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

///////////////////////////////////////////
// Running convGAN-proximary-5 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 GAN.predict
GAN tn, fp: 26787, 2104
GAN fn, tp: 32, 228
GAN f1 score: 0.176
GAN cohens kappa score: 0.162

-> test with 'LR'
LR tn, fp: 27679, 1212
LR fn, tp: 16, 244
LR f1 score: 0.284
LR cohens kappa score: 0.273
LR average precision score: 0.857

-> test with 'GB'
GB tn, fp: 28403, 488
GB fn, tp: 18, 242
GB f1 score: 0.489
GB cohens kappa score: 0.482

-> test with 'KNN'
KNN tn, fp: 28545, 346
KNN fn, tp: 96, 164
KNN f1 score: 0.426
KNN cohens kappa score: 0.419


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 26737, 2154
GAN fn, tp: 17, 243
GAN f1 score: 0.183
GAN cohens kappa score: 0.170

-> test with 'LR'
LR tn, fp: 27751, 1140
LR fn, tp: 13, 247
LR f1 score: 0.300
LR cohens kappa score: 0.289
LR average precision score: 0.884

-> test with 'GB'
GB tn, fp: 28426, 465
GB fn, tp: 16, 244
GB f1 score: 0.504
GB cohens kappa score: 0.497

-> test with 'KNN'
KNN tn, fp: 28528, 363
KNN fn, tp: 81, 179
KNN f1 score: 0.446
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 GAN.predict
GAN tn, fp: 28682, 209
GAN fn, tp: 85, 175
GAN f1 score: 0.543
GAN cohens kappa score: 0.539

-> test with 'LR'
LR tn, fp: 27759, 1132
LR fn, tp: 8, 252
LR f1 score: 0.307
LR cohens kappa score: 0.296
LR average precision score: 0.887

-> test with 'GB'
GB tn, fp: 28377, 514
GB fn, tp: 10, 250
GB f1 score: 0.488
GB cohens kappa score: 0.481

-> test with 'KNN'
KNN tn, fp: 28122, 769
KNN fn, tp: 97, 163
KNN f1 score: 0.273
KNN cohens kappa score: 0.263


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 26613, 2278
GAN fn, tp: 30, 230
GAN f1 score: 0.166
GAN cohens kappa score: 0.152

-> test with 'LR'
LR tn, fp: 27745, 1146
LR fn, tp: 14, 246
LR f1 score: 0.298
LR cohens kappa score: 0.287
LR average precision score: 0.856

-> test with 'GB'
GB tn, fp: 28431, 460
GB fn, tp: 20, 240
GB f1 score: 0.500
GB cohens kappa score: 0.493

-> 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 GAN.predict
GAN tn, fp: 27153, 1738
GAN fn, tp: 36, 220
GAN f1 score: 0.199
GAN cohens kappa score: 0.186

-> test with 'LR'
LR tn, fp: 27842, 1049
LR fn, tp: 22, 234
LR f1 score: 0.304
LR cohens kappa score: 0.294
LR average precision score: 0.817

-> test with 'GB'
GB tn, fp: 28515, 376
GB fn, tp: 26, 230
GB f1 score: 0.534
GB cohens kappa score: 0.528

-> 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 GAN.predict
GAN tn, fp: 26830, 2061
GAN fn, tp: 28, 232
GAN f1 score: 0.182
GAN cohens kappa score: 0.168

-> test with 'LR'
LR tn, fp: 27753, 1138
LR fn, tp: 11, 249
LR f1 score: 0.302
LR cohens kappa score: 0.292
LR average precision score: 0.866

-> test with 'GB'
GB tn, fp: 28453, 438
GB fn, tp: 17, 243
GB f1 score: 0.516
GB cohens kappa score: 0.510

-> 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 GAN.predict
GAN tn, fp: 27290, 1601
GAN fn, tp: 27, 233
GAN f1 score: 0.223
GAN cohens kappa score: 0.210

-> test with 'LR'
LR tn, fp: 27734, 1157
LR fn, tp: 13, 247
LR f1 score: 0.297
LR cohens kappa score: 0.286
LR average precision score: 0.891

-> test with 'GB'
GB tn, fp: 28380, 511
GB fn, tp: 18, 242
GB f1 score: 0.478
GB cohens kappa score: 0.471

