ConvGeNBenchmarkResults/data_result/convGAN-full/folding_flare-F.log

///////////////////////////////////////////
// Running convGAN-full 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
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 181, 24
LR fn, tp: 7, 2
LR f1 score: 0.114
LR cohens kappa score: 0.055
LR average precision score: 0.086

-> test with 'GB'
GB tn, fp: 198, 7
GB fn, tp: 8, 1
GB f1 score: 0.118
GB cohens kappa score: 0.081

-> test with 'KNN'
KNN tn, fp: 179, 26
KNN fn, tp: 4, 5
KNN f1 score: 0.250
KNN cohens kappa score: 0.198


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 163, 42
LR fn, tp: 1, 8
LR f1 score: 0.271
LR cohens kappa score: 0.215
LR average precision score: 0.322

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.131

-> test with 'KNN'
KNN tn, fp: 170, 35
KNN fn, tp: 2, 7
KNN f1 score: 0.275
KNN cohens kappa score: 0.221


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 180, 25
LR fn, tp: 4, 5
LR f1 score: 0.256
LR cohens kappa score: 0.205
LR average precision score: 0.433

-> 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: 175, 30
KNN fn, tp: 4, 5
KNN f1 score: 0.227
KNN cohens kappa score: 0.172


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 19
LR fn, tp: 0, 9
LR f1 score: 0.486
LR cohens kappa score: 0.452
LR average precision score: 0.718

-> 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: 187, 18
KNN fn, tp: 5, 4
KNN f1 score: 0.258
KNN cohens kappa score: 0.211


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 176, 27
LR fn, tp: 3, 4
LR f1 score: 0.211
LR cohens kappa score: 0.165
LR average precision score: 0.267

-> test with 'GB'
GB tn, fp: 201, 2
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

-> test with 'KNN'
KNN tn, fp: 172, 31
KNN fn, tp: 1, 6
KNN f1 score: 0.273
KNN cohens kappa score: 0.230


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 176, 29
LR fn, tp: 2, 7
LR f1 score: 0.311
LR cohens kappa score: 0.261
LR average precision score: 0.485

-> test with 'GB'
GB tn, fp: 201, 4
GB fn, tp: 8, 1
GB f1 score: 0.143
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 178, 27
KNN fn, tp: 2, 7
KNN f1 score: 0.326
KNN cohens kappa score: 0.278


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 177, 28
LR fn, tp: 5, 4
LR f1 score: 0.195
LR cohens kappa score: 0.139
LR average precision score: 0.405

-> 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: 178, 27
KNN fn, tp: 4, 5
KNN f1 score: 0.244
KNN cohens kappa score: 0.191


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 177, 28
LR fn, tp: 3, 6
LR f1 score: 0.279
LR cohens kappa score: 0.228
LR average precision score: 0.375

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.131

-> test with 'KNN'
KNN tn, fp: 182, 23
KNN fn, tp: 5, 4
KNN f1 score: 0.222
KNN cohens kappa score: 0.170


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 190, 15
LR fn, tp: 4, 5
LR f1 score: 0.345
LR cohens kappa score: 0.304
LR average precision score: 0.288

-> 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: 186, 19
KNN fn, tp: 3, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.310


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 174, 29
LR fn, tp: 0, 7
LR f1 score: 0.326
LR cohens kappa score: 0.286
LR average precision score: 0.374

-> 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: 174, 29
KNN fn, tp: 1, 6
KNN f1 score: 0.286
KNN cohens kappa score: 0.244


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 188, 17
LR fn, tp: 2, 7
LR f1 score: 0.424
LR cohens kappa score: 0.387
LR average precision score: 0.787

-> 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: 189, 16
KNN fn, tp: 3, 6
KNN f1 score: 0.387
KNN cohens kappa score: 0.348


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 174, 31
LR fn, tp: 2, 7
LR f1 score: 0.298
LR cohens kappa score: 0.247
LR average precision score: 0.288

-> test with 'GB'
GB tn, fp: 194, 11
GB fn, tp: 5, 4
GB f1 score: 0.333
GB cohens kappa score: 0.296

-> test with 'KNN'
KNN tn, fp: 172, 33
KNN fn, tp: 3, 6
KNN f1 score: 0.250
KNN cohens kappa score: 0.195


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 21
LR fn, tp: 3, 6
LR f1 score: 0.333
LR cohens kappa score: 0.288
LR average precision score: 0.432

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

-> test with 'KNN'
KNN tn, fp: 172, 33
KNN fn, tp: 2, 7
KNN f1 score: 0.286
KNN cohens kappa score: 0.233


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 188, 17
LR fn, tp: 4, 5
LR f1 score: 0.323
LR cohens kappa score: 0.280
LR average precision score: 0.233

-> 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: 184, 21
KNN fn, tp: 3, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.288


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 168, 35
LR fn, tp: 1, 6
LR f1 score: 0.250
LR cohens kappa score: 0.205
LR average precision score: 0.269

-> test with 'GB'
GB tn, fp: 197, 6
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.032

