ConvGeNBenchmarkResults/data_result/Repeater/imblearn_protein_homo.log

///////////////////////////////////////////
// Running Repeater on imblearn_protein_homo
///////////////////////////////////////////

Load 'data_input/imblearn_protein_homo'
from imblearn
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 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27649, 1242
LR fn, tp: 17, 243
LR f1 score: 0.279
LR cohens kappa score: 0.267
LR average precision score: 0.863

-> test with 'GB'
GB tn, fp: 28386, 505
GB fn, tp: 19, 241
GB f1 score: 0.479
GB cohens kappa score: 0.472

-> test with 'KNN'
KNN tn, fp: 28546, 345
KNN fn, tp: 96, 164
KNN f1 score: 0.427
KNN cohens kappa score: 0.420


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27695, 1196
LR fn, tp: 14, 246
LR f1 score: 0.289
LR cohens kappa score: 0.278
LR average precision score: 0.883

-> test with 'GB'
GB tn, fp: 28384, 507
GB fn, tp: 14, 246
GB f1 score: 0.486
GB cohens kappa score: 0.479

-> test with 'KNN'
KNN tn, fp: 28529, 362
KNN fn, tp: 81, 179
KNN f1 score: 0.447
KNN cohens kappa score: 0.440


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27630, 1261
LR fn, tp: 6, 254
LR f1 score: 0.286
LR cohens kappa score: 0.275
LR average precision score: 0.887

-> test with 'GB'
GB tn, fp: 28342, 549
GB fn, tp: 11, 249
GB f1 score: 0.471
GB cohens kappa score: 0.463

-> test with 'KNN'
KNN tn, fp: 28493, 398
KNN fn, tp: 110, 150
KNN f1 score: 0.371
KNN cohens kappa score: 0.364


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27663, 1228
LR fn, tp: 17, 243
LR f1 score: 0.281
LR cohens kappa score: 0.270
LR average precision score: 0.855

-> test with 'GB'
GB tn, fp: 28406, 485
GB fn, tp: 19, 241
GB f1 score: 0.489
GB cohens kappa score: 0.482

-> test with 'KNN'
KNN tn, fp: 28499, 392
KNN fn, tp: 94, 166
KNN f1 score: 0.406
KNN cohens kappa score: 0.399


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 27824, 1067
LR fn, tp: 22, 234
LR f1 score: 0.301
LR cohens kappa score: 0.290
LR average precision score: 0.812

-> test with 'GB'
GB tn, fp: 28481, 410
GB fn, tp: 26, 230
GB f1 score: 0.513
GB cohens kappa score: 0.507

-> test with 'KNN'
KNN tn, fp: 28504, 387
KNN fn, tp: 113, 143
KNN f1 score: 0.364
KNN cohens kappa score: 0.356


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27706, 1185
LR fn, tp: 12, 248
LR f1 score: 0.293
LR cohens kappa score: 0.282
LR average precision score: 0.870

-> test with 'GB'
GB tn, fp: 28416, 475
GB fn, tp: 17, 243
GB f1 score: 0.497
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 28549, 342
KNN fn, tp: 100, 160
KNN f1 score: 0.420
KNN cohens kappa score: 0.413


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27692, 1199
LR fn, tp: 10, 250
LR f1 score: 0.293
LR cohens kappa score: 0.282
LR average precision score: 0.892

-> test with 'GB'
GB tn, fp: 28359, 532
GB fn, tp: 17, 243
GB f1 score: 0.470
GB cohens kappa score: 0.462

-> test with 'KNN'
KNN tn, fp: 28527, 364
KNN fn, tp: 93, 167
KNN f1 score: 0.422
KNN cohens kappa score: 0.415


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27688, 1203
LR fn, tp: 18, 242
LR f1 score: 0.284
LR cohens kappa score: 0.273
LR average precision score: 0.830

-> test with 'GB'
GB tn, fp: 28423, 468
GB fn, tp: 19, 241
GB f1 score: 0.497
GB cohens kappa score: 0.491

-> test with 'KNN'
KNN tn, fp: 28497, 394
KNN fn, tp: 99, 161
KNN f1 score: 0.395
KNN cohens kappa score: 0.388


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27688, 1203
LR fn, tp: 13, 247
LR f1 score: 0.289
LR cohens kappa score: 0.278
LR average precision score: 0.864

-> test with 'GB'
GB tn, fp: 28399, 492
GB fn, tp: 16, 244
GB f1 score: 0.490
GB cohens kappa score: 0.483

-> test with 'KNN'
KNN tn, fp: 28502, 389
KNN fn, tp: 90, 170
KNN f1 score: 0.415
KNN cohens kappa score: 0.408


