ConvGeNBenchmarkResults/data_result/ProWRAS/imblearn_webpage.log

///////////////////////////////////////////
// Running ProWRAS 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: 6554, 206
LR fn, tp: 32, 165
LR f1 score: 0.581
LR cohens kappa score: 0.565
LR average precision score: 0.774

-> test with 'GB'
GB tn, fp: 6707, 53
GB fn, tp: 111, 86
GB f1 score: 0.512
GB cohens kappa score: 0.500

-> test with 'KNN'
KNN tn, fp: 5975, 785
KNN fn, tp: 10, 187
KNN f1 score: 0.320
KNN cohens kappa score: 0.286


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6559, 201
LR fn, tp: 38, 159
LR f1 score: 0.571
LR cohens kappa score: 0.555
LR average precision score: 0.770

-> test with 'GB'
GB tn, fp: 6710, 50
GB fn, tp: 108, 89
GB f1 score: 0.530
GB cohens kappa score: 0.518

-> test with 'KNN'
KNN tn, fp: 6119, 641
KNN fn, tp: 17, 180
KNN f1 score: 0.354
KNN cohens kappa score: 0.323


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6575, 185
LR fn, tp: 23, 174
LR f1 score: 0.626
LR cohens kappa score: 0.612
LR average precision score: 0.837

-> test with 'GB'
GB tn, fp: 6720, 40
GB fn, tp: 106, 91
GB f1 score: 0.555
GB cohens kappa score: 0.545

-> test with 'KNN'
KNN tn, fp: 6033, 727
KNN fn, tp: 18, 179
KNN f1 score: 0.325
KNN cohens kappa score: 0.292


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6557, 203
LR fn, tp: 32, 165
LR f1 score: 0.584
LR cohens kappa score: 0.568
LR average precision score: 0.750

-> test with 'GB'
GB tn, fp: 6699, 61
GB fn, tp: 115, 82
GB f1 score: 0.482
GB cohens kappa score: 0.470

-> test with 'KNN'
KNN tn, fp: 6024, 736
KNN fn, tp: 20, 177
KNN f1 score: 0.319
KNN cohens kappa score: 0.286


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6583, 176
LR fn, tp: 43, 150
LR f1 score: 0.578
LR cohens kappa score: 0.563
LR average precision score: 0.737

-> test with 'GB'
GB tn, fp: 6715, 44
GB fn, tp: 114, 79
GB f1 score: 0.500
GB cohens kappa score: 0.489

-> test with 'KNN'
KNN tn, fp: 6076, 683
KNN fn, tp: 27, 166
KNN f1 score: 0.319
KNN cohens kappa score: 0.286


====== 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: 6602, 158
LR fn, tp: 36, 161
LR f1 score: 0.624
LR cohens kappa score: 0.610
LR average precision score: 0.782

-> test with 'GB'
GB tn, fp: 6699, 61
GB fn, tp: 106, 91
GB f1 score: 0.521
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 6112, 648
KNN fn, tp: 17, 180
KNN f1 score: 0.351
KNN cohens kappa score: 0.320


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6563, 197
LR fn, tp: 31, 166
LR f1 score: 0.593
LR cohens kappa score: 0.577
LR average precision score: 0.804

-> test with 'GB'
GB tn, fp: 6694, 66
GB fn, tp: 102, 95
GB f1 score: 0.531
GB cohens kappa score: 0.518

-> test with 'KNN'
KNN tn, fp: 5926, 834
KNN fn, tp: 16, 181
KNN f1 score: 0.299
KNN cohens kappa score: 0.264


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6564, 196
LR fn, tp: 42, 155
LR f1 score: 0.566
LR cohens kappa score: 0.549
LR average precision score: 0.736

-> test with 'GB'
GB tn, fp: 6706, 54
GB fn, tp: 112, 85
GB f1 score: 0.506
GB cohens kappa score: 0.494

-> test with 'KNN'
KNN tn, fp: 6148, 612
KNN fn, tp: 21, 176
KNN f1 score: 0.357
KNN cohens kappa score: 0.327


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6534, 226
LR fn, tp: 34, 163
LR f1 score: 0.556
LR cohens kappa score: 0.539
LR average precision score: 0.754

-> test with 'GB'
GB tn, fp: 6716, 44
GB fn, tp: 120, 77
GB f1 score: 0.484
GB cohens kappa score: 0.473

-> test with 'KNN'
KNN tn, fp: 5999, 761
KNN fn, tp: 19, 178
KNN f1 score: 0.313
KNN cohens kappa score: 0.280


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6559, 200
LR fn, tp: 31, 162
LR f1 score: 0.584
LR cohens kappa score: 0.568
LR average precision score: 0.770

