ConvGeNBenchmarkResults/data_result/ProWRAS/folding_yeast4.log

///////////////////////////////////////////
// Running ProWRAS on folding_yeast4
///////////////////////////////////////////

Load 'data_input/folding_yeast4'
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 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 235, 52
LR fn, tp: 2, 9
LR f1 score: 0.250
LR cohens kappa score: 0.200
LR average precision score: 0.474

-> test with 'RF'
RF tn, fp: 284, 3
RF fn, tp: 8, 3
RF f1 score: 0.353
RF cohens kappa score: 0.336

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 7, 4
GB f1 score: 0.400
GB cohens kappa score: 0.379

-> test with 'KNN'
KNN tn, fp: 272, 15
KNN fn, tp: 5, 6
KNN f1 score: 0.375
KNN cohens kappa score: 0.343


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 229, 58
LR fn, tp: 1, 10
LR f1 score: 0.253
LR cohens kappa score: 0.202
LR average precision score: 0.675

-> test with 'RF'
RF tn, fp: 284, 3
RF fn, tp: 7, 4
RF f1 score: 0.444
RF cohens kappa score: 0.428

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 3, 8
GB f1 score: 0.667
GB cohens kappa score: 0.653

-> test with 'KNN'
KNN tn, fp: 272, 15
KNN fn, tp: 4, 7
KNN f1 score: 0.424
KNN cohens kappa score: 0.394


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 238, 49
LR fn, tp: 1, 10
LR f1 score: 0.286
LR cohens kappa score: 0.238
LR average precision score: 0.269

-> test with 'RF'
RF tn, fp: 286, 1
RF fn, tp: 7, 4
RF f1 score: 0.500
RF cohens kappa score: 0.488

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 8, 3
GB f1 score: 0.300
GB cohens kappa score: 0.276

-> test with 'KNN'
KNN tn, fp: 268, 19
KNN fn, tp: 5, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.297


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 254, 33
LR fn, tp: 6, 5
LR f1 score: 0.204
LR cohens kappa score: 0.156
LR average precision score: 0.212

-> test with 'RF'
RF tn, fp: 282, 5
RF fn, tp: 9, 2
RF f1 score: 0.222
RF cohens kappa score: 0.199

-> test with 'GB'
GB tn, fp: 275, 12
GB fn, tp: 8, 3
GB f1 score: 0.231
GB cohens kappa score: 0.197

-> test with 'KNN'
KNN tn, fp: 270, 17
KNN fn, tp: 7, 4
KNN f1 score: 0.250
KNN cohens kappa score: 0.212


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 238, 47
LR fn, tp: 1, 6
LR f1 score: 0.200
LR cohens kappa score: 0.165
LR average precision score: 0.381

-> test with 'RF'
RF tn, fp: 282, 3
RF fn, tp: 5, 2
RF f1 score: 0.333
RF cohens kappa score: 0.320

-> test with 'GB'
GB tn, fp: 280, 5
GB fn, tp: 4, 3
GB f1 score: 0.400
GB cohens kappa score: 0.384

-> test with 'KNN'
KNN tn, fp: 265, 20
KNN fn, tp: 1, 6
KNN f1 score: 0.364
KNN cohens kappa score: 0.339


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 248, 39
LR fn, tp: 2, 9
LR f1 score: 0.305
LR cohens kappa score: 0.261
LR average precision score: 0.274

-> test with 'RF'
RF tn, fp: 285, 2
RF fn, tp: 8, 3
RF f1 score: 0.375
RF cohens kappa score: 0.360

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 8, 3
GB f1 score: 0.300
GB cohens kappa score: 0.276

-> test with 'KNN'
KNN tn, fp: 278, 9
KNN fn, tp: 5, 6
KNN f1 score: 0.462
KNN cohens kappa score: 0.438


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 233, 54
LR fn, tp: 3, 8
LR f1 score: 0.219
LR cohens kappa score: 0.167
LR average precision score: 0.471

-> test with 'RF'
RF tn, fp: 282, 5
RF fn, tp: 7, 4
RF f1 score: 0.400
RF cohens kappa score: 0.379

-> test with 'GB'
GB tn, fp: 278, 9
GB fn, tp: 6, 5
GB f1 score: 0.400
GB cohens kappa score: 0.374

