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

///////////////////////////////////////////
// Running convGAN-proximary-5 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 GAN.predict
GAN tn, fp: 34356, 22507
GAN fn, tp: 4, 95
GAN f1 score: 0.008
GAN cohens kappa score: 0.005

-> test with 'LR'
LR tn, fp: 54228, 2635
LR fn, tp: 17, 82
LR f1 score: 0.058
LR cohens kappa score: 0.055
LR average precision score: 0.548

-> test with 'GB'
GB tn, fp: 56650, 213
GB fn, tp: 19, 80
GB f1 score: 0.408
GB cohens kappa score: 0.407

-> test with 'KNN'
KNN tn, fp: 56611, 252
KNN fn, tp: 79, 20
KNN f1 score: 0.108
KNN cohens kappa score: 0.106


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 14063, 42800
GAN fn, tp: 1, 98
GAN f1 score: 0.005
GAN cohens kappa score: 0.001

-> test with 'LR'
LR tn, fp: 54148, 2715
LR fn, tp: 6, 93
LR f1 score: 0.064
LR cohens kappa score: 0.061
LR average precision score: 0.725

-> test with 'GB'
GB tn, fp: 56631, 232
GB fn, tp: 10, 89
GB f1 score: 0.424
GB cohens kappa score: 0.422

-> test with 'KNN'
KNN tn, fp: 56682, 181
KNN fn, tp: 79, 20
KNN f1 score: 0.133
KNN cohens kappa score: 0.131


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 56736, 127
GAN fn, tp: 47, 52
GAN f1 score: 0.374
GAN cohens kappa score: 0.373

-> test with 'LR'
LR tn, fp: 54573, 2290
LR fn, tp: 9, 90
LR f1 score: 0.073
LR cohens kappa score: 0.070
LR average precision score: 0.663

-> test with 'GB'
GB tn, fp: 56540, 323
GB fn, tp: 13, 86
GB f1 score: 0.339
GB cohens kappa score: 0.337

-> test with 'KNN'
KNN tn, fp: 56565, 298
KNN fn, tp: 77, 22
KNN f1 score: 0.105
KNN cohens kappa score: 0.103


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 56733, 130
GAN fn, tp: 33, 66
GAN f1 score: 0.447
GAN cohens kappa score: 0.446

-> test with 'LR'
LR tn, fp: 55214, 1649
LR fn, tp: 7, 92
LR f1 score: 0.100
LR cohens kappa score: 0.097
LR average precision score: 0.751

-> test with 'GB'
GB tn, fp: 56500, 363
GB fn, tp: 7, 92
GB f1 score: 0.332
GB cohens kappa score: 0.330

-> test with 'KNN'
KNN tn, fp: 56575, 288
KNN fn, tp: 73, 26
KNN f1 score: 0.126
KNN cohens kappa score: 0.124


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with GAN.predict
GAN tn, fp: 56708, 155
GAN fn, tp: 23, 73
GAN f1 score: 0.451
GAN cohens kappa score: 0.449

-> test with 'LR'
LR tn, fp: 55107, 1756
LR fn, tp: 7, 89
LR f1 score: 0.092
LR cohens kappa score: 0.089
LR average precision score: 0.857

-> test with 'GB'
GB tn, fp: 56479, 384
GB fn, tp: 9, 87
GB f1 score: 0.307
GB cohens kappa score: 0.305

-> test with 'KNN'
KNN tn, fp: 56549, 314
KNN fn, tp: 70, 26
KNN f1 score: 0.119
KNN cohens kappa score: 0.117


====== 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 GAN.predict
GAN tn, fp: 56555, 308
GAN fn, tp: 29, 70
GAN f1 score: 0.294
GAN cohens kappa score: 0.292

-> test with 'LR'
LR tn, fp: 54544, 2319
LR fn, tp: 7, 92
LR f1 score: 0.073
LR cohens kappa score: 0.070
LR average precision score: 0.750

