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

///////////////////////////////////////////
// Running Repeater 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
-> 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: 0.997

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 567, 4
KNN fn, tp: 0, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 568, 3
KNN fn, tp: 0, 17
KNN f1 score: 0.919
KNN cohens kappa score: 0.916


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 567, 4
KNN fn, tp: 0, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.997

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 565, 6
KNN fn, tp: 0, 17
KNN f1 score: 0.850
KNN cohens kappa score: 0.845


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 566, 4
LR fn, tp: 1, 12
LR f1 score: 0.828
LR cohens kappa score: 0.823
LR average precision score: 0.850

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

-> test with 'GB'
GB tn, fp: 568, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.927

-> test with 'KNN'
KNN tn, fp: 567, 3
KNN fn, tp: 0, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.894


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 1.000

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 570, 1
KNN fn, tp: 0, 17
KNN f1 score: 0.971
KNN cohens kappa score: 0.971


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 569, 2
KNN fn, tp: 0, 17
KNN f1 score: 0.944
KNN cohens kappa score: 0.943


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.979

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

-> 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: 566, 5
KNN fn, tp: 0, 17
KNN f1 score: 0.872
KNN cohens kappa score: 0.867


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 1.000

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 567, 4
KNN fn, tp: 0, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 569, 1
LR fn, tp: 0, 13
LR f1 score: 0.963
LR cohens kappa score: 0.962
LR average precision score: 1.000

-> test with 'RF'
RF tn, fp: 570, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa 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: 565, 5
KNN fn, tp: 0, 13
KNN f1 score: 0.839
KNN cohens kappa score: 0.834


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.993

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 568, 3
KNN fn, tp: 0, 17
KNN f1 score: 0.919
KNN cohens kappa score: 0.916


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.997

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 567, 4
KNN fn, tp: 0, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 567, 4
KNN fn, tp: 0, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 569, 2
KNN fn, tp: 0, 17
KNN f1 score: 0.944
KNN cohens kappa score: 0.943


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with 'LR'
LR tn, fp: 567, 3
LR fn, tp: 1, 12
LR f1 score: 0.857
LR cohens kappa score: 0.854
LR average precision score: 0.976

-> test with 'RF'
RF tn, fp: 570, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa 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: 567, 3
KNN fn, tp: 0, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.894


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 1.000

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 564, 7
KNN fn, tp: 0, 17
KNN f1 score: 0.829
KNN cohens kappa score: 0.823


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 569, 2
KNN fn, tp: 0, 17
KNN f1 score: 0.944
KNN cohens kappa score: 0.943


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 1.000

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 566, 5
KNN fn, tp: 0, 17
KNN f1 score: 0.872
KNN cohens kappa score: 0.867


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.889

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

-> test with 'GB'
GB tn, fp: 569, 2
GB fn, tp: 0, 17
GB f1 score: 0.944
GB cohens kappa score: 0.943

-> test with 'KNN'
KNN tn, fp: 567, 4
KNN fn, tp: 0, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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 'RF'
RF tn, fp: 570, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa 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: 568, 2
KNN fn, tp: 0, 13
KNN f1 score: 0.929
KNN cohens kappa score: 0.927


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 1.000

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 565, 6
KNN fn, tp: 0, 17
KNN f1 score: 0.850
KNN cohens kappa score: 0.845


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.990

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 569, 2
KNN fn, tp: 0, 17
KNN f1 score: 0.944
KNN cohens kappa score: 0.943


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.997

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 567, 4
KNN fn, tp: 0, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.993

-> test with 'RF'
RF tn, fp: 571, 0
RF fn, tp: 0, 17
RF f1 score: 1.000
RF cohens kappa 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: 569, 2
KNN fn, tp: 0, 17
KNN f1 score: 0.944
KNN cohens kappa score: 0.943


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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 'RF'
RF tn, fp: 570, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa 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: 567, 3
KNN fn, tp: 0, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.894

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 571, 4
LR fn, tp: 1, 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: 0.36, 15.84
LR f1 score: 0.942
LR cohens kappa score: 0.941
LR average precision score: 0.986


minimum:
LR tn, fp: 566, 0
LR fn, tp: 0, 12
LR f1 score: 0.828
LR cohens kappa score: 0.823
LR average precision score: 0.850


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


average:
RF tn, fp: 570.8, 0.0
RF fn, tp: 0.04, 16.16
RF f1 score: 0.999
RF cohens kappa score: 0.999


minimum:
RF tn, fp: 570, 0
RF fn, tp: 0, 13
RF f1 score: 0.970
RF cohens kappa score: 0.969


-----[ GB ]-----
maximum:
GB tn, fp: 571, 2
GB fn, tp: 0, 17
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 570.64, 0.16
GB fn, tp: 0.0, 16.2
GB f1 score: 0.995
GB cohens kappa score: 0.995


minimum:
GB tn, fp: 568, 0
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.927


-----[ KNN ]-----
maximum:
KNN tn, fp: 570, 7
KNN fn, tp: 0, 17
KNN f1 score: 0.971
KNN cohens kappa score: 0.971


average:
KNN tn, fp: 567.2, 3.6
KNN fn, tp: 0.0, 16.2
KNN f1 score: 0.901
KNN cohens kappa score: 0.898


minimum:
KNN tn, fp: 564, 1
KNN fn, tp: 0, 13
KNN f1 score: 0.829
KNN cohens kappa score: 0.823