ConvGeNBenchmarkResults/data_result/ProWRAS/folding_yeast6.log

///////////////////////////////////////////
// Running ProWRAS 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: 262, 28
LR fn, tp: 0, 7
LR f1 score: 0.333
LR cohens kappa score: 0.306
LR average precision score: 0.688

-> 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: 287, 3
GB fn, tp: 3, 4
GB f1 score: 0.571
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 281, 9
KNN fn, tp: 2, 5
KNN f1 score: 0.476
KNN cohens kappa score: 0.459


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 23
LR fn, tp: 2, 5
LR f1 score: 0.286
LR cohens kappa score: 0.258
LR average precision score: 0.432

-> test with 'RF'
RF tn, fp: 286, 4
RF fn, tp: 4, 3
RF f1 score: 0.429
RF cohens kappa score: 0.415

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 4, 3
GB f1 score: 0.400
GB cohens kappa score: 0.385

-> test with 'KNN'
KNN tn, fp: 279, 11
KNN fn, tp: 3, 4
KNN f1 score: 0.364
KNN cohens kappa score: 0.343


------ Step 1/5: Slice 3/5 -------
-> 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: 0.268

-> 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: 290, 0
GB fn, tp: 3, 4
GB f1 score: 0.727
GB cohens kappa score: 0.723

-> 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 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 20
LR fn, tp: 2, 5
LR f1 score: 0.312
LR cohens kappa score: 0.286
LR average precision score: 0.554

-> 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: 281, 9
KNN fn, tp: 2, 5
KNN f1 score: 0.476
KNN cohens kappa score: 0.459


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 49
LR fn, tp: 0, 7
LR f1 score: 0.222
LR cohens kappa score: 0.188
LR average precision score: 0.536

-> test with 'RF'
RF tn, fp: 288, 1
RF fn, tp: 1, 6
RF f1 score: 0.857
RF cohens kappa score: 0.854

-> test with 'GB'
GB tn, fp: 283, 6
GB fn, tp: 2, 5
GB f1 score: 0.556
GB cohens kappa score: 0.542

-> test with 'KNN'
KNN tn, fp: 278, 11
KNN fn, tp: 1, 6
KNN f1 score: 0.500
KNN cohens kappa score: 0.483


====== 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: 262, 28
LR fn, tp: 0, 7
LR f1 score: 0.333
LR cohens kappa score: 0.306
LR average precision score: 0.682

-> 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: 286, 4
GB fn, tp: 3, 4
GB f1 score: 0.533
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 277, 13
KNN fn, tp: 2, 5
KNN f1 score: 0.400
KNN cohens kappa score: 0.379


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 251, 39
LR fn, tp: 0, 7
LR f1 score: 0.264
LR cohens kappa score: 0.233
LR average precision score: 0.242

-> 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: 285, 5
GB fn, tp: 1, 6
GB f1 score: 0.667
GB cohens kappa score: 0.657

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 0, 7
KNN f1 score: 0.483
KNN cohens kappa score: 0.464


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 261, 29
LR fn, tp: 1, 6
LR f1 score: 0.286
LR cohens kappa score: 0.257
LR average precision score: 0.536

-> 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: 290, 0
GB fn, tp: 4, 3
GB f1 score: 0.600
GB cohens kappa score: 0.594

-> test with 'KNN'
KNN tn, fp: 281, 9
KNN fn, tp: 2, 5
KNN f1 score: 0.476
KNN cohens kappa score: 0.459


------ Step 2/5: Slice 4/5 -------
-> 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: 0.524

-> 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: 286, 4
GB fn, tp: 5, 2
GB f1 score: 0.308
GB cohens kappa score: 0.292

-> test with 'KNN'
KNN tn, fp: 276, 14
KNN fn, tp: 3, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.296


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 19
LR fn, tp: 1, 6
LR f1 score: 0.375
LR cohens kappa score: 0.351
LR average precision score: 0.541

