ConvGeNBenchmarkResults/data_result/Repeater/imblearn_webpage.log

///////////////////////////////////////////
// Running Repeater 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: 6389, 371
LR fn, tp: 21, 176
LR f1 score: 0.473
LR cohens kappa score: 0.450
LR average precision score: 0.761

-> test with 'GB'
GB tn, fp: 6418, 342
GB fn, tp: 24, 173
GB f1 score: 0.486
GB cohens kappa score: 0.464

-> test with 'KNN'
KNN tn, fp: 6155, 605
KNN fn, tp: 16, 181
KNN f1 score: 0.368
KNN cohens kappa score: 0.338


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6449, 311
LR fn, tp: 24, 173
LR f1 score: 0.508
LR cohens kappa score: 0.487
LR average precision score: 0.766

-> test with 'GB'
GB tn, fp: 6446, 314
GB fn, tp: 28, 169
GB f1 score: 0.497
GB cohens kappa score: 0.476

-> test with 'KNN'
KNN tn, fp: 6175, 585
KNN fn, tp: 30, 167
KNN f1 score: 0.352
KNN cohens kappa score: 0.322


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6426, 334
LR fn, tp: 13, 184
LR f1 score: 0.515
LR cohens kappa score: 0.494
LR average precision score: 0.827

-> test with 'GB'
GB tn, fp: 6433, 327
GB fn, tp: 22, 175
GB f1 score: 0.501
GB cohens kappa score: 0.480

-> test with 'KNN'
KNN tn, fp: 6122, 638
KNN fn, tp: 20, 177
KNN f1 score: 0.350
KNN cohens kappa score: 0.319


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6412, 348
LR fn, tp: 18, 179
LR f1 score: 0.494
LR cohens kappa score: 0.473
LR average precision score: 0.753

-> test with 'GB'
GB tn, fp: 6433, 327
GB fn, tp: 26, 171
GB f1 score: 0.492
GB cohens kappa score: 0.471

-> test with 'KNN'
KNN tn, fp: 6071, 689
KNN fn, tp: 24, 173
KNN f1 score: 0.327
KNN cohens kappa score: 0.294


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6472, 287
LR fn, tp: 29, 164
LR f1 score: 0.509
LR cohens kappa score: 0.489
LR average precision score: 0.757

-> test with 'GB'
GB tn, fp: 6479, 280
GB fn, tp: 31, 162
GB f1 score: 0.510
GB cohens kappa score: 0.491

-> test with 'KNN'
KNN tn, fp: 6046, 713
KNN fn, tp: 25, 168
KNN f1 score: 0.313
KNN cohens kappa score: 0.280


====== 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: 6406, 354
LR fn, tp: 15, 182
LR f1 score: 0.497
LR cohens kappa score: 0.475
LR average precision score: 0.793

-> test with 'GB'
GB tn, fp: 6454, 306
GB fn, tp: 28, 169
GB f1 score: 0.503
GB cohens kappa score: 0.482

-> test with 'KNN'
KNN tn, fp: 6135, 625
KNN fn, tp: 20, 177
KNN f1 score: 0.354
KNN cohens kappa score: 0.324


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6457, 303
LR fn, tp: 25, 172
LR f1 score: 0.512
LR cohens kappa score: 0.492
LR average precision score: 0.792

-> test with 'GB'
GB tn, fp: 6455, 305
GB fn, tp: 24, 173
GB f1 score: 0.513
GB cohens kappa score: 0.492

-> test with 'KNN'
KNN tn, fp: 6076, 684
KNN fn, tp: 18, 179
KNN f1 score: 0.338
KNN cohens kappa score: 0.306


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6439, 321
LR fn, tp: 27, 170
LR f1 score: 0.494
LR cohens kappa score: 0.473
LR average precision score: 0.756

-> test with 'GB'
GB tn, fp: 6433, 327
GB fn, tp: 28, 169
GB f1 score: 0.488
GB cohens kappa score: 0.466

-> test with 'KNN'
KNN tn, fp: 6141, 619
KNN fn, tp: 25, 172
KNN f1 score: 0.348
KNN cohens kappa score: 0.317


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6381, 379
LR fn, tp: 21, 176
LR f1 score: 0.468
LR cohens kappa score: 0.445
LR average precision score: 0.746

-> test with 'GB'
GB tn, fp: 6453, 307
GB fn, tp: 28, 169
GB f1 score: 0.502
GB cohens kappa score: 0.481

-> test with 'KNN'
KNN tn, fp: 6108, 652
KNN fn, tp: 17, 180
KNN f1 score: 0.350
KNN cohens kappa score: 0.319


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6410, 349
LR fn, tp: 18, 175
LR f1 score: 0.488
LR cohens kappa score: 0.466
LR average precision score: 0.801

