ConvGeNBenchmarkResults/data_result/Repeater/folding_yeast4.log

///////////////////////////////////////////
// Running Repeater 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: 234, 53
LR fn, tp: 2, 9
LR f1 score: 0.247
LR cohens kappa score: 0.196
LR average precision score: 0.371

-> 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: 264, 23
KNN fn, tp: 3, 8
KNN f1 score: 0.381
KNN cohens kappa score: 0.345


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 232, 55
LR fn, tp: 0, 11
LR f1 score: 0.286
LR cohens kappa score: 0.237
LR average precision score: 0.529

-> test with 'GB'
GB tn, fp: 271, 16
GB fn, tp: 3, 8
GB f1 score: 0.457
GB cohens kappa score: 0.428

-> test with 'KNN'
KNN tn, fp: 260, 27
KNN fn, tp: 2, 9
KNN f1 score: 0.383
KNN cohens kappa score: 0.346


------ Step 1/5: Slice 3/5 -------
-> 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: 0.235

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

-> test with 'KNN'
KNN tn, fp: 265, 22
KNN fn, tp: 4, 7
KNN f1 score: 0.350
KNN cohens kappa score: 0.313


------ Step 1/5: 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: 6, 5
LR f1 score: 0.159
LR cohens kappa score: 0.104
LR average precision score: 0.164

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 7, 4
GB f1 score: 0.308
GB cohens kappa score: 0.277

-> test with 'KNN'
KNN tn, fp: 263, 24
KNN fn, tp: 4, 7
KNN f1 score: 0.333
KNN cohens kappa score: 0.295


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 228, 57
LR fn, tp: 0, 7
LR f1 score: 0.197
LR cohens kappa score: 0.161
LR average precision score: 0.429

-> test with 'GB'
GB tn, fp: 269, 16
GB fn, tp: 1, 6
GB f1 score: 0.414
GB cohens kappa score: 0.392

-> test with 'KNN'
KNN tn, fp: 261, 24
KNN fn, tp: 2, 5
KNN f1 score: 0.278
KNN cohens kappa score: 0.249


====== 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: 242, 45
LR fn, tp: 1, 10
LR f1 score: 0.303
LR cohens kappa score: 0.257
LR average precision score: 0.263

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 7, 4
GB f1 score: 0.348
GB cohens kappa score: 0.322

-> test with 'KNN'
KNN tn, fp: 263, 24
KNN fn, tp: 3, 8
KNN f1 score: 0.372
KNN cohens kappa score: 0.336


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 224, 63
LR fn, tp: 1, 10
LR f1 score: 0.238
LR cohens kappa score: 0.186
LR average precision score: 0.418

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

-> test with 'KNN'
KNN tn, fp: 247, 40
KNN fn, tp: 3, 8
KNN f1 score: 0.271
KNN cohens kappa score: 0.225


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 229, 58
LR fn, tp: 3, 8
LR f1 score: 0.208
LR cohens kappa score: 0.154
LR average precision score: 0.314

-> 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: 260, 27
KNN fn, tp: 3, 8
KNN f1 score: 0.348
KNN cohens kappa score: 0.309


------ Step 2/5: 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: 2, 9
LR f1 score: 0.265
LR cohens kappa score: 0.216
LR average precision score: 0.248

-> test with 'GB'
GB tn, fp: 272, 15
GB fn, tp: 3, 8
GB f1 score: 0.471
GB cohens kappa score: 0.443

-> 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 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 227, 58
LR fn, tp: 1, 6
LR f1 score: 0.169
LR cohens kappa score: 0.131
LR average precision score: 0.415

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

-> test with 'KNN'
KNN tn, fp: 264, 21
KNN fn, tp: 3, 4
KNN f1 score: 0.250
KNN cohens kappa score: 0.221


====== 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: 234, 53
LR fn, tp: 1, 10
LR f1 score: 0.270
LR cohens kappa score: 0.221
LR average precision score: 0.345

-> test with 'GB'
GB tn, fp: 275, 12
GB fn, tp: 4, 7
GB f1 score: 0.467
GB cohens kappa score: 0.441

-> test with 'KNN'
KNN tn, fp: 268, 19
KNN fn, tp: 4, 7
KNN f1 score: 0.378
KNN cohens kappa score: 0.344


------ Step 3/5: 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: 0.395

-> 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: 263, 24
KNN fn, tp: 2, 9
KNN f1 score: 0.409
KNN cohens kappa score: 0.374


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 239, 48
LR fn, tp: 3, 8
LR f1 score: 0.239
LR cohens kappa score: 0.189
LR average precision score: 0.219

-> test with 'GB'
GB tn, fp: 269, 18
GB fn, tp: 6, 5
GB f1 score: 0.294
GB cohens kappa score: 0.257

-> test with 'KNN'
KNN tn, fp: 260, 27
KNN fn, tp: 4, 7
KNN f1 score: 0.311
KNN cohens kappa score: 0.270


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 219, 68
LR fn, tp: 0, 11
LR f1 score: 0.244
LR cohens kappa score: 0.192
LR average precision score: 0.423

-> test with 'GB'
GB tn, fp: 275, 12
GB fn, tp: 3, 8
GB f1 score: 0.516
GB cohens kappa score: 0.492

-> test with 'KNN'
KNN tn, fp: 263, 24
KNN fn, tp: 5, 6
KNN f1 score: 0.293
KNN cohens kappa score: 0.252