-> test with 'RF'
RF tn, fp: 289, 0
RF fn, tp: 4, 3
RF f1 score: 0.600
RF cohens kappa score: 0.594

-> test with 'GB'
GB tn, fp: 289, 0
GB fn, tp: 4, 3
GB f1 score: 0.600
GB cohens kappa score: 0.594

-> test with 'KNN'
KNN tn, fp: 284, 5
KNN fn, tp: 3, 4
KNN f1 score: 0.500
KNN cohens kappa score: 0.486


====== 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: 266, 24
LR fn, tp: 1, 6
LR f1 score: 0.324
LR cohens kappa score: 0.297
LR average precision score: 0.640

-> 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: 289, 1
GB fn, tp: 3, 4
GB f1 score: 0.667
GB cohens kappa score: 0.660

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


------ Step 3/5: Slice 2/5 -------
-> 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: 0.753

-> 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: 287, 3
GB fn, tp: 3, 4
GB f1 score: 0.571
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 274, 16
KNN fn, tp: 1, 6
KNN f1 score: 0.414
KNN cohens kappa score: 0.392


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 24
LR fn, tp: 2, 5
LR f1 score: 0.278
LR cohens kappa score: 0.249
LR average precision score: 0.410

-> 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: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 282, 8
KNN fn, tp: 3, 4
KNN f1 score: 0.421
KNN cohens kappa score: 0.403


------ Step 3/5: Slice 4/5 -------
-> 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: 0.409

-> 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: 3, 4
GB f1 score: 0.500
GB cohens kappa score: 0.486

-> test with 'KNN'
KNN tn, fp: 276, 14
KNN fn, tp: 2, 5
KNN f1 score: 0.385
KNN cohens kappa score: 0.363


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 22
LR fn, tp: 1, 6
LR f1 score: 0.343
LR cohens kappa score: 0.317
LR average precision score: 0.424

-> test with 'RF'
RF tn, fp: 289, 0
RF fn, tp: 7, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

-> test with 'KNN'
KNN tn, fp: 285, 4
KNN fn, tp: 1, 6
KNN f1 score: 0.706
KNN cohens kappa score: 0.697


====== 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: 272, 18
LR fn, tp: 1, 6
LR f1 score: 0.387
LR cohens kappa score: 0.364
LR average precision score: 0.731

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 2, 5
RF f1 score: 0.833
RF cohens kappa score: 0.830

-> test with 'GB'
GB tn, fp: 290, 0
GB fn, tp: 2, 5
GB f1 score: 0.833
GB cohens kappa score: 0.830

-> 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: 259, 31
LR fn, tp: 1, 6
LR f1 score: 0.273
LR cohens kappa score: 0.243
LR average precision score: 0.233

-> 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: 285, 5
GB fn, tp: 4, 3
GB f1 score: 0.400
GB cohens kappa score: 0.385

-> test with 'KNN'
KNN tn, fp: 280, 10
KNN fn, tp: 2, 5
KNN f1 score: 0.455
KNN cohens kappa score: 0.436


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 250, 40
LR fn, tp: 1, 6
LR f1 score: 0.226
LR cohens kappa score: 0.193
LR average precision score: 0.482

-> test with 'RF'
RF tn, fp: 286, 4
RF fn, tp: 2, 5
RF f1 score: 0.625
RF cohens kappa score: 0.615

-> test with 'GB'
GB tn, fp: 284, 6
GB fn, tp: 2, 5
GB f1 score: 0.556
GB cohens kappa score: 0.542

-> test with 'KNN'
KNN tn, fp: 274, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.334


------ Step 4/5: Slice 4/5 -------
-> 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: 0.647

-> 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: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

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


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 22
LR fn, tp: 2, 5
LR f1 score: 0.294
LR cohens kappa score: 0.267
LR average precision score: 0.634

-> test with 'RF'
RF tn, fp: 286, 3
RF fn, tp: 4, 3
RF f1 score: 0.462
RF cohens kappa score: 0.450

