ConvGeNBenchmarkResults/data_result/convGAN-old-5/imblearn_protein_homo.log

///////////////////////////////////////////
// Running convGAN 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: 27668, 1223
LR fn, tp: 17, 243
LR f1 score: 0.282
LR cohens kappa score: 0.271
LR average precision score: 0.857

-> test with 'GB'
GB tn, fp: 28379, 512
GB fn, tp: 19, 241
GB f1 score: 0.476
GB cohens kappa score: 0.469

-> 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: 27748, 1143
LR fn, tp: 13, 247
LR f1 score: 0.299
LR cohens kappa score: 0.289
LR average precision score: 0.883

-> test with 'GB'
GB tn, fp: 28404, 487
GB fn, tp: 14, 246
GB f1 score: 0.495
GB cohens kappa score: 0.489

-> 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: 27639, 1252
LR fn, tp: 7, 253
LR f1 score: 0.287
LR cohens kappa score: 0.276
LR average precision score: 0.887

-> test with 'GB'
GB tn, fp: 28387, 504
GB fn, tp: 10, 250
GB f1 score: 0.493
GB cohens kappa score: 0.486

-> 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: 27753, 1138
LR fn, tp: 14, 246
LR f1 score: 0.299
LR cohens kappa score: 0.289
LR average precision score: 0.856

-> test with 'GB'
GB tn, fp: 28433, 458
GB fn, tp: 20, 240
GB f1 score: 0.501
GB cohens kappa score: 0.494

-> 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: 27804, 1087
LR fn, tp: 21, 235
LR f1 score: 0.298
LR cohens kappa score: 0.287
LR average precision score: 0.817

-> test with 'GB'
GB tn, fp: 28503, 388
GB fn, tp: 26, 230
GB f1 score: 0.526
GB cohens kappa score: 0.520

-> test with 'KNN'
KNN tn, fp: 28504, 387
KNN fn, tp: 111, 145
KNN f1 score: 0.368
KNN cohens kappa score: 0.360


====== 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: 27768, 1123
LR fn, tp: 11, 249
LR f1 score: 0.305
LR cohens kappa score: 0.295
LR average precision score: 0.866

-> test with 'GB'
GB tn, fp: 28467, 424
GB fn, tp: 18, 242
GB f1 score: 0.523
GB cohens kappa score: 0.516

-> 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: 27742, 1149
LR fn, tp: 13, 247
LR f1 score: 0.298
LR cohens kappa score: 0.288
LR average precision score: 0.890

-> test with 'GB'
GB tn, fp: 28395, 496
GB fn, tp: 17, 243
GB f1 score: 0.486
GB cohens kappa score: 0.480

-> test with 'KNN'
KNN tn, fp: 28524, 367
KNN fn, tp: 93, 167
KNN f1 score: 0.421
KNN cohens kappa score: 0.414


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27715, 1176
LR fn, tp: 17, 243
LR f1 score: 0.289
LR cohens kappa score: 0.279
LR average precision score: 0.832

-> test with 'GB'
GB tn, fp: 28433, 458
GB fn, tp: 22, 238
GB f1 score: 0.498
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: 27699, 1192
LR fn, tp: 13, 247
LR f1 score: 0.291
LR cohens kappa score: 0.280
LR average precision score: 0.863

-> test with 'GB'
GB tn, fp: 28404, 487
GB fn, tp: 17, 243
GB f1 score: 0.491
GB cohens kappa score: 0.484

-> 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: 27642, 1249
LR fn, tp: 12, 244
LR f1 score: 0.279
LR cohens kappa score: 0.268
LR average precision score: 0.843

-> test with 'GB'
GB tn, fp: 28404, 487
GB fn, tp: 18, 238
GB f1 score: 0.485
GB cohens kappa score: 0.478

-> 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: 27788, 1103
LR fn, tp: 17, 243
LR f1 score: 0.303
LR cohens kappa score: 0.292
LR average precision score: 0.868

-> test with 'GB'
GB tn, fp: 28458, 433
GB fn, tp: 19, 241
GB f1 score: 0.516
GB cohens kappa score: 0.510

-> 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: 27767, 1124
LR fn, tp: 14, 246
LR f1 score: 0.302
LR cohens kappa score: 0.291
LR average precision score: 0.863

-> test with 'GB'
GB tn, fp: 28392, 499
GB fn, tp: 18, 242
GB f1 score: 0.484
GB cohens kappa score: 0.477

-> test with 'KNN'
KNN tn, fp: 28333, 558
KNN fn, tp: 98, 162
KNN f1 score: 0.331
KNN cohens kappa score: 0.322


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27747, 1144
LR fn, tp: 16, 244
LR f1 score: 0.296
LR cohens kappa score: 0.285
LR average precision score: 0.831

-> test with 'GB'
GB tn, fp: 28427, 464
GB fn, tp: 23, 237
GB f1 score: 0.493
GB cohens kappa score: 0.487

-> test with 'KNN'
KNN tn, fp: 28520, 371
KNN fn, tp: 101, 159
KNN f1 score: 0.403
KNN cohens kappa score: 0.395


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27672, 1219
LR fn, tp: 12, 248
LR f1 score: 0.287
LR cohens kappa score: 0.276
LR average precision score: 0.865

-> test with 'GB'
GB tn, fp: 28413, 478
GB fn, tp: 13, 247
GB f1 score: 0.502
GB cohens kappa score: 0.495

