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

///////////////////////////////////////////
// Running convGAN-proximary-5 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 GAN.predict
GAN tn, fp: 6228, 532
GAN fn, tp: 35, 162
GAN f1 score: 0.364
GAN cohens kappa score: 0.334

-> test with 'LR'
LR tn, fp: 6351, 409
LR fn, tp: 25, 172
LR f1 score: 0.442
LR cohens kappa score: 0.418
LR average precision score: 0.766

-> test with 'GB'
GB tn, fp: 6400, 360
GB fn, tp: 94, 103
GB f1 score: 0.312
GB cohens kappa score: 0.284

-> test with 'KNN'
KNN tn, fp: 6264, 496
KNN fn, tp: 15, 182
KNN f1 score: 0.416
KNN cohens kappa score: 0.389


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 5812, 948
GAN fn, tp: 38, 159
GAN f1 score: 0.244
GAN cohens kappa score: 0.206

-> test with 'LR'
LR tn, fp: 6403, 357
LR fn, tp: 22, 175
LR f1 score: 0.480
LR cohens kappa score: 0.458
LR average precision score: 0.788

-> test with 'GB'
GB tn, fp: 6298, 462
GB fn, tp: 90, 107
GB f1 score: 0.279
GB cohens kappa score: 0.248

-> test with 'KNN'
KNN tn, fp: 6344, 416
KNN fn, tp: 33, 164
KNN f1 score: 0.422
KNN cohens kappa score: 0.397


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 5952, 808
GAN fn, tp: 28, 169
GAN f1 score: 0.288
GAN cohens kappa score: 0.253

-> test with 'LR'
LR tn, fp: 6385, 375
LR fn, tp: 15, 182
LR f1 score: 0.483
LR cohens kappa score: 0.460
LR average precision score: 0.838

-> 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: 6217, 543
KNN fn, tp: 23, 174
KNN f1 score: 0.381
KNN cohens kappa score: 0.352


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 5778, 982
GAN fn, tp: 43, 154
GAN f1 score: 0.231
GAN cohens kappa score: 0.192

-> test with 'LR'
LR tn, fp: 6370, 390
LR fn, tp: 17, 180
LR f1 score: 0.469
LR cohens kappa score: 0.446
LR average precision score: 0.754

-> test with 'GB'
GB tn, fp: 6352, 408
GB fn, tp: 90, 107
GB f1 score: 0.301
GB cohens kappa score: 0.271

-> test with 'KNN'
KNN tn, fp: 6261, 499
KNN fn, tp: 27, 170
KNN f1 score: 0.393
KNN cohens kappa score: 0.365


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with GAN.predict
GAN tn, fp: 6428, 331
GAN fn, tp: 42, 151
GAN f1 score: 0.447
GAN cohens kappa score: 0.425

-> test with 'LR'
LR tn, fp: 6397, 362
LR fn, tp: 32, 161
LR f1 score: 0.450
LR cohens kappa score: 0.426
LR average precision score: 0.741

-> test with 'GB'
GB tn, fp: 6376, 383
GB fn, tp: 93, 100
GB f1 score: 0.296
GB cohens kappa score: 0.267

-> 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 GAN.predict
GAN tn, fp: 5571, 1189
GAN fn, tp: 35, 162
GAN f1 score: 0.209
GAN cohens kappa score: 0.168

-> test with 'LR'
LR tn, fp: 6377, 383
LR fn, tp: 23, 174
LR f1 score: 0.462
LR cohens kappa score: 0.438
LR average precision score: 0.794

-> 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: 6192, 568
KNN fn, tp: 28, 169
KNN f1 score: 0.362
KNN cohens kappa score: 0.332


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 6194, 566
GAN fn, tp: 35, 162
GAN f1 score: 0.350
GAN cohens kappa score: 0.320

-> test with 'LR'
LR tn, fp: 6391, 369
LR fn, tp: 21, 176
LR f1 score: 0.474
LR cohens kappa score: 0.452
LR average precision score: 0.796

-> test with 'GB'
GB tn, fp: 6383, 377
GB fn, tp: 92, 105
GB f1 score: 0.309
GB cohens kappa score: 0.280

-> test with 'KNN'
KNN tn, fp: 6231, 529
KNN fn, tp: 27, 170
KNN f1 score: 0.379
KNN cohens kappa score: 0.351


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 6185, 575
GAN fn, tp: 36, 161
GAN f1 score: 0.345
GAN cohens kappa score: 0.314

