ConvGeNBenchmarkResults/data_result/ProWRAS/imblearn_protein_homo.log

///////////////////////////////////////////
// Running ProWRAS 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: 28420, 471
LR fn, tp: 23, 237
LR f1 score: 0.490
LR cohens kappa score: 0.483
LR average precision score: 0.865

-> test with 'GB'
GB tn, fp: 28766, 125
GB fn, tp: 46, 214
GB f1 score: 0.715
GB cohens kappa score: 0.712

-> test with 'KNN'
KNN tn, fp: 28330, 561
KNN fn, tp: 94, 166
KNN f1 score: 0.336
KNN cohens kappa score: 0.328


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28467, 424
LR fn, tp: 25, 235
LR f1 score: 0.511
LR cohens kappa score: 0.505
LR average precision score: 0.889

-> test with 'GB'
GB tn, fp: 28808, 83
GB fn, tp: 41, 219
GB f1 score: 0.779
GB cohens kappa score: 0.777

-> test with 'KNN'
KNN tn, fp: 28260, 631
KNN fn, tp: 75, 185
KNN f1 score: 0.344
KNN cohens kappa score: 0.335


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28373, 518
LR fn, tp: 9, 251
LR f1 score: 0.488
LR cohens kappa score: 0.481
LR average precision score: 0.891

-> test with 'GB'
GB tn, fp: 28787, 104
GB fn, tp: 51, 209
GB f1 score: 0.729
GB cohens kappa score: 0.727

-> test with 'KNN'
KNN tn, fp: 28282, 609
KNN fn, tp: 98, 162
KNN f1 score: 0.314
KNN cohens kappa score: 0.305


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28474, 417
LR fn, tp: 26, 234
LR f1 score: 0.514
LR cohens kappa score: 0.507
LR average precision score: 0.858

-> test with 'GB'
GB tn, fp: 28774, 117
GB fn, tp: 51, 209
GB f1 score: 0.713
GB cohens kappa score: 0.710

-> test with 'KNN'
KNN tn, fp: 28299, 592
KNN fn, tp: 96, 164
KNN f1 score: 0.323
KNN cohens kappa score: 0.314


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 28479, 412
LR fn, tp: 35, 221
LR f1 score: 0.497
LR cohens kappa score: 0.491
LR average precision score: 0.815

-> test with 'GB'
GB tn, fp: 28793, 98
GB fn, tp: 63, 193
GB f1 score: 0.706
GB cohens kappa score: 0.703

-> test with 'KNN'
KNN tn, fp: 28361, 530
KNN fn, tp: 105, 151
KNN f1 score: 0.322
KNN cohens kappa score: 0.314


====== 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: 28460, 431
LR fn, tp: 25, 235
LR f1 score: 0.508
LR cohens kappa score: 0.501
LR average precision score: 0.864

-> test with 'GB'
GB tn, fp: 28793, 98
GB fn, tp: 53, 207
GB f1 score: 0.733
GB cohens kappa score: 0.730

-> test with 'KNN'
KNN tn, fp: 28272, 619
KNN fn, tp: 99, 161
KNN f1 score: 0.310
KNN cohens kappa score: 0.300


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28432, 459
LR fn, tp: 18, 242
LR f1 score: 0.504
LR cohens kappa score: 0.497
LR average precision score: 0.896

-> test with 'GB'
GB tn, fp: 28753, 138
GB fn, tp: 43, 217
GB f1 score: 0.706
GB cohens kappa score: 0.703

-> test with 'KNN'
KNN tn, fp: 28275, 616
KNN fn, tp: 85, 175
KNN f1 score: 0.333
KNN cohens kappa score: 0.324


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28481, 410
LR fn, tp: 28, 232
LR f1 score: 0.514
LR cohens kappa score: 0.508
LR average precision score: 0.846

-> test with 'GB'
GB tn, fp: 28801, 90
GB fn, tp: 50, 210
GB f1 score: 0.750
GB cohens kappa score: 0.748

-> test with 'KNN'
KNN tn, fp: 28338, 553
KNN fn, tp: 92, 168
KNN f1 score: 0.343
KNN cohens kappa score: 0.334


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28435, 456
LR fn, tp: 23, 237
LR f1 score: 0.497
LR cohens kappa score: 0.491
LR average precision score: 0.867

-> test with 'GB'
GB tn, fp: 28787, 104
GB fn, tp: 51, 209
GB f1 score: 0.729
GB cohens kappa score: 0.727

-> test with 'KNN'
KNN tn, fp: 28321, 570
KNN fn, tp: 88, 172
KNN f1 score: 0.343
KNN cohens kappa score: 0.335


