ConvGeNBenchmarkResults/data_result/convGAN/imblearn_webpage.log

///////////////////////////////////////////
// Running convGAN on imblearn_webpage
///////////////////////////////////////////

Load 'data_input/imblearn_webpage'
from imblearn
non empty cut in data_input/imblearn_webpage! (76 points)
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 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6350, 410
LR fn, tp: 24, 173
LR f1 score: 0.444
LR cohens kappa score: 0.419
LR average precision score: 0.765

-> test with 'GB'
GB tn, fp: 6399, 361
GB fn, tp: 90, 107
GB f1 score: 0.322
GB cohens kappa score: 0.294

-> test with 'KNN'
KNN tn, fp: 6265, 495
KNN fn, tp: 15, 182
KNN f1 score: 0.416
KNN cohens kappa score: 0.390


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6402, 358
LR fn, tp: 21, 176
LR f1 score: 0.482
LR cohens kappa score: 0.459
LR average precision score: 0.793

-> test with 'GB'
GB tn, fp: 6324, 436
GB fn, tp: 87, 110
GB f1 score: 0.296
GB cohens kappa score: 0.266

-> test with 'KNN'
KNN tn, fp: 6370, 390
KNN fn, tp: 33, 164
KNN f1 score: 0.437
KNN cohens kappa score: 0.412


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6386, 374
LR fn, tp: 15, 182
LR f1 score: 0.483
LR cohens kappa score: 0.461
LR average precision score: 0.841

-> test with 'GB'
GB tn, fp: 6359, 401
GB fn, tp: 89, 108
GB f1 score: 0.306
GB cohens kappa score: 0.276

-> test with 'KNN'
KNN tn, fp: 6226, 534
KNN fn, tp: 26, 171
KNN f1 score: 0.379
KNN cohens kappa score: 0.350


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6372, 388
LR fn, tp: 17, 180
LR f1 score: 0.471
LR cohens kappa score: 0.447
LR average precision score: 0.754

-> test with 'GB'
GB tn, fp: 6348, 412
GB fn, tp: 94, 103
GB f1 score: 0.289
GB cohens kappa score: 0.259

-> test with 'KNN'
KNN tn, fp: 6270, 490
KNN fn, tp: 27, 170
KNN f1 score: 0.397
KNN cohens kappa score: 0.369


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6398, 361
LR fn, tp: 33, 160
LR f1 score: 0.448
LR cohens kappa score: 0.425
LR average precision score: 0.741

-> test with 'GB'
GB tn, fp: 6373, 386
GB fn, tp: 92, 101
GB f1 score: 0.297
GB cohens kappa score: 0.268

-> test with 'KNN'
KNN tn, fp: 6246, 513
KNN fn, tp: 30, 163
KNN f1 score: 0.375
KNN cohens kappa score: 0.347


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6374, 386
LR fn, tp: 23, 174
LR f1 score: 0.460
LR cohens kappa score: 0.436
LR average precision score: 0.793

-> test with 'GB'
GB tn, fp: 6353, 407
GB fn, tp: 87, 110
GB f1 score: 0.308
GB cohens kappa score: 0.279

-> test with 'KNN'
KNN tn, fp: 6243, 517
KNN fn, tp: 30, 167
KNN f1 score: 0.379
KNN cohens kappa score: 0.351


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6390, 370
LR fn, tp: 22, 175
LR f1 score: 0.472
LR cohens kappa score: 0.449
LR average precision score: 0.796

-> test with 'GB'
GB tn, fp: 6383, 377
GB fn, tp: 93, 104
GB f1 score: 0.307
GB cohens kappa score: 0.278

-> test with 'KNN'
KNN tn, fp: 6236, 524
KNN fn, tp: 27, 170
KNN f1 score: 0.382
KNN cohens kappa score: 0.353


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6400, 360
LR fn, tp: 28, 169
LR f1 score: 0.466
LR cohens kappa score: 0.443
LR average precision score: 0.758

-> test with 'GB'
GB tn, fp: 6320, 440
GB fn, tp: 95, 102
GB f1 score: 0.276
GB cohens kappa score: 0.245

-> test with 'KNN'
KNN tn, fp: 6260, 500
KNN fn, tp: 27, 170
KNN f1 score: 0.392
KNN cohens kappa score: 0.364


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6330, 430
LR fn, tp: 20, 177
LR f1 score: 0.440
LR cohens kappa score: 0.415
LR average precision score: 0.755

-> test with 'GB'
GB tn, fp: 6422, 338
GB fn, tp: 91, 106
GB f1 score: 0.331
GB cohens kappa score: 0.303

-> test with 'KNN'
KNN tn, fp: 6356, 404
KNN fn, tp: 20, 177
KNN f1 score: 0.455
KNN cohens kappa score: 0.431