-> test with 'LR'
LR tn, fp: 6399, 361
LR fn, tp: 28, 169
LR f1 score: 0.465
LR cohens kappa score: 0.442
LR average precision score: 0.758

-> test with 'GB'
GB tn, fp: 6325, 435
GB fn, tp: 94, 103
GB f1 score: 0.280
GB cohens kappa score: 0.249

-> test with 'KNN'
KNN tn, fp: 6243, 517
KNN fn, tp: 27, 170
KNN f1 score: 0.385
KNN cohens kappa score: 0.356


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 6011, 749
GAN fn, tp: 34, 163
GAN f1 score: 0.294
GAN cohens kappa score: 0.259

-> test with 'LR'
LR tn, fp: 6329, 431
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: 6441, 319
GB fn, tp: 93, 104
GB f1 score: 0.335
GB cohens kappa score: 0.309

-> test with 'KNN'
KNN tn, fp: 6339, 421
KNN fn, tp: 19, 178
KNN f1 score: 0.447
KNN cohens kappa score: 0.423


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with GAN.predict
GAN tn, fp: 5813, 946
GAN fn, tp: 34, 159
GAN f1 score: 0.245
GAN cohens kappa score: 0.208

-> test with 'LR'
LR tn, fp: 6372, 387
LR fn, tp: 19, 174
LR f1 score: 0.462
LR cohens kappa score: 0.438
LR average precision score: 0.800

-> test with 'GB'
GB tn, fp: 6355, 404
GB fn, tp: 105, 88
GB f1 score: 0.257
GB cohens kappa score: 0.226

-> test with 'KNN'
KNN tn, fp: 6287, 472
KNN fn, tp: 31, 162
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 GAN.predict
GAN tn, fp: 6043, 717
GAN fn, tp: 40, 157
GAN f1 score: 0.293
GAN cohens kappa score: 0.259

-> test with 'LR'
LR tn, fp: 6359, 401
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: 6366, 394
GB fn, tp: 92, 105
GB f1 score: 0.302
GB cohens kappa score: 0.272

-> test with 'KNN'
KNN tn, fp: 6341, 419
KNN fn, tp: 29, 168
KNN f1 score: 0.429
KNN cohens kappa score: 0.403


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 6180, 580
GAN fn, tp: 35, 162
GAN f1 score: 0.345
GAN cohens kappa score: 0.314

-> 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: 0.799

-> test with 'GB'
GB tn, fp: 6344, 416
GB fn, tp: 93, 104
GB f1 score: 0.290
GB cohens kappa score: 0.260

-> test with 'KNN'
KNN tn, fp: 6233, 527
KNN fn, tp: 24, 173
KNN f1 score: 0.386
KNN cohens kappa score: 0.357


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 6239, 521
GAN fn, tp: 49, 148
GAN f1 score: 0.342
GAN cohens kappa score: 0.312

-> test with 'LR'
LR tn, fp: 6399, 361
LR fn, tp: 32, 165
LR f1 score: 0.456
LR cohens kappa score: 0.433
LR average precision score: 0.713

-> test with 'GB'
GB tn, fp: 6401, 359
GB fn, tp: 100, 97
GB f1 score: 0.297
GB cohens kappa score: 0.268

-> test with 'KNN'
KNN tn, fp: 6297, 463
KNN fn, tp: 41, 156
KNN f1 score: 0.382
KNN cohens kappa score: 0.355


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 6238, 522
GAN fn, tp: 29, 168
GAN f1 score: 0.379
GAN cohens kappa score: 0.350

-> test with 'LR'
LR tn, fp: 6359, 401
LR fn, tp: 17, 180
LR f1 score: 0.463
LR cohens kappa score: 0.439
LR average precision score: 0.811

-> test with 'GB'
GB tn, fp: 6320, 440
GB fn, tp: 88, 109
GB f1 score: 0.292
GB cohens kappa score: 0.261

-> test with 'KNN'
KNN tn, fp: 6258, 502
KNN fn, tp: 21, 176
KNN f1 score: 0.402
KNN cohens kappa score: 0.375


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with GAN.predict
GAN tn, fp: 5751, 1008
GAN fn, tp: 37, 156
GAN f1 score: 0.230
GAN cohens kappa score: 0.191

-> test with 'LR'
LR tn, fp: 6374, 385
LR fn, tp: 16, 177
LR f1 score: 0.469
LR cohens kappa score: 0.446
LR average precision score: 0.775

-> test with 'GB'
GB tn, fp: 6393, 366
GB fn, tp: 95, 98
GB f1 score: 0.298
GB cohens kappa score: 0.270