-> test with 'KNN'
KNN tn, fp: 256, 31
KNN fn, tp: 3, 8
KNN f1 score: 0.320
KNN cohens kappa score: 0.278


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 244, 43
LR fn, tp: 4, 7
LR f1 score: 0.230
LR cohens kappa score: 0.180
LR average precision score: 0.356

-> test with 'RF'
RF tn, fp: 286, 1
RF fn, tp: 9, 2
RF f1 score: 0.286
RF cohens kappa score: 0.274

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 8, 3
GB f1 score: 0.333
GB cohens kappa score: 0.314

-> test with 'KNN'
KNN tn, fp: 270, 17
KNN fn, tp: 6, 5
KNN f1 score: 0.303
KNN cohens kappa score: 0.267


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 247, 40
LR fn, tp: 3, 8
LR f1 score: 0.271
LR cohens kappa score: 0.225
LR average precision score: 0.338

-> test with 'RF'
RF tn, fp: 283, 4
RF fn, tp: 5, 6
RF f1 score: 0.571
RF cohens kappa score: 0.556

-> test with 'GB'
GB tn, fp: 277, 10
GB fn, tp: 5, 6
GB f1 score: 0.444
GB cohens kappa score: 0.419

-> test with 'KNN'
KNN tn, fp: 274, 13
KNN fn, tp: 4, 7
KNN f1 score: 0.452
KNN cohens kappa score: 0.424


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 230, 55
LR fn, tp: 1, 6
LR f1 score: 0.176
LR cohens kappa score: 0.139
LR average precision score: 0.409

-> test with 'RF'
RF tn, fp: 283, 2
RF fn, tp: 6, 1
RF f1 score: 0.200
RF cohens kappa score: 0.188

-> test with 'GB'
GB tn, fp: 279, 6
GB fn, tp: 5, 2
GB f1 score: 0.267
GB cohens kappa score: 0.247

-> test with 'KNN'
KNN tn, fp: 274, 11
KNN fn, tp: 3, 4
KNN f1 score: 0.364
KNN cohens kappa score: 0.342


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 241, 46
LR fn, tp: 2, 9
LR f1 score: 0.273
LR cohens kappa score: 0.225
LR average precision score: 0.428

-> test with 'RF'
RF tn, fp: 281, 6
RF fn, tp: 9, 2
RF f1 score: 0.211
RF cohens kappa score: 0.185

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 8, 3
GB f1 score: 0.300
GB cohens kappa score: 0.276

-> test with 'KNN'
KNN tn, fp: 270, 17
KNN fn, tp: 5, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.319


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 239, 48
LR fn, tp: 2, 9
LR f1 score: 0.265
LR cohens kappa score: 0.216
LR average precision score: 0.379

-> test with 'RF'
RF tn, fp: 287, 0
RF fn, tp: 10, 1
RF f1 score: 0.167
RF cohens kappa score: 0.162

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 9, 2
GB f1 score: 0.211
GB cohens kappa score: 0.185

-> test with 'KNN'
KNN tn, fp: 275, 12
KNN fn, tp: 5, 6
KNN f1 score: 0.414
KNN cohens kappa score: 0.386


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 247, 40
LR fn, tp: 3, 8
LR f1 score: 0.271
LR cohens kappa score: 0.225
LR average precision score: 0.245

-> test with 'RF'
RF tn, fp: 284, 3
RF fn, tp: 8, 3
RF f1 score: 0.353
RF cohens kappa score: 0.336

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 5, 6
GB f1 score: 0.500
GB cohens kappa score: 0.479

-> test with 'KNN'
KNN tn, fp: 270, 17
KNN fn, tp: 2, 9
KNN f1 score: 0.486
KNN cohens kappa score: 0.458


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 47
LR fn, tp: 1, 10
LR f1 score: 0.294
LR cohens kappa score: 0.248
LR average precision score: 0.526

-> test with 'RF'
RF tn, fp: 281, 6
RF fn, tp: 5, 6
RF f1 score: 0.522
RF cohens kappa score: 0.503

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 3, 8
GB f1 score: 0.593
GB cohens kappa score: 0.574

