ConvGeNBenchmarkResults/data_result/SimpleGAN/folding_shuttle-2_vs_5.log

///////////////////////////////////////////
// Running SimpleGAN 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 652, 2
LR fn, tp: 0, 10
LR f1 score: 0.909
LR cohens kappa score: 0.908
LR average precision score: 0.901

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

-> 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 2, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 0.892

-> 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: 1, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2576 synthetic samples
-> 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 2, 8
KNN f1 score: 0.889
KNN cohens kappa score: 0.887


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 652, 2
GB fn, tp: 0, 10
GB f1 score: 0.909
GB cohens kappa score: 0.908

-> 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 653, 1
GB fn, tp: 0, 10
GB f1 score: 0.952
GB cohens kappa score: 0.952

-> 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 0.909

-> 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2576 synthetic samples
-> test with 'LR'
LR tn, fp: 650, 1
LR fn, tp: 0, 9
LR f1 score: 0.947
LR cohens kappa score: 0.947
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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 0.909

-> 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 2, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2576 synthetic samples
-> 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: 650, 1
GB fn, tp: 0, 9
GB f1 score: 0.947
GB cohens kappa score: 0.947

-> test with 'KNN'
KNN tn, fp: 650, 1
KNN fn, tp: 1, 8
KNN f1 score: 0.889
KNN cohens kappa score: 0.887


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 653, 1
GB fn, tp: 0, 10
GB f1 score: 0.952
GB cohens kappa score: 0.952

-> 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 1, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 2, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2576 synthetic samples
-> 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 1, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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: 0.882

-> 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 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2574 synthetic samples
-> 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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 2576 synthetic samples
-> test with 'LR'
LR tn, fp: 650, 1
LR fn, tp: 0, 9
LR f1 score: 0.947
LR cohens kappa score: 0.947
LR average precision score: 0.890

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

-> test with 'KNN'
KNN tn, fp: 650, 1
KNN fn, tp: 1, 8
KNN f1 score: 0.889
KNN cohens kappa score: 0.887

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 654, 2
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: 653.04, 0.36
LR fn, tp: 0.0, 9.8
LR f1 score: 0.983
LR cohens kappa score: 0.982
LR average precision score: 0.975


minimum:
LR tn, fp: 650, 0
LR fn, tp: 0, 9
LR f1 score: 0.909
LR cohens kappa score: 0.908
LR average precision score: 0.882


-----[ GB ]-----
maximum:
GB tn, fp: 654, 2
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 653.12, 0.28
GB fn, tp: 0.0, 9.8
GB f1 score: 0.986
GB cohens kappa score: 0.986


minimum:
GB tn, fp: 650, 0
GB fn, tp: 0, 9
GB f1 score: 0.909
GB cohens kappa score: 0.908


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


average:
KNN tn, fp: 652.76, 0.64
KNN fn, tp: 0.72, 9.08
KNN f1 score: 0.930
KNN cohens kappa score: 0.929


minimum:
KNN tn, fp: 650, 0
KNN fn, tp: 0, 8
KNN f1 score: 0.800
KNN cohens kappa score: 0.797