ConvGeNBenchmarkResults/data_result/convGAN-old-5/kaggle_creditcard.log

///////////////////////////////////////////
// Running convGAN 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: 54819, 2044
LR fn, tp: 16, 83
LR f1 score: 0.075
LR cohens kappa score: 0.071
LR average precision score: 0.579

-> test with 'GB'
GB tn, fp: 56577, 286
GB fn, tp: 20, 79
GB f1 score: 0.341
GB cohens kappa score: 0.339

-> test with 'KNN'
KNN tn, fp: 56521, 342
KNN fn, tp: 76, 23
KNN f1 score: 0.099
KNN cohens kappa score: 0.097


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54137, 2726
LR fn, tp: 10, 89
LR f1 score: 0.061
LR cohens kappa score: 0.058
LR average precision score: 0.703

-> test with 'GB'
GB tn, fp: 56550, 313
GB fn, tp: 10, 89
GB f1 score: 0.355
GB cohens kappa score: 0.353

-> test with 'KNN'
KNN tn, fp: 56389, 474
KNN fn, tp: 76, 23
KNN f1 score: 0.077
KNN cohens kappa score: 0.074


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 55190, 1673
LR fn, tp: 10, 89
LR f1 score: 0.096
LR cohens kappa score: 0.093
LR average precision score: 0.682

-> test with 'GB'
GB tn, fp: 56545, 318
GB fn, tp: 13, 86
GB f1 score: 0.342
GB cohens kappa score: 0.340

-> test with 'KNN'
KNN tn, fp: 56445, 418
KNN fn, tp: 77, 22
KNN f1 score: 0.082
KNN cohens kappa score: 0.079


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 55233, 1630
LR fn, tp: 7, 92
LR f1 score: 0.101
LR cohens kappa score: 0.098
LR average precision score: 0.758

-> test with 'GB'
GB tn, fp: 56495, 368
GB fn, tp: 8, 91
GB f1 score: 0.326
GB cohens kappa score: 0.324

-> test with 'KNN'
KNN tn, fp: 56516, 347
KNN fn, tp: 67, 32
KNN f1 score: 0.134
KNN cohens kappa score: 0.131


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 54918, 1945
LR fn, tp: 9, 87
LR f1 score: 0.082
LR cohens kappa score: 0.079
LR average precision score: 0.801

-> test with 'GB'
GB tn, fp: 56450, 413
GB fn, tp: 9, 87
GB f1 score: 0.292
GB cohens kappa score: 0.290

-> test with 'KNN'
KNN tn, fp: 56499, 364
KNN fn, tp: 70, 26
KNN f1 score: 0.107
KNN cohens kappa score: 0.105


====== 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: 53350, 3513
LR fn, tp: 12, 87
LR f1 score: 0.047
LR cohens kappa score: 0.044
LR average precision score: 0.699

-> test with 'GB'
GB tn, fp: 56557, 306
GB fn, tp: 10, 89
GB f1 score: 0.360
GB cohens kappa score: 0.359

-> test with 'KNN'
KNN tn, fp: 56382, 481
KNN fn, tp: 73, 26
KNN f1 score: 0.086
KNN cohens kappa score: 0.083


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54196, 2667
LR fn, tp: 10, 89
LR f1 score: 0.062
LR cohens kappa score: 0.059
LR average precision score: 0.652

-> test with 'GB'
GB tn, fp: 56434, 429
GB fn, tp: 10, 89
GB f1 score: 0.288
GB cohens kappa score: 0.286

-> test with 'KNN'
KNN tn, fp: 56532, 331
KNN fn, tp: 72, 27
KNN f1 score: 0.118
KNN cohens kappa score: 0.116


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54521, 2342
LR fn, tp: 9, 90
LR f1 score: 0.071
LR cohens kappa score: 0.068
LR average precision score: 0.714

-> test with 'GB'
GB tn, fp: 56529, 334
GB fn, tp: 11, 88
GB f1 score: 0.338
GB cohens kappa score: 0.336

-> test with 'KNN'
KNN tn, fp: 56627, 236
KNN fn, tp: 69, 30
KNN f1 score: 0.164
KNN cohens kappa score: 0.162


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54666, 2197
LR fn, tp: 10, 89
LR f1 score: 0.075
LR cohens kappa score: 0.072
LR average precision score: 0.716

-> test with 'GB'
GB tn, fp: 56528, 335
GB fn, tp: 12, 87
GB f1 score: 0.334
GB cohens kappa score: 0.332

-> test with 'KNN'
KNN tn, fp: 56610, 253
KNN fn, tp: 77, 22
KNN f1 score: 0.118
KNN cohens kappa score: 0.115


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 54988, 1875
LR fn, tp: 10, 86
LR f1 score: 0.084
LR cohens kappa score: 0.081
LR average precision score: 0.749

