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

///////////////////////////////////////////
// Running convGAN-majority-full 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: 137, 1
GAN fn, tp: 4, 5
GAN f1 score: 0.667
GAN cohens kappa score: 0.649

-> 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: 0.892

-> 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: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


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

-> 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: 0.544

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 8, 1
GB f1 score: 0.143
GB cohens kappa score: 0.104

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.032


------ 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: 136, 2
GAN fn, tp: 8, 1
GAN f1 score: 0.167
GAN cohens kappa score: 0.140

-> 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: 0.802

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

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ 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: 135, 3
GAN fn, tp: 6, 3
GAN f1 score: 0.400
GAN cohens kappa score: 0.369

-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 3, 6
LR f1 score: 0.545
LR cohens kappa score: 0.510
LR average precision score: 0.593

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

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


------ 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: 136, 1
GAN fn, tp: 4, 2
GAN f1 score: 0.444
GAN cohens kappa score: 0.428

-> test with 'LR'
LR tn, fp: 130, 7
LR fn, tp: 2, 4
LR f1 score: 0.471
LR cohens kappa score: 0.440
LR average precision score: 0.480

-> test with 'GB'
GB tn, fp: 137, 0
GB fn, tp: 5, 1
GB f1 score: 0.286
GB cohens kappa score: 0.277

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


====== 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: 132, 6
GAN fn, tp: 8, 1
GAN f1 score: 0.125
GAN cohens kappa score: 0.075

-> 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: 0.632

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

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


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

-> 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: 0.759

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

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ 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: 138, 0
GAN fn, tp: 5, 4
GAN f1 score: 0.615
GAN cohens kappa score: 0.600

-> 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: 0.730

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

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ 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: 136, 2
GAN fn, tp: 7, 2
GAN f1 score: 0.308
GAN cohens kappa score: 0.281

-> 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: 0.739

-> 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: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ 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: 134, 3
GAN fn, tp: 3, 3
GAN f1 score: 0.500
GAN cohens kappa score: 0.478

-> 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: 0.657

-> test with 'GB'
GB tn, fp: 136, 1
GB fn, tp: 3, 3
GB f1 score: 0.600
GB cohens kappa score: 0.586

-> test with 'KNN'
KNN tn, fp: 136, 1
KNN fn, tp: 4, 2
KNN f1 score: 0.444
KNN cohens kappa score: 0.428


====== 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: 134, 4
GAN fn, tp: 5, 4
GAN f1 score: 0.471
GAN cohens kappa score: 0.438

-> 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: 0.547

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 8, 1
GB f1 score: 0.143
GB cohens kappa score: 0.104

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


------ 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: 134, 4
GAN fn, tp: 5, 4
GAN f1 score: 0.471
GAN cohens kappa score: 0.438

-> 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: 136, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.281

-> test with 'KNN'
KNN tn, fp: 134, 4
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.039


------ 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: 136, 2
GAN fn, tp: 7, 2
GAN f1 score: 0.308
GAN cohens kappa score: 0.281

-> 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: 0.632

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

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ 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: 135, 3
GAN fn, tp: 6, 3
GAN f1 score: 0.400
GAN cohens kappa score: 0.369

-> 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: 0.659

-> 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: 136, 2
KNN fn, tp: 6, 3
KNN f1 score: 0.429
KNN cohens kappa score: 0.402


------ 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: 5, 1
GAN f1 score: 0.182
GAN cohens kappa score: 0.149

-> 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: 0.528

-> test with 'GB'
GB tn, fp: 133, 4
GB fn, tp: 5, 1
GB f1 score: 0.182
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 136, 1
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


====== 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: 136, 2
GAN fn, tp: 6, 3
GAN f1 score: 0.429
GAN cohens kappa score: 0.402

-> 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: 0.532

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

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ 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: 136, 2
GAN fn, tp: 5, 4
GAN f1 score: 0.533
GAN cohens kappa score: 0.509

-> 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: 0.681

-> 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: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


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

-> 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: 0.652

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

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ 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: 133, 5
GAN fn, tp: 6, 3
GAN f1 score: 0.353
GAN cohens kappa score: 0.313

-> 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: 0.906

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

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.032


------ 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: 134, 3
GAN fn, tp: 3, 3
GAN f1 score: 0.500
GAN cohens kappa score: 0.478

-> test with 'LR'
LR tn, fp: 132, 5
LR fn, tp: 2, 4
LR f1 score: 0.533
LR cohens kappa score: 0.509
LR average precision score: 0.623

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

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


====== 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: 133, 5
GAN fn, tp: 6, 3
GAN f1 score: 0.353
GAN cohens kappa score: 0.313

-> 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: 0.705

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

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


------ 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: 138, 0
GAN fn, tp: 7, 2
GAN f1 score: 0.364
GAN cohens kappa score: 0.349

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 2, 7
LR f1 score: 0.583
LR cohens kappa score: 0.549
LR average precision score: 0.717

-> 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: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


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

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 4, 5
LR f1 score: 0.455
LR cohens kappa score: 0.412
LR average precision score: 0.537

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

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


------ 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: 134, 4
GAN fn, tp: 6, 3
GAN f1 score: 0.375
GAN cohens kappa score: 0.340

-> 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: 0.891

-> test with 'GB'
GB tn, fp: 138, 0
GB fn, tp: 6, 3
GB f1 score: 0.500
GB cohens kappa score: 0.484

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


------ 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: 136, 1
GAN fn, tp: 3, 3
GAN f1 score: 0.600
GAN cohens kappa score: 0.586

-> 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: 0.827

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

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

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 134, 16
LR fn, tp: 5, 9
LR f1 score: 0.783
LR cohens kappa score: 0.765
LR average precision score: 0.906


average:
LR tn, fp: 130.52, 7.28
LR fn, tp: 2.2, 6.2
LR f1 score: 0.564
LR cohens kappa score: 0.531
LR average precision score: 0.687


minimum:
LR tn, fp: 122, 4
LR fn, tp: 0, 4
LR f1 score: 0.381
LR cohens kappa score: 0.334
LR average precision score: 0.480


-----[ GB ]-----
maximum:
GB tn, fp: 138, 6
GB fn, tp: 8, 4
GB f1 score: 0.600
GB cohens kappa score: 0.586


average:
GB tn, fp: 135.64, 2.16
GB fn, tp: 6.32, 2.08
GB f1 score: 0.327
GB cohens kappa score: 0.301


minimum:
GB tn, fp: 132, 0
GB fn, tp: 3, 1
GB f1 score: 0.143
GB cohens kappa score: 0.104


-----[ KNN ]-----
maximum:
KNN tn, fp: 138, 4
KNN fn, tp: 9, 3
KNN f1 score: 0.500
KNN cohens kappa score: 0.489


average:
KNN tn, fp: 136.56, 1.24
KNN fn, tp: 7.56, 0.84
KNN f1 score: 0.161
KNN cohens kappa score: 0.143


minimum:
KNN tn, fp: 134, 0
KNN fn, tp: 4, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.039


-----[ GAN ]-----
maximum:
GAN tn, fp: 138, 8
GAN fn, tp: 8, 5
GAN f1 score: 0.667
GAN cohens kappa score: 0.649


average:
GAN tn, fp: 134.92, 2.88
GAN fn, tp: 5.72, 2.68
GAN f1 score: 0.388
GAN cohens kappa score: 0.360


minimum:
GAN tn, fp: 130, 0
GAN fn, tp: 3, 1
GAN f1 score: 0.111
GAN cohens kappa score: 0.053