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6372, 387
LR fn, tp: 20, 173
LR f1 score: 0.459
LR cohens kappa score: 0.436
LR average precision score: 0.791

-> test with 'GB'
GB tn, fp: 6341, 418
GB fn, tp: 99, 94
GB f1 score: 0.267
GB cohens kappa score: 0.236

-> test with 'KNN'
KNN tn, fp: 6292, 467
KNN fn, tp: 32, 161
KNN f1 score: 0.392
KNN cohens kappa score: 0.365


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6360, 400
LR fn, tp: 30, 167
LR f1 score: 0.437
LR cohens kappa score: 0.412
LR average precision score: 0.733

-> test with 'GB'
GB tn, fp: 6361, 399
GB fn, tp: 93, 104
GB f1 score: 0.297
GB cohens kappa score: 0.267

-> test with 'KNN'
KNN tn, fp: 6339, 421
KNN fn, tp: 29, 168
KNN f1 score: 0.427
KNN cohens kappa score: 0.402


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6410, 350
LR fn, tp: 18, 179
LR f1 score: 0.493
LR cohens kappa score: 0.471
LR average precision score: 0.798

-> test with 'GB'
GB tn, fp: 6342, 418
GB fn, tp: 94, 103
GB f1 score: 0.287
GB cohens kappa score: 0.256

-> test with 'KNN'
KNN tn, fp: 6234, 526
KNN fn, tp: 23, 174
KNN f1 score: 0.388
KNN cohens kappa score: 0.360


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6400, 360
LR fn, tp: 32, 165
LR f1 score: 0.457
LR cohens kappa score: 0.434
LR average precision score: 0.706

-> test with 'GB'
GB tn, fp: 6390, 370
GB fn, tp: 100, 97
GB f1 score: 0.292
GB cohens kappa score: 0.263

-> test with 'KNN'
KNN tn, fp: 6282, 478
KNN fn, tp: 40, 157
KNN f1 score: 0.377
KNN cohens kappa score: 0.349


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6360, 400
LR fn, tp: 17, 180
LR f1 score: 0.463
LR cohens kappa score: 0.440
LR average precision score: 0.808

-> test with 'GB'
GB tn, fp: 6320, 440
GB fn, tp: 86, 111
GB f1 score: 0.297
GB cohens kappa score: 0.266

-> test with 'KNN'
KNN tn, fp: 6253, 507
KNN fn, tp: 21, 176
KNN f1 score: 0.400
KNN cohens kappa score: 0.372


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6372, 387
LR fn, tp: 17, 176
LR f1 score: 0.466
LR cohens kappa score: 0.443
LR average precision score: 0.773

-> test with 'GB'
GB tn, fp: 6391, 368
GB fn, tp: 91, 102
GB f1 score: 0.308
GB cohens kappa score: 0.279

-> test with 'KNN'
KNN tn, fp: 6240, 519
KNN fn, tp: 16, 177
KNN f1 score: 0.398
KNN cohens kappa score: 0.371


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6398, 362
LR fn, tp: 27, 170
LR f1 score: 0.466
LR cohens kappa score: 0.443
LR average precision score: 0.745

-> test with 'GB'
GB tn, fp: 6387, 373
GB fn, tp: 100, 97
GB f1 score: 0.291
GB cohens kappa score: 0.261

-> test with 'KNN'
KNN tn, fp: 6270, 490
KNN fn, tp: 37, 160
KNN f1 score: 0.378
KNN cohens kappa score: 0.350


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6351, 409
LR fn, tp: 21, 176
LR f1 score: 0.450
LR cohens kappa score: 0.426
LR average precision score: 0.756

-> test with 'GB'
GB tn, fp: 6331, 429
GB fn, tp: 101, 96
GB f1 score: 0.266
GB cohens kappa score: 0.234

-> test with 'KNN'
KNN tn, fp: 6238, 522
KNN fn, tp: 26, 171
KNN f1 score: 0.384
KNN cohens kappa score: 0.356


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6377, 383
LR fn, tp: 17, 180
LR f1 score: 0.474
LR cohens kappa score: 0.451
LR average precision score: 0.807

-> test with 'GB'
GB tn, fp: 6421, 339
GB fn, tp: 79, 118
GB f1 score: 0.361
GB cohens kappa score: 0.335

-> test with 'KNN'
KNN tn, fp: 6346, 414
KNN fn, tp: 19, 178
KNN f1 score: 0.451
KNN cohens kappa score: 0.427


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6377, 383
LR fn, tp: 21, 176
LR f1 score: 0.466
LR cohens kappa score: 0.442
LR average precision score: 0.752

-> test with 'GB'
GB tn, fp: 6317, 443
GB fn, tp: 92, 105
GB f1 score: 0.282
GB cohens kappa score: 0.251

