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

///////////////////////////////////////////
// Running convGAN-majority-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: 6714, 46
GAN fn, tp: 42, 155
GAN f1 score: 0.779
GAN cohens kappa score: 0.772

-> 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: 0.765

-> test with 'GB'
GB tn, fp: 6399, 361
GB fn, tp: 91, 106
GB f1 score: 0.319
GB cohens kappa score: 0.291

-> test with 'KNN'
KNN tn, fp: 6247, 513
KNN fn, tp: 14, 183
KNN f1 score: 0.410
KNN cohens kappa score: 0.383


------ 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: 6717, 43
GAN fn, tp: 48, 149
GAN f1 score: 0.766
GAN cohens kappa score: 0.759

-> test with 'LR'
LR tn, fp: 6401, 359
LR fn, tp: 21, 176
LR f1 score: 0.481
LR cohens kappa score: 0.458
LR average precision score: 0.790

-> test with 'GB'
GB tn, fp: 6326, 434
GB fn, tp: 91, 106
GB f1 score: 0.288
GB cohens kappa score: 0.257

-> test with 'KNN'
KNN tn, fp: 6290, 470
KNN fn, tp: 31, 166
KNN f1 score: 0.399
KNN cohens kappa score: 0.371


------ 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: 6719, 41
GAN fn, tp: 35, 162
GAN f1 score: 0.810
GAN cohens kappa score: 0.804

-> 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: 0.839

-> test with 'GB'
GB tn, fp: 6356, 404
GB fn, tp: 95, 102
GB f1 score: 0.290
GB cohens kappa score: 0.260

-> test with 'KNN'
KNN tn, fp: 6217, 543
KNN fn, tp: 25, 172
KNN f1 score: 0.377
KNN cohens kappa score: 0.348


------ 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: 6698, 62
GAN fn, tp: 47, 150
GAN f1 score: 0.733
GAN cohens kappa score: 0.725

-> 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: 0.753

-> test with 'GB'
GB tn, fp: 6353, 407
GB fn, tp: 96, 101
GB f1 score: 0.287
GB cohens kappa score: 0.256

-> test with 'KNN'
KNN tn, fp: 6259, 501
KNN fn, tp: 27, 170
KNN f1 score: 0.392
KNN cohens kappa score: 0.364


------ 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: 6713, 46
GAN fn, tp: 59, 134
GAN f1 score: 0.718
GAN cohens kappa score: 0.711

-> 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: 6364, 395
GB fn, tp: 91, 102
GB f1 score: 0.296
GB cohens kappa score: 0.266

-> 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: 6723, 37
GAN fn, tp: 59, 138
GAN f1 score: 0.742
GAN cohens kappa score: 0.735

-> test with 'LR'
LR tn, fp: 6376, 384
LR fn, tp: 24, 173
LR f1 score: 0.459
LR cohens kappa score: 0.435
LR average precision score: 0.793

-> test with 'GB'
GB tn, fp: 6354, 406
GB fn, tp: 96, 101
GB f1 score: 0.287
GB cohens kappa score: 0.257

-> test with 'KNN'
KNN tn, fp: 6212, 548
KNN fn, tp: 30, 167
KNN f1 score: 0.366
KNN cohens kappa score: 0.337


------ 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: 6709, 51
GAN fn, tp: 55, 142
GAN f1 score: 0.728
GAN cohens kappa score: 0.720

-> 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: 0.795

-> test with 'GB'
GB tn, fp: 6393, 367
GB fn, tp: 92, 105
GB f1 score: 0.314
GB cohens kappa score: 0.285

-> test with 'KNN'
KNN tn, fp: 6224, 536
KNN fn, tp: 27, 170
KNN f1 score: 0.377
KNN cohens kappa score: 0.348


------ 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: 6710, 50
GAN fn, tp: 51, 146
GAN f1 score: 0.743
GAN cohens kappa score: 0.736

-> test with 'LR'
LR tn, fp: 6401, 359
LR fn, tp: 28, 169
LR f1 score: 0.466
LR cohens kappa score: 0.443
LR average precision score: 0.759

-> test with 'GB'
GB tn, fp: 6312, 448
GB fn, tp: 93, 104
GB f1 score: 0.278
GB cohens kappa score: 0.246