-> 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: 281, 8
KNN fn, tp: 2, 5
KNN f1 score: 0.500
KNN cohens kappa score: 0.484


====== 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: 250, 40
LR fn, tp: 0, 7
LR f1 score: 0.259
LR cohens kappa score: 0.228
LR average precision score: 0.454

-> 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: 3, 4
GB f1 score: 0.471
GB cohens kappa score: 0.455

-> test with 'KNN'
KNN tn, fp: 274, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.334


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 23
LR fn, tp: 3, 4
LR f1 score: 0.235
LR cohens kappa score: 0.206
LR average precision score: 0.227

-> 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: 281, 9
KNN fn, tp: 3, 4
KNN f1 score: 0.400
KNN cohens kappa score: 0.381


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 259, 31
LR fn, tp: 0, 7
LR f1 score: 0.311
LR cohens kappa score: 0.283
LR average precision score: 0.688

-> 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: 287, 3
GB fn, tp: 0, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 281, 9
KNN fn, tp: 0, 7
KNN f1 score: 0.609
KNN cohens kappa score: 0.595


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 256, 34
LR fn, tp: 0, 7
LR f1 score: 0.292
LR cohens kappa score: 0.262
LR average precision score: 0.339

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 5, 2
RF f1 score: 0.444
RF cohens kappa score: 0.439

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

-> test with 'KNN'
KNN tn, fp: 281, 9
KNN fn, tp: 2, 5
KNN f1 score: 0.476
KNN cohens kappa score: 0.459


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 272, 17
LR fn, tp: 2, 5
LR f1 score: 0.345
LR cohens kappa score: 0.320
LR average precision score: 0.418

-> test with 'RF'
RF tn, fp: 288, 1
RF fn, tp: 4, 3
RF f1 score: 0.545
RF cohens kappa score: 0.537

-> 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: 2, 5
KNN f1 score: 0.500
KNN cohens kappa score: 0.484

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 272, 49
LR fn, tp: 3, 7
LR f1 score: 0.387
LR cohens kappa score: 0.364
LR average precision score: 0.753


average:
LR tn, fp: 260.84, 28.96
LR fn, tp: 1.0, 6.0
LR f1 score: 0.292
LR cohens kappa score: 0.263
LR average precision score: 0.500


minimum:
LR tn, fp: 240, 17
LR fn, tp: 0, 4
LR f1 score: 0.222
LR cohens kappa score: 0.188
LR average precision score: 0.227


-----[ RF ]-----
maximum:
RF tn, fp: 290, 4
RF fn, tp: 7, 6
RF f1 score: 0.857
RF cohens kappa score: 0.854


average:
RF tn, fp: 288.28, 1.52
RF fn, tp: 3.64, 3.36
RF f1 score: 0.551
RF cohens kappa score: 0.543


minimum:
RF tn, fp: 286, 0
RF fn, tp: 1, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000


-----[ GB ]-----
maximum:
GB tn, fp: 290, 6
GB fn, tp: 5, 7
GB f1 score: 0.833
GB cohens kappa score: 0.830


average:
GB tn, fp: 286.76, 3.04
GB fn, tp: 3.28, 3.72
GB f1 score: 0.539
GB cohens kappa score: 0.529


minimum:
GB tn, fp: 283, 0
GB fn, tp: 0, 2
GB f1 score: 0.308
GB cohens kappa score: 0.292


-----[ KNN ]-----
maximum:
KNN tn, fp: 285, 16
KNN fn, tp: 3, 7
KNN f1 score: 0.706
KNN cohens kappa score: 0.697


average:
KNN tn, fp: 279.36, 10.44
KNN fn, tp: 1.84, 5.16
KNN f1 score: 0.464
KNN cohens kappa score: 0.447


minimum:
KNN tn, fp: 274, 4
KNN fn, tp: 0, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.296