ConvGeNBenchmarkResults/data_result/ProWRAS/folding_flare-F.log

///////////////////////////////////////////
// Running ProWRAS 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: 178, 27
LR fn, tp: 6, 3
LR f1 score: 0.154
LR cohens kappa score: 0.095
LR average precision score: 0.091

-> test with 'GB'
GB tn, fp: 196, 9
GB fn, tp: 8, 1
GB f1 score: 0.105
GB cohens kappa score: 0.064

-> test with 'KNN'
KNN tn, fp: 184, 21
KNN fn, tp: 5, 4
KNN f1 score: 0.235
KNN cohens kappa score: 0.185


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 157, 48
LR fn, tp: 1, 8
LR f1 score: 0.246
LR cohens kappa score: 0.187
LR average precision score: 0.381

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.264

-> 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 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: 4, 5
LR f1 score: 0.238
LR cohens kappa score: 0.184
LR average precision score: 0.271

-> 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: 5, 4
KNN f1 score: 0.216
KNN cohens kappa score: 0.163


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 20
LR fn, tp: 0, 9
LR f1 score: 0.474
LR cohens kappa score: 0.438
LR average precision score: 0.590

-> 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: 189, 16
KNN fn, tp: 5, 4
KNN f1 score: 0.276
KNN cohens kappa score: 0.231


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 175, 28
LR fn, tp: 3, 4
LR f1 score: 0.205
LR cohens kappa score: 0.159
LR average precision score: 0.198

-> test with 'GB'
GB tn, fp: 201, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.348

-> test with 'KNN'
KNN tn, fp: 187, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.323


====== 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: 171, 34
LR fn, tp: 2, 7
LR f1 score: 0.280
LR cohens kappa score: 0.227
LR average precision score: 0.365

-> test with 'GB'
GB tn, fp: 199, 6
GB fn, tp: 7, 2
GB f1 score: 0.235
GB cohens kappa score: 0.204

-> test with 'KNN'
KNN tn, fp: 184, 21
KNN fn, tp: 2, 7
KNN f1 score: 0.378
KNN cohens kappa score: 0.336


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 172, 33
LR fn, tp: 3, 6
LR f1 score: 0.250
LR cohens kappa score: 0.195
LR average precision score: 0.391

-> 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: 183, 22
KNN fn, tp: 4, 5
KNN f1 score: 0.278
KNN cohens kappa score: 0.229


------ 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: 0.253

-> 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: 189, 16
KNN fn, tp: 5, 4
KNN f1 score: 0.276
KNN cohens kappa score: 0.231


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 20
LR fn, tp: 4, 5
LR f1 score: 0.294
LR cohens kappa score: 0.248
LR average precision score: 0.315

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


------ Step 2/5: 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: 0, 7
LR f1 score: 0.304
LR cohens kappa score: 0.263
LR average precision score: 0.312

-> 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: 184, 19
KNN fn, tp: 2, 5
KNN f1 score: 0.323
KNN cohens kappa score: 0.286


====== 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: 0.739

-> 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: 198, 7
KNN fn, tp: 4, 5
KNN f1 score: 0.476
KNN cohens kappa score: 0.450


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 168, 37
LR fn, tp: 2, 7
LR f1 score: 0.264
LR cohens kappa score: 0.209
LR average precision score: 0.243

-> test with 'GB'
GB tn, fp: 193, 12
GB fn, tp: 5, 4
GB f1 score: 0.320
GB cohens kappa score: 0.281

-> test with 'KNN'
KNN tn, fp: 182, 23
KNN fn, tp: 4, 5
KNN f1 score: 0.270
KNN cohens kappa score: 0.221


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 174, 31
LR fn, tp: 3, 6
LR f1 score: 0.261
LR cohens kappa score: 0.207
LR average precision score: 0.466

-> 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: 182, 23
KNN fn, tp: 3, 6
KNN f1 score: 0.316
KNN cohens kappa score: 0.269


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 23
LR fn, tp: 4, 5
LR f1 score: 0.270
LR cohens kappa score: 0.221
LR average precision score: 0.313

-> 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: 189, 16
KNN fn, tp: 4, 5
KNN f1 score: 0.333
KNN cohens kappa score: 0.292


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 162, 41
LR fn, tp: 1, 6
LR f1 score: 0.222
LR cohens kappa score: 0.174
LR average precision score: 0.277

-> test with 'GB'
GB tn, fp: 198, 5
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.029

