ KNN ]-----
maximum:
KNN tn, fp: 129, 21
KNN fn, tp: 6, 8
KNN f1 score: 0.500
KNN cohens kappa score: 0.459


average:
KNN tn, fp: 122.72, 15.08
KNN fn, tp: 3.16, 5.24
KNN f1 score: 0.366
KNN cohens kappa score: 0.310


minimum:
KNN tn, fp: 117, 9
KNN fn, tp: 1, 3
KNN f1 score: 0.222
KNN cohens kappa score: 0.153


-----[ GAN ]-----
maximum:
GAN tn, fp: 131, 20
GAN fn, tp: 4, 9
GAN f1 score: 0.581
GAN cohens kappa score: 0.541


average:
GAN tn, fp: 124.16, 13.64
GAN fn, tp: 2.16, 6.24
GAN f1 score: 0.442
GAN cohens kappa score: 0.394


minimum:
GAN tn, fp: 118, 6
GAN fn, tp: 0, 4
GAN f1 score: 0.320
GAN cohens kappa score: 0.258