ConvGeNBenchmarkResults/data_result/SpheredNoise/folding_kr-vs-k-zero-one_vs_draw.log

///////////////////////////////////////////
// Running SpheredNoise on folding_kr-vs-k-zero-one_vs_draw
///////////////////////////////////////////

Load 'data_input/folding_kr-vs-k-zero-one_vs_draw'
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 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.082762530298219
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 4
LR fn, tp: 3, 18
LR f1 score: 0.837
LR cohens kappa score: 0.831
LR average precision score: 0.833

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 2, 19
GB f1 score: 0.950
GB cohens kappa score: 0.948

-> test with 'KNN'
KNN tn, fp: 559, 1
KNN fn, tp: 7, 14
KNN f1 score: 0.778
KNN cohens kappa score: 0.771


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 6
LR fn, tp: 5, 16
LR f1 score: 0.744
LR cohens kappa score: 0.734
LR average precision score: 0.892

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 1, 20
GB f1 score: 0.952
GB cohens kappa score: 0.951

-> test with 'KNN'
KNN tn, fp: 559, 1
KNN fn, tp: 4, 17
KNN f1 score: 0.872
KNN cohens kappa score: 0.867


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 548, 12
LR fn, tp: 5, 16
LR f1 score: 0.653
LR cohens kappa score: 0.638
LR average precision score: 0.771

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 558, 2
KNN fn, tp: 3, 18
KNN f1 score: 0.878
KNN cohens kappa score: 0.874


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:5.5677643628300215
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 549, 11
LR fn, tp: 3, 18
LR f1 score: 0.720
LR cohens kappa score: 0.708
LR average precision score: 0.838

-> test with 'GB'
GB tn, fp: 557, 3
GB fn, tp: 1, 20
GB f1 score: 0.909
GB cohens kappa score: 0.906

-> test with 'KNN'
KNN tn, fp: 560, 0
KNN fn, tp: 4, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2240/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 550, 6
LR fn, tp: 3, 18
LR f1 score: 0.800
LR cohens kappa score: 0.792
LR average precision score: 0.921

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 556, 0
KNN fn, tp: 3, 18
KNN f1 score: 0.923
KNN cohens kappa score: 0.920


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 553, 7
LR fn, tp: 2, 19
LR f1 score: 0.809
LR cohens kappa score: 0.801
LR average precision score: 0.893

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 560, 0
KNN fn, tp: 3, 18
KNN f1 score: 0.923
KNN cohens kappa score: 0.920


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.082762530298219
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 557, 3
LR fn, tp: 4, 17
LR f1 score: 0.829
LR cohens kappa score: 0.823
LR average precision score: 0.917

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 2, 19
GB f1 score: 0.950
GB cohens kappa score: 0.948

-> test with 'KNN'
KNN tn, fp: 560, 0
KNN fn, tp: 4, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 555, 5
LR fn, tp: 3, 18
LR f1 score: 0.818
LR cohens kappa score: 0.811
LR average precision score: 0.894

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 0, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 559, 1
KNN fn, tp: 6, 15
KNN f1 score: 0.811
KNN cohens kappa score: 0.805


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:5.5677643628300215
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 547, 13
LR fn, tp: 3, 18
LR f1 score: 0.692
LR cohens kappa score: 0.678
LR average precision score: 0.826

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 1, 20
GB f1 score: 0.952
GB cohens kappa score: 0.951

-> test with 'KNN'
KNN tn, fp: 556, 4
KNN fn, tp: 6, 15
KNN f1 score: 0.750
KNN cohens kappa score: 0.741


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2240/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 546, 10
LR fn, tp: 6, 15
LR f1 score: 0.652
LR cohens kappa score: 0.638
LR average precision score: 0.813

-> test with 'GB'
GB tn, fp: 555, 1
GB fn, tp: 0, 21
GB f1 score: 0.977
GB cohens kappa score: 0.976

-> test with 'KNN'
KNN tn, fp: 556, 0
KNN fn, tp: 5, 16
KNN f1 score: 0.865
KNN cohens kappa score: 0.860


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 552, 8
LR fn, tp: 3, 18
LR f1 score: 0.766
LR cohens kappa score: 0.756
LR average precision score: 0.902

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 1, 20
GB f1 score: 0.952
GB cohens kappa score: 0.951

-> test with 'KNN'
KNN tn, fp: 558, 2
KNN fn, tp: 3, 18
KNN f1 score: 0.878
KNN cohens kappa score: 0.874


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 558, 2
LR fn, tp: 4, 17
LR f1 score: 0.850
LR cohens kappa score: 0.845
LR average precision score: 0.868

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 558, 2
KNN fn, tp: 7, 14
KNN f1 score: 0.757
KNN cohens kappa score: 0.749


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 546, 14
LR fn, tp: 5, 16
LR f1 score: 0.627
LR cohens kappa score: 0.611
LR average precision score: 0.763

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 1, 20
GB f1 score: 0.952
GB cohens kappa score: 0.951

-> test with 'KNN'
KNN tn, fp: 560, 0
KNN fn, tp: 6, 15
KNN f1 score: 0.833
KNN cohens kappa score: 0.828


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:5.5677643628300215
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 549, 11
LR fn, tp: 3, 18
LR f1 score: 0.720
LR cohens kappa score: 0.708
LR average precision score: 0.891

