ConvGeNBenchmarkResults/data_result/ProWRAS/kaggle_creditcard.log

///////////////////////////////////////////
// Running ProWRAS on kaggle_creditcard
///////////////////////////////////////////

Load 'data_input/kaggle_creditcard'
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 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56773, 90
LR fn, tp: 25, 74
LR f1 score: 0.563
LR cohens kappa score: 0.562
LR average precision score: 0.550

-> test with 'GB'
GB tn, fp: 56841, 22
GB fn, tp: 27, 72
GB f1 score: 0.746
GB cohens kappa score: 0.746

-> test with 'KNN'
KNN tn, fp: 56730, 133
KNN fn, tp: 84, 15
KNN f1 score: 0.121
KNN cohens kappa score: 0.120


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56357, 506
LR fn, tp: 13, 86
LR f1 score: 0.249
LR cohens kappa score: 0.247
LR average precision score: 0.713

-> test with 'GB'
GB tn, fp: 56852, 11
GB fn, tp: 19, 80
GB f1 score: 0.842
GB cohens kappa score: 0.842

-> test with 'KNN'
KNN tn, fp: 56696, 167
KNN fn, tp: 88, 11
KNN f1 score: 0.079
KNN cohens kappa score: 0.077


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56625, 238
LR fn, tp: 14, 85
LR f1 score: 0.403
LR cohens kappa score: 0.401
LR average precision score: 0.721

-> test with 'GB'
GB tn, fp: 56847, 16
GB fn, tp: 16, 83
GB f1 score: 0.838
GB cohens kappa score: 0.838

-> test with 'KNN'
KNN tn, fp: 56725, 138
KNN fn, tp: 91, 8
KNN f1 score: 0.065
KNN cohens kappa score: 0.063


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56688, 175
LR fn, tp: 12, 87
LR f1 score: 0.482
LR cohens kappa score: 0.481
LR average precision score: 0.776

-> test with 'GB'
GB tn, fp: 56848, 15
GB fn, tp: 17, 82
GB f1 score: 0.837
GB cohens kappa score: 0.836

-> test with 'KNN'
KNN tn, fp: 56744, 119
KNN fn, tp: 85, 14
KNN f1 score: 0.121
KNN cohens kappa score: 0.119


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56571, 292
LR fn, tp: 11, 85
LR f1 score: 0.359
LR cohens kappa score: 0.358
LR average precision score: 0.845

-> test with 'GB'
GB tn, fp: 56854, 9
GB fn, tp: 16, 80
GB f1 score: 0.865
GB cohens kappa score: 0.865

-> test with 'KNN'
KNN tn, fp: 56730, 133
KNN fn, tp: 86, 10
KNN f1 score: 0.084
KNN cohens kappa score: 0.082


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56593, 270
LR fn, tp: 13, 86
LR f1 score: 0.378
LR cohens kappa score: 0.376
LR average precision score: 0.746

-> test with 'GB'
GB tn, fp: 56843, 20
GB fn, tp: 14, 85
GB f1 score: 0.833
GB cohens kappa score: 0.833

-> test with 'KNN'
KNN tn, fp: 56675, 188
KNN fn, tp: 85, 14
KNN f1 score: 0.093
KNN cohens kappa score: 0.091


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56531, 332
LR fn, tp: 13, 86
LR f1 score: 0.333
LR cohens kappa score: 0.331
LR average precision score: 0.666

-> test with 'GB'
GB tn, fp: 56840, 23
GB fn, tp: 17, 82
GB f1 score: 0.804
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 56724, 139
KNN fn, tp: 89, 10
KNN f1 score: 0.081
KNN cohens kappa score: 0.079


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56701, 162
LR fn, tp: 16, 83
LR f1 score: 0.483
LR cohens kappa score: 0.481
LR average precision score: 0.728

-> test with 'GB'
GB tn, fp: 56851, 12
GB fn, tp: 21, 78
GB f1 score: 0.825
GB cohens kappa score: 0.825

-> test with 'KNN'
KNN tn, fp: 56709, 154
KNN fn, tp: 86, 13
KNN f1 score: 0.098
KNN cohens kappa score: 0.096


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56521, 342
LR fn, tp: 18, 81
LR f1 score: 0.310
LR cohens kappa score: 0.308
LR average precision score: 0.702

-> test with 'GB'
GB tn, fp: 56853, 10
GB fn, tp: 25, 74
GB f1 score: 0.809
GB cohens kappa score: 0.808

-> test with 'KNN'
KNN tn, fp: 56742, 121
KNN fn, tp: 86, 13
KNN f1 score: 0.112
KNN cohens kappa score: 0.110


