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: 0.093

-> test with 'RF'
RF tn, fp: 196, 9
RF fn, tp: 7, 2
RF f1 score: 0.200
RF cohens kappa score: 0.161

-> test with 'GB'
GB tn, fp: 197, 8
GB fn, tp: 8, 1
GB f1 score: 0.111
GB cohens kappa score: 0.072

-> 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: 0.363

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

-> 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: 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: 178, 27
LR fn, tp: 4, 5
LR f1 score: 0.244
LR cohens kappa score: 0.191
LR average precision score: 0.307

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

-> 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: 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: 183, 22
LR fn, tp: 0, 9
LR f1 score: 0.450
LR cohens kappa score: 0.412
LR average precision score: 0.684

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

-> 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: 173, 30
LR fn, tp: 3, 4
LR f1 score: 0.195
LR cohens kappa score: 0.148
LR average precision score: 0.205

-> test with 'RF'
RF tn, fp: 198, 5
RF fn, tp: 6, 1
RF f1 score: 0.154
RF cohens kappa score: 0.127

-> 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: 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: 170, 35
LR fn, tp: 2, 7
LR f1 score: 0.275
LR cohens kappa score: 0.221
LR average precision score: 0.395

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

-> test with 'GB'
GB tn, fp: 198, 7
GB fn, tp: 7, 2
GB f1 score: 0.222
GB cohens kappa score: 0.188

-> 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: 0.399

-> test with 'RF'
RF tn, fp: 202, 3
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.021

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.021

-> 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: 0.246

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

-> 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: 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: 184, 21
LR fn, tp: 4, 5
LR f1 score: 0.286
LR cohens kappa score: 0.238
LR average precision score: 0.315

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

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


------ 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: 0.287

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

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

-> 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: 196, 9
KNN fn, tp: 4, 5
KNN f1 score: 0.435
KNN cohens kappa score: 0.404


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 169, 36
LR fn, tp: 2, 7
LR f1 score: 0.269
LR cohens kappa score: 0.215
LR average precision score: 0.243

-> test with 'RF'
RF tn, fp: 194, 11
RF fn, tp: 6, 3
RF f1 score: 0.261
RF cohens kappa score: 0.221

-> 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: 175, 30
LR fn, tp: 3, 6
LR f1 score: 0.267
LR cohens kappa score: 0.214
LR average precision score: 0.498

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

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

-> 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: 0.291

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

-> 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: 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: 173, 32
LR fn, tp: 1, 8
LR f1 score: 0.327
LR cohens kappa score: 0.277
LR average precision score: 0.218

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

-> 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: 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: 183, 22
LR fn, tp: 2, 7
LR f1 score: 0.368
LR cohens kappa score: 0.325
LR average precision score: 0.545

-> test with 'RF'
RF tn, fp: 202, 3
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.021

-> 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: 187, 18
KNN fn, tp: 6, 3
KNN f1 score: 0.200
KNN cohens kappa score: 0.150


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 171, 34
LR fn, tp: 4, 5
LR f1 score: 0.208
LR cohens kappa score: 0.150
LR average precision score: 0.250

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

-> 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: 171, 34
LR fn, tp: 1, 8
LR f1 score: 0.314
LR cohens kappa score: 0.263
LR average precision score: 0.431

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

-> test with 'GB'
GB tn, fp: 199, 6
GB fn, tp: 5, 4
GB f1 score: 0.421
GB cohens kappa score: 0.394

-> 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: 0.566

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

-> 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: 185, 18
KNN fn, tp: 3, 4
KNN f1 score: 0.276
KNN cohens kappa score: 0.237


====== 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: 184, 21
LR fn, tp: 4, 5
LR f1 score: 0.286
LR cohens kappa score: 0.238
LR average precision score: 0.197

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

-> 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: 192, 13
KNN fn, tp: 6, 3
KNN f1 score: 0.240
KNN cohens kappa score: 0.197


------ 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: 0.434

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

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


------ 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: 0.439

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

-> 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: 0.187

-> test with 'RF'
RF tn, fp: 202, 3
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.021

-> 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: 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: 0.454

-> test with 'RF'
RF tn, fp: 196, 7
RF fn, tp: 7, 0
RF f1 score: 0.000
RF cohens kappa score: -0.034

-> 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: 182, 21
KNN fn, tp: 4, 3
KNN f1 score: 0.194
KNN cohens kappa score: 0.150

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 188, 48
LR fn, tp: 6, 9
LR f1 score: 0.450
LR cohens kappa score: 0.412
LR average precision score: 0.739


average:
LR tn, fp: 174.88, 29.72
LR fn, tp: 2.48, 6.12
LR f1 score: 0.278
LR cohens kappa score: 0.228
LR average precision score: 0.364


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: 0.093


-----[ RF ]-----
maximum:
RF tn, fp: 205, 11
RF fn, tp: 9, 3
RF f1 score: 0.261
RF cohens kappa score: 0.221


average:
RF tn, fp: 201.04, 3.56
RF fn, tp: 8.0, 0.6
RF f1 score: 0.080
RF cohens kappa score: 0.059


minimum:
RF tn, fp: 194, 0
RF fn, tp: 6, 0
RF f1 score: 0.000
RF cohens kappa score: -0.035


-----[ GB ]-----
maximum:
GB tn, fp: 205, 12
GB fn, tp: 9, 4
GB f1 score: 0.421
GB cohens kappa score: 0.394


average:
GB tn, fp: 201.2, 3.4
GB fn, tp: 7.6, 1.0
GB f1 score: 0.133
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: -0.035


-----[ KNN ]-----
maximum:
KNN tn, fp: 196, 26
KNN fn, tp: 6, 7
KNN f1 score: 0.438
KNN cohens kappa score: 0.404


average:
KNN tn, fp: 186.0, 18.6
KNN fn, tp: 3.92, 4.68
KNN f1 score: 0.294
KNN cohens kappa score: 0.251


minimum:
KNN tn, fp: 179, 9
KNN fn, tp: 2, 3
KNN f1 score: 0.194
KNN cohens kappa score: 0.150