-> test with 'KNN'
KNN tn, fp: 6248, 512
KNN fn, tp: 26, 171
KNN f1 score: 0.389
KNN cohens kappa score: 0.361


------ 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: 6714, 46
GAN fn, tp: 52, 145
GAN f1 score: 0.747
GAN cohens kappa score: 0.740

-> test with 'LR'
LR tn, fp: 6331, 429
LR fn, tp: 20, 177
LR f1 score: 0.441
LR cohens kappa score: 0.416
LR average precision score: 0.756

-> test with 'GB'
GB tn, fp: 6417, 343
GB fn, tp: 92, 105
GB f1 score: 0.326
GB cohens kappa score: 0.298

-> test with 'KNN'
KNN tn, fp: 6334, 426
KNN fn, tp: 19, 178
KNN f1 score: 0.444
KNN cohens kappa score: 0.420


------ 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: 6722, 37
GAN fn, tp: 51, 142
GAN f1 score: 0.763
GAN cohens kappa score: 0.757

-> test with 'LR'
LR tn, fp: 6371, 388
LR fn, tp: 19, 174
LR f1 score: 0.461
LR cohens kappa score: 0.438
LR average precision score: 0.786

-> test with 'GB'
GB tn, fp: 6358, 401
GB fn, tp: 108, 85
GB f1 score: 0.250
GB cohens kappa score: 0.219

-> test with 'KNN'
KNN tn, fp: 6267, 492
KNN fn, tp: 30, 163
KNN f1 score: 0.384
KNN cohens kappa score: 0.357


====== 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: 6709, 51
GAN fn, tp: 48, 149
GAN f1 score: 0.751
GAN cohens kappa score: 0.743

-> 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: 0.734

-> test with 'GB'
GB tn, fp: 6345, 415
GB fn, tp: 93, 104
GB f1 score: 0.291
GB cohens kappa score: 0.260

-> test with 'KNN'
KNN tn, fp: 6335, 425
KNN fn, tp: 29, 168
KNN f1 score: 0.425
KNN cohens kappa score: 0.400


------ 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: 6723, 37
GAN fn, tp: 57, 140
GAN f1 score: 0.749
GAN cohens kappa score: 0.742

-> 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: 6338, 422
GB fn, tp: 93, 104
GB f1 score: 0.288
GB cohens kappa score: 0.257

-> test with 'KNN'
KNN tn, fp: 6202, 558
KNN fn, tp: 23, 174
KNN f1 score: 0.375
KNN cohens kappa score: 0.345


------ 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: 6718, 42
GAN fn, tp: 62, 135
GAN f1 score: 0.722
GAN cohens kappa score: 0.714

-> 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: 0.708

-> test with 'GB'
GB tn, fp: 6399, 361
GB fn, tp: 95, 102
GB f1 score: 0.309
GB cohens kappa score: 0.281

-> test with 'KNN'
KNN tn, fp: 6301, 459
KNN fn, tp: 40, 157
KNN f1 score: 0.386
KNN cohens kappa score: 0.359


------ 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: 6707, 53
GAN fn, tp: 38, 159
GAN f1 score: 0.778
GAN cohens kappa score: 0.771

-> test with 'LR'
LR tn, fp: 6358, 402
LR fn, tp: 17, 180
LR f1 score: 0.462
LR cohens kappa score: 0.438
LR average precision score: 0.802

-> test with 'GB'
GB tn, fp: 6329, 431
GB fn, tp: 89, 108
GB f1 score: 0.293
GB cohens kappa score: 0.263

-> test with 'KNN'
KNN tn, fp: 6252, 508
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 GAN.predict
GAN tn, fp: 6719, 40
GAN fn, tp: 42, 151
GAN f1 score: 0.786
GAN cohens kappa score: 0.780

-> test with 'LR'
LR tn, fp: 6372, 387
LR fn, tp: 16, 177
LR f1 score: 0.468
LR cohens kappa score: 0.445
LR average precision score: 0.775

-> test with 'GB'
GB tn, fp: 6385, 374
GB fn, tp: 94, 99
GB f1 score: 0.297
GB cohens kappa score: 0.268