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 27631, 1260
LR fn, tp: 13, 243
LR f1 score: 0.276
LR cohens kappa score: 0.265
LR average precision score: 0.840

-> test with 'GB'
GB tn, fp: 28376, 515
GB fn, tp: 20, 236
GB f1 score: 0.469
GB cohens kappa score: 0.462

-> test with 'KNN'
KNN tn, fp: 28518, 373
KNN fn, tp: 97, 159
KNN f1 score: 0.404
KNN cohens kappa score: 0.396


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27777, 1114
LR fn, tp: 16, 244
LR f1 score: 0.302
LR cohens kappa score: 0.291
LR average precision score: 0.868

-> test with 'GB'
GB tn, fp: 28417, 474
GB fn, tp: 19, 241
GB f1 score: 0.494
GB cohens kappa score: 0.488

-> test with 'KNN'
KNN tn, fp: 28501, 390
KNN fn, tp: 91, 169
KNN f1 score: 0.413
KNN cohens kappa score: 0.405


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27678, 1213
LR fn, tp: 13, 247
LR f1 score: 0.287
LR cohens kappa score: 0.276
LR average precision score: 0.865

-> test with 'GB'
GB tn, fp: 28404, 487
GB fn, tp: 18, 242
GB f1 score: 0.489
GB cohens kappa score: 0.483

-> test with 'KNN'
KNN tn, fp: 28539, 352
KNN fn, tp: 108, 152
KNN f1 score: 0.398
KNN cohens kappa score: 0.391


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27722, 1169
LR fn, tp: 17, 243
LR f1 score: 0.291
LR cohens kappa score: 0.280
LR average precision score: 0.834

-> test with 'GB'
GB tn, fp: 28409, 482
GB fn, tp: 21, 239
GB f1 score: 0.487
GB cohens kappa score: 0.480

-> test with 'KNN'
KNN tn, fp: 28523, 368
KNN fn, tp: 101, 159
KNN f1 score: 0.404
KNN cohens kappa score: 0.397


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27635, 1256
LR fn, tp: 14, 246
LR f1 score: 0.279
LR cohens kappa score: 0.268
LR average precision score: 0.855

-> test with 'GB'
GB tn, fp: 28359, 532
GB fn, tp: 12, 248
GB f1 score: 0.477
GB cohens kappa score: 0.470

-> test with 'KNN'
KNN tn, fp: 28495, 396
KNN fn, tp: 99, 161
KNN f1 score: 0.394
KNN cohens kappa score: 0.387


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 27643, 1248
LR fn, tp: 11, 245
LR f1 score: 0.280
LR cohens kappa score: 0.269
LR average precision score: 0.882

-> test with 'GB'
GB tn, fp: 28384, 507
GB fn, tp: 16, 240
GB f1 score: 0.479
GB cohens kappa score: 0.472

-> test with 'KNN'
KNN tn, fp: 28505, 386
KNN fn, tp: 91, 165
KNN f1 score: 0.409
KNN cohens kappa score: 0.402


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27672, 1219
LR fn, tp: 13, 247
LR f1 score: 0.286
LR cohens kappa score: 0.275
LR average precision score: 0.873

-> test with 'GB'
GB tn, fp: 28378, 513
GB fn, tp: 15, 245
GB f1 score: 0.481
GB cohens kappa score: 0.474

-> test with 'KNN'
KNN tn, fp: 28514, 377
KNN fn, tp: 96, 164
KNN f1 score: 0.409
KNN cohens kappa score: 0.402


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27703, 1188
LR fn, tp: 16, 244
LR f1 score: 0.288
LR cohens kappa score: 0.278
LR average precision score: 0.835

-> test with 'GB'
GB tn, fp: 28382, 509
GB fn, tp: 18, 242
GB f1 score: 0.479
GB cohens kappa score: 0.472

-> test with 'KNN'
KNN tn, fp: 28527, 364
KNN fn, tp: 105, 155
KNN f1 score: 0.398
KNN cohens kappa score: 0.391


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27707, 1184
LR fn, tp: 17, 243
LR f1 score: 0.288
LR cohens kappa score: 0.277
LR average precision score: 0.859

-> test with 'GB'
GB tn, fp: 28415, 476
GB fn, tp: 20, 240
GB f1 score: 0.492
GB cohens kappa score: 0.485

-> test with 'KNN'
KNN tn, fp: 28501, 390
KNN fn, tp: 89, 171
KNN f1 score: 0.417
KNN cohens kappa score: 0.409


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27671, 1220
LR fn, tp: 12, 248
LR f1 score: 0.287
LR cohens kappa score: 0.276
LR average precision score: 0.878

-> test with 'GB'
GB tn, fp: 28427, 464
GB fn, tp: 15, 245
GB f1 score: 0.506
GB cohens kappa score: 0.499