-> test with 'GB'
GB tn, fp: 56467, 396
GB fn, tp: 10, 89
GB f1 score: 0.305
GB cohens kappa score: 0.303

-> test with 'KNN'
KNN tn, fp: 56641, 222
KNN fn, tp: 78, 21
KNN f1 score: 0.123
KNN cohens kappa score: 0.121


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 56756, 107
GAN fn, tp: 48, 51
GAN f1 score: 0.397
GAN cohens kappa score: 0.396

-> test with 'LR'
LR tn, fp: 54054, 2809
LR fn, tp: 12, 87
LR f1 score: 0.058
LR cohens kappa score: 0.055
LR average precision score: 0.608

-> test with 'GB'
GB tn, fp: 56422, 441
GB fn, tp: 9, 90
GB f1 score: 0.286
GB cohens kappa score: 0.284

-> test with 'KNN'
KNN tn, fp: 56512, 351
KNN fn, tp: 69, 30
KNN f1 score: 0.125
KNN cohens kappa score: 0.123


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 52985, 3878
GAN fn, tp: 8, 91
GAN f1 score: 0.045
GAN cohens kappa score: 0.041

-> test with 'LR'
LR tn, fp: 54553, 2310
LR fn, tp: 10, 89
LR f1 score: 0.071
LR cohens kappa score: 0.068
LR average precision score: 0.663

-> test with 'GB'
GB tn, fp: 56604, 259
GB fn, tp: 12, 87
GB f1 score: 0.391
GB cohens kappa score: 0.389

-> test with 'KNN'
KNN tn, fp: 56523, 340
KNN fn, tp: 67, 32
KNN f1 score: 0.136
KNN cohens kappa score: 0.134


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 56661, 202
GAN fn, tp: 54, 45
GAN f1 score: 0.260
GAN cohens kappa score: 0.258

-> test with 'LR'
LR tn, fp: 55078, 1785
LR fn, tp: 9, 90
LR f1 score: 0.091
LR cohens kappa score: 0.088
LR average precision score: 0.673

-> test with 'GB'
GB tn, fp: 56636, 227
GB fn, tp: 13, 86
GB f1 score: 0.417
GB cohens kappa score: 0.416

-> test with 'KNN'
KNN tn, fp: 56592, 271
KNN fn, tp: 77, 22
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 GAN.predict
GAN tn, fp: 56727, 136
GAN fn, tp: 27, 69
GAN f1 score: 0.458
GAN cohens kappa score: 0.457

-> test with 'LR'
LR tn, fp: 54621, 2242
LR fn, tp: 13, 83
LR f1 score: 0.069
LR cohens kappa score: 0.066
LR average precision score: 0.737

-> test with 'GB'
GB tn, fp: 56472, 391
GB fn, tp: 16, 80
GB f1 score: 0.282
GB cohens kappa score: 0.280

-> test with 'KNN'
KNN tn, fp: 56609, 254
KNN fn, tp: 75, 21
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 GAN.predict
GAN tn, fp: 55865, 998
GAN fn, tp: 15, 84
GAN f1 score: 0.142
GAN cohens kappa score: 0.140

-> test with 'LR'
LR tn, fp: 55632, 1231
LR fn, tp: 10, 89
LR f1 score: 0.125
LR cohens kappa score: 0.123
LR average precision score: 0.665

-> test with 'GB'
GB tn, fp: 56688, 175
GB fn, tp: 15, 84
GB f1 score: 0.469
GB cohens kappa score: 0.468

-> test with 'KNN'
KNN tn, fp: 56287, 576
KNN fn, tp: 76, 23
KNN f1 score: 0.066
KNN cohens kappa score: 0.063


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 4941, 51922
GAN fn, tp: 0, 99
GAN f1 score: 0.004
GAN cohens kappa score: 0.000

