ConvGeNBenchmarkResults/data_result/convGAN-majority-full/folding_kr-vs-k-three_vs_eleven.log

///////////////////////////////////////////
// Running convGAN-majority-full 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 GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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: 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 GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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: 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 GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

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


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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 '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 GAN.predict
GAN tn, fp: 569, 1
GAN fn, tp: 0, 13
GAN f1 score: 0.963
GAN cohens kappa score: 0.962

-> 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: 0.911

-> 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: 569, 1
KNN fn, tp: 0, 13
KNN f1 score: 0.963
KNN cohens kappa score: 0.962


====== 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 GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

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


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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: 570, 1
KNN fn, tp: 0, 17
KNN f1 score: 0.971
KNN cohens kappa score: 0.971


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 570, 1
LR fn, tp: 2, 15
LR f1 score: 0.909
LR cohens kappa score: 0.906
LR average precision score: 0.979

-> 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 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 571, 0
LR fn, tp: 0, 17
LR f1 score: 1.000
LR cohens kappa score: 1.000
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: 568, 3
KNN fn, tp: 1, 16
KNN f1 score: 0.889
KNN cohens kappa score: 0.885


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with GAN.predict
GAN tn, fp: 570, 0
GAN fn, tp: 0, 13
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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 '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: 564, 6
KNN fn, tp: 0, 13
KNN f1 score: 0.813
KNN cohens kappa score: 0.807


====== 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 GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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 '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 GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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: 564, 7
KNN fn, tp: 0, 17
KNN f1 score: 0.829
KNN cohens kappa score: 0.823


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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: 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 GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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: 567, 4
KNN fn, tp: 0, 17
KNN f1 score: 0.895
KNN cohens kappa score: 0.891


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with GAN.predict
GAN tn, fp: 570, 0
GAN fn, tp: 1, 12
GAN f1 score: 0.960
GAN cohens kappa score: 0.959

-> test with 'LR'
LR tn, fp: 569, 1
LR fn, tp: 2, 11
LR f1 score: 0.880
LR cohens kappa score: 0.877
LR average precision score: 0.973

-> 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 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 GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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: 565, 6
KNN fn, tp: 0, 17
KNN f1 score: 0.850
KNN cohens kappa score: 0.845


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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: 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 GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

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


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with GAN.predict
GAN tn, fp: 569, 2
GAN fn, tp: 0, 17
GAN f1 score: 0.944
GAN cohens kappa score: 0.943

-> 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: 0.935

-> 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: 569, 2
KNN fn, tp: 0, 17
KNN f1 score: 0.944
KNN cohens kappa score: 0.943


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with GAN.predict
GAN tn, fp: 570, 0
GAN fn, tp: 0, 13
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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: 0.982

-> 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 GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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 '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 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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: 0.990

-> 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: 570, 1
KNN fn, tp: 0, 17
KNN f1 score: 0.971
KNN cohens kappa score: 0.971


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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: 0.994

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


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2219 synthetic samples
-> test with GAN.predict
GAN tn, fp: 571, 0
GAN fn, tp: 0, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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 '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 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2216 synthetic samples
-> test with GAN.predict
GAN tn, fp: 570, 0
GAN fn, tp: 0, 13
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> 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: 568, 2
KNN fn, tp: 0, 13
KNN f1 score: 0.929
KNN cohens kappa score: 0.927

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 571, 4
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: 569.84, 0.96
LR fn, tp: 0.44, 15.76
LR f1 score: 0.956
LR cohens kappa score: 0.955
LR average precision score: 0.990


minimum:
LR tn, fp: 566, 0
LR fn, tp: 0, 11
LR f1 score: 0.828
LR cohens kappa score: 0.823
LR average precision score: 0.911


-----[ 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: 571, 7
KNN fn, tp: 1, 17
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


average:
KNN tn, fp: 567.52, 3.28
KNN fn, tp: 0.04, 16.16
KNN f1 score: 0.909
KNN cohens kappa score: 0.907


minimum:
KNN tn, fp: 564, 0
KNN fn, tp: 0, 13
KNN f1 score: 0.813
KNN cohens kappa score: 0.807


-----[ GAN ]-----
maximum:
GAN tn, fp: 571, 2
GAN fn, tp: 1, 17
GAN f1 score: 1.000
GAN cohens kappa score: 1.000


average:
GAN tn, fp: 570.68, 0.12
GAN fn, tp: 0.04, 16.16
GAN f1 score: 0.995
GAN cohens kappa score: 0.995


minimum:
GAN tn, fp: 569, 0
GAN fn, tp: 0, 12
GAN f1 score: 0.944
GAN cohens kappa score: 0.943