-> test with 'GB'
GB tn, fp: 6713, 46
GB fn, tp: 119, 74
GB f1 score: 0.473
GB cohens kappa score: 0.461

-> test with 'KNN'
KNN tn, fp: 6025, 734
KNN fn, tp: 23, 170
KNN f1 score: 0.310
KNN cohens kappa score: 0.277


====== 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: 6551, 209
LR fn, tp: 34, 163
LR f1 score: 0.573
LR cohens kappa score: 0.557
LR average precision score: 0.756

-> test with 'GB'
GB tn, fp: 6699, 61
GB fn, tp: 108, 89
GB f1 score: 0.513
GB cohens kappa score: 0.501

-> test with 'KNN'
KNN tn, fp: 6038, 722
KNN fn, tp: 23, 174
KNN f1 score: 0.318
KNN cohens kappa score: 0.285


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6578, 182
LR fn, tp: 29, 168
LR f1 score: 0.614
LR cohens kappa score: 0.600
LR average precision score: 0.796

-> test with 'GB'
GB tn, fp: 6703, 57
GB fn, tp: 108, 89
GB f1 score: 0.519
GB cohens kappa score: 0.507

-> test with 'KNN'
KNN tn, fp: 6025, 735
KNN fn, tp: 17, 180
KNN f1 score: 0.324
KNN cohens kappa score: 0.291


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6591, 169
LR fn, tp: 44, 153
LR f1 score: 0.590
LR cohens kappa score: 0.575
LR average precision score: 0.727

-> test with 'GB'
GB tn, fp: 6726, 34
GB fn, tp: 121, 76
GB f1 score: 0.495
GB cohens kappa score: 0.485

-> test with 'KNN'
KNN tn, fp: 6124, 636
KNN fn, tp: 27, 170
KNN f1 score: 0.339
KNN cohens kappa score: 0.307


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6541, 219
LR fn, tp: 26, 171
LR f1 score: 0.583
LR cohens kappa score: 0.566
LR average precision score: 0.804

-> test with 'GB'
GB tn, fp: 6695, 65
GB fn, tp: 107, 90
GB f1 score: 0.511
GB cohens kappa score: 0.499

-> test with 'KNN'
KNN tn, fp: 5968, 792
KNN fn, tp: 18, 179
KNN f1 score: 0.307
KNN cohens kappa score: 0.272


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6569, 190
LR fn, tp: 33, 160
LR f1 score: 0.589
LR cohens kappa score: 0.574
LR average precision score: 0.787

-> test with 'GB'
GB tn, fp: 6710, 49
GB fn, tp: 105, 88
GB f1 score: 0.533
GB cohens kappa score: 0.522

-> test with 'KNN'
KNN tn, fp: 6042, 717
KNN fn, tp: 12, 181
KNN f1 score: 0.332
KNN cohens kappa score: 0.300


====== 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: 6563, 197
LR fn, tp: 48, 149
LR f1 score: 0.549
LR cohens kappa score: 0.532
LR average precision score: 0.728

-> test with 'GB'
GB tn, fp: 6724, 36
GB fn, tp: 115, 82
GB f1 score: 0.521
GB cohens kappa score: 0.510

-> test with 'KNN'
KNN tn, fp: 6102, 658
KNN fn, tp: 25, 172
KNN f1 score: 0.335
KNN cohens kappa score: 0.303


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6549, 211
LR fn, tp: 28, 169
LR f1 score: 0.586
LR cohens kappa score: 0.570
LR average precision score: 0.769

-> test with 'GB'
GB tn, fp: 6704, 56
GB fn, tp: 110, 87
GB f1 score: 0.512
GB cohens kappa score: 0.500

-> test with 'KNN'
KNN tn, fp: 6013, 747
KNN fn, tp: 18, 179
KNN f1 score: 0.319
KNN cohens kappa score: 0.285


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6577, 183
LR fn, tp: 27, 170
LR f1 score: 0.618
LR cohens kappa score: 0.604
LR average precision score: 0.786

-> test with 'GB'
GB tn, fp: 6723, 37
GB fn, tp: 109, 88
GB f1 score: 0.547
GB cohens kappa score: 0.536

-> test with 'KNN'
KNN tn, fp: 5999, 761
KNN fn, tp: 19, 178
KNN f1 score: 0.313
KNN cohens kappa score: 0.280


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6561, 199
LR fn, tp: 40, 157
LR f1 score: 0.568
LR cohens kappa score: 0.551
LR average precision score: 0.747

-> test with 'GB'
GB tn, fp: 6700, 60
GB fn, tp: 125, 72
GB f1 score: 0.438
GB cohens kappa score: 0.425

-> test with 'KNN'
KNN tn, fp: 5996, 764
KNN fn, tp: 17, 180
KNN f1 score: 0.316
KNN cohens kappa score: 0.282