-> test with 'LR'
LR tn, fp: 54257, 2606
LR fn, tp: 7, 92
LR f1 score: 0.066
LR cohens kappa score: 0.063
LR average precision score: 0.638

-> test with 'GB'
GB tn, fp: 56482, 381
GB fn, tp: 10, 89
GB f1 score: 0.313
GB cohens kappa score: 0.311

-> test with 'KNN'
KNN tn, fp: 56687, 176
KNN fn, tp: 78, 21
KNN f1 score: 0.142
KNN cohens kappa score: 0.140


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2800, 54063
GAN fn, tp: 0, 99
GAN f1 score: 0.004
GAN cohens kappa score: 0.000

-> test with 'LR'
LR tn, fp: 54766, 2097
LR fn, tp: 10, 89
LR f1 score: 0.078
LR cohens kappa score: 0.075
LR average precision score: 0.722

-> test with 'GB'
GB tn, fp: 56595, 268
GB fn, tp: 13, 86
GB f1 score: 0.380
GB cohens kappa score: 0.378

-> test with 'KNN'
KNN tn, fp: 56578, 285
KNN fn, tp: 77, 22
KNN f1 score: 0.108
KNN cohens kappa score: 0.106


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 3270, 53593
GAN fn, tp: 1, 98
GAN f1 score: 0.004
GAN cohens kappa score: 0.000

-> test with 'LR'
LR tn, fp: 54299, 2564
LR fn, tp: 9, 90
LR f1 score: 0.065
LR cohens kappa score: 0.062
LR average precision score: 0.745

-> test with 'GB'
GB tn, fp: 56540, 323
GB fn, tp: 9, 90
GB f1 score: 0.352
GB cohens kappa score: 0.350

-> test with 'KNN'
KNN tn, fp: 56682, 181
KNN fn, tp: 79, 20
KNN f1 score: 0.133
KNN cohens kappa score: 0.131


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with GAN.predict
GAN tn, fp: 47210, 9653
GAN fn, tp: 8, 88
GAN f1 score: 0.018
GAN cohens kappa score: 0.015

-> test with 'LR'
LR tn, fp: 55383, 1480
LR fn, tp: 8, 88
LR f1 score: 0.106
LR cohens kappa score: 0.103
LR average precision score: 0.745

-> test with 'GB'
GB tn, fp: 56536, 327
GB fn, tp: 11, 85
GB f1 score: 0.335
GB cohens kappa score: 0.333

-> test with 'KNN'
KNN tn, fp: 56423, 440
KNN fn, tp: 67, 29
KNN f1 score: 0.103
KNN cohens kappa score: 0.100


====== 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 GAN.predict
GAN tn, fp: 56263, 600
GAN fn, tp: 14, 85
GAN f1 score: 0.217
GAN cohens kappa score: 0.214

-> test with 'LR'
LR tn, fp: 54684, 2179
LR fn, tp: 3, 96
LR f1 score: 0.081
LR cohens kappa score: 0.078
LR average precision score: 0.708

-> test with 'GB'
GB tn, fp: 56502, 361
GB fn, tp: 7, 92
GB f1 score: 0.333
GB cohens kappa score: 0.331

-> test with 'KNN'
KNN tn, fp: 56547, 316
KNN fn, tp: 79, 20
KNN f1 score: 0.092
KNN cohens kappa score: 0.090


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 56725, 138
GAN fn, tp: 46, 53
GAN f1 score: 0.366
GAN cohens kappa score: 0.364

-> test with 'LR'
LR tn, fp: 54793, 2070
LR fn, tp: 10, 89
LR f1 score: 0.079
LR cohens kappa score: 0.076
LR average precision score: 0.617

-> test with 'GB'
GB tn, fp: 56571, 292
GB fn, tp: 13, 86
GB f1 score: 0.361
GB cohens kappa score: 0.359

-> test with 'KNN'
KNN tn, fp: 56584, 279
KNN fn, tp: 81, 18
KNN f1 score: 0.091
KNN cohens kappa score: 0.089


