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: 239, 48
LR fn, tp: 2, 9
LR f1 score: 0.265
LR cohens kappa score: 0.216
LR average precision score: 0.426

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

-> 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: 231, 56
LR fn, tp: 1, 10
LR f1 score: 0.260
LR cohens kappa score: 0.210
LR average precision score: 0.659

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 3, 8
GB f1 score: 0.615
GB cohens kappa score: 0.598

-> test with 'KNN'
KNN tn, fp: 273, 14
KNN fn, tp: 4, 7
KNN f1 score: 0.437
KNN cohens kappa score: 0.409


------ Step 1/5: 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: 1, 10
LR f1 score: 0.278
LR cohens kappa score: 0.230
LR average precision score: 0.268

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

-> 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: 253, 34
LR fn, tp: 6, 5
LR f1 score: 0.200
LR cohens kappa score: 0.151
LR average precision score: 0.201

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 8, 3
GB f1 score: 0.240
GB cohens kappa score: 0.207

-> 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: 242, 43
LR fn, tp: 1, 6
LR f1 score: 0.214
LR cohens kappa score: 0.180
LR average precision score: 0.379

-> 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: 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: 244, 43
LR fn, tp: 2, 9
LR f1 score: 0.286
LR cohens kappa score: 0.239
LR average precision score: 0.344

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 8, 3
GB f1 score: 0.286
GB cohens kappa score: 0.260

-> 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: 234, 53
LR fn, tp: 3, 8
LR f1 score: 0.222
LR cohens kappa score: 0.170
LR average precision score: 0.472

-> 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: 255, 32
KNN fn, tp: 3, 8
KNN f1 score: 0.314
KNN cohens kappa score: 0.272


------ 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: 0.354

-> 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: 245, 42
LR fn, tp: 3, 8
LR f1 score: 0.262
LR cohens kappa score: 0.215
LR average precision score: 0.326

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

-> 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: 231, 54
LR fn, tp: 1, 6
LR f1 score: 0.179
LR cohens kappa score: 0.142
LR average precision score: 0.423

-> test with 'GB'
GB tn, fp: 281, 4
GB fn, tp: 6, 1
GB f1 score: 0.167
GB cohens kappa score: 0.150

-> 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: 242, 45
LR fn, tp: 3, 8
LR f1 score: 0.250
LR cohens kappa score: 0.201
LR average precision score: 0.416

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

-> test with 'KNN'
KNN tn, fp: 271, 16
KNN fn, tp: 5, 6
KNN f1 score: 0.364
KNN cohens kappa score: 0.331


------ 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: 0.372

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 9, 2
GB f1 score: 0.235
GB cohens kappa score: 0.215

-> 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: 0.255

-> 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: 239, 48
LR fn, tp: 1, 10
LR f1 score: 0.290
LR cohens kappa score: 0.243
LR average precision score: 0.522

-> test with 'GB'
GB tn, fp: 278, 9
GB fn, tp: 3, 8
GB f1 score: 0.571
GB cohens kappa score: 0.551

-> 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: 0.454

-> test with 'GB'
GB tn, fp: 276, 9
GB fn, tp: 5, 2
GB f1 score: 0.222
GB cohens kappa score: 0.199

-> 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: 253, 34
LR fn, tp: 3, 8
LR f1 score: 0.302
LR cohens kappa score: 0.259
LR average precision score: 0.489

-> 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: 276, 11
KNN fn, tp: 6, 5
KNN f1 score: 0.370
KNN cohens kappa score: 0.342


------ Step 4/5: 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: 0.345

-> 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: 235, 52
LR fn, tp: 3, 8
LR f1 score: 0.225
LR cohens kappa score: 0.174
LR average precision score: 0.298

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 7, 4
GB f1 score: 0.381
GB cohens kappa score: 0.358

-> 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: 244, 43
LR fn, tp: 4, 7
LR f1 score: 0.230
LR cohens kappa score: 0.180
LR average precision score: 0.289

-> 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: 237, 48
LR fn, tp: 2, 5
LR f1 score: 0.167
LR cohens kappa score: 0.130
LR average precision score: 0.563

-> test with 'GB'
GB tn, fp: 277, 8
GB fn, tp: 2, 5
GB f1 score: 0.500
GB cohens kappa score: 0.484

-> 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: 0.227

-> 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: 229, 58
LR fn, tp: 2, 9
LR f1 score: 0.231
LR cohens kappa score: 0.179
LR average precision score: 0.535

-> 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: 247, 40
LR fn, tp: 3, 8
LR f1 score: 0.271
LR cohens kappa score: 0.225
LR average precision score: 0.554

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

-> 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: 243, 44
LR fn, tp: 1, 10
LR f1 score: 0.308
LR cohens kappa score: 0.262
LR average precision score: 0.509

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 5, 6
GB f1 score: 0.429
GB cohens kappa score: 0.402

-> 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: 241, 44
LR fn, tp: 3, 4
LR f1 score: 0.145
LR cohens kappa score: 0.108
LR average precision score: 0.122

-> 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: 253, 58
LR fn, tp: 6, 10
LR f1 score: 0.308
LR cohens kappa score: 0.262
LR average precision score: 0.659


average:
LR tn, fp: 240.96, 45.64
LR fn, tp: 2.44, 7.76
LR f1 score: 0.243
LR cohens kappa score: 0.197
LR average precision score: 0.392


minimum:
LR tn, fp: 229, 34
LR fn, tp: 1, 4
LR f1 score: 0.145
LR cohens kappa score: 0.108
LR average precision score: 0.122


-----[ GB ]-----
maximum:
GB tn, fp: 285, 11
GB fn, tp: 9, 8
GB f1 score: 0.615
GB cohens kappa score: 0.598


average:
GB tn, fp: 279.92, 6.68
GB fn, tp: 6.4, 3.8
GB f1 score: 0.352
GB cohens kappa score: 0.330


minimum:
GB tn, fp: 276, 2
GB fn, tp: 2, 1
GB f1 score: 0.167
GB cohens kappa score: 0.150


-----[ KNN ]-----
maximum:
KNN tn, fp: 278, 32
KNN fn, tp: 7, 9
KNN f1 score: 0.486
KNN cohens kappa score: 0.458


average:
KNN tn, fp: 270.44, 16.16
KNN fn, tp: 4.32, 5.88
KNN f1 score: 0.367
KNN cohens kappa score: 0.336


minimum:
KNN tn, fp: 255, 9
KNN fn, tp: 1, 4
KNN f1 score: 0.250
KNN cohens kappa score: 0.212