ConvGeNBenchmarkResults/data_result/Repeater/folding_yeast6.log

///////////////////////////////////////////
// Running Repeater on folding_yeast6
///////////////////////////////////////////

Load 'data_input/folding_yeast6'
from pickle file
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 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 252, 38
LR fn, tp: 0, 7
LR f1 score: 0.269
LR cohens kappa score: 0.238
LR average precision score: 0.706

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 4, 3
RF f1 score: 0.500
RF cohens kappa score: 0.490

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 3, 4
GB f1 score: 0.444
GB cohens kappa score: 0.428

-> test with 'KNN'
KNN tn, fp: 277, 13
KNN fn, tp: 1, 6
KNN f1 score: 0.462
KNN cohens kappa score: 0.442


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 255, 35
LR fn, tp: 2, 5
LR f1 score: 0.213
LR cohens kappa score: 0.180
LR average precision score: 0.426

-> test with 'RF'
RF tn, fp: 285, 5
RF fn, tp: 3, 4
RF f1 score: 0.500
RF cohens kappa score: 0.486

-> test with 'GB'
GB tn, fp: 284, 6
GB fn, tp: 3, 4
GB f1 score: 0.471
GB cohens kappa score: 0.455

-> test with 'KNN'
KNN tn, fp: 270, 20
KNN fn, tp: 2, 5
KNN f1 score: 0.312
KNN cohens kappa score: 0.286


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 245, 45
LR fn, tp: 1, 6
LR f1 score: 0.207
LR cohens kappa score: 0.173
LR average precision score: 0.272

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 3, 4
RF f1 score: 0.667
RF cohens kappa score: 0.660

-> test with 'GB'
GB tn, fp: 284, 6
GB fn, tp: 1, 6
GB f1 score: 0.632
GB cohens kappa score: 0.620

-> test with 'KNN'
KNN tn, fp: 279, 11
KNN fn, tp: 1, 6
KNN f1 score: 0.500
KNN cohens kappa score: 0.483


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 263, 27
LR fn, tp: 1, 6
LR f1 score: 0.300
LR cohens kappa score: 0.272
LR average precision score: 0.623

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 5, 2
RF f1 score: 0.364
RF cohens kappa score: 0.353

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 4, 3
GB f1 score: 0.353
GB cohens kappa score: 0.334

-> test with 'KNN'
KNN tn, fp: 276, 14
KNN fn, tp: 1, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.424


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 235, 54
LR fn, tp: 0, 7
LR f1 score: 0.206
LR cohens kappa score: 0.171
LR average precision score: 0.491

-> test with 'RF'
RF tn, fp: 287, 2
RF fn, tp: 2, 5
RF f1 score: 0.714
RF cohens kappa score: 0.707

-> test with 'GB'
GB tn, fp: 281, 8
GB fn, tp: 1, 6
GB f1 score: 0.571
GB cohens kappa score: 0.557

-> test with 'KNN'
KNN tn, fp: 268, 21
KNN fn, tp: 0, 7
KNN f1 score: 0.400
KNN cohens kappa score: 0.376


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 244, 46
LR fn, tp: 0, 7
LR f1 score: 0.233
LR cohens kappa score: 0.200
LR average precision score: 0.657

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 4, 3
RF f1 score: 0.500
RF cohens kappa score: 0.490

-> test with 'GB'
GB tn, fp: 284, 6
GB fn, tp: 3, 4
GB f1 score: 0.471
GB cohens kappa score: 0.455

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 1, 6
KNN f1 score: 0.429
KNN cohens kappa score: 0.408


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 236, 54
LR fn, tp: 0, 7
LR f1 score: 0.206
LR cohens kappa score: 0.171
LR average precision score: 0.197

-> test with 'RF'
RF tn, fp: 287, 3
RF fn, tp: 4, 3
RF f1 score: 0.462
RF cohens kappa score: 0.450

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 3, 4
GB f1 score: 0.444
GB cohens kappa score: 0.428

-> test with 'KNN'
KNN tn, fp: 274, 16
KNN fn, tp: 0, 7
KNN f1 score: 0.467
KNN cohens kappa score: 0.447


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 251, 39
LR fn, tp: 1, 6
LR f1 score: 0.231
LR cohens kappa score: 0.198
LR average precision score: 0.525

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 3, 4
RF f1 score: 0.615
RF cohens kappa score: 0.607

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 3, 4
GB f1 score: 0.444
GB cohens kappa score: 0.428

-> test with 'KNN'
KNN tn, fp: 272, 18
KNN fn, tp: 1, 6
KNN f1 score: 0.387
KNN cohens kappa score: 0.364


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 248, 42
LR fn, tp: 2, 5
LR f1 score: 0.185
LR cohens kappa score: 0.150
LR average precision score: 0.485

-> test with 'RF'
RF tn, fp: 286, 4
RF fn, tp: 5, 2
RF f1 score: 0.308
RF cohens kappa score: 0.292

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 4, 3
GB f1 score: 0.353
GB cohens kappa score: 0.334

