ConvGeNBenchmarkResults/data_result/SimpleGAN/folding_flare-F.log

///////////////////////////////////////////
// Running SimpleGAN on folding_flare-F
///////////////////////////////////////////

Load 'data_input/folding_flare-F'
from pickle file
non empty cut in data_input/folding_flare-F! (23 points)
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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 203, 2
LR fn, tp: 8, 1
LR f1 score: 0.167
LR cohens kappa score: 0.149
LR average precision score: 0.192

-> test with 'GB'
GB tn, fp: 199, 6
GB fn, tp: 8, 1
GB f1 score: 0.125
GB cohens kappa score: 0.092

-> test with 'KNN'
KNN tn, fp: 200, 5
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.031


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 203, 2
LR fn, tp: 5, 4
LR f1 score: 0.533
LR cohens kappa score: 0.517
LR average precision score: 0.471

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 204, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.008


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 205, 0
LR fn, tp: 7, 2
LR f1 score: 0.364
LR cohens kappa score: 0.354
LR average precision score: 0.440

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 8, 1
GB f1 score: 0.200
GB cohens kappa score: 0.193

-> test with 'KNN'
KNN tn, fp: 205, 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 205, 0
LR fn, tp: 6, 3
LR f1 score: 0.500
LR cohens kappa score: 0.489
LR average precision score: 0.744

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.319

-> test with 'KNN'
KNN tn, fp: 205, 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 3
LR fn, tp: 5, 2
LR f1 score: 0.333
LR cohens kappa score: 0.314
LR average precision score: 0.271

-> test with 'GB'
GB tn, fp: 201, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.184

-> test with 'KNN'
KNN tn, fp: 203, 0
KNN fn, tp: 6, 1
KNN f1 score: 0.250
KNN cohens kappa score: 0.244


====== 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 204, 1
LR fn, tp: 5, 4
LR f1 score: 0.571
LR cohens kappa score: 0.558
LR average precision score: 0.612

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.289

-> test with 'KNN'
KNN tn, fp: 204, 1
KNN fn, tp: 8, 1
KNN f1 score: 0.182
KNN cohens kappa score: 0.169


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 203, 2
LR fn, tp: 7, 2
LR f1 score: 0.308
LR cohens kappa score: 0.289
LR average precision score: 0.305

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.016

-> test with 'KNN'
KNN tn, fp: 205, 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 205, 0
LR fn, tp: 8, 1
LR f1 score: 0.200
LR cohens kappa score: 0.193
LR average precision score: 0.437

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 8, 1
GB f1 score: 0.200
GB cohens kappa score: 0.193

-> test with 'KNN'
KNN tn, fp: 203, 2
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.016


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 205, 0
LR fn, tp: 8, 1
LR f1 score: 0.200
LR cohens kappa score: 0.193
LR average precision score: 0.360

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 204, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.008


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 3
LR fn, tp: 4, 3
LR f1 score: 0.462
LR cohens kappa score: 0.444
LR average precision score: 0.391

-> test with 'GB'
GB tn, fp: 201, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.184

-> test with 'KNN'
KNN tn, fp: 202, 1
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.008


====== 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 205, 0
LR fn, tp: 4, 5
LR f1 score: 0.714
LR cohens kappa score: 0.705
LR average precision score: 0.834

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 205, 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 5
LR fn, tp: 7, 2
LR f1 score: 0.250
LR cohens kappa score: 0.221
LR average precision score: 0.210

-> test with 'GB'
GB tn, fp: 199, 6
GB fn, tp: 6, 3
GB f1 score: 0.333
GB cohens kappa score: 0.304

-> test with 'KNN'
KNN tn, fp: 201, 4
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.027


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 3
LR fn, tp: 6, 3
LR f1 score: 0.400
LR cohens kappa score: 0.379
LR average precision score: 0.483

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 205, 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 204, 1
LR fn, tp: 8, 1
LR f1 score: 0.182
LR cohens kappa score: 0.169
LR average precision score: 0.330

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 205, 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 201, 2
LR fn, tp: 6, 1
LR f1 score: 0.200
LR cohens kappa score: 0.184
LR average precision score: 0.212

-> test with 'GB'
GB tn, fp: 199, 4
GB fn, tp: 5, 2
GB f1 score: 0.308
GB cohens kappa score: 0.286

