ConvGeNBenchmarkResults/data_result/Repeater/folding_flare-F.log

///////////////////////////////////////////
// Running Repeater 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: 165, 40
LR fn, tp: 5, 4
LR f1 score: 0.151
LR cohens kappa score: 0.087
LR average precision score: 0.070

-> test with 'GB'
GB tn, fp: 177, 28
GB fn, tp: 6, 3
GB f1 score: 0.150
GB cohens kappa score: 0.091

-> test with 'KNN'
KNN tn, fp: 168, 37
KNN fn, tp: 4, 5
KNN f1 score: 0.196
KNN cohens kappa score: 0.136


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 153, 52
LR fn, tp: 0, 9
LR f1 score: 0.257
LR cohens kappa score: 0.198
LR average precision score: 0.371

-> test with 'GB'
GB tn, fp: 175, 30
GB fn, tp: 3, 6
GB f1 score: 0.267
GB cohens kappa score: 0.214

-> test with 'KNN'
KNN tn, fp: 161, 44
KNN fn, tp: 1, 8
KNN f1 score: 0.262
KNN cohens kappa score: 0.205


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 167, 38
LR fn, tp: 2, 7
LR f1 score: 0.259
LR cohens kappa score: 0.203
LR average precision score: 0.397

-> test with 'GB'
GB tn, fp: 184, 21
GB fn, tp: 3, 6
GB f1 score: 0.333
GB cohens kappa score: 0.288

-> test with 'KNN'
KNN tn, fp: 178, 27
KNN fn, tp: 5, 4
KNN f1 score: 0.200
KNN cohens kappa score: 0.144


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 26
LR fn, tp: 0, 9
LR f1 score: 0.409
LR cohens kappa score: 0.367
LR average precision score: 0.768

-> test with 'GB'
GB tn, fp: 181, 24
GB fn, tp: 2, 7
GB f1 score: 0.350
GB cohens kappa score: 0.305

-> test with 'KNN'
KNN tn, fp: 175, 30
KNN fn, tp: 2, 7
KNN f1 score: 0.304
KNN cohens kappa score: 0.254


------ Step 1/5: 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: 2, 5
LR f1 score: 0.213
LR cohens kappa score: 0.165
LR average precision score: 0.222

-> test with 'GB'
GB tn, fp: 175, 28
GB fn, tp: 1, 6
GB f1 score: 0.293
GB cohens kappa score: 0.251

-> test with 'KNN'
KNN tn, fp: 170, 33
KNN fn, tp: 2, 5
KNN f1 score: 0.222
KNN cohens kappa score: 0.176


====== 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: 166, 39
LR fn, tp: 1, 8
LR f1 score: 0.286
LR cohens kappa score: 0.231
LR average precision score: 0.401

-> test with 'GB'
GB tn, fp: 179, 26
GB fn, tp: 2, 7
GB f1 score: 0.333
GB cohens kappa score: 0.286

-> test with 'KNN'
KNN tn, fp: 169, 36
KNN fn, tp: 2, 7
KNN f1 score: 0.269
KNN cohens kappa score: 0.215


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 166, 39
LR fn, tp: 2, 7
LR f1 score: 0.255
LR cohens kappa score: 0.198
LR average precision score: 0.368

-> test with 'GB'
GB tn, fp: 175, 30
GB fn, tp: 1, 8
GB f1 score: 0.340
GB cohens kappa score: 0.292

-> test with 'KNN'
KNN tn, fp: 169, 36
KNN fn, tp: 2, 7
KNN f1 score: 0.269
KNN cohens kappa score: 0.215


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 165, 40
LR fn, tp: 1, 8
LR f1 score: 0.281
LR cohens kappa score: 0.226
LR average precision score: 0.322

-> test with 'GB'
GB tn, fp: 174, 31
GB fn, tp: 4, 5
GB f1 score: 0.222
GB cohens kappa score: 0.166

-> test with 'KNN'
KNN tn, fp: 168, 37
KNN fn, tp: 3, 6
KNN f1 score: 0.231
KNN cohens kappa score: 0.173


------ Step 2/5: Slice 4/5 -------
-> 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: 0.310

-> test with 'GB'
GB tn, fp: 187, 18
GB fn, tp: 4, 5
GB f1 score: 0.312
GB cohens kappa score: 0.268

-> 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 2/5: 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: 0, 7
LR f1 score: 0.255
LR cohens kappa score: 0.208
LR average precision score: 0.416

