ConvGeNBenchmarkResults/data_result/SpheredNoise/folding_abalone9-18.log

///////////////////////////////////////////
// Running SpheredNoise 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
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.3975704340113837
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 3, 6
LR f1 score: 0.750
LR cohens kappa score: 0.736
LR average precision score: 0.829

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 4, 5
GB f1 score: 0.556
GB cohens kappa score: 0.527

-> 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 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03311344137959691 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 3
LR fn, tp: 5, 4
LR f1 score: 0.500
LR cohens kappa score: 0.472
LR average precision score: 0.499

-> 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 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 5, 4
LR f1 score: 0.533
LR cohens kappa score: 0.509
LR average precision score: 0.711

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


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 6, 3
LR f1 score: 0.462
LR cohens kappa score: 0.440
LR average precision score: 0.619

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 5, 4
GB f1 score: 0.571
GB cohens kappa score: 0.552

-> 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 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 552/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:0.03211308144666282 max:0.6863390197271315
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 3
LR fn, tp: 3, 3
LR f1 score: 0.500
LR cohens kappa score: 0.478
LR average precision score: 0.498

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

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03311344137959691 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 5, 4
LR f1 score: 0.533
LR cohens kappa score: 0.509
LR average precision score: 0.667

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


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03311344137959691 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 3, 6
LR f1 score: 0.706
LR cohens kappa score: 0.688
LR average precision score: 0.726

-> 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 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.3752825602129679
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 6, 3
LR f1 score: 0.429
LR cohens kappa score: 0.402
LR average precision score: 0.537

-> 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 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 6, 3
LR f1 score: 0.429
LR cohens kappa score: 0.402
LR average precision score: 0.657

-> 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 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 552/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:0.03211308144666282 max:0.6176281243596343
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 3
LR fn, tp: 2, 4
LR f1 score: 0.615
LR cohens kappa score: 0.597
LR average precision score: 0.510

-> test with 'GB'
GB tn, fp: 135, 2
GB fn, tp: 3, 3
GB f1 score: 0.545
GB cohens kappa score: 0.527

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 6, 3
LR f1 score: 0.375
LR cohens kappa score: 0.340
LR average precision score: 0.503

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

-> 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 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 4, 5
LR f1 score: 0.667
LR cohens kappa score: 0.649
LR average precision score: 0.766

-> 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 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03311344137959691 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 5, 4
LR f1 score: 0.533
LR cohens kappa score: 0.509
LR average precision score: 0.588

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

-> 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 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.6610300673948196
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 3
LR fn, tp: 3, 6
LR f1 score: 0.667
LR cohens kappa score: 0.645
LR average precision score: 0.630

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 4, 5
GB f1 score: 0.625
GB cohens kappa score: 0.604

-> 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 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 552/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:0.0358887168898527 max:0.4738359420727812
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 2
LR fn, tp: 4, 2
LR f1 score: 0.400
LR cohens kappa score: 0.379
LR average precision score: 0.582

-> 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: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03311344137959691 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 5, 4
LR f1 score: 0.471
LR cohens kappa score: 0.438
LR average precision score: 0.524

-> 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: 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
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03311344137959691 max:0.3975704340113837
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 5, 4
LR f1 score: 0.571
LR cohens kappa score: 0.552
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: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 4, 5
LR f1 score: 0.667
LR cohens kappa score: 0.649
LR average precision score: 0.610

-> 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: 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
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 5, 4
LR f1 score: 0.571
LR cohens kappa score: 0.552
LR average precision score: 0.805

-> 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 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 552/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:0.03211308144666282 max:0.6610300673948196
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 2
LR fn, tp: 4, 2
LR f1 score: 0.400
LR cohens kappa score: 0.379
LR average precision score: 0.439

-> test with 'GB'
GB tn, fp: 134, 3
GB fn, tp: 5, 1
GB f1 score: 0.200
GB cohens kappa score: 0.172

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.4217019089356842
-> 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: 0.707

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


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.6610300673948196
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 4, 5
LR f1 score: 0.625
LR cohens kappa score: 0.604
LR average precision score: 0.713

-> 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: 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
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.037393181196576475 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 3
LR fn, tp: 6, 3
LR f1 score: 0.400
LR cohens kappa score: 0.369
LR average precision score: 0.479

-> 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 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 551/33 points
-> new disc
-> calc distances
-> statistics
trained 33 points min:0.03211308144666282 max:0.6176281243596343
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 5, 4
LR f1 score: 0.571
LR cohens kappa score: 0.552
LR average precision score: 0.711

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


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 552/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:0.03311344137959691 max:0.6176281243596343
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 0
LR fn, tp: 3, 3
LR f1 score: 0.667
LR cohens kappa score: 0.657
LR average precision score: 0.785

-> 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: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 137, 5
LR fn, tp: 6, 6
LR f1 score: 0.750
LR cohens kappa score: 0.736
LR average precision score: 0.829


average:
LR tn, fp: 135.68, 2.12
LR fn, tp: 4.4, 4.0
LR f1 score: 0.546
LR cohens kappa score: 0.523
LR average precision score: 0.628


minimum:
LR tn, fp: 133, 0
LR fn, tp: 2, 2
LR f1 score: 0.375
LR cohens kappa score: 0.340
LR average precision score: 0.439


-----[ GB ]-----
maximum:
GB tn, fp: 138, 6
GB fn, tp: 8, 5
GB f1 score: 0.625
GB cohens kappa score: 0.604


average:
GB tn, fp: 135.56, 2.24
GB fn, tp: 5.6, 2.8
GB f1 score: 0.404
GB cohens kappa score: 0.379


minimum:
GB tn, fp: 132, 0
GB fn, tp: 3, 0
GB f1 score: 0.000
GB cohens kappa score: -0.012


-----[ KNN ]-----
maximum:
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


average:
KNN tn, fp: 137.8, 0.0
KNN fn, tp: 8.4, 0.0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


minimum:
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000