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

///////////////////////////////////////////
// Running convGAN-proximary-full 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: 52781, 4082
GAN fn, tp: 13, 86
GAN f1 score: 0.040
GAN cohens kappa score: 0.037

-> test with 'LR'
LR tn, fp: 53586, 3277
LR fn, tp: 16, 83
LR f1 score: 0.048
LR cohens kappa score: 0.045
LR average precision score: 0.563

-> test with 'GB'
GB tn, fp: 56611, 252
GB fn, tp: 19, 80
GB f1 score: 0.371
GB cohens kappa score: 0.370

-> test with 'KNN'
KNN tn, fp: 55607, 1256
KNN fn, tp: 77, 22
KNN f1 score: 0.032
KNN cohens kappa score: 0.029


------ 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: 56311, 552
GAN fn, tp: 13, 86
GAN f1 score: 0.233
GAN cohens kappa score: 0.231

-> test with 'LR'
LR tn, fp: 53657, 3206
LR fn, tp: 6, 93
LR f1 score: 0.055
LR cohens kappa score: 0.052
LR average precision score: 0.734

-> test with 'GB'
GB tn, fp: 56464, 399
GB fn, tp: 8, 91
GB f1 score: 0.309
GB cohens kappa score: 0.307

-> test with 'KNN'
KNN tn, fp: 56363, 500
KNN fn, tp: 77, 22
KNN f1 score: 0.071
KNN cohens kappa score: 0.068


------ 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: 56461, 402
GAN fn, tp: 15, 84
GAN f1 score: 0.287
GAN cohens kappa score: 0.285

-> test with 'LR'
LR tn, fp: 54172, 2691
LR fn, tp: 9, 90
LR f1 score: 0.062
LR cohens kappa score: 0.059
LR average precision score: 0.658

-> test with 'GB'
GB tn, fp: 56516, 347
GB fn, tp: 12, 87
GB f1 score: 0.326
GB cohens kappa score: 0.325

-> test with 'KNN'
KNN tn, fp: 55941, 922
KNN fn, tp: 72, 27
KNN f1 score: 0.052
KNN cohens kappa score: 0.049


------ 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: 43317, 13546
GAN fn, tp: 3, 96
GAN f1 score: 0.014
GAN cohens kappa score: 0.011

-> test with 'LR'
LR tn, fp: 55008, 1855
LR fn, tp: 6, 93
LR f1 score: 0.091
LR cohens kappa score: 0.088
LR average precision score: 0.791

-> test with 'GB'
GB tn, fp: 56502, 361
GB fn, tp: 8, 91
GB f1 score: 0.330
GB cohens kappa score: 0.328

-> test with 'KNN'
KNN tn, fp: 55804, 1059
KNN fn, tp: 69, 30
KNN f1 score: 0.051
KNN cohens kappa score: 0.047


------ 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: 51762, 5101
GAN fn, tp: 6, 90
GAN f1 score: 0.034
GAN cohens kappa score: 0.031

-> test with 'LR'
LR tn, fp: 54715, 2148
LR fn, tp: 9, 87
LR f1 score: 0.075
LR cohens kappa score: 0.072
LR average precision score: 0.793

-> test with 'GB'
GB tn, fp: 56391, 472
GB fn, tp: 9, 87
GB f1 score: 0.266
GB cohens kappa score: 0.264

-> test with 'KNN'
KNN tn, fp: 56285, 578
KNN fn, tp: 70, 26
KNN f1 score: 0.074
KNN cohens kappa score: 0.072


====== 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: 55210, 1653
GAN fn, tp: 12, 87
GAN f1 score: 0.095
GAN cohens kappa score: 0.092

-> test with 'LR'
LR tn, fp: 52822, 4041
LR fn, tp: 12, 87
LR f1 score: 0.041
LR cohens kappa score: 0.038
LR average precision score: 0.701

-> test with 'GB'
GB tn, fp: 56487, 376
GB fn, tp: 10, 89
GB f1 score: 0.316
GB cohens kappa score: 0.314

-> test with 'KNN'
KNN tn, fp: 55426, 1437
KNN fn, tp: 72, 27
KNN f1 score: 0.035
KNN cohens kappa score: 0.031