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56663, 200
LR fn, tp: 19, 77
LR f1 score: 0.413
LR cohens kappa score: 0.411
LR average precision score: 0.751

-> test with 'GB'
GB tn, fp: 56848, 15
GB fn, tp: 21, 75
GB f1 score: 0.806
GB cohens kappa score: 0.806

-> test with 'KNN'
KNN tn, fp: 56742, 121
KNN fn, tp: 83, 13
KNN f1 score: 0.113
KNN cohens kappa score: 0.111


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56781, 82
LR fn, tp: 21, 78
LR f1 score: 0.602
LR cohens kappa score: 0.601
LR average precision score: 0.657

-> test with 'GB'
GB tn, fp: 56835, 28
GB fn, tp: 24, 75
GB f1 score: 0.743
GB cohens kappa score: 0.742

-> test with 'KNN'
KNN tn, fp: 56708, 155
KNN fn, tp: 88, 11
KNN f1 score: 0.083
KNN cohens kappa score: 0.081


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56753, 110
LR fn, tp: 17, 82
LR f1 score: 0.564
LR cohens kappa score: 0.563
LR average precision score: 0.660

-> test with 'GB'
GB tn, fp: 56844, 19
GB fn, tp: 17, 82
GB f1 score: 0.820
GB cohens kappa score: 0.820

-> test with 'KNN'
KNN tn, fp: 56710, 153
KNN fn, tp: 88, 11
KNN f1 score: 0.084
KNN cohens kappa score: 0.082


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 55881, 982
LR fn, tp: 15, 84
LR f1 score: 0.144
LR cohens kappa score: 0.141
LR average precision score: 0.702

-> test with 'GB'
GB tn, fp: 56853, 10
GB fn, tp: 18, 81
GB f1 score: 0.853
GB cohens kappa score: 0.852

-> test with 'KNN'
KNN tn, fp: 56714, 149
KNN fn, tp: 85, 14
KNN f1 score: 0.107
KNN cohens kappa score: 0.105


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56492, 371
LR fn, tp: 11, 88
LR f1 score: 0.315
LR cohens kappa score: 0.313
LR average precision score: 0.796

-> test with 'GB'
GB tn, fp: 56850, 13
GB fn, tp: 17, 82
GB f1 score: 0.845
GB cohens kappa score: 0.845

-> test with 'KNN'
KNN tn, fp: 56709, 154
KNN fn, tp: 87, 12
KNN f1 score: 0.091
KNN cohens kappa score: 0.089


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56660, 203
LR fn, tp: 17, 79
LR f1 score: 0.418
LR cohens kappa score: 0.417
LR average precision score: 0.761

-> test with 'GB'
GB tn, fp: 56851, 12
GB fn, tp: 21, 75
GB f1 score: 0.820
GB cohens kappa score: 0.819

-> test with 'KNN'
KNN tn, fp: 56747, 116
KNN fn, tp: 85, 11
KNN f1 score: 0.099
KNN cohens kappa score: 0.097


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56418, 445
LR fn, tp: 11, 88
LR f1 score: 0.278
LR cohens kappa score: 0.276
LR average precision score: 0.717

-> test with 'GB'
GB tn, fp: 56849, 14
GB fn, tp: 16, 83
GB f1 score: 0.847
GB cohens kappa score: 0.847

-> test with 'KNN'
KNN tn, fp: 56722, 141
KNN fn, tp: 89, 10
KNN f1 score: 0.080
KNN cohens kappa score: 0.078


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 55857, 1006
LR fn, tp: 16, 83
LR f1 score: 0.140
LR cohens kappa score: 0.137
LR average precision score: 0.593

-> test with 'GB'
GB tn, fp: 56845, 18
GB fn, tp: 18, 81
GB f1 score: 0.818
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 56726, 137
KNN fn, tp: 91, 8
KNN f1 score: 0.066
KNN cohens kappa score: 0.064


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 55612, 1251
LR fn, tp: 17, 82
LR f1 score: 0.115
LR cohens kappa score: 0.112
LR average precision score: 0.675

-> test with 'GB'
GB tn, fp: 56853, 10
GB fn, tp: 26, 73
GB f1 score: 0.802
GB cohens kappa score: 0.802

-> test with 'KNN'
KNN tn, fp: 56710, 153
KNN fn, tp: 84, 15
KNN f1 score: 0.112
KNN cohens kappa score: 0.110


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56830, 33
LR fn, tp: 17, 82
LR f1 score: 0.766
LR cohens kappa score: 0.766
LR average precision score: 0.763

-> test with 'GB'
GB tn, fp: 56855, 8
GB fn, tp: 21, 78
GB f1 score: 0.843
GB cohens kappa score: 0.843