-> test with 'KNN'
KNN tn, fp: 273, 14
KNN fn, tp: 5, 6
KNN f1 score: 0.387
KNN cohens kappa score: 0.356


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 239, 46
LR fn, tp: 2, 5
LR f1 score: 0.172
LR cohens kappa score: 0.136
LR average precision score: 0.452

-> test with 'RF'
RF tn, fp: 281, 4
RF fn, tp: 5, 2
RF f1 score: 0.308
RF cohens kappa score: 0.292

-> test with 'GB'
GB tn, fp: 274, 11
GB fn, tp: 4, 3
GB f1 score: 0.286
GB cohens kappa score: 0.262

-> test with 'KNN'
KNN tn, fp: 269, 16
KNN fn, tp: 3, 4
KNN f1 score: 0.296
KNN cohens kappa score: 0.270


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 254, 33
LR fn, tp: 3, 8
LR f1 score: 0.308
LR cohens kappa score: 0.265
LR average precision score: 0.485

-> test with 'RF'
RF tn, fp: 287, 0
RF fn, tp: 8, 3
RF f1 score: 0.429
RF cohens kappa score: 0.419

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 7, 4
GB f1 score: 0.421
GB cohens kappa score: 0.402

-> test with 'KNN'
KNN tn, fp: 275, 12
KNN fn, tp: 6, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.327


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 246, 41
LR fn, tp: 2, 9
LR f1 score: 0.295
LR cohens kappa score: 0.250
LR average precision score: 0.380

-> test with 'RF'
RF tn, fp: 286, 1
RF fn, tp: 10, 1
RF f1 score: 0.154
RF cohens kappa score: 0.144

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 9, 2
GB f1 score: 0.211
GB cohens kappa score: 0.185

-> test with 'KNN'
KNN tn, fp: 264, 23
KNN fn, tp: 5, 6
KNN f1 score: 0.300
KNN cohens kappa score: 0.260


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 236, 51
LR fn, tp: 3, 8
LR f1 score: 0.229
LR cohens kappa score: 0.177
LR average precision score: 0.306

-> test with 'RF'
RF tn, fp: 283, 4
RF fn, tp: 10, 1
RF f1 score: 0.125
RF cohens kappa score: 0.104

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 7, 4
GB f1 score: 0.400
GB cohens kappa score: 0.379

-> test with 'KNN'
KNN tn, fp: 267, 20
KNN fn, tp: 4, 7
KNN f1 score: 0.368
KNN cohens kappa score: 0.333


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 245, 42
LR fn, tp: 4, 7
LR f1 score: 0.233
LR cohens kappa score: 0.184
LR average precision score: 0.291

-> test with 'RF'
RF tn, fp: 282, 5
RF fn, tp: 8, 3
RF f1 score: 0.316
RF cohens kappa score: 0.294

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 6, 5
GB f1 score: 0.417
GB cohens kappa score: 0.392

-> test with 'KNN'
KNN tn, fp: 269, 18
KNN fn, tp: 4, 7
KNN f1 score: 0.389
KNN cohens kappa score: 0.356


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 45
LR fn, tp: 2, 5
LR f1 score: 0.175
LR cohens kappa score: 0.139
LR average precision score: 0.523

-> test with 'RF'
RF tn, fp: 281, 4
RF fn, tp: 3, 4
RF f1 score: 0.533
RF cohens kappa score: 0.521

-> test with 'GB'
GB tn, fp: 276, 9
GB fn, tp: 3, 4
GB f1 score: 0.400
GB cohens kappa score: 0.381

-> test with 'KNN'
KNN tn, fp: 269, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.333


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 251, 36
LR fn, tp: 3, 8
LR f1 score: 0.291
LR cohens kappa score: 0.246
LR average precision score: 0.224

-> test with 'RF'
RF tn, fp: 285, 2
RF fn, tp: 10, 1
RF f1 score: 0.143
RF cohens kappa score: 0.129

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 9, 2
GB f1 score: 0.200
GB cohens kappa score: 0.173

-> test with 'KNN'
KNN tn, fp: 270, 17
KNN fn, tp: 4, 7
KNN f1 score: 0.400
KNN cohens kappa score: 0.368


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 235, 52
LR fn, tp: 2, 9
LR f1 score: 0.250
LR cohens kappa score: 0.200
LR average precision score: 0.538