------ 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: 52251, 4612
GAN fn, tp: 7, 92
GAN f1 score: 0.038
GAN cohens kappa score: 0.035

-> test with 'LR'
LR tn, fp: 53964, 2899
LR fn, tp: 9, 90
LR f1 score: 0.058
LR cohens kappa score: 0.055
LR average precision score: 0.643

-> test with 'GB'
GB tn, fp: 56403, 460
GB fn, tp: 10, 89
GB f1 score: 0.275
GB cohens kappa score: 0.273

-> test with 'KNN'
KNN tn, fp: 56508, 355
KNN fn, tp: 71, 28
KNN f1 score: 0.116
KNN cohens kappa score: 0.114


------ 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: 51974, 4889
GAN fn, tp: 4, 95
GAN f1 score: 0.037
GAN cohens kappa score: 0.034

-> test with 'LR'
LR tn, fp: 54274, 2589
LR fn, tp: 10, 89
LR f1 score: 0.064
LR cohens kappa score: 0.061
LR average precision score: 0.659

-> test with 'GB'
GB tn, fp: 56521, 342
GB fn, tp: 11, 88
GB f1 score: 0.333
GB cohens kappa score: 0.331

-> test with 'KNN'
KNN tn, fp: 56110, 753
KNN fn, tp: 68, 31
KNN f1 score: 0.070
KNN cohens kappa score: 0.067


------ 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: 51109, 5754
GAN fn, tp: 6, 93
GAN f1 score: 0.031
GAN cohens kappa score: 0.028

-> test with 'LR'
LR tn, fp: 54871, 1992
LR fn, tp: 8, 91
LR f1 score: 0.083
LR cohens kappa score: 0.080
LR average precision score: 0.737

-> test with 'GB'
GB tn, fp: 56509, 354
GB fn, tp: 10, 89
GB f1 score: 0.328
GB cohens kappa score: 0.326

-> test with 'KNN'
KNN tn, fp: 56552, 311
KNN fn, tp: 78, 21
KNN f1 score: 0.097
KNN cohens kappa score: 0.095


------ 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: 56454, 409
GAN fn, tp: 16, 80
GAN f1 score: 0.274
GAN cohens kappa score: 0.271

-> test with 'LR'
LR tn, fp: 54634, 2229
LR fn, tp: 12, 84
LR f1 score: 0.070
LR cohens kappa score: 0.067
LR average precision score: 0.759

-> test with 'GB'
GB tn, fp: 56431, 432
GB fn, tp: 15, 81
GB f1 score: 0.266
GB cohens kappa score: 0.264

-> test with 'KNN'
KNN tn, fp: 56517, 346
KNN fn, tp: 76, 20
KNN f1 score: 0.087
KNN cohens kappa score: 0.084


====== 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: 52135, 4728
GAN fn, tp: 6, 93
GAN f1 score: 0.038
GAN cohens kappa score: 0.035

-> test with 'LR'
LR tn, fp: 53496, 3367
LR fn, tp: 9, 90
LR f1 score: 0.051
LR cohens kappa score: 0.047
LR average precision score: 0.673

-> test with 'GB'
GB tn, fp: 56438, 425
GB fn, tp: 14, 85
GB f1 score: 0.279
GB cohens kappa score: 0.277

-> test with 'KNN'
KNN tn, fp: 55897, 966
KNN fn, tp: 72, 27
KNN f1 score: 0.049
KNN cohens kappa score: 0.046


------ 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: 55694, 1169
GAN fn, tp: 12, 87
GAN f1 score: 0.128
GAN cohens kappa score: 0.126

-> test with 'LR'
LR tn, fp: 54640, 2223
LR fn, tp: 7, 92
LR f1 score: 0.076
LR cohens kappa score: 0.073
LR average precision score: 0.686

-> test with 'GB'
GB tn, fp: 56465, 398
GB fn, tp: 10, 89
GB f1 score: 0.304
GB cohens kappa score: 0.302

-> test with 'KNN'
KNN tn, fp: 56045, 818
KNN fn, tp: 70, 29
KNN f1 score: 0.061
KNN cohens kappa score: 0.058