-> test with 'KNN'
KNN tn, fp: 271, 19
KNN fn, tp: 2, 5
KNN f1 score: 0.323
KNN cohens kappa score: 0.297


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 259, 30
LR fn, tp: 1, 6
LR f1 score: 0.279
LR cohens kappa score: 0.249
LR average precision score: 0.503

-> test with 'RF'
RF tn, fp: 289, 0
RF fn, tp: 6, 1
RF f1 score: 0.250
RF cohens kappa score: 0.246

-> test with 'GB'
GB tn, fp: 287, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.353

-> test with 'KNN'
KNN tn, fp: 275, 14
KNN fn, tp: 3, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.296


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 251, 39
LR fn, tp: 1, 6
LR f1 score: 0.231
LR cohens kappa score: 0.198
LR average precision score: 0.603

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 2, 5
RF f1 score: 0.714
RF cohens kappa score: 0.707

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 2, 5
GB f1 score: 0.526
GB cohens kappa score: 0.512

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 2, 5
KNN f1 score: 0.370
KNN cohens kappa score: 0.348


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 238, 52
LR fn, tp: 0, 7
LR f1 score: 0.212
LR cohens kappa score: 0.177
LR average precision score: 0.656

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 3, 4
RF f1 score: 0.615
RF cohens kappa score: 0.607

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 2, 5
GB f1 score: 0.588
GB cohens kappa score: 0.576

-> test with 'KNN'
KNN tn, fp: 269, 21
KNN fn, tp: 0, 7
KNN f1 score: 0.400
KNN cohens kappa score: 0.376


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 255, 35
LR fn, tp: 2, 5
LR f1 score: 0.213
LR cohens kappa score: 0.180
LR average precision score: 0.373

-> test with 'RF'
RF tn, fp: 287, 3
RF fn, tp: 5, 2
RF f1 score: 0.333
RF cohens kappa score: 0.320

-> test with 'GB'
GB tn, fp: 284, 6
GB fn, tp: 3, 4
GB f1 score: 0.471
GB cohens kappa score: 0.455

-> test with 'KNN'
KNN tn, fp: 279, 11
KNN fn, tp: 2, 5
KNN f1 score: 0.435
KNN cohens kappa score: 0.416


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 248, 42
LR fn, tp: 0, 7
LR f1 score: 0.250
LR cohens kappa score: 0.218
LR average precision score: 0.378

-> test with 'RF'
RF tn, fp: 287, 3
RF fn, tp: 3, 4
RF f1 score: 0.571
RF cohens kappa score: 0.561

-> test with 'GB'
GB tn, fp: 277, 13
GB fn, tp: 3, 4
GB f1 score: 0.333
GB cohens kappa score: 0.310

-> test with 'KNN'
KNN tn, fp: 266, 24
KNN fn, tp: 1, 6
KNN f1 score: 0.324
KNN cohens kappa score: 0.297


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 23
LR fn, tp: 1, 6
LR f1 score: 0.333
LR cohens kappa score: 0.307
LR average precision score: 0.355

-> test with 'RF'
RF tn, fp: 288, 1
RF fn, tp: 6, 1
RF f1 score: 0.222
RF cohens kappa score: 0.214

-> test with 'GB'
GB tn, fp: 287, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.353

-> test with 'KNN'
KNN tn, fp: 281, 8
KNN fn, tp: 1, 6
KNN f1 score: 0.571
KNN cohens kappa score: 0.557


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 262, 28
LR fn, tp: 1, 6
LR f1 score: 0.293
LR cohens kappa score: 0.264
LR average precision score: 0.552

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 3, 4
RF f1 score: 0.727
RF cohens kappa score: 0.723

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 3, 4
GB f1 score: 0.571
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 277, 13
KNN fn, tp: 1, 6
KNN f1 score: 0.462
KNN cohens kappa score: 0.442


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 249, 41
LR fn, tp: 0, 7
LR f1 score: 0.255
LR cohens kappa score: 0.223
LR average precision score: 0.272

-> test with 'RF'
RF tn, fp: 286, 4
RF fn, tp: 5, 2
RF f1 score: 0.308
RF cohens kappa score: 0.292

-> test with 'GB'
GB tn, fp: 284, 6
GB fn, tp: 3, 4
GB f1 score: 0.471
GB cohens kappa score: 0.455

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 1, 6
KNN f1 score: 0.429
KNN cohens kappa score: 0.408


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 50
LR fn, tp: 1, 6
LR f1 score: 0.190
LR cohens kappa score: 0.155
LR average precision score: 0.428

-> test with 'RF'
RF tn, fp: 287, 3
RF fn, tp: 2, 5
RF f1 score: 0.667
RF cohens kappa score: 0.658

-> test with 'GB'
GB tn, fp: 284, 6
GB fn, tp: 1, 6
GB f1 score: 0.632
GB cohens kappa score: 0.620

-> test with 'KNN'
KNN tn, fp: 265, 25
KNN fn, tp: 0, 7
KNN f1 score: 0.359
KNN cohens kappa score: 0.333


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 258, 32
LR fn, tp: 1, 6
LR f1 score: 0.267
LR cohens kappa score: 0.236
LR average precision score: 0.594