-> test with 'GB'
GB tn, fp: 56518, 345
GB fn, tp: 15, 81
GB f1 score: 0.310
GB cohens kappa score: 0.308

-> test with 'KNN'
KNN tn, fp: 56611, 252
KNN fn, tp: 74, 22
KNN f1 score: 0.119
KNN cohens kappa score: 0.117


====== 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: 54577, 2286
LR fn, tp: 10, 89
LR f1 score: 0.072
LR cohens kappa score: 0.069
LR average precision score: 0.672

-> test with 'GB'
GB tn, fp: 56596, 267
GB fn, tp: 14, 85
GB f1 score: 0.377
GB cohens kappa score: 0.375

-> test with 'KNN'
KNN tn, fp: 56344, 519
KNN fn, tp: 75, 24
KNN f1 score: 0.075
KNN cohens kappa score: 0.072


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 55026, 1837
LR fn, tp: 9, 90
LR f1 score: 0.089
LR cohens kappa score: 0.086
LR average precision score: 0.642

-> test with 'GB'
GB tn, fp: 56583, 280
GB fn, tp: 11, 88
GB f1 score: 0.377
GB cohens kappa score: 0.375

-> test with 'KNN'
KNN tn, fp: 56348, 515
KNN fn, tp: 73, 26
KNN f1 score: 0.081
KNN cohens kappa score: 0.079


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54796, 2067
LR fn, tp: 10, 89
LR f1 score: 0.079
LR cohens kappa score: 0.076
LR average precision score: 0.703

-> test with 'GB'
GB tn, fp: 56500, 363
GB fn, tp: 13, 86
GB f1 score: 0.314
GB cohens kappa score: 0.312

-> test with 'KNN'
KNN tn, fp: 56714, 149
KNN fn, tp: 79, 20
KNN f1 score: 0.149
KNN cohens kappa score: 0.147


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 55442, 1421
LR fn, tp: 9, 90
LR f1 score: 0.112
LR cohens kappa score: 0.109
LR average precision score: 0.780

-> test with 'GB'
GB tn, fp: 56547, 316
GB fn, tp: 11, 88
GB f1 score: 0.350
GB cohens kappa score: 0.348

-> test with 'KNN'
KNN tn, fp: 56278, 585
KNN fn, tp: 70, 29
KNN f1 score: 0.081
KNN cohens kappa score: 0.079


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 55364, 1499
LR fn, tp: 11, 85
LR f1 score: 0.101
LR cohens kappa score: 0.098
LR average precision score: 0.770

-> test with 'GB'
GB tn, fp: 56585, 278
GB fn, tp: 13, 83
GB f1 score: 0.363
GB cohens kappa score: 0.362

-> test with 'KNN'
KNN tn, fp: 56435, 428
KNN fn, tp: 69, 27
KNN f1 score: 0.098
KNN cohens kappa score: 0.095


====== 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: 54526, 2337
LR fn, tp: 6, 93
LR f1 score: 0.074
LR cohens kappa score: 0.070
LR average precision score: 0.693

-> test with 'GB'
GB tn, fp: 56538, 325
GB fn, tp: 8, 91
GB f1 score: 0.353
GB cohens kappa score: 0.352

-> test with 'KNN'
KNN tn, fp: 56573, 290
KNN fn, tp: 80, 19
KNN f1 score: 0.093
KNN cohens kappa score: 0.091


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54747, 2116
LR fn, tp: 12, 87
LR f1 score: 0.076
LR cohens kappa score: 0.073
LR average precision score: 0.650

-> test with 'GB'
GB tn, fp: 56562, 301
GB fn, tp: 13, 86
GB f1 score: 0.354
GB cohens kappa score: 0.352

-> test with 'KNN'
KNN tn, fp: 56524, 339
KNN fn, tp: 79, 20
KNN f1 score: 0.087
KNN cohens kappa score: 0.085


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 55021, 1842
LR fn, tp: 10, 89
LR f1 score: 0.088
LR cohens kappa score: 0.085
LR average precision score: 0.733

-> test with 'GB'
GB tn, fp: 56539, 324
GB fn, tp: 13, 86
GB f1 score: 0.338
GB cohens kappa score: 0.336

-> test with 'KNN'
KNN tn, fp: 56689, 174
KNN fn, tp: 77, 22
KNN f1 score: 0.149
KNN cohens kappa score: 0.147


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 55209, 1654
LR fn, tp: 7, 92
LR f1 score: 0.100
LR cohens kappa score: 0.097
LR average precision score: 0.777

-> test with 'GB'
GB tn, fp: 56435, 428
GB fn, tp: 10, 89
GB f1 score: 0.289
GB cohens kappa score: 0.287