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 28425, 466
LR fn, tp: 23, 233
LR f1 score: 0.488
LR cohens kappa score: 0.481
LR average precision score: 0.850

-> test with 'GB'
GB tn, fp: 28796, 95
GB fn, tp: 57, 199
GB f1 score: 0.724
GB cohens kappa score: 0.721

-> test with 'KNN'
KNN tn, fp: 28293, 598
KNN fn, tp: 97, 159
KNN f1 score: 0.314
KNN cohens kappa score: 0.305


====== 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: 28429, 462
LR fn, tp: 23, 237
LR f1 score: 0.494
LR cohens kappa score: 0.488
LR average precision score: 0.872

-> test with 'GB'
GB tn, fp: 28791, 100
GB fn, tp: 48, 212
GB f1 score: 0.741
GB cohens kappa score: 0.739

-> test with 'KNN'
KNN tn, fp: 28282, 609
KNN fn, tp: 89, 171
KNN f1 score: 0.329
KNN cohens kappa score: 0.320


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28432, 459
LR fn, tp: 23, 237
LR f1 score: 0.496
LR cohens kappa score: 0.489
LR average precision score: 0.868

-> test with 'GB'
GB tn, fp: 28795, 96
GB fn, tp: 53, 207
GB f1 score: 0.735
GB cohens kappa score: 0.733

-> test with 'KNN'
KNN tn, fp: 28331, 560
KNN fn, tp: 103, 157
KNN f1 score: 0.321
KNN cohens kappa score: 0.312


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28440, 451
LR fn, tp: 33, 227
LR f1 score: 0.484
LR cohens kappa score: 0.477
LR average precision score: 0.836

-> test with 'GB'
GB tn, fp: 28778, 113
GB fn, tp: 50, 210
GB f1 score: 0.720
GB cohens kappa score: 0.718

-> test with 'KNN'
KNN tn, fp: 28298, 593
KNN fn, tp: 103, 157
KNN f1 score: 0.311
KNN cohens kappa score: 0.302


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28434, 457
LR fn, tp: 25, 235
LR f1 score: 0.494
LR cohens kappa score: 0.487
LR average precision score: 0.868

-> test with 'GB'
GB tn, fp: 28783, 108
GB fn, tp: 53, 207
GB f1 score: 0.720
GB cohens kappa score: 0.717

-> test with 'KNN'
KNN tn, fp: 28322, 569
KNN fn, tp: 95, 165
KNN f1 score: 0.332
KNN cohens kappa score: 0.323


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 28416, 475
LR fn, tp: 19, 237
LR f1 score: 0.490
LR cohens kappa score: 0.483
LR average precision score: 0.887

-> test with 'GB'
GB tn, fp: 28780, 111
GB fn, tp: 47, 209
GB f1 score: 0.726
GB cohens kappa score: 0.723

-> test with 'KNN'
KNN tn, fp: 28299, 592
KNN fn, tp: 84, 172
KNN f1 score: 0.337
KNN cohens kappa score: 0.328


====== 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: 28437, 454
LR fn, tp: 20, 240
LR f1 score: 0.503
LR cohens kappa score: 0.497
LR average precision score: 0.880

-> test with 'GB'
GB tn, fp: 28787, 104
GB fn, tp: 47, 213
GB f1 score: 0.738
GB cohens kappa score: 0.736

-> test with 'KNN'
KNN tn, fp: 28301, 590
KNN fn, tp: 86, 174
KNN f1 score: 0.340
KNN cohens kappa score: 0.331


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28450, 441
LR fn, tp: 22, 238
LR f1 score: 0.507
LR cohens kappa score: 0.500
LR average precision score: 0.846

-> test with 'GB'
GB tn, fp: 28802, 89
GB fn, tp: 63, 197
GB f1 score: 0.722
GB cohens kappa score: 0.719

-> test with 'KNN'
KNN tn, fp: 28323, 568
KNN fn, tp: 104, 156
KNN f1 score: 0.317
KNN cohens kappa score: 0.308


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28493, 398
LR fn, tp: 27, 233
LR f1 score: 0.523
LR cohens kappa score: 0.517
LR average precision score: 0.859

-> test with 'GB'
GB tn, fp: 28803, 88
GB fn, tp: 48, 212
GB f1 score: 0.757
GB cohens kappa score: 0.755

-> test with 'KNN'
KNN tn, fp: 28281, 610
KNN fn, tp: 87, 173
KNN f1 score: 0.332
KNN cohens kappa score: 0.323


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28423, 468
LR fn, tp: 24, 236
LR f1 score: 0.490
LR cohens kappa score: 0.483
LR average precision score: 0.877