-> test with 'GB'
GB tn, fp: 173, 30
GB fn, tp: 3, 4
GB f1 score: 0.195
GB cohens kappa score: 0.148

-> test with 'KNN'
KNN tn, fp: 164, 39
KNN fn, tp: 0, 7
KNN f1 score: 0.264
KNN cohens kappa score: 0.219


====== 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: 180, 25
LR fn, tp: 0, 9
LR f1 score: 0.419
LR cohens kappa score: 0.377
LR average precision score: 0.779

-> test with 'GB'
GB tn, fp: 191, 14
GB fn, tp: 3, 6
GB f1 score: 0.414
GB cohens kappa score: 0.378

-> 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 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 159, 46
LR fn, tp: 2, 7
LR f1 score: 0.226
LR cohens kappa score: 0.166
LR average precision score: 0.243

-> test with 'GB'
GB tn, fp: 173, 32
GB fn, tp: 3, 6
GB f1 score: 0.255
GB cohens kappa score: 0.201

-> test with 'KNN'
KNN tn, fp: 156, 49
KNN fn, tp: 2, 7
KNN f1 score: 0.215
KNN cohens kappa score: 0.154


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 162, 43
LR fn, tp: 2, 7
LR f1 score: 0.237
LR cohens kappa score: 0.179
LR average precision score: 0.353

-> test with 'GB'
GB tn, fp: 180, 25
GB fn, tp: 2, 7
GB f1 score: 0.341
GB cohens kappa score: 0.295

-> test with 'KNN'
KNN tn, fp: 157, 48
KNN fn, tp: 1, 8
KNN f1 score: 0.246
KNN cohens kappa score: 0.187


------ Step 3/5: Slice 4/5 -------
-> 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: 0.294

-> test with 'GB'
GB tn, fp: 184, 21
GB fn, tp: 3, 6
GB f1 score: 0.333
GB cohens kappa score: 0.288

-> test with 'KNN'
KNN tn, fp: 173, 32
KNN fn, tp: 3, 6
KNN f1 score: 0.255
KNN cohens kappa score: 0.201


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 159, 44
LR fn, tp: 1, 6
LR f1 score: 0.211
LR cohens kappa score: 0.161
LR average precision score: 0.224

-> test with 'GB'
GB tn, fp: 169, 34
GB fn, tp: 4, 3
GB f1 score: 0.136
GB cohens kappa score: 0.085

-> test with 'KNN'
KNN tn, fp: 166, 37
KNN fn, tp: 4, 3
KNN f1 score: 0.128
KNN cohens kappa score: 0.075


====== 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: 163, 42
LR fn, tp: 1, 8
LR f1 score: 0.271
LR cohens kappa score: 0.215
LR average precision score: 0.186

-> test with 'GB'
GB tn, fp: 176, 29
GB fn, tp: 3, 6
GB f1 score: 0.273
GB cohens kappa score: 0.221

-> test with 'KNN'
KNN tn, fp: 165, 40
KNN fn, tp: 2, 7
KNN f1 score: 0.250
KNN cohens kappa score: 0.193


------ Step 4/5: 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: 2, 7
LR f1 score: 0.286
LR cohens kappa score: 0.233
LR average precision score: 0.537

-> test with 'GB'
GB tn, fp: 188, 17
GB fn, tp: 4, 5
GB f1 score: 0.323
GB cohens kappa score: 0.280

-> test with 'KNN'
KNN tn, fp: 180, 25
KNN fn, tp: 2, 7
KNN f1 score: 0.341
KNN cohens kappa score: 0.295


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 165, 40
LR fn, tp: 3, 6
LR f1 score: 0.218
LR cohens kappa score: 0.159
LR average precision score: 0.244

-> test with 'GB'
GB tn, fp: 168, 37
GB fn, tp: 4, 5
GB f1 score: 0.196
GB cohens kappa score: 0.136

-> test with 'KNN'
KNN tn, fp: 159, 46
KNN fn, tp: 4, 5
KNN f1 score: 0.167
KNN cohens kappa score: 0.102


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 168, 37
LR fn, tp: 0, 9
LR f1 score: 0.327
LR cohens kappa score: 0.276
LR average precision score: 0.387

-> test with 'GB'
GB tn, fp: 177, 28
GB fn, tp: 3, 6
GB f1 score: 0.279
GB cohens kappa score: 0.228