-> test with 'KNN'
KNN tn, fp: 28523, 368
KNN fn, tp: 93, 167
KNN f1 score: 0.420
KNN cohens kappa score: 0.413


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 27674, 1217
LR fn, tp: 16, 240
LR f1 score: 0.280
LR cohens kappa score: 0.269
LR average precision score: 0.841

-> test with 'GB'
GB tn, fp: 28383, 508
GB fn, tp: 16, 240
GB f1 score: 0.478
GB cohens kappa score: 0.471

-> test with 'KNN'
KNN tn, fp: 28492, 399
KNN fn, tp: 89, 167
KNN f1 score: 0.406
KNN cohens kappa score: 0.399


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27688, 1203
LR fn, tp: 13, 247
LR f1 score: 0.289
LR cohens kappa score: 0.278
LR average precision score: 0.866

-> test with 'GB'
GB tn, fp: 28385, 506
GB fn, tp: 15, 245
GB f1 score: 0.485
GB cohens kappa score: 0.478

-> test with 'KNN'
KNN tn, fp: 28548, 343
KNN fn, tp: 100, 160
KNN f1 score: 0.419
KNN cohens kappa score: 0.412


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27724, 1167
LR fn, tp: 15, 245
LR f1 score: 0.293
LR cohens kappa score: 0.282
LR average precision score: 0.870

-> test with 'GB'
GB tn, fp: 28424, 467
GB fn, tp: 19, 241
GB f1 score: 0.498
GB cohens kappa score: 0.491

-> test with 'KNN'
KNN tn, fp: 28520, 371
KNN fn, tp: 100, 160
KNN f1 score: 0.405
KNN cohens kappa score: 0.397


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27654, 1237
LR fn, tp: 16, 244
LR f1 score: 0.280
LR cohens kappa score: 0.269
LR average precision score: 0.854

-> test with 'GB'
GB tn, fp: 28444, 447
GB fn, tp: 18, 242
GB f1 score: 0.510
GB cohens kappa score: 0.504

-> test with 'KNN'
KNN tn, fp: 28515, 376
KNN fn, tp: 106, 154
KNN f1 score: 0.390
KNN cohens kappa score: 0.382


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27677, 1214
LR fn, tp: 12, 248
LR f1 score: 0.288
LR cohens kappa score: 0.277
LR average precision score: 0.863

-> test with 'GB'
GB tn, fp: 28386, 505
GB fn, tp: 15, 245
GB f1 score: 0.485
GB cohens kappa score: 0.478

-> test with 'KNN'
KNN tn, fp: 28522, 369
KNN fn, tp: 95, 165
KNN f1 score: 0.416
KNN cohens kappa score: 0.409


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 27691, 1200
LR fn, tp: 13, 243
LR f1 score: 0.286
LR cohens kappa score: 0.275
LR average precision score: 0.851

-> test with 'GB'
GB tn, fp: 28373, 518
GB fn, tp: 14, 242
GB f1 score: 0.476
GB cohens kappa score: 0.469

-> test with 'KNN'
KNN tn, fp: 28520, 371
KNN fn, tp: 91, 165
KNN f1 score: 0.417
KNN cohens kappa score: 0.410

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 27824, 1261
LR fn, tp: 22, 254
LR f1 score: 0.302
LR cohens kappa score: 0.291
LR average precision score: 0.892


average:
LR tn, fp: 27687.28, 1203.72
LR fn, tp: 14.24, 244.96
LR f1 score: 0.287
LR cohens kappa score: 0.276
LR average precision score: 0.860


minimum:
LR tn, fp: 27630, 1067
LR fn, tp: 6, 234
LR f1 score: 0.276
LR cohens kappa score: 0.265
LR average precision score: 0.812


-----[ GB ]-----
maximum:
GB tn, fp: 28481, 549
GB fn, tp: 26, 249
GB f1 score: 0.513
GB cohens kappa score: 0.507


average:
GB tn, fp: 28397.68, 493.32
GB fn, tp: 17.16, 242.04
GB f1 score: 0.487
GB cohens kappa score: 0.480


minimum:
GB tn, fp: 28342, 410
GB fn, tp: 11, 230
GB f1 score: 0.469
GB cohens kappa score: 0.462


-----[ KNN ]-----
maximum:
KNN tn, fp: 28549, 399
KNN fn, tp: 113, 179
KNN f1 score: 0.447
KNN cohens kappa score: 0.440


average:
KNN tn, fp: 28516.36, 374.64
KNN fn, tp: 97.08, 162.12
KNN f1 score: 0.407
KNN cohens kappa score: 0.400


minimum:
KNN tn, fp: 28492, 342
KNN fn, tp: 81, 143
KNN f1 score: 0.364
KNN cohens kappa score: 0.356