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 560, 0
KNN fn, tp: 3, 18
KNN f1 score: 0.923
KNN cohens kappa score: 0.920


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2240/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 2
LR fn, tp: 3, 18
LR f1 score: 0.878
LR cohens kappa score: 0.874
LR average precision score: 0.906

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 0, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 556, 0
KNN fn, tp: 4, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 552, 8
LR fn, tp: 3, 18
LR f1 score: 0.766
LR cohens kappa score: 0.756
LR average precision score: 0.877

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 0, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 558, 2
KNN fn, tp: 3, 18
KNN f1 score: 0.878
KNN cohens kappa score: 0.874


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 552, 8
LR fn, tp: 2, 19
LR f1 score: 0.792
LR cohens kappa score: 0.783
LR average precision score: 0.937

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 0, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 560, 0
KNN fn, tp: 3, 18
KNN f1 score: 0.923
KNN cohens kappa score: 0.920


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 555, 5
LR fn, tp: 7, 14
LR f1 score: 0.700
LR cohens kappa score: 0.689
LR average precision score: 0.777

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 1, 20
GB f1 score: 0.952
GB cohens kappa score: 0.951

-> test with 'KNN'
KNN tn, fp: 558, 2
KNN fn, tp: 8, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.714


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:5.5677643628300215
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 551, 9
LR fn, tp: 3, 18
LR f1 score: 0.750
LR cohens kappa score: 0.739
LR average precision score: 0.855

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 0, 21
GB f1 score: 0.977
GB cohens kappa score: 0.976

-> test with 'KNN'
KNN tn, fp: 559, 1
KNN fn, tp: 3, 18
KNN f1 score: 0.900
KNN cohens kappa score: 0.896


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2240/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.082762530298219
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 550, 6
LR fn, tp: 3, 18
LR f1 score: 0.800
LR cohens kappa score: 0.792
LR average precision score: 0.868

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 2, 19
GB f1 score: 0.950
GB cohens kappa score: 0.948

-> test with 'KNN'
KNN tn, fp: 556, 0
KNN fn, tp: 6, 15
KNN f1 score: 0.833
KNN cohens kappa score: 0.828


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:5.5677643628300215
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 4
LR fn, tp: 3, 18
LR f1 score: 0.837
LR cohens kappa score: 0.831
LR average precision score: 0.952

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 1, 20
GB f1 score: 0.952
GB cohens kappa score: 0.951

-> test with 'KNN'
KNN tn, fp: 560, 0
KNN fn, tp: 4, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.082762530298219
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 553, 7
LR fn, tp: 7, 14
LR f1 score: 0.667
LR cohens kappa score: 0.654
LR average precision score: 0.836

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 3, 18
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 560, 0
KNN fn, tp: 5, 16
KNN f1 score: 0.865
KNN cohens kappa score: 0.861


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 552, 8
LR fn, tp: 4, 17
LR f1 score: 0.739
LR cohens kappa score: 0.728
LR average precision score: 0.785

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 558, 2
KNN fn, tp: 5, 16
KNN f1 score: 0.821
KNN cohens kappa score: 0.814


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2236/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 550, 10
LR fn, tp: 5, 16
LR f1 score: 0.681
LR cohens kappa score: 0.668
LR average precision score: 0.816

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 559, 1
KNN fn, tp: 5, 16
KNN f1 score: 0.842
KNN cohens kappa score: 0.837


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2240/84 points
-> new disc
-> calc distances
-> statistics
trained 84 points min:1.0 max:6.0
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 548, 8
LR fn, tp: 1, 20
LR f1 score: 0.816
LR cohens kappa score: 0.808
LR average precision score: 0.900

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 0, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 555, 1
KNN fn, tp: 2, 19
KNN f1 score: 0.927
KNN cohens kappa score: 0.924

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 558, 14
LR fn, tp: 7, 20
LR f1 score: 0.878
LR cohens kappa score: 0.874
LR average precision score: 0.952


average:
LR tn, fp: 551.72, 7.48
LR fn, tp: 3.72, 17.28
LR f1 score: 0.758
LR cohens kappa score: 0.748
LR average precision score: 0.861


minimum:
LR tn, fp: 546, 2
LR fn, tp: 1, 14
LR f1 score: 0.627
LR cohens kappa score: 0.611
LR average precision score: 0.763


-----[ GB ]-----
maximum:
GB tn, fp: 560, 3
GB fn, tp: 3, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 558.76, 0.44
GB fn, tp: 0.92, 20.08
GB f1 score: 0.967
GB cohens kappa score: 0.966


minimum:
GB tn, fp: 555, 0
GB fn, tp: 0, 18
GB f1 score: 0.909
GB cohens kappa score: 0.906


-----[ KNN ]-----
maximum:
KNN tn, fp: 560, 4
KNN fn, tp: 8, 19
KNN f1 score: 0.927
KNN cohens kappa score: 0.924


average:
KNN tn, fp: 558.32, 0.88
KNN fn, tp: 4.48, 16.52
KNN f1 score: 0.859
KNN cohens kappa score: 0.855


minimum:
KNN tn, fp: 555, 0
KNN fn, tp: 2, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.714