-> test with 'GB'
GB tn, fp: 28799, 92
GB fn, tp: 51, 209
GB f1 score: 0.745
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 28327, 564
KNN fn, tp: 95, 165
KNN f1 score: 0.334
KNN cohens kappa score: 0.325


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 28487, 404
LR fn, tp: 27, 229
LR f1 score: 0.515
LR cohens kappa score: 0.509
LR average precision score: 0.860

-> test with 'GB'
GB tn, fp: 28781, 110
GB fn, tp: 53, 203
GB f1 score: 0.714
GB cohens kappa score: 0.711

-> test with 'KNN'
KNN tn, fp: 28280, 611
KNN fn, tp: 83, 173
KNN f1 score: 0.333
KNN cohens kappa score: 0.324


====== 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: 28406, 485
LR fn, tp: 22, 238
LR f1 score: 0.484
LR cohens kappa score: 0.477
LR average precision score: 0.871

-> test with 'GB'
GB tn, fp: 28770, 121
GB fn, tp: 47, 213
GB f1 score: 0.717
GB cohens kappa score: 0.714

-> test with 'KNN'
KNN tn, fp: 28323, 568
KNN fn, tp: 102, 158
KNN f1 score: 0.320
KNN cohens kappa score: 0.311


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28486, 405
LR fn, tp: 28, 232
LR f1 score: 0.517
LR cohens kappa score: 0.511
LR average precision score: 0.866

-> test with 'GB'
GB tn, fp: 28804, 87
GB fn, tp: 53, 207
GB f1 score: 0.747
GB cohens kappa score: 0.745

-> test with 'KNN'
KNN tn, fp: 28316, 575
KNN fn, tp: 98, 162
KNN f1 score: 0.325
KNN cohens kappa score: 0.316


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28438, 453
LR fn, tp: 24, 236
LR f1 score: 0.497
LR cohens kappa score: 0.491
LR average precision score: 0.854

-> test with 'GB'
GB tn, fp: 28813, 78
GB fn, tp: 59, 201
GB f1 score: 0.746
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 28348, 543
KNN fn, tp: 102, 158
KNN f1 score: 0.329
KNN cohens kappa score: 0.320


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with 'LR'
LR tn, fp: 28453, 438
LR fn, tp: 20, 240
LR f1 score: 0.512
LR cohens kappa score: 0.505
LR average precision score: 0.872

-> test with 'GB'
GB tn, fp: 28778, 113
GB fn, tp: 54, 206
GB f1 score: 0.712
GB cohens kappa score: 0.709

-> test with 'KNN'
KNN tn, fp: 28282, 609
KNN fn, tp: 91, 169
KNN f1 score: 0.326
KNN cohens kappa score: 0.316


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with 'LR'
LR tn, fp: 28436, 455
LR fn, tp: 28, 228
LR f1 score: 0.486
LR cohens kappa score: 0.479
LR average precision score: 0.855

-> test with 'GB'
GB tn, fp: 28776, 115
GB fn, tp: 49, 207
GB f1 score: 0.716
GB cohens kappa score: 0.713

-> test with 'KNN'
KNN tn, fp: 28316, 575
KNN fn, tp: 89, 167
KNN f1 score: 0.335
KNN cohens kappa score: 0.326

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 28493, 518
LR fn, tp: 35, 251
LR f1 score: 0.523
LR cohens kappa score: 0.517
LR average precision score: 0.896


average:
LR tn, fp: 28444.24, 446.76
LR fn, tp: 24.0, 235.2
LR f1 score: 0.500
LR cohens kappa score: 0.494
LR average precision score: 0.865


minimum:
LR tn, fp: 28373, 398
LR fn, tp: 9, 221
LR f1 score: 0.484
LR cohens kappa score: 0.477
LR average precision score: 0.815


-----[ GB ]-----
maximum:
GB tn, fp: 28813, 138
GB fn, tp: 63, 219
GB f1 score: 0.779
GB cohens kappa score: 0.777


average:
GB tn, fp: 28787.92, 103.08
GB fn, tp: 51.24, 207.96
GB f1 score: 0.730
GB cohens kappa score: 0.727


minimum:
GB tn, fp: 28753, 78
GB fn, tp: 41, 193
GB f1 score: 0.706
GB cohens kappa score: 0.703


-----[ KNN ]-----
maximum:
KNN tn, fp: 28361, 631
KNN fn, tp: 105, 185
KNN f1 score: 0.344
KNN cohens kappa score: 0.335


average:
KNN tn, fp: 28306.4, 584.6
KNN fn, tp: 93.6, 165.6
KNN f1 score: 0.328
KNN cohens kappa score: 0.319


minimum:
KNN tn, fp: 28260, 530
KNN fn, tp: 75, 151
KNN f1 score: 0.310
KNN cohens kappa score: 0.300