-> test with 'KNN'
KNN tn, fp: 6241, 518
KNN fn, tp: 16, 177
KNN f1 score: 0.399
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 GAN.predict
GAN tn, fp: 5798, 962
GAN fn, tp: 39, 158
GAN f1 score: 0.240
GAN cohens kappa score: 0.201

-> test with 'LR'
LR tn, fp: 6397, 363
LR fn, tp: 27, 170
LR f1 score: 0.466
LR cohens kappa score: 0.443
LR average precision score: 0.742

-> test with 'GB'
GB tn, fp: 6389, 371
GB fn, tp: 100, 97
GB f1 score: 0.292
GB cohens kappa score: 0.262

-> test with 'KNN'
KNN tn, fp: 6277, 483
KNN fn, tp: 37, 160
KNN f1 score: 0.381
KNN cohens kappa score: 0.353


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 6150, 610
GAN fn, tp: 42, 155
GAN f1 score: 0.322
GAN cohens kappa score: 0.290

-> test with 'LR'
LR tn, fp: 6353, 407
LR fn, tp: 22, 175
LR f1 score: 0.449
LR cohens kappa score: 0.425
LR average precision score: 0.748

-> test with 'GB'
GB tn, fp: 6342, 418
GB fn, tp: 96, 101
GB f1 score: 0.282
GB cohens kappa score: 0.251

-> test with 'KNN'
KNN tn, fp: 6235, 525
KNN fn, tp: 26, 171
KNN f1 score: 0.383
KNN cohens kappa score: 0.354


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 5932, 828
GAN fn, tp: 26, 171
GAN f1 score: 0.286
GAN cohens kappa score: 0.250

-> test with 'LR'
LR tn, fp: 6378, 382
LR fn, tp: 17, 180
LR f1 score: 0.474
LR cohens kappa score: 0.451
LR average precision score: 0.808

-> test with 'GB'
GB tn, fp: 6430, 330
GB fn, tp: 79, 118
GB f1 score: 0.366
GB cohens kappa score: 0.340

-> test with 'KNN'
KNN tn, fp: 6331, 429
KNN fn, tp: 19, 178
KNN f1 score: 0.443
KNN cohens kappa score: 0.418


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 6109, 651
GAN fn, tp: 45, 152
GAN f1 score: 0.304
GAN cohens kappa score: 0.271

-> 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: 6322, 438
GB fn, tp: 88, 109
GB f1 score: 0.293
GB cohens kappa score: 0.262

-> test with 'KNN'
KNN tn, fp: 6238, 522
KNN fn, tp: 29, 168
KNN f1 score: 0.379
KNN cohens kappa score: 0.350


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with GAN.predict
GAN tn, fp: 6057, 702
GAN fn, tp: 28, 165
GAN f1 score: 0.311
GAN cohens kappa score: 0.279

-> test with 'LR'
LR tn, fp: 6357, 402
LR fn, tp: 20, 173
LR f1 score: 0.451
LR cohens kappa score: 0.427
LR average precision score: 0.792

-> test with 'GB'
GB tn, fp: 6349, 410
GB fn, tp: 91, 102
GB f1 score: 0.289
GB cohens kappa score: 0.260

-> test with 'KNN'
KNN tn, fp: 6232, 527
KNN fn, tp: 21, 172
KNN f1 score: 0.386
KNN cohens kappa score: 0.358


====== 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 GAN.predict
GAN tn, fp: 5930, 830
GAN fn, tp: 33, 164
GAN f1 score: 0.275
GAN cohens kappa score: 0.239

-> test with 'LR'
LR tn, fp: 6412, 348
LR fn, tp: 22, 175
LR f1 score: 0.486
LR cohens kappa score: 0.464
LR average precision score: 0.766

-> test with 'GB'
GB tn, fp: 6380, 380
GB fn, tp: 85, 112
GB f1 score: 0.325
GB cohens kappa score: 0.297

-> test with 'KNN'
KNN tn, fp: 6303, 457
KNN fn, tp: 23, 174
KNN f1 score: 0.420
KNN cohens kappa score: 0.394


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 6249, 511
GAN fn, tp: 51, 146
GAN f1 score: 0.342
GAN cohens kappa score: 0.312

-> test with 'LR'
LR tn, fp: 6406, 354
LR fn, tp: 26, 171
LR f1 score: 0.474
LR cohens kappa score: 0.451
LR average precision score: 0.750