-> test with 'RF'
RF tn, fp: 282, 5
RF fn, tp: 9, 2
RF f1 score: 0.222
RF cohens kappa score: 0.199

-> test with 'GB'
GB tn, fp: 277, 10
GB fn, tp: 7, 4
GB f1 score: 0.320
GB cohens kappa score: 0.291

-> test with 'KNN'
KNN tn, fp: 266, 21
KNN fn, tp: 4, 7
KNN f1 score: 0.359
KNN cohens kappa score: 0.323


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 252, 35
LR fn, tp: 3, 8
LR f1 score: 0.296
LR cohens kappa score: 0.252
LR average precision score: 0.560

-> test with 'RF'
RF tn, fp: 284, 3
RF fn, tp: 9, 2
RF f1 score: 0.250
RF cohens kappa score: 0.232

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 9, 2
GB f1 score: 0.267
GB cohens kappa score: 0.252

-> test with 'KNN'
KNN tn, fp: 275, 12
KNN fn, tp: 6, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.327


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 47
LR fn, tp: 1, 10
LR f1 score: 0.294
LR cohens kappa score: 0.248
LR average precision score: 0.505

-> test with 'RF'
RF tn, fp: 283, 4
RF fn, tp: 7, 4
RF f1 score: 0.421
RF cohens kappa score: 0.402

-> test with 'GB'
GB tn, fp: 275, 12
GB fn, tp: 7, 4
GB f1 score: 0.296
GB cohens kappa score: 0.264

-> test with 'KNN'
KNN tn, fp: 272, 15
KNN fn, tp: 7, 4
KNN f1 score: 0.267
KNN cohens kappa score: 0.231


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 45
LR fn, tp: 3, 4
LR f1 score: 0.143
LR cohens kappa score: 0.105
LR average precision score: 0.119

-> test with 'RF'
RF tn, fp: 284, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 280, 5
GB fn, tp: 5, 2
GB f1 score: 0.286
GB cohens kappa score: 0.268

-> test with 'KNN'
KNN tn, fp: 276, 9
KNN fn, tp: 3, 4
KNN f1 score: 0.400
KNN cohens kappa score: 0.381

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 254, 58
LR fn, tp: 6, 10
LR f1 score: 0.308
LR cohens kappa score: 0.265
LR average precision score: 0.675


average:
LR tn, fp: 241.64, 44.96
LR fn, tp: 2.4, 7.8
LR f1 score: 0.247
LR cohens kappa score: 0.202
LR average precision score: 0.393


minimum:
LR tn, fp: 229, 33
LR fn, tp: 1, 4
LR f1 score: 0.143
LR cohens kappa score: 0.105
LR average precision score: 0.119


-----[ RF ]-----
maximum:
RF tn, fp: 287, 6
RF fn, tp: 10, 6
RF f1 score: 0.571
RF cohens kappa score: 0.556


average:
RF tn, fp: 283.52, 3.08
RF fn, tp: 7.48, 2.72
RF f1 score: 0.329
RF cohens kappa score: 0.314


minimum:
RF tn, fp: 281, 0
RF fn, tp: 3, 1
RF f1 score: 0.125
RF cohens kappa score: 0.104


-----[ GB ]-----
maximum:
GB tn, fp: 285, 12
GB fn, tp: 9, 8
GB f1 score: 0.667
GB cohens kappa score: 0.653


average:
GB tn, fp: 279.64, 6.96
GB fn, tp: 6.4, 3.8
GB f1 score: 0.354
GB cohens kappa score: 0.331


minimum:
GB tn, fp: 274, 2
GB fn, tp: 3, 2
GB f1 score: 0.200
GB cohens kappa score: 0.173


-----[ KNN ]-----
maximum:
KNN tn, fp: 278, 31
KNN fn, tp: 7, 9
KNN f1 score: 0.486
KNN cohens kappa score: 0.458


average:
KNN tn, fp: 270.36, 16.24
KNN fn, tp: 4.32, 5.88
KNN f1 score: 0.365
KNN cohens kappa score: 0.335


minimum:
KNN tn, fp: 256, 9
KNN fn, tp: 1, 4
KNN f1 score: 0.250
KNN cohens kappa score: 0.212