-> test with 'GB'
GB tn, fp: 6420, 339
GB fn, tp: 22, 171
GB f1 score: 0.486
GB cohens kappa score: 0.465

-> test with 'KNN'
KNN tn, fp: 6097, 662
KNN fn, tp: 28, 165
KNN f1 score: 0.324
KNN cohens kappa score: 0.292


====== 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: 6383, 377
LR fn, tp: 26, 171
LR f1 score: 0.459
LR cohens kappa score: 0.436
LR average precision score: 0.729

-> test with 'GB'
GB tn, fp: 6427, 333
GB fn, tp: 27, 170
GB f1 score: 0.486
GB cohens kappa score: 0.464

-> test with 'KNN'
KNN tn, fp: 6158, 602
KNN fn, tp: 24, 173
KNN f1 score: 0.356
KNN cohens kappa score: 0.326


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6462, 298
LR fn, tp: 21, 176
LR f1 score: 0.525
LR cohens kappa score: 0.505
LR average precision score: 0.795

-> test with 'GB'
GB tn, fp: 6456, 304
GB fn, tp: 27, 170
GB f1 score: 0.507
GB cohens kappa score: 0.486

-> test with 'KNN'
KNN tn, fp: 6118, 642
KNN fn, tp: 18, 179
KNN f1 score: 0.352
KNN cohens kappa score: 0.321


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6446, 314
LR fn, tp: 26, 171
LR f1 score: 0.501
LR cohens kappa score: 0.481
LR average precision score: 0.723

-> test with 'GB'
GB tn, fp: 6450, 310
GB fn, tp: 32, 165
GB f1 score: 0.491
GB cohens kappa score: 0.470

-> test with 'KNN'
KNN tn, fp: 6071, 689
KNN fn, tp: 34, 163
KNN f1 score: 0.311
KNN cohens kappa score: 0.278


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6382, 378
LR fn, tp: 11, 186
LR f1 score: 0.489
LR cohens kappa score: 0.466
LR average precision score: 0.809

-> test with 'GB'
GB tn, fp: 6415, 345
GB fn, tp: 23, 174
GB f1 score: 0.486
GB cohens kappa score: 0.464

-> test with 'KNN'
KNN tn, fp: 6045, 715
KNN fn, tp: 18, 179
KNN f1 score: 0.328
KNN cohens kappa score: 0.295


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6422, 337
LR fn, tp: 21, 172
LR f1 score: 0.490
LR cohens kappa score: 0.469
LR average precision score: 0.757

-> test with 'GB'
GB tn, fp: 6424, 335
GB fn, tp: 23, 170
GB f1 score: 0.487
GB cohens kappa score: 0.466

-> test with 'KNN'
KNN tn, fp: 6139, 620
KNN fn, tp: 13, 180
KNN f1 score: 0.363
KNN cohens kappa score: 0.333


====== 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: 6420, 340
LR fn, tp: 26, 171
LR f1 score: 0.483
LR cohens kappa score: 0.461
LR average precision score: 0.751

-> test with 'GB'
GB tn, fp: 6421, 339
GB fn, tp: 28, 169
GB f1 score: 0.479
GB cohens kappa score: 0.457

-> test with 'KNN'
KNN tn, fp: 6149, 611
KNN fn, tp: 30, 167
KNN f1 score: 0.343
KNN cohens kappa score: 0.311


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6452, 308
LR fn, tp: 26, 171
LR f1 score: 0.506
LR cohens kappa score: 0.485
LR average precision score: 0.749

-> test with 'GB'
GB tn, fp: 6479, 281
GB fn, tp: 31, 166
GB f1 score: 0.516
GB cohens kappa score: 0.496

-> test with 'KNN'
KNN tn, fp: 6086, 674
KNN fn, tp: 18, 179
KNN f1 score: 0.341
KNN cohens kappa score: 0.309


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6427, 333
LR fn, tp: 15, 182
LR f1 score: 0.511
LR cohens kappa score: 0.490
LR average precision score: 0.800

-> test with 'GB'
GB tn, fp: 6420, 340
GB fn, tp: 23, 174
GB f1 score: 0.489
GB cohens kappa score: 0.468

-> test with 'KNN'
KNN tn, fp: 6145, 615
KNN fn, tp: 20, 177
KNN f1 score: 0.358
KNN cohens kappa score: 0.327


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6418, 342
LR fn, tp: 19, 178
LR f1 score: 0.497
LR cohens kappa score: 0.475
LR average precision score: 0.740

-> test with 'GB'
GB tn, fp: 6453, 307
GB fn, tp: 26, 171
GB f1 score: 0.507
GB cohens kappa score: 0.486