-> test with 'KNN'
KNN tn, fp: 56729, 134
KNN fn, tp: 80, 19
KNN f1 score: 0.151
KNN cohens kappa score: 0.149


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56653, 210
LR fn, tp: 21, 75
LR f1 score: 0.394
LR cohens kappa score: 0.392
LR average precision score: 0.710

-> test with 'GB'
GB tn, fp: 56850, 13
GB fn, tp: 23, 73
GB f1 score: 0.802
GB cohens kappa score: 0.802

-> test with 'KNN'
KNN tn, fp: 56733, 130
KNN fn, tp: 85, 11
KNN f1 score: 0.093
KNN cohens kappa score: 0.091


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56750, 113
LR fn, tp: 24, 75
LR f1 score: 0.523
LR cohens kappa score: 0.522
LR average precision score: 0.644

-> test with 'GB'
GB tn, fp: 56848, 15
GB fn, tp: 26, 73
GB f1 score: 0.781
GB cohens kappa score: 0.780

-> test with 'KNN'
KNN tn, fp: 56731, 132
KNN fn, tp: 90, 9
KNN f1 score: 0.075
KNN cohens kappa score: 0.073


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56593, 270
LR fn, tp: 12, 87
LR f1 score: 0.382
LR cohens kappa score: 0.380
LR average precision score: 0.759

-> test with 'GB'
GB tn, fp: 56845, 18
GB fn, tp: 17, 82
GB f1 score: 0.824
GB cohens kappa score: 0.824

-> test with 'KNN'
KNN tn, fp: 56716, 147
KNN fn, tp: 83, 16
KNN f1 score: 0.122
KNN cohens kappa score: 0.120


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56695, 168
LR fn, tp: 20, 79
LR f1 score: 0.457
LR cohens kappa score: 0.455
LR average precision score: 0.689

-> test with 'GB'
GB tn, fp: 56845, 18
GB fn, tp: 21, 78
GB f1 score: 0.800
GB cohens kappa score: 0.800

-> test with 'KNN'
KNN tn, fp: 56735, 128
KNN fn, tp: 90, 9
KNN f1 score: 0.076
KNN cohens kappa score: 0.074


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56407, 456
LR fn, tp: 13, 86
LR f1 score: 0.268
LR cohens kappa score: 0.266
LR average precision score: 0.764

-> test with 'GB'
GB tn, fp: 56856, 7
GB fn, tp: 19, 80
GB f1 score: 0.860
GB cohens kappa score: 0.860

-> test with 'KNN'
KNN tn, fp: 56736, 127
KNN fn, tp: 83, 16
KNN f1 score: 0.132
KNN cohens kappa score: 0.130


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56670, 193
LR fn, tp: 15, 81
LR f1 score: 0.438
LR cohens kappa score: 0.436
LR average precision score: 0.712

-> test with 'GB'
GB tn, fp: 56847, 16
GB fn, tp: 21, 75
GB f1 score: 0.802
GB cohens kappa score: 0.802

-> test with 'KNN'
KNN tn, fp: 56730, 133
KNN fn, tp: 84, 12
KNN f1 score: 0.100
KNN cohens kappa score: 0.098

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 56830, 1251
LR fn, tp: 25, 88
LR f1 score: 0.766
LR cohens kappa score: 0.766
LR average precision score: 0.845


average:
LR tn, fp: 56523.0, 340.0
LR fn, tp: 16.04, 82.36
LR f1 score: 0.391
LR cohens kappa score: 0.389
LR average precision score: 0.712


minimum:
LR tn, fp: 55612, 33
LR fn, tp: 11, 74
LR f1 score: 0.115
LR cohens kappa score: 0.112
LR average precision score: 0.550


-----[ GB ]-----
maximum:
GB tn, fp: 56856, 28
GB fn, tp: 27, 85
GB f1 score: 0.865
GB cohens kappa score: 0.865


average:
GB tn, fp: 56848.12, 14.88
GB fn, tp: 19.92, 78.48
GB f1 score: 0.819
GB cohens kappa score: 0.818


minimum:
GB tn, fp: 56835, 7
GB fn, tp: 14, 72
GB f1 score: 0.743
GB cohens kappa score: 0.742


-----[ KNN ]-----
maximum:
KNN tn, fp: 56747, 188
KNN fn, tp: 91, 19
KNN f1 score: 0.151
KNN cohens kappa score: 0.149


average:
KNN tn, fp: 56722.92, 140.08
KNN fn, tp: 86.2, 12.2
KNN f1 score: 0.097
KNN cohens kappa score: 0.096


minimum:
KNN tn, fp: 56675, 116
KNN fn, tp: 80, 8
KNN f1 score: 0.065
KNN cohens kappa score: 0.063