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 12795, 44068
GAN fn, tp: 2, 97
GAN f1 score: 0.004
GAN cohens kappa score: 0.001

-> test with 'LR'
LR tn, fp: 54916, 1947
LR fn, tp: 11, 88
LR f1 score: 0.082
LR cohens kappa score: 0.079
LR average precision score: 0.712

-> test with 'GB'
GB tn, fp: 56534, 329
GB fn, tp: 13, 86
GB f1 score: 0.335
GB cohens kappa score: 0.333

-> test with 'KNN'
KNN tn, fp: 56686, 177
KNN fn, tp: 77, 22
KNN f1 score: 0.148
KNN cohens kappa score: 0.146


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 56655, 208
GAN fn, tp: 21, 78
GAN f1 score: 0.405
GAN cohens kappa score: 0.404

-> test with 'LR'
LR tn, fp: 54798, 2065
LR fn, tp: 8, 91
LR f1 score: 0.081
LR cohens kappa score: 0.078
LR average precision score: 0.766

-> test with 'GB'
GB tn, fp: 56494, 369
GB fn, tp: 11, 88
GB f1 score: 0.317
GB cohens kappa score: 0.315

-> test with 'KNN'
KNN tn, fp: 56532, 331
KNN fn, tp: 64, 35
KNN f1 score: 0.151
KNN cohens kappa score: 0.148


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with GAN.predict
GAN tn, fp: 56750, 113
GAN fn, tp: 49, 47
GAN f1 score: 0.367
GAN cohens kappa score: 0.366

-> test with 'LR'
LR tn, fp: 55501, 1362
LR fn, tp: 11, 85
LR f1 score: 0.110
LR cohens kappa score: 0.107
LR average precision score: 0.716

-> test with 'GB'
GB tn, fp: 56658, 205
GB fn, tp: 14, 82
GB f1 score: 0.428
GB cohens kappa score: 0.427

-> test with 'KNN'
KNN tn, fp: 56454, 409
KNN fn, tp: 72, 24
KNN f1 score: 0.091
KNN cohens kappa score: 0.088


====== 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 GAN.predict
GAN tn, fp: 56685, 178
GAN fn, tp: 44, 55
GAN f1 score: 0.331
GAN cohens kappa score: 0.330

-> test with 'LR'
LR tn, fp: 55050, 1813
LR fn, tp: 15, 84
LR f1 score: 0.084
LR cohens kappa score: 0.081
LR average precision score: 0.652

-> test with 'GB'
GB tn, fp: 56633, 230
GB fn, tp: 19, 80
GB f1 score: 0.391
GB cohens kappa score: 0.390

-> test with 'KNN'
KNN tn, fp: 56440, 423
KNN fn, tp: 72, 27
KNN f1 score: 0.098
KNN cohens kappa score: 0.096


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 56704, 159
GAN fn, tp: 28, 71
GAN f1 score: 0.432
GAN cohens kappa score: 0.430

-> test with 'LR'
LR tn, fp: 54787, 2076
LR fn, tp: 5, 94
LR f1 score: 0.083
LR cohens kappa score: 0.080
LR average precision score: 0.767

-> test with 'GB'
GB tn, fp: 56528, 335
GB fn, tp: 8, 91
GB f1 score: 0.347
GB cohens kappa score: 0.345

-> test with 'KNN'
KNN tn, fp: 56461, 402
KNN fn, tp: 69, 30
KNN f1 score: 0.113
KNN cohens kappa score: 0.110


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 3197, 53666
GAN fn, tp: 0, 99
GAN f1 score: 0.004
GAN cohens kappa score: 0.000

-> test with 'LR'
LR tn, fp: 55134, 1729
LR fn, tp: 12, 87
LR f1 score: 0.091
LR cohens kappa score: 0.088
LR average precision score: 0.691