-> 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 GAN.predict
GAN tn, fp: 26904, 1987
GAN fn, tp: 37, 223
GAN f1 score: 0.181
GAN cohens kappa score: 0.167

-> test with 'LR'
LR tn, fp: 27726, 1165
LR fn, tp: 17, 243
LR f1 score: 0.291
LR cohens kappa score: 0.281
LR average precision score: 0.833

-> test with 'GB'
GB tn, fp: 28431, 460
GB fn, tp: 23, 237
GB f1 score: 0.495
GB cohens kappa score: 0.489

-> 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 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 26919, 1972
GAN fn, tp: 25, 235
GAN f1 score: 0.191
GAN cohens kappa score: 0.177

-> test with 'LR'
LR tn, fp: 27704, 1187
LR fn, tp: 15, 245
LR f1 score: 0.290
LR cohens kappa score: 0.279
LR average precision score: 0.861

-> test with 'GB'
GB tn, fp: 28422, 469
GB fn, tp: 15, 245
GB f1 score: 0.503
GB cohens kappa score: 0.496

-> 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 GAN.predict
GAN tn, fp: 27373, 1518
GAN fn, tp: 34, 222
GAN f1 score: 0.222
GAN cohens kappa score: 0.210

-> test with 'LR'
LR tn, fp: 27641, 1250
LR fn, tp: 13, 243
LR f1 score: 0.278
LR cohens kappa score: 0.267
LR average precision score: 0.844

-> test with 'GB'
GB tn, fp: 28374, 517
GB fn, tp: 20, 236
GB f1 score: 0.468
GB cohens kappa score: 0.461

-> test with 'KNN'
KNN tn, fp: 28517, 374
KNN fn, tp: 97, 159
KNN f1 score: 0.403
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 GAN.predict
GAN tn, fp: 27072, 1819
GAN fn, tp: 26, 234
GAN f1 score: 0.202
GAN cohens kappa score: 0.190

-> test with 'LR'
LR tn, fp: 27791, 1100
LR fn, tp: 17, 243
LR f1 score: 0.303
LR cohens kappa score: 0.293
LR average precision score: 0.869

-> test with 'GB'
GB tn, fp: 28459, 432
GB fn, tp: 20, 240
GB f1 score: 0.515
GB cohens kappa score: 0.509

-> 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 GAN.predict
GAN tn, fp: 25799, 3092
GAN fn, tp: 22, 238
GAN f1 score: 0.133
GAN cohens kappa score: 0.118

-> test with 'LR'
LR tn, fp: 27731, 1160
LR fn, tp: 15, 245
LR f1 score: 0.294
LR cohens kappa score: 0.284
LR average precision score: 0.864

-> test with 'GB'
GB tn, fp: 28418, 473
GB fn, tp: 18, 242
GB f1 score: 0.496
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 28227, 664
KNN fn, tp: 91, 169
KNN f1 score: 0.309
KNN cohens kappa score: 0.300


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 26408, 2483
GAN fn, tp: 30, 230
GAN f1 score: 0.155
GAN cohens kappa score: 0.141

-> test with 'LR'
LR tn, fp: 27734, 1157
LR fn, tp: 16, 244
LR f1 score: 0.294
LR cohens kappa score: 0.283
LR average precision score: 0.831

-> test with 'GB'
GB tn, fp: 28442, 449
GB fn, tp: 24, 236
GB f1 score: 0.499
GB cohens kappa score: 0.493

-> 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 GAN.predict
GAN tn, fp: 27430, 1461
GAN fn, tp: 44, 216
GAN f1 score: 0.223
GAN cohens kappa score: 0.211

-> test with 'LR'
LR tn, fp: 27682, 1209
LR fn, tp: 11, 249
LR f1 score: 0.290
LR cohens kappa score: 0.279
LR average precision score: 0.865

-> test with 'GB'
GB tn, fp: 28401, 490
GB fn, tp: 13, 247
GB f1 score: 0.495
GB cohens kappa score: 0.489

-> 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 GAN.predict
GAN tn, fp: 27162, 1729
GAN fn, tp: 22, 234
GAN f1 score: 0.211
GAN cohens kappa score: 0.198