-> 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: 27699, 1192
LR fn, tp: 13, 243
LR f1 score: 0.287
LR cohens kappa score: 0.277
LR average precision score: 0.883

-> test with 'GB'
GB tn, fp: 28398, 493
GB fn, tp: 16, 240
GB f1 score: 0.485
GB cohens kappa score: 0.479

-> test with 'KNN'
KNN tn, fp: 28505, 386
KNN fn, tp: 92, 164
KNN f1 score: 0.407
KNN cohens kappa score: 0.400


====== 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: 27722, 1169
LR fn, tp: 14, 246
LR f1 score: 0.294
LR cohens kappa score: 0.283
LR average precision score: 0.872

-> test with 'GB'
GB tn, fp: 28402, 489
GB fn, tp: 15, 245
GB f1 score: 0.493
GB cohens kappa score: 0.486

-> 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: 27757, 1134
LR fn, tp: 15, 245
LR f1 score: 0.299
LR cohens kappa score: 0.288
LR average precision score: 0.840

-> test with 'GB'
GB tn, fp: 28392, 499
GB fn, tp: 22, 238
GB f1 score: 0.477
GB cohens kappa score: 0.470

-> 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: 27735, 1156
LR fn, tp: 17, 243
LR f1 score: 0.293
LR cohens kappa score: 0.282
LR average precision score: 0.855

-> test with 'GB'
GB tn, fp: 28445, 446
GB fn, tp: 20, 240
GB f1 score: 0.507
GB cohens kappa score: 0.501

-> 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: 27719, 1172
LR fn, tp: 11, 249
LR f1 score: 0.296
LR cohens kappa score: 0.285
LR average precision score: 0.879

-> test with 'GB'
GB tn, fp: 28487, 404
GB fn, tp: 15, 245
GB f1 score: 0.539
GB cohens kappa score: 0.533

-> 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: 27754, 1137
LR fn, tp: 16, 240
LR f1 score: 0.294
LR cohens kappa score: 0.283
LR average precision score: 0.839

-> test with 'GB'
GB tn, fp: 28415, 476
GB fn, tp: 15, 241
GB f1 score: 0.495
GB cohens kappa score: 0.489

-> 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: 27746, 1145
LR fn, tp: 13, 247
LR f1 score: 0.299
LR cohens kappa score: 0.288
LR average precision score: 0.863

-> test with 'GB'
GB tn, fp: 28425, 466
GB fn, tp: 18, 242
GB f1 score: 0.500
GB cohens kappa score: 0.493

-> 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: 27736, 1155
LR fn, tp: 14, 246
LR f1 score: 0.296
LR cohens kappa score: 0.285
LR average precision score: 0.867

-> test with 'GB'
GB tn, fp: 28461, 430
GB fn, tp: 19, 241
GB f1 score: 0.518
GB cohens kappa score: 0.511

-> 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: 27717, 1174
LR fn, tp: 18, 242
LR f1 score: 0.289
LR cohens kappa score: 0.278
LR average precision score: 0.854

-> test with 'GB'
GB tn, fp: 28430, 461
GB fn, tp: 16, 244
GB f1 score: 0.506
GB cohens kappa score: 0.499

-> test with 'KNN'
KNN tn, fp: 28223, 668
KNN fn, tp: 98, 162
KNN f1 score: 0.297
KNN cohens kappa score: 0.288


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 27720, 1171
LR fn, tp: 10, 250
LR f1 score: 0.297
LR cohens kappa score: 0.287
LR average precision score: 0.863

-> test with 'GB'
GB tn, fp: 28426, 465
GB fn, tp: 18, 242
GB f1 score: 0.501
GB cohens kappa score: 0.494

-> 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: 27732, 1159
LR fn, tp: 14, 242
LR f1 score: 0.292
LR cohens kappa score: 0.281
LR average precision score: 0.849

-> test with 'GB'
GB tn, fp: 28405, 486
GB fn, tp: 15, 241
GB f1 score: 0.490
GB cohens kappa score: 0.484

-> 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: 27804, 1252
LR fn, tp: 21, 253
LR f1 score: 0.305
LR cohens kappa score: 0.295
LR average precision score: 0.890


average:
LR tn, fp: 27727.56, 1163.44
LR fn, tp: 14.08, 245.12
LR f1 score: 0.294
LR cohens kappa score: 0.283
LR average precision score: 0.859


minimum:
LR tn, fp: 27639, 1087
LR fn, tp: 7, 235
LR f1 score: 0.279
LR cohens kappa score: 0.268
LR average precision score: 0.817


-----[ GB ]-----
maximum:
GB tn, fp: 28503, 512
GB fn, tp: 26, 250
GB f1 score: 0.539
GB cohens kappa score: 0.533


average:
GB tn, fp: 28423.4, 467.6
GB fn, tp: 17.72, 241.48
GB f1 score: 0.499
GB cohens kappa score: 0.493


minimum:
GB tn, fp: 28379, 388
GB fn, tp: 10, 230
GB f1 score: 0.476
GB cohens kappa score: 0.469


-----[ KNN ]-----
maximum:
KNN tn, fp: 28549, 668
KNN fn, tp: 111, 179
KNN f1 score: 0.447
KNN cohens kappa score: 0.440


average:
KNN tn, fp: 28496.2, 394.8
KNN fn, tp: 96.32, 162.88
KNN f1 score: 0.401
KNN cohens kappa score: 0.394


minimum:
KNN tn, fp: 28223, 342
KNN fn, tp: 81, 145
KNN f1 score: 0.297
KNN cohens kappa score: 0.288