-> test with 'KNN'
KNN tn, fp: 56295, 568
KNN fn, tp: 62, 37
KNN f1 score: 0.105
KNN cohens kappa score: 0.102


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 55824, 1039
LR fn, tp: 12, 84
LR f1 score: 0.138
LR cohens kappa score: 0.135
LR average precision score: 0.729

-> test with 'GB'
GB tn, fp: 56665, 198
GB fn, tp: 16, 80
GB f1 score: 0.428
GB cohens kappa score: 0.426

-> test with 'KNN'
KNN tn, fp: 56285, 578
KNN fn, tp: 69, 27
KNN f1 score: 0.077
KNN cohens kappa score: 0.074


====== 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: 54476, 2387
LR fn, tp: 14, 85
LR f1 score: 0.066
LR cohens kappa score: 0.063
LR average precision score: 0.664

-> test with 'GB'
GB tn, fp: 56599, 264
GB fn, tp: 18, 81
GB f1 score: 0.365
GB cohens kappa score: 0.363

-> test with 'KNN'
KNN tn, fp: 56702, 161
KNN fn, tp: 77, 22
KNN f1 score: 0.156
KNN cohens kappa score: 0.154


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54533, 2330
LR fn, tp: 7, 92
LR f1 score: 0.073
LR cohens kappa score: 0.070
LR average precision score: 0.764

-> test with 'GB'
GB tn, fp: 56521, 342
GB fn, tp: 8, 91
GB f1 score: 0.342
GB cohens kappa score: 0.340

-> test with 'KNN'
KNN tn, fp: 56554, 309
KNN fn, tp: 74, 25
KNN f1 score: 0.115
KNN cohens kappa score: 0.113


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54993, 1870
LR fn, tp: 11, 88
LR f1 score: 0.086
LR cohens kappa score: 0.083
LR average precision score: 0.687

-> test with 'GB'
GB tn, fp: 56481, 382
GB fn, tp: 13, 86
GB f1 score: 0.303
GB cohens kappa score: 0.301

-> test with 'KNN'
KNN tn, fp: 56727, 136
KNN fn, tp: 76, 23
KNN f1 score: 0.178
KNN cohens kappa score: 0.177


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54666, 2197
LR fn, tp: 8, 91
LR f1 score: 0.076
LR cohens kappa score: 0.073
LR average precision score: 0.775

-> test with 'GB'
GB tn, fp: 56535, 328
GB fn, tp: 10, 89
GB f1 score: 0.345
GB cohens kappa score: 0.343

-> test with 'KNN'
KNN tn, fp: 56518, 345
KNN fn, tp: 76, 23
KNN f1 score: 0.099
KNN cohens kappa score: 0.096


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 55361, 1502
LR fn, tp: 8, 88
LR f1 score: 0.104
LR cohens kappa score: 0.102
LR average precision score: 0.688

-> test with 'GB'
GB tn, fp: 56557, 306
GB fn, tp: 10, 86
GB f1 score: 0.352
GB cohens kappa score: 0.351

-> test with 'KNN'
KNN tn, fp: 56283, 580
KNN fn, tp: 67, 29
KNN f1 score: 0.082
KNN cohens kappa score: 0.080

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 55824, 3513
LR fn, tp: 16, 93
LR f1 score: 0.138
LR cohens kappa score: 0.135
LR average precision score: 0.801


average:
LR tn, fp: 54823.16, 2039.84
LR fn, tp: 9.88, 88.52
LR f1 score: 0.083
LR cohens kappa score: 0.080
LR average precision score: 0.711


minimum:
LR tn, fp: 53350, 1039
LR fn, tp: 6, 83
LR f1 score: 0.047
LR cohens kappa score: 0.044
LR average precision score: 0.579


-----[ GB ]-----
maximum:
GB tn, fp: 56665, 429
GB fn, tp: 20, 91
GB f1 score: 0.428
GB cohens kappa score: 0.426


average:
GB tn, fp: 56537.04, 325.96
GB fn, tp: 11.96, 86.44
GB f1 score: 0.341
GB cohens kappa score: 0.340


minimum:
GB tn, fp: 56434, 198
GB fn, tp: 8, 79
GB f1 score: 0.288
GB cohens kappa score: 0.286


-----[ KNN ]-----
maximum:
KNN tn, fp: 56727, 585
KNN fn, tp: 80, 37
KNN f1 score: 0.178
KNN cohens kappa score: 0.177


average:
KNN tn, fp: 56496.04, 366.96
KNN fn, tp: 73.36, 25.04
KNN f1 score: 0.109
KNN cohens kappa score: 0.107


minimum:
KNN tn, fp: 56278, 136
KNN fn, tp: 62, 19
KNN f1 score: 0.075
KNN cohens kappa score: 0.072