-> test with 'KNN'
KNN tn, fp: 173, 32
KNN fn, tp: 2, 7
KNN f1 score: 0.292
KNN cohens kappa score: 0.240


------ Step 4/5: 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: 0, 7
LR f1 score: 0.269
LR cohens kappa score: 0.224
LR average precision score: 0.592

-> test with 'GB'
GB tn, fp: 179, 24
GB fn, tp: 2, 5
GB f1 score: 0.278
GB cohens kappa score: 0.237

-> test with 'KNN'
KNN tn, fp: 164, 39
KNN fn, tp: 2, 5
KNN f1 score: 0.196
KNN cohens kappa score: 0.147


====== 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: 168, 37
LR fn, tp: 2, 7
LR f1 score: 0.264
LR cohens kappa score: 0.209
LR average precision score: 0.200

-> test with 'GB'
GB tn, fp: 180, 25
GB fn, tp: 3, 6
GB f1 score: 0.300
GB cohens kappa score: 0.251

-> 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 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 158, 47
LR fn, tp: 1, 8
LR f1 score: 0.250
LR cohens kappa score: 0.192
LR average precision score: 0.418

-> test with 'GB'
GB tn, fp: 180, 25
GB fn, tp: 3, 6
GB f1 score: 0.300
GB cohens kappa score: 0.251

-> test with 'KNN'
KNN tn, fp: 165, 40
KNN fn, tp: 2, 7
KNN f1 score: 0.250
KNN cohens kappa score: 0.193


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 164, 41
LR fn, tp: 0, 9
LR f1 score: 0.305
LR cohens kappa score: 0.252
LR average precision score: 0.500

-> test with 'GB'
GB tn, fp: 181, 24
GB fn, tp: 3, 6
GB f1 score: 0.308
GB cohens kappa score: 0.260

-> test with 'KNN'
KNN tn, fp: 166, 39
KNN fn, tp: 1, 8
KNN f1 score: 0.286
KNN cohens kappa score: 0.231


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 175, 30
LR fn, tp: 4, 5
LR f1 score: 0.227
LR cohens kappa score: 0.172
LR average precision score: 0.187

-> test with 'GB'
GB tn, fp: 183, 22
GB fn, tp: 3, 6
GB f1 score: 0.324
GB cohens kappa score: 0.278

-> 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 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 160, 43
LR fn, tp: 2, 5
LR f1 score: 0.182
LR cohens kappa score: 0.131
LR average precision score: 0.430

-> test with 'GB'
GB tn, fp: 171, 32
GB fn, tp: 2, 5
GB f1 score: 0.227
GB cohens kappa score: 0.181

-> test with 'KNN'
KNN tn, fp: 159, 44
KNN fn, tp: 3, 4
KNN f1 score: 0.145
KNN cohens kappa score: 0.093

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 180, 52
LR fn, tp: 5, 9
LR f1 score: 0.419
LR cohens kappa score: 0.377
LR average precision score: 0.779


average:
LR tn, fp: 166.32, 38.28
LR fn, tp: 1.56, 7.04
LR f1 score: 0.263
LR cohens kappa score: 0.210
LR average precision score: 0.369


minimum:
LR tn, fp: 153, 25
LR fn, tp: 0, 4
LR f1 score: 0.151
LR cohens kappa score: 0.087
LR average precision score: 0.070


-----[ GB ]-----
maximum:
GB tn, fp: 191, 37
GB fn, tp: 6, 8
GB f1 score: 0.414
GB cohens kappa score: 0.378


average:
GB tn, fp: 178.4, 26.2
GB fn, tp: 2.96, 5.64
GB f1 score: 0.283
GB cohens kappa score: 0.235


minimum:
GB tn, fp: 168, 14
GB fn, tp: 1, 3
GB f1 score: 0.136
GB cohens kappa score: 0.085


-----[ KNN ]-----
maximum:
KNN tn, fp: 186, 49
KNN fn, tp: 5, 8
KNN f1 score: 0.353
KNN cohens kappa score: 0.310


average:
KNN tn, fp: 168.68, 35.92
KNN fn, tp: 2.48, 6.12
KNN f1 score: 0.245
KNN cohens kappa score: 0.191


minimum:
KNN tn, fp: 156, 19
KNN fn, tp: 0, 3
KNN f1 score: 0.128
KNN cohens kappa score: 0.075