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 4, 3
RF f1 score: 0.545
RF cohens kappa score: 0.538

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 276, 14
KNN fn, tp: 1, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.424


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 258, 31
LR fn, tp: 1, 6
LR f1 score: 0.273
LR cohens kappa score: 0.243
LR average precision score: 0.625

-> test with 'RF'
RF tn, fp: 287, 2
RF fn, tp: 4, 3
RF f1 score: 0.500
RF cohens kappa score: 0.490

-> test with 'GB'
GB tn, fp: 284, 5
GB fn, tp: 4, 3
GB f1 score: 0.400
GB cohens kappa score: 0.384

-> test with 'KNN'
KNN tn, fp: 278, 11
KNN fn, tp: 2, 5
KNN f1 score: 0.435
KNN cohens kappa score: 0.416


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 241, 49
LR fn, tp: 0, 7
LR f1 score: 0.222
LR cohens kappa score: 0.188
LR average precision score: 0.516

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 2, 5
RF f1 score: 0.714
RF cohens kappa score: 0.707

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 2, 5
GB f1 score: 0.526
GB cohens kappa score: 0.512

-> test with 'KNN'
KNN tn, fp: 269, 21
KNN fn, tp: 1, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.328


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 257, 33
LR fn, tp: 2, 5
LR f1 score: 0.222
LR cohens kappa score: 0.190
LR average precision score: 0.225

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 4, 3
RF f1 score: 0.545
RF cohens kappa score: 0.538

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 3, 4
GB f1 score: 0.571
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 3, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.283


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 249, 41
LR fn, tp: 0, 7
LR f1 score: 0.255
LR cohens kappa score: 0.223
LR average precision score: 0.711

-> test with 'RF'
RF tn, fp: 285, 5
RF fn, tp: 1, 6
RF f1 score: 0.667
RF cohens kappa score: 0.657

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 0, 7
GB f1 score: 0.737
GB cohens kappa score: 0.729

-> test with 'KNN'
KNN tn, fp: 273, 17
KNN fn, tp: 0, 7
KNN f1 score: 0.452
KNN cohens kappa score: 0.431


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 250, 40
LR fn, tp: 0, 7
LR f1 score: 0.259
LR cohens kappa score: 0.228
LR average precision score: 0.267

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 5, 2
GB f1 score: 0.250
GB cohens kappa score: 0.230

-> test with 'KNN'
KNN tn, fp: 278, 12
KNN fn, tp: 1, 6
KNN f1 score: 0.480
KNN cohens kappa score: 0.462


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 260, 29
LR fn, tp: 2, 5
LR f1 score: 0.244
LR cohens kappa score: 0.213
LR average precision score: 0.403

-> test with 'RF'
RF tn, fp: 287, 2
RF fn, tp: 4, 3
RF f1 score: 0.500
RF cohens kappa score: 0.490

-> test with 'GB'
GB tn, fp: 285, 4
GB fn, tp: 4, 3
GB f1 score: 0.429
GB cohens kappa score: 0.415

-> test with 'KNN'
KNN tn, fp: 273, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.334

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 266, 54
LR fn, tp: 2, 7
LR f1 score: 0.333
LR cohens kappa score: 0.307
LR average precision score: 0.711


average:
LR tn, fp: 250.8, 39.0
LR fn, tp: 0.8, 6.2
LR f1 score: 0.242
LR cohens kappa score: 0.210
LR average precision score: 0.474


minimum:
LR tn, fp: 235, 23
LR fn, tp: 0, 5
LR f1 score: 0.185
LR cohens kappa score: 0.150
LR average precision score: 0.197


-----[ RF ]-----
maximum:
RF tn, fp: 290, 5
RF fn, tp: 6, 6
RF f1 score: 0.727
RF cohens kappa score: 0.723


average:
RF tn, fp: 287.6, 2.2
RF fn, tp: 3.68, 3.32
RF f1 score: 0.516
RF cohens kappa score: 0.507


minimum:
RF tn, fp: 285, 0
RF fn, tp: 1, 1
RF f1 score: 0.222
RF cohens kappa score: 0.214


-----[ GB ]-----
maximum:
GB tn, fp: 287, 13
GB fn, tp: 5, 7
GB f1 score: 0.737
GB cohens kappa score: 0.729


average:
GB tn, fp: 284.0, 5.8
GB fn, tp: 2.96, 4.04
GB f1 score: 0.475
GB cohens kappa score: 0.461


minimum:
GB tn, fp: 277, 2
GB fn, tp: 0, 2
GB f1 score: 0.250
GB cohens kappa score: 0.230


-----[ KNN ]-----
maximum:
KNN tn, fp: 281, 25
KNN fn, tp: 3, 7
KNN f1 score: 0.571
KNN cohens kappa score: 0.557


average:
KNN tn, fp: 273.84, 15.96
KNN fn, tp: 1.2, 5.8
KNN f1 score: 0.409
KNN cohens kappa score: 0.387


minimum:
KNN tn, fp: 265, 8
KNN fn, tp: 0, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.283