-> test with 'LR'
LR tn, fp: 27670, 1221
LR fn, tp: 13, 243
LR f1 score: 0.283
LR cohens kappa score: 0.272
LR average precision score: 0.882

-> test with 'GB'
GB tn, fp: 28397, 494
GB fn, tp: 14, 242
GB f1 score: 0.488
GB cohens kappa score: 0.481

-> test with 'KNN'
KNN tn, fp: 28414, 477
KNN fn, tp: 88, 168
KNN f1 score: 0.373
KNN cohens kappa score: 0.365


====== 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 GAN.predict
GAN tn, fp: 27177, 1714
GAN fn, tp: 27, 233
GAN f1 score: 0.211
GAN cohens kappa score: 0.199

-> test with 'LR'
LR tn, fp: 27698, 1193
LR fn, tp: 13, 247
LR f1 score: 0.291
LR cohens kappa score: 0.280
LR average precision score: 0.873

-> test with 'GB'
GB tn, fp: 28433, 458
GB fn, tp: 16, 244
GB f1 score: 0.507
GB cohens kappa score: 0.501

-> 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 GAN.predict
GAN tn, fp: 27168, 1723
GAN fn, tp: 34, 226
GAN f1 score: 0.205
GAN cohens kappa score: 0.192

-> test with 'LR'
LR tn, fp: 27762, 1129
LR fn, tp: 16, 244
LR f1 score: 0.299
LR cohens kappa score: 0.288
LR average precision score: 0.839

-> test with 'GB'
GB tn, fp: 28405, 486
GB fn, tp: 20, 240
GB f1 score: 0.487
GB cohens kappa score: 0.480

-> test with 'KNN'
KNN tn, fp: 28518, 373
KNN fn, tp: 105, 155
KNN f1 score: 0.393
KNN cohens kappa score: 0.386


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28015, 876
GAN fn, tp: 27, 233
GAN f1 score: 0.340
GAN cohens kappa score: 0.331

-> test with 'LR'
LR tn, fp: 27740, 1151
LR fn, tp: 18, 242
LR f1 score: 0.293
LR cohens kappa score: 0.282
LR average precision score: 0.855

-> 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: 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 GAN.predict
GAN tn, fp: 26083, 2808
GAN fn, tp: 20, 240
GAN f1 score: 0.145
GAN cohens kappa score: 0.131

-> test with 'LR'
LR tn, fp: 27727, 1164
LR fn, tp: 11, 249
LR f1 score: 0.298
LR cohens kappa score: 0.287
LR average precision score: 0.878

-> test with 'GB'
GB tn, fp: 28475, 416
GB fn, tp: 15, 245
GB f1 score: 0.532
GB cohens kappa score: 0.526

-> 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 GAN.predict
GAN tn, fp: 27049, 1842
GAN fn, tp: 29, 227
GAN f1 score: 0.195
GAN cohens kappa score: 0.182

-> test with 'LR'
LR tn, fp: 27746, 1145
LR fn, tp: 16, 240
LR f1 score: 0.293
LR cohens kappa score: 0.282
LR average precision score: 0.840

-> test with 'GB'
GB tn, fp: 28403, 488
GB fn, tp: 17, 239
GB f1 score: 0.486
GB cohens kappa score: 0.480

-> 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 GAN.predict
GAN tn, fp: 25883, 3008
GAN fn, tp: 16, 244
GAN f1 score: 0.139
GAN cohens kappa score: 0.124

-> test with 'LR'
LR tn, fp: 27840, 1051
LR fn, tp: 13, 247
LR f1 score: 0.317
LR cohens kappa score: 0.307
LR average precision score: 0.865

-> 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: 28142, 749
KNN fn, tp: 89, 171
KNN f1 score: 0.290
KNN cohens kappa score: 0.280


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 26648, 2243
GAN fn, tp: 33, 227
GAN f1 score: 0.166
GAN cohens kappa score: 0.153

-> test with 'LR'
LR tn, fp: 27748, 1143
LR fn, tp: 14, 246
LR f1 score: 0.298
LR cohens kappa score: 0.288
LR average precision score: 0.867

-> test with 'GB'
GB tn, fp: 28428, 463
GB fn, tp: 19, 241
GB f1 score: 0.500
GB cohens kappa score: 0.493