-> test with 'KNN'
KNN tn, fp: 6117, 643
KNN fn, tp: 21, 176
KNN f1 score: 0.346
KNN cohens kappa score: 0.315


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6392, 367
LR fn, tp: 16, 177
LR f1 score: 0.480
LR cohens kappa score: 0.458
LR average precision score: 0.796

-> test with 'GB'
GB tn, fp: 6409, 350
GB fn, tp: 18, 175
GB f1 score: 0.487
GB cohens kappa score: 0.466

-> test with 'KNN'
KNN tn, fp: 6104, 655
KNN fn, tp: 12, 181
KNN f1 score: 0.352
KNN cohens kappa score: 0.321


====== 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: 6435, 325
LR fn, tp: 22, 175
LR f1 score: 0.502
LR cohens kappa score: 0.481
LR average precision score: 0.761

-> test with 'GB'
GB tn, fp: 6469, 291
GB fn, tp: 27, 170
GB f1 score: 0.517
GB cohens kappa score: 0.497

-> test with 'KNN'
KNN tn, fp: 6158, 602
KNN fn, tp: 16, 181
KNN f1 score: 0.369
KNN cohens kappa score: 0.340


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6431, 329
LR fn, tp: 30, 167
LR f1 score: 0.482
LR cohens kappa score: 0.460
LR average precision score: 0.728

-> test with 'GB'
GB tn, fp: 6413, 347
GB fn, tp: 27, 170
GB f1 score: 0.476
GB cohens kappa score: 0.454

-> test with 'KNN'
KNN tn, fp: 6158, 602
KNN fn, tp: 30, 167
KNN f1 score: 0.346
KNN cohens kappa score: 0.315


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6360, 400
LR fn, tp: 26, 171
LR f1 score: 0.445
LR cohens kappa score: 0.421
LR average precision score: 0.731

-> test with 'GB'
GB tn, fp: 6400, 360
GB fn, tp: 23, 174
GB f1 score: 0.476
GB cohens kappa score: 0.453

-> test with 'KNN'
KNN tn, fp: 6074, 686
KNN fn, tp: 23, 174
KNN f1 score: 0.329
KNN cohens kappa score: 0.297


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6430, 330
LR fn, tp: 17, 180
LR f1 score: 0.509
LR cohens kappa score: 0.488
LR average precision score: 0.819

-> test with 'GB'
GB tn, fp: 6469, 291
GB fn, tp: 24, 173
GB f1 score: 0.523
GB cohens kappa score: 0.504

-> test with 'KNN'
KNN tn, fp: 6086, 674
KNN fn, tp: 24, 173
KNN f1 score: 0.331
KNN cohens kappa score: 0.299


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6409, 350
LR fn, tp: 18, 175
LR f1 score: 0.487
LR cohens kappa score: 0.466
LR average precision score: 0.764

-> test with 'GB'
GB tn, fp: 6476, 283
GB fn, tp: 27, 166
GB f1 score: 0.517
GB cohens kappa score: 0.498

-> test with 'KNN'
KNN tn, fp: 6094, 665
KNN fn, tp: 23, 170
KNN f1 score: 0.331
KNN cohens kappa score: 0.299

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 6472, 400
LR fn, tp: 30, 186
LR f1 score: 0.525
LR cohens kappa score: 0.505
LR average precision score: 0.827


average:
LR tn, fp: 6420.4, 339.4
LR fn, tp: 21.24, 174.96
LR f1 score: 0.493
LR cohens kappa score: 0.471
LR average precision score: 0.768


minimum:
LR tn, fp: 6360, 287
LR fn, tp: 11, 164
LR f1 score: 0.445
LR cohens kappa score: 0.421
LR average precision score: 0.723


-----[ GB ]-----
maximum:
GB tn, fp: 6479, 360
GB fn, tp: 32, 175
GB f1 score: 0.523
GB cohens kappa score: 0.504


average:
GB tn, fp: 6440.2, 319.6
GB fn, tp: 25.88, 170.32
GB f1 score: 0.497
GB cohens kappa score: 0.476


minimum:
GB tn, fp: 6400, 280
GB fn, tp: 18, 162
GB f1 score: 0.476
GB cohens kappa score: 0.453


-----[ KNN ]-----
maximum:
KNN tn, fp: 6175, 715
KNN fn, tp: 34, 181
KNN f1 score: 0.369
KNN cohens kappa score: 0.340


average:
KNN tn, fp: 6113.12, 646.68
KNN fn, tp: 21.88, 174.32
KNN f1 score: 0.343
KNN cohens kappa score: 0.312


minimum:
KNN tn, fp: 6045, 585
KNN fn, tp: 12, 163
KNN f1 score: 0.311
KNN cohens kappa score: 0.278