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6574, 185
LR fn, tp: 29, 164
LR f1 score: 0.605
LR cohens kappa score: 0.591
LR average precision score: 0.795

-> test with 'GB'
GB tn, fp: 6716, 43
GB fn, tp: 106, 87
GB f1 score: 0.539
GB cohens kappa score: 0.528

-> test with 'KNN'
KNN tn, fp: 6021, 738
KNN fn, tp: 10, 183
KNN f1 score: 0.329
KNN cohens kappa score: 0.296


====== 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: 6548, 212
LR fn, tp: 36, 161
LR f1 score: 0.565
LR cohens kappa score: 0.548
LR average precision score: 0.750

-> test with 'GB'
GB tn, fp: 6715, 45
GB fn, tp: 106, 91
GB f1 score: 0.547
GB cohens kappa score: 0.536

-> test with 'KNN'
KNN tn, fp: 6080, 680
KNN fn, tp: 16, 181
KNN f1 score: 0.342
KNN cohens kappa score: 0.310


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6566, 194
LR fn, tp: 32, 165
LR f1 score: 0.594
LR cohens kappa score: 0.578
LR average precision score: 0.762

-> test with 'GB'
GB tn, fp: 6683, 77
GB fn, tp: 122, 75
GB f1 score: 0.430
GB cohens kappa score: 0.415

-> test with 'KNN'
KNN tn, fp: 6068, 692
KNN fn, tp: 22, 175
KNN f1 score: 0.329
KNN cohens kappa score: 0.296


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6538, 222
LR fn, tp: 39, 158
LR f1 score: 0.548
LR cohens kappa score: 0.530
LR average precision score: 0.741

-> test with 'GB'
GB tn, fp: 6709, 51
GB fn, tp: 105, 92
GB f1 score: 0.541
GB cohens kappa score: 0.530

-> test with 'KNN'
KNN tn, fp: 5967, 793
KNN fn, tp: 16, 181
KNN f1 score: 0.309
KNN cohens kappa score: 0.275


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26255 synthetic samples
-> test with 'LR'
LR tn, fp: 6570, 190
LR fn, tp: 33, 164
LR f1 score: 0.595
LR cohens kappa score: 0.580
LR average precision score: 0.811

-> test with 'GB'
GB tn, fp: 6714, 46
GB fn, tp: 113, 84
GB f1 score: 0.514
GB cohens kappa score: 0.503

-> test with 'KNN'
KNN tn, fp: 6012, 748
KNN fn, tp: 19, 178
KNN f1 score: 0.317
KNN cohens kappa score: 0.284


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 26252 synthetic samples
-> test with 'LR'
LR tn, fp: 6572, 187
LR fn, tp: 42, 151
LR f1 score: 0.569
LR cohens kappa score: 0.553
LR average precision score: 0.726

-> test with 'GB'
GB tn, fp: 6695, 64
GB fn, tp: 105, 88
GB f1 score: 0.510
GB cohens kappa score: 0.498

-> test with 'KNN'
KNN tn, fp: 6127, 632
KNN fn, tp: 17, 176
KNN f1 score: 0.352
KNN cohens kappa score: 0.321

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 6602, 226
LR fn, tp: 48, 174
LR f1 score: 0.626
LR cohens kappa score: 0.612
LR average precision score: 0.837


average:
LR tn, fp: 6563.92, 195.88
LR fn, tp: 34.48, 161.72
LR f1 score: 0.584
LR cohens kappa score: 0.569
LR average precision score: 0.768


minimum:
LR tn, fp: 6534, 158
LR fn, tp: 23, 149
LR f1 score: 0.548
LR cohens kappa score: 0.530
LR average precision score: 0.726


-----[ GB ]-----
maximum:
GB tn, fp: 6726, 77
GB fn, tp: 125, 95
GB f1 score: 0.555
GB cohens kappa score: 0.545


average:
GB tn, fp: 6707.8, 52.0
GB fn, tp: 111.12, 85.08
GB f1 score: 0.511
GB cohens kappa score: 0.499


minimum:
GB tn, fp: 6683, 34
GB fn, tp: 102, 72
GB f1 score: 0.430
GB cohens kappa score: 0.415


-----[ KNN ]-----
maximum:
KNN tn, fp: 6148, 834
KNN fn, tp: 27, 187
KNN f1 score: 0.357
KNN cohens kappa score: 0.327


average:
KNN tn, fp: 6040.76, 719.04
KNN fn, tp: 18.56, 177.64
KNN f1 score: 0.326
KNN cohens kappa score: 0.293


minimum:
KNN tn, fp: 5926, 612
KNN fn, tp: 10, 166
KNN f1 score: 0.299
KNN cohens kappa score: 0.264