-> test with 'KNN'
KNN tn, fp: 28518, 373
KNN fn, tp: 100, 160
KNN f1 score: 0.404
KNN cohens kappa score: 0.396


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 26641, 2250
GAN fn, tp: 29, 231
GAN f1 score: 0.169
GAN cohens kappa score: 0.155

-> test with 'LR'
LR tn, fp: 27677, 1214
LR fn, tp: 18, 242
LR f1 score: 0.282
LR cohens kappa score: 0.271
LR average precision score: 0.854

-> test with 'GB'
GB tn, fp: 28472, 419
GB fn, tp: 17, 243
GB f1 score: 0.527
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 28515, 376
KNN fn, tp: 105, 155
KNN f1 score: 0.392
KNN cohens kappa score: 0.385


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 27392, 1499
GAN fn, tp: 33, 227
GAN f1 score: 0.229
GAN cohens kappa score: 0.216

-> test with 'LR'
LR tn, fp: 27795, 1096
LR fn, tp: 11, 249
LR f1 score: 0.310
LR cohens kappa score: 0.300
LR average precision score: 0.863

-> test with 'GB'
GB tn, fp: 28420, 471
GB fn, tp: 19, 241
GB f1 score: 0.496
GB cohens kappa score: 0.489

-> test with 'KNN'
KNN tn, fp: 28256, 635
KNN fn, tp: 87, 173
KNN f1 score: 0.324
KNN cohens kappa score: 0.315


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with GAN.predict
GAN tn, fp: 26974, 1917
GAN fn, tp: 23, 233
GAN f1 score: 0.194
GAN cohens kappa score: 0.181

-> test with 'LR'
LR tn, fp: 27744, 1147
LR fn, tp: 16, 240
LR f1 score: 0.292
LR cohens kappa score: 0.281
LR average precision score: 0.848

-> test with 'GB'
GB tn, fp: 28398, 493
GB fn, tp: 15, 241
GB f1 score: 0.487
GB cohens kappa score: 0.480

-> test with 'KNN'
KNN tn, fp: 28519, 372
KNN fn, tp: 91, 165
KNN f1 score: 0.416
KNN cohens kappa score: 0.409

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 27842, 1250
LR fn, tp: 22, 252
LR f1 score: 0.317
LR cohens kappa score: 0.307
LR average precision score: 0.891


average:
LR tn, fp: 27736.76, 1154.24
LR fn, tp: 14.4, 244.8
LR f1 score: 0.295
LR cohens kappa score: 0.285
LR average precision score: 0.860


minimum:
LR tn, fp: 27641, 1049
LR fn, tp: 8, 234
LR f1 score: 0.278
LR cohens kappa score: 0.267
LR average precision score: 0.817


-----[ GB ]-----
maximum:
GB tn, fp: 28515, 517
GB fn, tp: 26, 250
GB f1 score: 0.534
GB cohens kappa score: 0.528


average:
GB tn, fp: 28424.88, 466.12
GB fn, tp: 17.92, 241.28
GB f1 score: 0.500
GB cohens kappa score: 0.493


minimum:
GB tn, fp: 28374, 376
GB fn, tp: 10, 230
GB f1 score: 0.468
GB cohens kappa score: 0.461


-----[ KNN ]-----
maximum:
KNN tn, fp: 28549, 769
KNN fn, tp: 113, 179
KNN f1 score: 0.446
KNN cohens kappa score: 0.440


average:
KNN tn, fp: 28457.84, 433.16
KNN fn, tp: 94.96, 164.24
KNN f1 score: 0.389
KNN cohens kappa score: 0.382


minimum:
KNN tn, fp: 28122, 342
KNN fn, tp: 81, 143
KNN f1 score: 0.273
KNN cohens kappa score: 0.263


-----[ GAN ]-----
maximum:
GAN tn, fp: 28682, 3092
GAN fn, tp: 85, 244
GAN f1 score: 0.543
GAN cohens kappa score: 0.539


average:
GAN tn, fp: 26967.56, 1923.44
GAN fn, tp: 30.64, 228.56
GAN f1 score: 0.207
GAN cohens kappa score: 0.195


minimum:
GAN tn, fp: 25799, 209
GAN fn, tp: 16, 175
GAN f1 score: 0.133
GAN cohens kappa score: 0.118