-> test with 'KNN'
KNN tn, fp: 6197, 562
KNN fn, tp: 15, 178
KNN f1 score: 0.382
KNN cohens kappa score: 0.353


====== 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: 6716, 44
GAN fn, tp: 61, 136
GAN f1 score: 0.721
GAN cohens kappa score: 0.714

-> 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: 6390, 370
GB fn, tp: 98, 99
GB f1 score: 0.297
GB cohens kappa score: 0.268

-> test with 'KNN'
KNN tn, fp: 6256, 504
KNN fn, tp: 36, 161
KNN f1 score: 0.374
KNN cohens kappa score: 0.345


------ 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: 6710, 50
GAN fn, tp: 54, 143
GAN f1 score: 0.733
GAN cohens kappa score: 0.726

-> test with 'LR'
LR tn, fp: 6352, 408
LR fn, tp: 22, 175
LR f1 score: 0.449
LR cohens kappa score: 0.424
LR average precision score: 0.751

-> test with 'GB'
GB tn, fp: 6346, 414
GB fn, tp: 97, 100
GB f1 score: 0.281
GB cohens kappa score: 0.251

-> test with 'KNN'
KNN tn, fp: 6207, 553
KNN fn, tp: 26, 171
KNN f1 score: 0.371
KNN cohens kappa score: 0.342


------ 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: 6725, 35
GAN fn, tp: 41, 156
GAN f1 score: 0.804
GAN cohens kappa score: 0.799

-> 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: 0.808

-> test with 'GB'
GB tn, fp: 6399, 361
GB fn, tp: 81, 116
GB f1 score: 0.344
GB cohens kappa score: 0.317

-> test with 'KNN'
KNN tn, fp: 6323, 437
KNN fn, tp: 19, 178
KNN f1 score: 0.438
KNN cohens kappa score: 0.413


------ 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: 6727, 33
GAN fn, tp: 71, 126
GAN f1 score: 0.708
GAN cohens kappa score: 0.700

-> test with 'LR'
LR tn, fp: 6380, 380
LR fn, tp: 22, 175
LR f1 score: 0.465
LR cohens kappa score: 0.442
LR average precision score: 0.753

-> test with 'GB'
GB tn, fp: 6312, 448
GB fn, tp: 88, 109
GB f1 score: 0.289
GB cohens kappa score: 0.258

-> test with 'KNN'
KNN tn, fp: 6195, 565
KNN fn, tp: 29, 168
KNN f1 score: 0.361
KNN cohens kappa score: 0.331


------ 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: 6730, 29
GAN fn, tp: 42, 151
GAN f1 score: 0.810
GAN cohens kappa score: 0.804

-> test with 'LR'
LR tn, fp: 6355, 404
LR fn, tp: 20, 173
LR f1 score: 0.449
LR cohens kappa score: 0.425
LR average precision score: 0.792

-> test with 'GB'
GB tn, fp: 6336, 423
GB fn, tp: 88, 105
GB f1 score: 0.291
GB cohens kappa score: 0.261

-> test with 'KNN'
KNN tn, fp: 6234, 525
KNN fn, tp: 21, 172
KNN f1 score: 0.387
KNN cohens kappa score: 0.359


====== 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: 6707, 53
GAN fn, tp: 48, 149
GAN f1 score: 0.747
GAN cohens kappa score: 0.739

-> test with 'LR'
LR tn, fp: 6413, 347
LR fn, tp: 22, 175
LR f1 score: 0.487
LR cohens kappa score: 0.465
LR average precision score: 0.757

-> test with 'GB'
GB tn, fp: 6383, 377
GB fn, tp: 85, 112
GB f1 score: 0.327
GB cohens kappa score: 0.298

-> test with 'KNN'
KNN tn, fp: 6295, 465
KNN fn, tp: 22, 175
KNN f1 score: 0.418
KNN cohens kappa score: 0.392


------ 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: 6720, 40
GAN fn, tp: 65, 132
GAN f1 score: 0.715
GAN cohens kappa score: 0.708

-> 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: 0.743

-> test with 'GB'
GB tn, fp: 6365, 395
GB fn, tp: 98, 99
GB f1 score: 0.287
GB cohens kappa score: 0.256

-> test with 'KNN'
KNN tn, fp: 6229, 531
KNN fn, tp: 31, 166
KNN f1 score: 0.371
KNN cohens kappa score: 0.342


