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

///////////////////////////////////////////
// Running convGAN-proximary-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: 653, 1
LR fn, tp: 0, 10
LR f1 score: 0.952
LR cohens kappa score: 0.952
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: 645, 9
GAN fn, tp: 0, 10
GAN f1 score: 0.690
GAN cohens kappa score: 0.683

-> 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: 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: 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: 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: 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: 646, 5
GAN fn, tp: 0, 9
GAN f1 score: 0.783
GAN cohens kappa score: 0.779

-> 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 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: 652, 2
GAN fn, tp: 0, 10
GAN f1 score: 0.909
GAN cohens kappa score: 0.908

-> 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: 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 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: 2, 8
KNN f1 score: 0.800
KNN cohens kappa score: 0.797


------ 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: 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 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2576 synthetic samples
-> test with GAN.predict
GAN tn, fp: 648, 3
GAN fn, tp: 0, 9
GAN f1 score: 0.857
GAN cohens kappa score: 0.855

-> 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 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: 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 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: 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: 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: 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: 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: 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: 644, 7
GAN fn, tp: 1, 8
GAN f1 score: 0.667
GAN cohens kappa score: 0.661

-> 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: 1, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


====== 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: 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 4/5: 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: 651, 3
KNN fn, tp: 0, 10
KNN f1 score: 0.870
KNN cohens kappa score: 0.867


------ 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: 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 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 644, 10
GAN fn, tp: 1, 9
GAN f1 score: 0.621
GAN cohens kappa score: 0.613

-> 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 4/5: 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


====== 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: 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: 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: 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 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 2574 synthetic samples
-> test with GAN.predict
GAN tn, fp: 647, 7
GAN fn, tp: 2, 8
GAN f1 score: 0.640
GAN cohens kappa score: 0.633

-> 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: 650, 4
KNN fn, tp: 1, 9
KNN f1 score: 0.783
KNN cohens kappa score: 0.779


------ 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: 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 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

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 654, 1
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: 653.36, 0.04
LR fn, tp: 0.0, 9.8
LR f1 score: 0.998
LR cohens kappa score: 0.998
LR average precision score: 1.000


minimum:
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 0.952
LR cohens kappa score: 0.952
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, 4
KNN fn, tp: 2, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


average:
KNN tn, fp: 652.36, 1.04
KNN fn, tp: 0.16, 9.64
KNN f1 score: 0.943
KNN cohens kappa score: 0.942


minimum:
KNN tn, fp: 649, 0
KNN fn, tp: 0, 8
KNN f1 score: 0.783
KNN cohens kappa score: 0.779


-----[ GAN ]-----
maximum:
GAN tn, fp: 652, 11
GAN fn, tp: 2, 10
GAN f1 score: 0.909
GAN cohens kappa score: 0.908


average:
GAN tn, fp: 646.84, 6.56
GAN fn, tp: 0.24, 9.56
GAN f1 score: 0.743
GAN cohens kappa score: 0.738


minimum:
GAN tn, fp: 640, 2
GAN fn, tp: 0, 8
GAN f1 score: 0.621
GAN cohens kappa score: 0.613