-> test with 'GB'
GB tn, fp: 56505, 358
GB fn, tp: 13, 86
GB f1 score: 0.317
GB cohens kappa score: 0.315

-> test with 'KNN'
KNN tn, fp: 56427, 436
KNN fn, tp: 73, 26
KNN f1 score: 0.093
KNN cohens kappa score: 0.090


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with GAN.predict
GAN tn, fp: 56450, 413
GAN fn, tp: 13, 86
GAN f1 score: 0.288
GAN cohens kappa score: 0.286

-> test with 'LR'
LR tn, fp: 54893, 1970
LR fn, tp: 9, 90
LR f1 score: 0.083
LR cohens kappa score: 0.080
LR average precision score: 0.768

-> test with 'GB'
GB tn, fp: 56469, 394
GB fn, tp: 9, 90
GB f1 score: 0.309
GB cohens kappa score: 0.307

-> test with 'KNN'
KNN tn, fp: 56459, 404
KNN fn, tp: 77, 22
KNN f1 score: 0.084
KNN cohens kappa score: 0.081


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with GAN.predict
GAN tn, fp: 51540, 5323
GAN fn, tp: 6, 90
GAN f1 score: 0.033
GAN cohens kappa score: 0.029

-> test with 'LR'
LR tn, fp: 54706, 2157
LR fn, tp: 11, 85
LR f1 score: 0.073
LR cohens kappa score: 0.070
LR average precision score: 0.663

-> test with 'GB'
GB tn, fp: 56556, 307
GB fn, tp: 10, 86
GB f1 score: 0.352
GB cohens kappa score: 0.350

-> test with 'KNN'
KNN tn, fp: 56520, 343
KNN fn, tp: 73, 23
KNN f1 score: 0.100
KNN cohens kappa score: 0.097

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 55632, 2809
LR fn, tp: 17, 96
LR f1 score: 0.125
LR cohens kappa score: 0.123
LR average precision score: 0.857


average:
LR tn, fp: 54788.76, 2074.24
LR fn, tp: 9.44, 88.96
LR f1 score: 0.081
LR cohens kappa score: 0.078
LR average precision score: 0.702


minimum:
LR tn, fp: 54054, 1231
LR fn, tp: 3, 82
LR f1 score: 0.058
LR cohens kappa score: 0.055
LR average precision score: 0.548


-----[ GB ]-----
maximum:
GB tn, fp: 56688, 441
GB fn, tp: 19, 92
GB f1 score: 0.469
GB cohens kappa score: 0.468


average:
GB tn, fp: 56547.68, 315.32
GB fn, tp: 11.72, 86.68
GB f1 score: 0.353
GB cohens kappa score: 0.351


minimum:
GB tn, fp: 56422, 175
GB fn, tp: 7, 80
GB f1 score: 0.282
GB cohens kappa score: 0.280


-----[ KNN ]-----
maximum:
KNN tn, fp: 56687, 576
KNN fn, tp: 81, 35
KNN f1 score: 0.151
KNN cohens kappa score: 0.148


average:
KNN tn, fp: 56545.04, 317.96
KNN fn, tp: 74.32, 24.08
KNN f1 score: 0.112
KNN cohens kappa score: 0.110


minimum:
KNN tn, fp: 56287, 176
KNN fn, tp: 64, 18
KNN f1 score: 0.066
KNN cohens kappa score: 0.063


-----[ GAN ]-----
maximum:
GAN tn, fp: 56756, 54063
GAN fn, tp: 54, 99
GAN f1 score: 0.458
GAN cohens kappa score: 0.457


average:
GAN tn, fp: 43045.2, 13817.8
GAN fn, tp: 20.84, 77.56
GAN f1 score: 0.214
GAN cohens kappa score: 0.212


minimum:
GAN tn, fp: 2800, 107
GAN fn, tp: 0, 45
GAN f1 score: 0.004
GAN cohens kappa score: 0.000