-> test with 'KNN'
KNN tn, fp: 200, 3
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.020


====== 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 199, 6
LR fn, tp: 9, 0
LR f1 score: 0.000
LR cohens kappa score: -0.035
LR average precision score: 0.203

-> test with 'GB'
GB tn, fp: 199, 6
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.035

-> test with 'KNN'
KNN tn, fp: 205, 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 205, 0
LR fn, tp: 6, 3
LR f1 score: 0.500
LR cohens kappa score: 0.489
LR average precision score: 0.564

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 8, 1
GB f1 score: 0.200
GB cohens kappa score: 0.193

-> test with 'KNN'
KNN tn, fp: 205, 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 5
LR fn, tp: 6, 3
LR f1 score: 0.353
LR cohens kappa score: 0.326
LR average precision score: 0.382

-> test with 'GB'
GB tn, fp: 201, 4
GB fn, tp: 7, 2
GB f1 score: 0.267
GB cohens kappa score: 0.241

-> test with 'KNN'
KNN tn, fp: 204, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.008


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 201, 4
LR fn, tp: 7, 2
LR f1 score: 0.267
LR cohens kappa score: 0.241
LR average precision score: 0.333

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 200, 5
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.031


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 1
LR fn, tp: 5, 2
LR f1 score: 0.400
LR cohens kappa score: 0.388
LR average precision score: 0.596

-> test with 'GB'
GB tn, fp: 203, 0
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 203, 0
KNN fn, tp: 7, 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 205, 0
LR fn, tp: 8, 1
LR f1 score: 0.200
LR cohens kappa score: 0.193
LR average precision score: 0.288

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 205, 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 203, 2
LR fn, tp: 8, 1
LR f1 score: 0.167
LR cohens kappa score: 0.149
LR average precision score: 0.410

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 205, 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 201, 4
LR fn, tp: 4, 5
LR f1 score: 0.556
LR cohens kappa score: 0.536
LR average precision score: 0.473

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 7, 2
GB f1 score: 0.364
GB cohens kappa score: 0.354

-> test with 'KNN'
KNN tn, fp: 204, 1
KNN fn, tp: 8, 1
KNN f1 score: 0.182
KNN cohens kappa score: 0.169


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 201, 4
LR fn, tp: 8, 1
LR f1 score: 0.143
LR cohens kappa score: 0.116
LR average precision score: 0.248

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.016

-> test with 'KNN'
KNN tn, fp: 204, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.008


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 1
LR fn, tp: 6, 1
LR f1 score: 0.222
LR cohens kappa score: 0.211
LR average precision score: 0.342

-> test with 'GB'
GB tn, fp: 199, 4
GB fn, tp: 6, 1
GB f1 score: 0.167
GB cohens kappa score: 0.143

-> test with 'KNN'
KNN tn, fp: 199, 4
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.025

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 205, 6
LR fn, tp: 9, 5
LR f1 score: 0.714
LR cohens kappa score: 0.705
LR average precision score: 0.834


average:
LR tn, fp: 202.56, 2.04
LR fn, tp: 6.44, 2.16
LR f1 score: 0.328
LR cohens kappa score: 0.311
LR average precision score: 0.405


minimum:
LR tn, fp: 199, 0
LR fn, tp: 4, 0
LR f1 score: 0.000
LR cohens kappa score: -0.035
LR average precision score: 0.192


-----[ GB ]-----
maximum:
GB tn, fp: 205, 6
GB fn, tp: 9, 3
GB f1 score: 0.364
GB cohens kappa score: 0.354


average:
GB tn, fp: 202.68, 1.92
GB fn, tp: 7.56, 1.04
GB f1 score: 0.171
GB cohens kappa score: 0.156


minimum:
GB tn, fp: 199, 0
GB fn, tp: 5, 0
GB f1 score: 0.000
GB cohens kappa score: -0.035


-----[ KNN ]-----
maximum:
KNN tn, fp: 205, 5
KNN fn, tp: 9, 1
KNN f1 score: 0.250
KNN cohens kappa score: 0.244


average:
KNN tn, fp: 203.4, 1.2
KNN fn, tp: 8.48, 0.12
KNN f1 score: 0.025
KNN cohens kappa score: 0.016


minimum:
KNN tn, fp: 199, 0
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.031