ConvGeNBenchmarkResults/data_result/SpheredNoise/folding_kr-vs-k-three_vs_eleven.log

///////////////////////////////////////////
// Running SpheredNoise on folding_kr-vs-k-three_vs_eleven
///////////////////////////////////////////

Load 'data_input/folding_kr-vs-k-three_vs_eleven'
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 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 570, 1
LR fn, tp: 0, 17
LR f1 score: 0.971
LR cohens kappa score: 0.971
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 1, 16
KNN f1 score: 0.970
KNN cohens kappa score: 0.969


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 571, 0
LR fn, tp: 2, 15
LR f1 score: 0.938
LR cohens kappa score: 0.936
LR average precision score: 0.990

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 1, 16
KNN f1 score: 0.970
KNN cohens kappa score: 0.969


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 568, 3
LR fn, tp: 1, 16
LR f1 score: 0.889
LR cohens kappa score: 0.885
LR average precision score: 0.991

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 1, 16
KNN f1 score: 0.970
KNN cohens kappa score: 0.969


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 570, 1
LR fn, tp: 1, 16
LR f1 score: 0.941
LR cohens kappa score: 0.939
LR average precision score: 0.994

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 2, 15
KNN f1 score: 0.938
KNN cohens kappa score: 0.936


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2284/68 points
-> new disc
-> calc distances
-> statistics
trained 68 points min:1.0 max:3.3166247903554
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 565, 5
LR fn, tp: 1, 12
LR f1 score: 0.800
LR cohens kappa score: 0.795
LR average precision score: 0.782

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 568, 2
KNN fn, tp: 1, 12
KNN f1 score: 0.889
KNN cohens kappa score: 0.886


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.1622776601683795
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 570, 1
LR fn, tp: 0, 17
LR f1 score: 0.971
LR cohens kappa score: 0.971
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 2, 15
KNN f1 score: 0.938
KNN cohens kappa score: 0.936


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 571, 0
LR fn, tp: 1, 16
LR f1 score: 0.970
LR cohens kappa score: 0.969
LR average precision score: 0.991

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 1, 16
KNN f1 score: 0.970
KNN cohens kappa score: 0.969


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 568, 3
LR fn, tp: 2, 15
LR f1 score: 0.857
LR cohens kappa score: 0.853
LR average precision score: 0.978

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 570, 1
KNN fn, tp: 2, 15
KNN f1 score: 0.909
KNN cohens kappa score: 0.906


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 569, 2
LR fn, tp: 1, 16
LR f1 score: 0.914
LR cohens kappa score: 0.912
LR average precision score: 0.994

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 3, 14
KNN f1 score: 0.903
KNN cohens kappa score: 0.901


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2284/68 points
-> new disc
-> calc distances
-> statistics
trained 68 points min:1.0 max:3.3166247903554
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 569, 1
LR fn, tp: 1, 12
LR f1 score: 0.923
LR cohens kappa score: 0.921
LR average precision score: 0.990

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 569, 1
KNN fn, tp: 0, 13
KNN f1 score: 0.963
KNN cohens kappa score: 0.962


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 569, 2
LR fn, tp: 0, 17
LR f1 score: 0.944
LR cohens kappa score: 0.943
LR average precision score: 0.990

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 0, 17
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 570, 1
LR fn, tp: 1, 16
LR f1 score: 0.941
LR cohens kappa score: 0.939
LR average precision score: 0.994

-> test with 'GB'
GB tn, fp: 570, 1
GB fn, tp: 0, 17
GB f1 score: 0.971
GB cohens kappa score: 0.971

-> test with 'KNN'
KNN tn, fp: 570, 1
KNN fn, tp: 3, 14
KNN f1 score: 0.875
KNN cohens kappa score: 0.872


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 569, 2
LR fn, tp: 1, 16
LR f1 score: 0.914
LR cohens kappa score: 0.912
LR average precision score: 0.994

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 570, 1
KNN fn, tp: 0, 17
KNN f1 score: 0.971
KNN cohens kappa score: 0.971


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 568, 3
LR fn, tp: 1, 16
LR f1 score: 0.889
LR cohens kappa score: 0.885
LR average precision score: 0.987

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 1, 16
KNN f1 score: 0.970
KNN cohens kappa score: 0.969


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2284/68 points
-> new disc
-> calc distances
-> statistics
trained 68 points min:1.0 max:3.3166247903554
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 569, 1
LR fn, tp: 1, 12
LR f1 score: 0.923
LR cohens kappa score: 0.921
LR average precision score: 0.990