-> test with 'KNN'
KNN tn, fp: 184, 19
KNN fn, tp: 3, 4
KNN f1 score: 0.267
KNN cohens kappa score: 0.227


====== 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: 177, 28
LR fn, tp: 2, 7
LR f1 score: 0.318
LR cohens kappa score: 0.269
LR average precision score: 0.212

-> 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: 183, 22
KNN fn, tp: 3, 6
KNN f1 score: 0.324
KNN cohens kappa score: 0.278


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 23
LR fn, tp: 2, 7
LR f1 score: 0.359
LR cohens kappa score: 0.315
LR average precision score: 0.545

-> 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: 6, 3
KNN f1 score: 0.194
KNN cohens kappa score: 0.142


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 170, 35
LR fn, tp: 4, 5
LR f1 score: 0.204
LR cohens kappa score: 0.145
LR average precision score: 0.286

-> 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: 3, 6
KNN f1 score: 0.316
KNN cohens kappa score: 0.269


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 173, 32
LR fn, tp: 2, 7
LR f1 score: 0.292
LR cohens kappa score: 0.240
LR average precision score: 0.437

-> test with 'GB'
GB tn, fp: 200, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.326

-> test with 'KNN'
KNN tn, fp: 190, 15
KNN fn, tp: 3, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.362


------ Step 4/5: 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: 0.562

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

-> test with 'KNN'
KNN tn, fp: 184, 19
KNN fn, tp: 3, 4
KNN f1 score: 0.267
KNN cohens kappa score: 0.227


====== 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: 180, 25
LR fn, tp: 4, 5
LR f1 score: 0.256
LR cohens kappa score: 0.205
LR average precision score: 0.189

-> 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: 190, 15
KNN fn, tp: 6, 3
KNN f1 score: 0.222
KNN cohens kappa score: 0.176


------ Step 5/5: 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: 0.463

-> 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: 182, 23
KNN fn, tp: 4, 5
KNN f1 score: 0.270
KNN cohens kappa score: 0.221


------ Step 5/5: 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: 0, 9
LR f1 score: 0.419
LR cohens kappa score: 0.377
LR average precision score: 0.434

-> 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: 2, 7
KNN f1 score: 0.438
KNN cohens kappa score: 0.401


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 181, 24
LR fn, tp: 6, 3
LR f1 score: 0.167
LR cohens kappa score: 0.111
LR average precision score: 0.185

-> 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: 194, 11
KNN fn, tp: 6, 3
KNN f1 score: 0.261
KNN cohens kappa score: 0.221


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 165, 38
LR fn, tp: 2, 5
LR f1 score: 0.200
LR cohens kappa score: 0.151
LR average precision score: 0.389

-> 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: 183, 20
KNN fn, tp: 4, 3
KNN f1 score: 0.200
KNN cohens kappa score: 0.157

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 188, 48
LR fn, tp: 6, 9
LR f1 score: 0.474
LR cohens kappa score: 0.438
LR average precision score: 0.739


average:
LR tn, fp: 175.0, 29.6
LR fn, tp: 2.56, 6.04
LR f1 score: 0.276
LR cohens kappa score: 0.226
LR average precision score: 0.356


minimum:
LR tn, fp: 157, 17
LR fn, tp: 0, 3
LR f1 score: 0.154
LR cohens kappa score: 0.095
LR average precision score: 0.091


-----[ GB ]-----
maximum:
GB tn, fp: 205, 12
GB fn, tp: 9, 4
GB f1 score: 0.364
GB cohens kappa score: 0.348


average:
GB tn, fp: 201.4, 3.2
GB fn, tp: 7.64, 0.96
GB f1 score: 0.132
GB cohens kappa score: 0.113


minimum:
GB tn, fp: 193, 0
GB fn, tp: 5, 0
GB f1 score: 0.000
GB cohens kappa score: -0.038


-----[ KNN ]-----
maximum:
KNN tn, fp: 198, 26
KNN fn, tp: 6, 7
KNN f1 score: 0.476
KNN cohens kappa score: 0.450


average:
KNN tn, fp: 185.88, 18.72
KNN fn, tp: 3.92, 4.68
KNN f1 score: 0.294
KNN cohens kappa score: 0.250


minimum:
KNN tn, fp: 179, 7
KNN fn, tp: 2, 3
KNN f1 score: 0.194
KNN cohens kappa score: 0.142