ConvGeNBenchmarkResults/data_result/convGAN-proximary-full/folding_shuttle-2_vs_5.log

///////////////////////////////////////////
// Running convGAN-proxymary-full on folding_shuttle-2_vs_5
///////////////////////////////////////////

Load 'data_input/folding_shuttle-2_vs_5'
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 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 649, 5
GAN fn, tp: 0, 10
GAN f1 score: 0.800
GAN cohens kappa score: 0.796

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 0, 10
KNN f1 score: 0.952
KNN cohens kappa score: 0.952


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 640, 14
GAN fn, tp: 0, 10
GAN f1 score: 0.588
GAN cohens kappa score: 0.579

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 0, 10
KNN f1 score: 0.952
KNN cohens kappa score: 0.952


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 644, 10
GAN fn, tp: 0, 10
GAN f1 score: 0.667
GAN cohens kappa score: 0.660

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 650, 4
GAN fn, tp: 0, 10
GAN f1 score: 0.833
GAN cohens kappa score: 0.830

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 652, 2
KNN fn, tp: 0, 10
KNN f1 score: 0.909
KNN cohens kappa score: 0.908


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2576 synthetic samples
-> test with GAN.predict
GAN tn, fp: 647, 4
GAN fn, tp: 0, 9
GAN f1 score: 0.818
GAN cohens kappa score: 0.815

-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 651, 0
KNN fn, tp: 0, 9
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 649, 5
GAN fn, tp: 0, 10
GAN f1 score: 0.800
GAN cohens kappa score: 0.796

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 0, 10
KNN f1 score: 0.952
KNN cohens kappa score: 0.952


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 646, 8
GAN fn, tp: 0, 10
GAN f1 score: 0.714
GAN cohens kappa score: 0.709

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 648, 6
GAN fn, tp: 2, 8
GAN f1 score: 0.667
GAN cohens kappa score: 0.661

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 652, 2
KNN fn, tp: 0, 10
KNN f1 score: 0.909
KNN cohens kappa score: 0.908


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 649, 5
GAN fn, tp: 0, 10
GAN f1 score: 0.800
GAN cohens kappa score: 0.796

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2576 synthetic samples
-> test with GAN.predict
GAN tn, fp: 644, 7
GAN fn, tp: 0, 9
GAN f1 score: 0.720
GAN cohens kappa score: 0.715

-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 650, 1
KNN fn, tp: 0, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 646, 8
GAN fn, tp: 0, 10
GAN f1 score: 0.714
GAN cohens kappa score: 0.709

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
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
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 647, 7
GAN fn, tp: 0, 10
GAN f1 score: 0.741
GAN cohens kappa score: 0.736

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 0, 10
KNN f1 score: 0.952
KNN cohens kappa score: 0.952


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 651, 3
GAN fn, tp: 0, 10
GAN f1 score: 0.870
GAN cohens kappa score: 0.867

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 647, 7
GAN fn, tp: 1, 9
GAN f1 score: 0.692
GAN cohens kappa score: 0.686

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 0, 10
KNN f1 score: 0.952
KNN cohens kappa score: 0.952


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2576 synthetic samples
-> test with GAN.predict
GAN tn, fp: 647, 4
GAN fn, tp: 0, 9
GAN f1 score: 0.818
GAN cohens kappa score: 0.815

-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 649, 2
KNN fn, tp: 0, 9
KNN f1 score: 0.900
KNN cohens kappa score: 0.898


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 651, 3
GAN fn, tp: 0, 10
GAN f1 score: 0.870
GAN cohens kappa score: 0.867

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 642, 12
GAN fn, tp: 0, 10
GAN f1 score: 0.625
GAN cohens kappa score: 0.617

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 652, 2
KNN fn, tp: 0, 10
KNN f1 score: 0.909
KNN cohens kappa score: 0.908


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 643, 11
GAN fn, tp: 0, 10
GAN f1 score: 0.645
GAN cohens kappa score: 0.638

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 649, 5
GAN fn, tp: 2, 8
GAN f1 score: 0.696
GAN cohens kappa score: 0.690

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 1, 9
KNN f1 score: 0.900
KNN cohens kappa score: 0.898


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2576 synthetic samples
-> test with GAN.predict
GAN tn, fp: 643, 8
GAN fn, tp: 0, 9
GAN f1 score: 0.692
GAN cohens kappa score: 0.687

-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 650, 1
KNN fn, tp: 0, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 654, 0
GAN fn, tp: 0, 10
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 0, 10
KNN f1 score: 0.952
KNN cohens kappa score: 0.952


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 648, 6
GAN fn, tp: 0, 10
GAN f1 score: 0.769
GAN cohens kappa score: 0.765

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 646, 8
GAN fn, tp: 2, 8
GAN f1 score: 0.615
GAN cohens kappa score: 0.608

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 651, 3
KNN fn, tp: 2, 8
KNN f1 score: 0.762
KNN cohens kappa score: 0.758


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 640, 14
GAN fn, tp: 0, 10
GAN f1 score: 0.588
GAN cohens kappa score: 0.579

-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2576 synthetic samples
-> test with GAN.predict
GAN tn, fp: 640, 11
GAN fn, tp: 0, 9
GAN f1 score: 0.621
GAN cohens kappa score: 0.613

-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 650, 1
KNN fn, tp: 0, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000


average:
LR tn, fp: 653.4, 0.0
LR fn, tp: 0.0, 9.8
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000


minimum:
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000


-----[ GB ]-----
maximum:
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 653.4, 0.0
GB fn, tp: 0.0, 9.8
GB f1 score: 1.000
GB cohens kappa score: 1.000


minimum:
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000


-----[ KNN ]-----
maximum:
KNN tn, fp: 654, 3
KNN fn, tp: 2, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


average:
KNN tn, fp: 652.56, 0.84
KNN fn, tp: 0.12, 9.68
KNN f1 score: 0.954
KNN cohens kappa score: 0.953


minimum:
KNN tn, fp: 649, 0
KNN fn, tp: 0, 8
KNN f1 score: 0.762
KNN cohens kappa score: 0.758


-----[ GAN ]-----
maximum:
GAN tn, fp: 654, 14
GAN fn, tp: 2, 10
GAN f1 score: 1.000
GAN cohens kappa score: 1.000


average:
GAN tn, fp: 646.4, 7.0
GAN fn, tp: 0.28, 9.52
GAN f1 score: 0.735
GAN cohens kappa score: 0.729


minimum:
GAN tn, fp: 640, 0
GAN fn, tp: 0, 8
GAN f1 score: 0.588
GAN cohens kappa score: 0.579