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 570, 0
KNN fn, tp: 3, 10
KNN f1 score: 0.870
KNN cohens kappa score: 0.867


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 569, 2
LR fn, tp: 1, 16
LR f1 score: 0.914
LR cohens kappa score: 0.912
LR average precision score: 0.989

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 1, 16
KNN f1 score: 0.970
KNN cohens kappa score: 0.969


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 571, 0
LR fn, tp: 1, 16
LR f1 score: 0.970
LR cohens kappa score: 0.969
LR average precision score: 0.989

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 3, 14
KNN f1 score: 0.903
KNN cohens kappa score: 0.901


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 571, 0
LR fn, tp: 1, 16
LR f1 score: 0.970
LR cohens kappa score: 0.969
LR average precision score: 0.997

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 570, 1
KNN fn, tp: 0, 17
KNN f1 score: 0.971
KNN cohens kappa score: 0.971


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 568, 3
LR fn, tp: 0, 17
LR f1 score: 0.919
LR cohens kappa score: 0.916
LR average precision score: 0.936

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 570, 1
KNN fn, tp: 0, 17
KNN f1 score: 0.971
KNN cohens kappa score: 0.971


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2284/68 points
-> new disc
-> calc distances
-> statistics
trained 68 points min:1.0 max:3.3166247903554
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 569, 1
LR fn, tp: 1, 12
LR f1 score: 0.923
LR cohens kappa score: 0.921
LR average precision score: 0.990

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 570, 0
KNN fn, tp: 2, 11
KNN f1 score: 0.917
KNN cohens kappa score: 0.915


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 567, 4
LR fn, tp: 0, 17
LR f1 score: 0.895
LR cohens kappa score: 0.891
LR average precision score: 0.991

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 570, 1
KNN fn, tp: 2, 15
KNN f1 score: 0.909
KNN cohens kappa score: 0.906


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 568, 3
LR fn, tp: 1, 16
LR f1 score: 0.889
LR cohens kappa score: 0.885
LR average precision score: 0.970

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 2, 15
KNN f1 score: 0.938
KNN cohens kappa score: 0.936


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 571, 0
LR fn, tp: 1, 16
LR f1 score: 0.970
LR cohens kappa score: 0.969
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 571, 0
KNN fn, tp: 1, 16
KNN f1 score: 0.970
KNN cohens kappa score: 0.969


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2283/64 points
-> new disc
-> calc distances
-> statistics
trained 64 points min:1.0 max:3.3166247903554
-> create 2219 synthetic samples
-> test with 'LR'
LR tn, fp: 571, 0
LR fn, tp: 2, 15
LR f1 score: 0.938
LR cohens kappa score: 0.936
LR average precision score: 0.977

-> test with 'GB'
GB tn, fp: 571, 0
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 570, 1
KNN fn, tp: 0, 17
KNN f1 score: 0.971
KNN cohens kappa score: 0.971


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2284/68 points
-> new disc
-> calc distances
-> statistics
trained 68 points min:1.0 max:3.3166247903554
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 570, 0
LR fn, tp: 0, 13
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 570, 0
KNN fn, tp: 1, 12
KNN f1 score: 0.960
KNN cohens kappa score: 0.959

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 571, 5
LR fn, tp: 2, 17
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000


average:
LR tn, fp: 569.24, 1.56
LR fn, tp: 0.88, 15.32
LR f1 score: 0.927
LR cohens kappa score: 0.925
LR average precision score: 0.980


minimum:
LR tn, fp: 565, 0
LR fn, tp: 0, 12
LR f1 score: 0.800
LR cohens kappa score: 0.795
LR average precision score: 0.782


-----[ GB ]-----
maximum:
GB tn, fp: 571, 1
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 570.76, 0.04
GB fn, tp: 0.0, 16.2
GB f1 score: 0.999
GB cohens kappa score: 0.999


minimum:
GB tn, fp: 570, 0
GB fn, tp: 0, 13
GB f1 score: 0.971
GB cohens kappa score: 0.971


-----[ KNN ]-----
maximum:
KNN tn, fp: 571, 2
KNN fn, tp: 3, 17
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


average:
KNN tn, fp: 570.4, 0.4
KNN fn, tp: 1.32, 14.88
KNN f1 score: 0.943
KNN cohens kappa score: 0.942


minimum:
KNN tn, fp: 568, 0
KNN fn, tp: 0, 10
KNN f1 score: 0.870
KNN cohens kappa score: 0.867