-> test with 'KNN'
KNN tn, fp: 6230, 530
KNN fn, tp: 29, 168
KNN f1 score: 0.375
KNN cohens kappa score: 0.347


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6354, 405
LR fn, tp: 20, 173
LR f1 score: 0.449
LR cohens kappa score: 0.425
LR average precision score: 0.791

-> test with 'GB'
GB tn, fp: 6351, 408
GB fn, tp: 87, 106
GB f1 score: 0.300
GB cohens kappa score: 0.270

-> test with 'KNN'
KNN tn, fp: 6250, 509
KNN fn, tp: 21, 172
KNN f1 score: 0.394
KNN cohens kappa score: 0.366


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6411, 349
LR fn, tp: 22, 175
LR f1 score: 0.485
LR cohens kappa score: 0.463
LR average precision score: 0.769

-> test with 'GB'
GB tn, fp: 6387, 373
GB fn, tp: 91, 106
GB f1 score: 0.314
GB cohens kappa score: 0.285

-> test with 'KNN'
KNN tn, fp: 6320, 440
KNN fn, tp: 23, 174
KNN f1 score: 0.429
KNN cohens kappa score: 0.404


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6407, 353
LR fn, tp: 26, 171
LR f1 score: 0.474
LR cohens kappa score: 0.452
LR average precision score: 0.741

-> test with 'GB'
GB tn, fp: 6355, 405
GB fn, tp: 94, 103
GB f1 score: 0.292
GB cohens kappa score: 0.262

-> test with 'KNN'
KNN tn, fp: 6243, 517
KNN fn, tp: 31, 166
KNN f1 score: 0.377
KNN cohens kappa score: 0.349


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6311, 449
LR fn, tp: 25, 172
LR f1 score: 0.421
LR cohens kappa score: 0.395
LR average precision score: 0.746

-> test with 'GB'
GB tn, fp: 6335, 425
GB fn, tp: 85, 112
GB f1 score: 0.305
GB cohens kappa score: 0.275

-> test with 'KNN'
KNN tn, fp: 6301, 459
KNN fn, tp: 21, 176
KNN f1 score: 0.423
KNN cohens kappa score: 0.397


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6375, 385
LR fn, tp: 17, 180
LR f1 score: 0.472
LR cohens kappa score: 0.449
LR average precision score: 0.824

-> test with 'GB'
GB tn, fp: 6353, 407
GB fn, tp: 92, 105
GB f1 score: 0.296
GB cohens kappa score: 0.266

-> test with 'KNN'
KNN tn, fp: 6276, 484
KNN fn, tp: 30, 167
KNN f1 score: 0.394
KNN cohens kappa score: 0.366


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6388, 371
LR fn, tp: 25, 168
LR f1 score: 0.459
LR cohens kappa score: 0.436
LR average precision score: 0.755

-> test with 'GB'
GB tn, fp: 6372, 387
GB fn, tp: 100, 93
GB f1 score: 0.276
GB cohens kappa score: 0.247

-> test with 'KNN'
KNN tn, fp: 6213, 546
KNN fn, tp: 31, 162
KNN f1 score: 0.360
KNN cohens kappa score: 0.330

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 6411, 449
LR fn, tp: 33, 182
LR f1 score: 0.493
LR cohens kappa score: 0.471
LR average precision score: 0.841


average:
LR tn, fp: 6377.0, 382.8
LR fn, tp: 22.32, 173.88
LR f1 score: 0.462
LR cohens kappa score: 0.439
LR average precision score: 0.772


minimum:
LR tn, fp: 6311, 349
LR fn, tp: 15, 160
LR f1 score: 0.421
LR cohens kappa score: 0.395
LR average precision score: 0.706


-----[ GB ]-----
maximum:
GB tn, fp: 6422, 443
GB fn, tp: 101, 118
GB f1 score: 0.361
GB cohens kappa score: 0.335


average:
GB tn, fp: 6361.4, 398.4
GB fn, tp: 92.08, 104.12
GB f1 score: 0.298
GB cohens kappa score: 0.269


minimum:
GB tn, fp: 6317, 338
GB fn, tp: 79, 93
GB f1 score: 0.266
GB cohens kappa score: 0.234


-----[ KNN ]-----
maximum:
KNN tn, fp: 6370, 546
KNN fn, tp: 40, 182
KNN f1 score: 0.455
KNN cohens kappa score: 0.431


average:
KNN tn, fp: 6271.96, 487.84
KNN fn, tp: 26.56, 169.64
KNN f1 score: 0.398
KNN cohens kappa score: 0.371


minimum:
KNN tn, fp: 6213, 390
KNN fn, tp: 15, 157
KNN f1 score: 0.360
KNN cohens kappa score: 0.330