ConvGeNBenchmarkResults/data_result/SimpleGAN/folding_abalone9-18.log

///////////////////////////////////////////
// Running SimpleGAN on folding_abalone9-18
///////////////////////////////////////////

Load 'data_input/folding_abalone9-18'
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 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 2, 7
LR f1 score: 0.824
LR cohens kappa score: 0.813
LR average precision score: 0.917

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 5, 4
LR f1 score: 0.533
LR cohens kappa score: 0.509
LR average precision score: 0.646

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 6, 3
GB f1 score: 0.429
GB cohens kappa score: 0.402

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 5, 4
LR f1 score: 0.571
LR cohens kappa score: 0.552
LR average precision score: 0.716

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 6, 3
GB f1 score: 0.429
GB cohens kappa score: 0.402

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 3
LR fn, tp: 6, 3
LR f1 score: 0.400
LR cohens kappa score: 0.369
LR average precision score: 0.527

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 4, 5
GB f1 score: 0.556
GB cohens kappa score: 0.527

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 3
LR fn, tp: 4, 2
LR f1 score: 0.364
LR cohens kappa score: 0.338
LR average precision score: 0.530

-> test with 'GB'
GB tn, fp: 137, 0
GB fn, tp: 6, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


====== 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 518 synthetic samples
-> test with 'LR'
LR tn, fp: 138, 0
LR fn, tp: 6, 3
LR f1 score: 0.500
LR cohens kappa score: 0.484
LR average precision score: 0.558

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.253

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 4, 5
LR f1 score: 0.625
LR cohens kappa score: 0.604
LR average precision score: 0.720

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 5, 4
GB f1 score: 0.571
GB cohens kappa score: 0.552

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 5, 4
LR f1 score: 0.571
LR cohens kappa score: 0.552
LR average precision score: 0.674

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 5, 4
LR f1 score: 0.571
LR cohens kappa score: 0.552
LR average precision score: 0.717

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 5, 4
GB f1 score: 0.533
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 2
LR fn, tp: 2, 4
LR f1 score: 0.667
LR cohens kappa score: 0.652
LR average precision score: 0.719

-> test with 'GB'
GB tn, fp: 136, 1
GB fn, tp: 4, 2
GB f1 score: 0.444
GB cohens kappa score: 0.428

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


====== 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 518 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 6, 3
LR f1 score: 0.375
LR cohens kappa score: 0.340
LR average precision score: 0.521

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.121

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 4, 5
LR f1 score: 0.667
LR cohens kappa score: 0.649
LR average precision score: 0.825

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 138, 0
LR fn, tp: 5, 4
LR f1 score: 0.615
LR cohens kappa score: 0.600
LR average precision score: 0.675

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.281

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 4, 5
LR f1 score: 0.625
LR cohens kappa score: 0.604
LR average precision score: 0.790

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 2
LR fn, tp: 3, 3
LR f1 score: 0.545
LR cohens kappa score: 0.527
LR average precision score: 0.550

-> test with 'GB'
GB tn, fp: 135, 2
GB fn, tp: 3, 3
GB f1 score: 0.545
GB cohens kappa score: 0.527

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


====== 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 518 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 5, 4
LR f1 score: 0.471
LR cohens kappa score: 0.438
LR average precision score: 0.498

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 3
LR fn, tp: 5, 4
LR f1 score: 0.500
LR cohens kappa score: 0.472
LR average precision score: 0.656

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 5, 4
GB f1 score: 0.471
GB cohens kappa score: 0.438

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 4, 5
LR f1 score: 0.667
LR cohens kappa score: 0.649
LR average precision score: 0.719

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 5, 4
LR f1 score: 0.571
LR cohens kappa score: 0.552
LR average precision score: 0.879

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 2
LR fn, tp: 4, 2
LR f1 score: 0.400
LR cohens kappa score: 0.379
LR average precision score: 0.605

-> test with 'GB'
GB tn, fp: 135, 2
GB fn, tp: 5, 1
GB f1 score: 0.222
GB cohens kappa score: 0.200

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


====== 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 518 synthetic samples
-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 5, 4
LR f1 score: 0.444
LR cohens kappa score: 0.408
LR average precision score: 0.610

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 7, 2
GB f1 score: 0.250
GB cohens kappa score: 0.208

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 3, 6
LR f1 score: 0.706
LR cohens kappa score: 0.688
LR average precision score: 0.686

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.281

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 135, 3
LR fn, tp: 5, 4
LR f1 score: 0.500
LR cohens kappa score: 0.472
LR average precision score: 0.535

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 6, 3
GB f1 score: 0.429
GB cohens kappa score: 0.402

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 4, 5
LR f1 score: 0.667
LR cohens kappa score: 0.649
LR average precision score: 0.842

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.312

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 0
LR fn, tp: 3, 3
LR f1 score: 0.667
LR cohens kappa score: 0.657
LR average precision score: 0.760

-> test with 'GB'
GB tn, fp: 136, 1
GB fn, tp: 3, 3
GB f1 score: 0.600
GB cohens kappa score: 0.586

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 138, 5
LR fn, tp: 6, 7
LR f1 score: 0.824
LR cohens kappa score: 0.813
LR average precision score: 0.917


average:
LR tn, fp: 135.92, 1.88
LR fn, tp: 4.36, 4.04
LR f1 score: 0.562
LR cohens kappa score: 0.540
LR average precision score: 0.675


minimum:
LR tn, fp: 133, 0
LR fn, tp: 2, 2
LR f1 score: 0.364
LR cohens kappa score: 0.338
LR average precision score: 0.498


-----[ GB ]-----
maximum:
GB tn, fp: 137, 5
GB fn, tp: 8, 5
GB f1 score: 0.600
GB cohens kappa score: 0.586


average:
GB tn, fp: 135.64, 2.16
GB fn, tp: 5.72, 2.68
GB f1 score: 0.395
GB cohens kappa score: 0.370


minimum:
GB tn, fp: 133, 0
GB fn, tp: 3, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000


-----[ KNN ]-----
maximum:
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


average:
KNN tn, fp: 137.8, 0.0
KNN fn, tp: 8.4, 0.0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


minimum:
KNN tn, fp: 137, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000