-> test with 'GB'
GB tn, fp: 6351, 409
GB fn, tp: 100, 97
GB f1 score: 0.276
GB cohens kappa score: 0.245

-> test with 'KNN'
KNN tn, fp: 6222, 538
KNN fn, tp: 31, 166
KNN f1 score: 0.368
KNN cohens kappa score: 0.339


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 6133, 627
GAN fn, tp: 35, 162
GAN f1 score: 0.329
GAN cohens kappa score: 0.297

-> test with 'LR'
LR tn, fp: 6309, 451
LR fn, tp: 25, 172
LR f1 score: 0.420
LR cohens kappa score: 0.393
LR average precision score: 0.745

-> test with 'GB'
GB tn, fp: 6328, 432
GB fn, tp: 80, 117
GB f1 score: 0.314
GB cohens kappa score: 0.284

-> test with 'KNN'
KNN tn, fp: 6281, 479
KNN fn, tp: 21, 176
KNN f1 score: 0.413
KNN cohens kappa score: 0.386


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with GAN.predict
GAN tn, fp: 5751, 1009
GAN fn, tp: 33, 164
GAN f1 score: 0.239
GAN cohens kappa score: 0.201

-> test with 'LR'
LR tn, fp: 6376, 384
LR fn, tp: 17, 180
LR f1 score: 0.473
LR cohens kappa score: 0.450
LR average precision score: 0.824

-> test with 'GB'
GB tn, fp: 6360, 400
GB fn, tp: 96, 101
GB f1 score: 0.289
GB cohens kappa score: 0.259

-> test with 'KNN'
KNN tn, fp: 6275, 485
KNN fn, tp: 28, 169
KNN f1 score: 0.397
KNN cohens kappa score: 0.370


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with GAN.predict
GAN tn, fp: 5570, 1189
GAN fn, tp: 34, 159
GAN f1 score: 0.206
GAN cohens kappa score: 0.166

-> test with 'LR'
LR tn, fp: 6388, 371
LR fn, tp: 24, 169
LR f1 score: 0.461
LR cohens kappa score: 0.438
LR average precision score: 0.754

-> test with 'GB'
GB tn, fp: 6378, 381
GB fn, tp: 104, 89
GB f1 score: 0.268
GB cohens kappa score: 0.239

-> test with 'KNN'
KNN tn, fp: 6202, 557
KNN fn, tp: 31, 162
KNN f1 score: 0.355
KNN cohens kappa score: 0.326

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 6412, 451
LR fn, tp: 32, 182
LR f1 score: 0.493
LR cohens kappa score: 0.471
LR average precision score: 0.838


average:
LR tn, fp: 6377.12, 382.68
LR fn, tp: 22.24, 173.96
LR f1 score: 0.463
LR cohens kappa score: 0.439
LR average precision score: 0.772


minimum:
LR tn, fp: 6309, 348
LR fn, tp: 15, 161
LR f1 score: 0.420
LR cohens kappa score: 0.393
LR average precision score: 0.713


-----[ GB ]-----
maximum:
GB tn, fp: 6441, 462
GB fn, tp: 105, 118
GB f1 score: 0.366
GB cohens kappa score: 0.340


average:
GB tn, fp: 6363.56, 396.24
GB fn, tp: 92.68, 103.52
GB f1 score: 0.298
GB cohens kappa score: 0.268


minimum:
GB tn, fp: 6298, 319
GB fn, tp: 79, 88
GB f1 score: 0.257
GB cohens kappa score: 0.226


-----[ KNN ]-----
maximum:
KNN tn, fp: 6344, 568
KNN fn, tp: 41, 182
KNN f1 score: 0.447
KNN cohens kappa score: 0.423


average:
KNN tn, fp: 6263.6, 496.2
KNN fn, tp: 26.28, 169.92
KNN f1 score: 0.395
KNN cohens kappa score: 0.367


minimum:
KNN tn, fp: 6192, 416
KNN fn, tp: 15, 156
KNN f1 score: 0.355
KNN cohens kappa score: 0.326


-----[ GAN ]-----
maximum:
GAN tn, fp: 6428, 1189
GAN fn, tp: 51, 171
GAN f1 score: 0.447
GAN cohens kappa score: 0.425


average:
GAN tn, fp: 6004.08, 755.72
GAN fn, tp: 36.64, 159.56
GAN f1 score: 0.298
GAN cohens kappa score: 0.264


minimum:
GAN tn, fp: 5570, 331
GAN fn, tp: 26, 146
GAN f1 score: 0.206
GAN cohens kappa score: 0.166