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 235, 50
LR fn, tp: 1, 6
LR f1 score: 0.190
LR cohens kappa score: 0.154
LR average precision score: 0.384

-> test with 'GB'
GB tn, fp: 268, 17
GB fn, tp: 3, 4
GB f1 score: 0.286
GB cohens kappa score: 0.259

-> test with 'KNN'
KNN tn, fp: 259, 26
KNN fn, tp: 2, 5
KNN f1 score: 0.263
KNN cohens kappa score: 0.233


====== 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: 246, 41
LR fn, tp: 4, 7
LR f1 score: 0.237
LR cohens kappa score: 0.189
LR average precision score: 0.462

-> 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: 272, 15
KNN fn, tp: 8, 3
KNN f1 score: 0.207
KNN cohens kappa score: 0.169


------ Step 4/5: 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: 1, 10
LR f1 score: 0.274
LR cohens kappa score: 0.225
LR average precision score: 0.308

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

-> test with 'KNN'
KNN tn, fp: 263, 24
KNN fn, tp: 3, 8
KNN f1 score: 0.372
KNN cohens kappa score: 0.336


------ Step 4/5: Slice 3/5 -------
-> 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: 0.219

-> 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: 260, 27
KNN fn, tp: 2, 9
KNN f1 score: 0.383
KNN cohens kappa score: 0.346


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 226, 61
LR fn, tp: 3, 8
LR f1 score: 0.200
LR cohens kappa score: 0.146
LR average precision score: 0.274

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

-> test with 'KNN'
KNN tn, fp: 261, 26
KNN fn, tp: 4, 7
KNN f1 score: 0.318
KNN cohens kappa score: 0.278


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 235, 50
LR fn, tp: 0, 7
LR f1 score: 0.219
LR cohens kappa score: 0.184
LR average precision score: 0.422

-> test with 'GB'
GB tn, fp: 272, 13
GB fn, tp: 3, 4
GB f1 score: 0.333
GB cohens kappa score: 0.310

-> test with 'KNN'
KNN tn, fp: 259, 26
KNN fn, tp: 2, 5
KNN f1 score: 0.263
KNN cohens kappa score: 0.233


====== 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: 242, 45
LR fn, tp: 3, 8
LR f1 score: 0.250
LR cohens kappa score: 0.201
LR average precision score: 0.204

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 7, 4
GB f1 score: 0.308
GB cohens kappa score: 0.277

-> test with 'KNN'
KNN tn, fp: 267, 20
KNN fn, tp: 3, 8
KNN f1 score: 0.410
KNN cohens kappa score: 0.377


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 225, 62
LR fn, tp: 1, 10
LR f1 score: 0.241
LR cohens kappa score: 0.189
LR average precision score: 0.438

-> test with 'GB'
GB tn, fp: 268, 19
GB fn, tp: 4, 7
GB f1 score: 0.378
GB cohens kappa score: 0.344

-> test with 'KNN'
KNN tn, fp: 259, 28
KNN fn, tp: 1, 10
KNN f1 score: 0.408
KNN cohens kappa score: 0.372


------ Step 5/5: 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: 3, 8
LR f1 score: 0.235
LR cohens kappa score: 0.185
LR average precision score: 0.431

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

-> test with 'KNN'
KNN tn, fp: 263, 24
KNN fn, tp: 5, 6
KNN f1 score: 0.293
KNN cohens kappa score: 0.252


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 234, 53
LR fn, tp: 1, 10
LR f1 score: 0.270
LR cohens kappa score: 0.221
LR average precision score: 0.499

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

-> test with 'KNN'
KNN tn, fp: 259, 28
KNN fn, tp: 6, 5
KNN f1 score: 0.227
KNN cohens kappa score: 0.182


------ Step 5/5: 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: 0.130

-> test with 'GB'
GB tn, fp: 270, 15
GB fn, tp: 4, 3
GB f1 score: 0.240
GB cohens kappa score: 0.213

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

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 246, 68
LR fn, tp: 6, 11
LR f1 score: 0.303
LR cohens kappa score: 0.257
LR average precision score: 0.529


average:
LR tn, fp: 233.0, 53.6
LR fn, tp: 1.64, 8.56
LR f1 score: 0.236
LR cohens kappa score: 0.188
LR average precision score: 0.342


minimum:
LR tn, fp: 219, 41
LR fn, tp: 0, 5
LR f1 score: 0.159
LR cohens kappa score: 0.104
LR average precision score: 0.130


-----[ GB ]-----
maximum:
GB tn, fp: 281, 20
GB fn, tp: 7, 8
GB f1 score: 0.516
GB cohens kappa score: 0.492


average:
GB tn, fp: 273.0, 13.6
GB fn, tp: 4.64, 5.56
GB f1 score: 0.375
GB cohens kappa score: 0.347


minimum:
GB tn, fp: 267, 6
GB fn, tp: 1, 3
GB f1 score: 0.240
GB cohens kappa score: 0.213


-----[ KNN ]-----
maximum:
KNN tn, fp: 272, 40
KNN fn, tp: 8, 10
KNN f1 score: 0.410
KNN cohens kappa score: 0.377


average:
KNN tn, fp: 262.52, 24.08
KNN fn, tp: 3.4, 6.8
KNN f1 score: 0.329
KNN cohens kappa score: 0.293


minimum:
KNN tn, fp: 247, 15
KNN fn, tp: 1, 3
KNN f1 score: 0.207
KNN cohens kappa score: 0.169