-> test with 'KNN'
KNN tn, fp: 183, 20
KNN fn, tp: 4, 3
KNN f1 score: 0.200
KNN cohens kappa score: 0.157


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 178, 27
LR fn, tp: 2, 7
LR f1 score: 0.326
LR cohens kappa score: 0.278
LR average precision score: 0.191

-> test with 'GB'
GB tn, fp: 198, 7
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.038

-> test with 'KNN'
KNN tn, fp: 179, 26
KNN fn, tp: 3, 6
KNN f1 score: 0.293
KNN cohens kappa score: 0.243


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 21
LR fn, tp: 3, 6
LR f1 score: 0.333
LR cohens kappa score: 0.288
LR average precision score: 0.593

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.131

-> test with 'KNN'
KNN tn, fp: 186, 19
KNN fn, tp: 4, 5
KNN f1 score: 0.303
KNN cohens kappa score: 0.258


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 173, 32
LR fn, tp: 4, 5
LR f1 score: 0.217
LR cohens kappa score: 0.161
LR average precision score: 0.273

-> 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: 181, 24
KNN fn, tp: 3, 6
KNN f1 score: 0.308
KNN cohens kappa score: 0.260


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 176, 29
LR fn, tp: 2, 7
LR f1 score: 0.311
LR cohens kappa score: 0.261
LR average precision score: 0.379

-> 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: 179, 26
KNN fn, tp: 2, 7
KNN f1 score: 0.333
KNN cohens kappa score: 0.286


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 177, 26
LR fn, tp: 1, 6
LR f1 score: 0.308
LR cohens kappa score: 0.268
LR average precision score: 0.397

-> test with 'GB'
GB tn, fp: 201, 2
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

-> test with 'KNN'
KNN tn, fp: 168, 35
KNN fn, tp: 3, 4
KNN f1 score: 0.174
KNN cohens kappa score: 0.124


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 26
LR fn, tp: 4, 5
LR f1 score: 0.250
LR cohens kappa score: 0.198
LR average precision score: 0.263

-> 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: 184, 21
KNN fn, tp: 3, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.288


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 175, 30
LR fn, tp: 2, 7
LR f1 score: 0.304
LR cohens kappa score: 0.254
LR average precision score: 0.418

-> 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: 167, 38
KNN fn, tp: 2, 7
KNN f1 score: 0.259
KNN cohens kappa score: 0.203


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 177, 28
LR fn, tp: 0, 9
LR f1 score: 0.391
LR cohens kappa score: 0.347
LR average precision score: 0.508

-> 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: 177, 28
KNN fn, tp: 2, 7
KNN f1 score: 0.318
KNN cohens kappa score: 0.269


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 187, 18
LR fn, tp: 5, 4
LR f1 score: 0.258
LR cohens kappa score: 0.211
LR average precision score: 0.194

-> 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: 189, 16
KNN fn, tp: 6, 3
KNN f1 score: 0.214
KNN cohens kappa score: 0.167


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 171, 32
LR fn, tp: 2, 5
LR f1 score: 0.227
LR cohens kappa score: 0.181
LR average precision score: 0.440

-> test with 'GB'
GB tn, fp: 196, 7
GB fn, tp: 5, 2
GB f1 score: 0.250
GB cohens kappa score: 0.221

-> test with 'KNN'
KNN tn, fp: 178, 25
KNN fn, tp: 3, 4
KNN f1 score: 0.222
KNN cohens kappa score: 0.178

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 190, 42
LR fn, tp: 7, 9
LR f1 score: 0.486
LR cohens kappa score: 0.452
LR average precision score: 0.787


average:
LR tn, fp: 178.36, 26.24
LR fn, tp: 2.64, 5.96
LR f1 score: 0.294
LR cohens kappa score: 0.247
LR average precision score: 0.377


minimum:
LR tn, fp: 163, 15
LR fn, tp: 0, 2
LR f1 score: 0.114
LR cohens kappa score: 0.055
LR average precision score: 0.086


-----[ GB ]-----
maximum:
GB tn, fp: 205, 11
GB fn, tp: 9, 4
GB f1 score: 0.364
GB cohens kappa score: 0.354


average:
GB tn, fp: 201.6, 3.0
GB fn, tp: 7.72, 0.88
GB f1 score: 0.129
GB cohens kappa score: 0.109


minimum:
GB tn, fp: 194, 0
GB fn, tp: 5, 0
GB f1 score: 0.000
GB cohens kappa score: -0.038


-----[ KNN ]-----
maximum:
KNN tn, fp: 189, 38
KNN fn, tp: 6, 7
KNN f1 score: 0.387
KNN cohens kappa score: 0.348


average:
KNN tn, fp: 178.76, 25.84
KNN fn, tp: 3.08, 5.52
KNN f1 score: 0.277
KNN cohens kappa score: 0.229


minimum:
KNN tn, fp: 167, 16
KNN fn, tp: 1, 3
KNN f1 score: 0.174
KNN cohens kappa score: 0.124