------ 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: 6696, 64
GAN fn, tp: 45, 152
GAN f1 score: 0.736
GAN cohens kappa score: 0.728

-> test with 'LR'
LR tn, fp: 6313, 447
LR fn, tp: 25, 172
LR f1 score: 0.422
LR cohens kappa score: 0.396
LR average precision score: 0.749

-> test with 'GB'
GB tn, fp: 6346, 414
GB fn, tp: 80, 117
GB f1 score: 0.321
GB cohens kappa score: 0.292

-> test with 'KNN'
KNN tn, fp: 6294, 466
KNN fn, tp: 21, 176
KNN f1 score: 0.420
KNN cohens kappa score: 0.393


------ 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: 6713, 47
GAN fn, tp: 48, 149
GAN f1 score: 0.758
GAN cohens kappa score: 0.751

-> test with 'LR'
LR tn, fp: 6374, 386
LR fn, tp: 18, 179
LR f1 score: 0.470
LR cohens kappa score: 0.447
LR average precision score: 0.826

-> test with 'GB'
GB tn, fp: 6345, 415
GB fn, tp: 93, 104
GB f1 score: 0.291
GB cohens kappa score: 0.260

-> test with 'KNN'
KNN tn, fp: 6234, 526
KNN fn, tp: 29, 168
KNN f1 score: 0.377
KNN cohens kappa score: 0.348


------ 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: 6720, 39
GAN fn, tp: 63, 130
GAN f1 score: 0.718
GAN cohens kappa score: 0.711

-> test with 'LR'
LR tn, fp: 6392, 367
LR fn, tp: 25, 168
LR f1 score: 0.462
LR cohens kappa score: 0.439
LR average precision score: 0.754

-> test with 'GB'
GB tn, fp: 6374, 385
GB fn, tp: 100, 93
GB f1 score: 0.277
GB cohens kappa score: 0.247

-> test with 'KNN'
KNN tn, fp: 6224, 535
KNN fn, tp: 29, 164
KNN f1 score: 0.368
KNN cohens kappa score: 0.339

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 6413, 447
LR fn, tp: 33, 182
LR f1 score: 0.493
LR cohens kappa score: 0.471
LR average precision score: 0.839


average:
LR tn, fp: 6377.32, 382.48
LR fn, tp: 22.44, 173.76
LR f1 score: 0.462
LR cohens kappa score: 0.439
LR average precision score: 0.771


minimum:
LR tn, fp: 6313, 347
LR fn, tp: 15, 160
LR f1 score: 0.422
LR cohens kappa score: 0.396
LR average precision score: 0.708


-----[ GB ]-----
maximum:
GB tn, fp: 6417, 448
GB fn, tp: 108, 117
GB f1 score: 0.344
GB cohens kappa score: 0.317


average:
GB tn, fp: 6360.96, 398.84
GB fn, tp: 92.68, 103.52
GB f1 score: 0.297
GB cohens kappa score: 0.267


minimum:
GB tn, fp: 6312, 343
GB fn, tp: 80, 85
GB f1 score: 0.250
GB cohens kappa score: 0.219


-----[ KNN ]-----
maximum:
KNN tn, fp: 6335, 565
KNN fn, tp: 40, 183
KNN f1 score: 0.444
KNN cohens kappa score: 0.420


average:
KNN tn, fp: 6252.88, 506.92
KNN fn, tp: 26.0, 170.2
KNN f1 score: 0.391
KNN cohens kappa score: 0.363


minimum:
KNN tn, fp: 6195, 425
KNN fn, tp: 14, 157
KNN f1 score: 0.361
KNN cohens kappa score: 0.331


-----[ GAN ]-----
maximum:
GAN tn, fp: 6730, 64
GAN fn, tp: 71, 162
GAN f1 score: 0.810
GAN cohens kappa score: 0.804


average:
GAN tn, fp: 6715.16, 44.64
GAN fn, tp: 51.36, 144.84
GAN f1 score: 0.751
GAN cohens kappa score: 0.744


minimum:
GAN tn, fp: 6696, 29
GAN fn, tp: 35, 126
GAN f1 score: 0.708
GAN cohens kappa score: 0.700