------ 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: 51836, 5027
GAN fn, tp: 7, 92
GAN f1 score: 0.035
GAN cohens kappa score: 0.032

-> test with 'LR'
LR tn, fp: 54671, 2192
LR fn, tp: 11, 88
LR f1 score: 0.074
LR cohens kappa score: 0.071
LR average precision score: 0.698

-> test with 'GB'
GB tn, fp: 56516, 347
GB fn, tp: 14, 85
GB f1 score: 0.320
GB cohens kappa score: 0.318

-> test with 'KNN'
KNN tn, fp: 56040, 823
KNN fn, tp: 75, 24
KNN f1 score: 0.051
KNN cohens kappa score: 0.048


------ 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: 56227, 636
GAN fn, tp: 20, 79
GAN f1 score: 0.194
GAN cohens kappa score: 0.192

-> test with 'LR'
LR tn, fp: 54738, 2125
LR fn, tp: 8, 91
LR f1 score: 0.079
LR cohens kappa score: 0.076
LR average precision score: 0.788

-> test with 'GB'
GB tn, fp: 56437, 426
GB fn, tp: 10, 89
GB f1 score: 0.290
GB cohens kappa score: 0.288

-> test with 'KNN'
KNN tn, fp: 56028, 835
KNN fn, tp: 70, 29
KNN f1 score: 0.060
KNN cohens kappa score: 0.057


------ 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: 46516, 10347
GAN fn, tp: 4, 92
GAN f1 score: 0.017
GAN cohens kappa score: 0.014

-> test with 'LR'
LR tn, fp: 54971, 1892
LR fn, tp: 5, 91
LR f1 score: 0.088
LR cohens kappa score: 0.085
LR average precision score: 0.710

-> test with 'GB'
GB tn, fp: 56528, 335
GB fn, tp: 12, 84
GB f1 score: 0.326
GB cohens kappa score: 0.324

-> test with 'KNN'
KNN tn, fp: 56065, 798
KNN fn, tp: 67, 29
KNN f1 score: 0.063
KNN cohens kappa score: 0.060


====== 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: 25153, 31710
GAN fn, tp: 2, 97
GAN f1 score: 0.006
GAN cohens kappa score: 0.003

-> test with 'LR'
LR tn, fp: 54080, 2783
LR fn, tp: 3, 96
LR f1 score: 0.064
LR cohens kappa score: 0.061
LR average precision score: 0.680

-> 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: 55887, 976
KNN fn, tp: 72, 27
KNN f1 score: 0.049
KNN cohens kappa score: 0.046


------ 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: 56570, 293
GAN fn, tp: 22, 77
GAN f1 score: 0.328
GAN cohens kappa score: 0.327

-> test with 'LR'
LR tn, fp: 54746, 2117
LR fn, tp: 11, 88
LR f1 score: 0.076
LR cohens kappa score: 0.073
LR average precision score: 0.672

-> test with 'GB'
GB tn, fp: 56506, 357
GB fn, tp: 13, 86
GB f1 score: 0.317
GB cohens kappa score: 0.315

-> test with 'KNN'
KNN tn, fp: 56042, 821
KNN fn, tp: 77, 22
KNN f1 score: 0.047
KNN cohens kappa score: 0.044


------ 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: 53376, 3487
GAN fn, tp: 11, 88
GAN f1 score: 0.048
GAN cohens kappa score: 0.045

-> test with 'LR'
LR tn, fp: 54690, 2173
LR fn, tp: 10, 89
LR f1 score: 0.075
LR cohens kappa score: 0.072
LR average precision score: 0.723

-> test with 'GB'
GB tn, fp: 56522, 341
GB fn, tp: 11, 88
GB f1 score: 0.333
GB cohens kappa score: 0.331

-> test with 'KNN'
KNN tn, fp: 56189, 674
KNN fn, tp: 74, 25
KNN f1 score: 0.063
KNN cohens kappa score: 0.060


------ 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: 12505, 44358
GAN fn, tp: 1, 98
GAN f1 score: 0.004
GAN cohens kappa score: 0.001

-> test with 'LR'
LR tn, fp: 54384, 2479
LR fn, tp: 9, 90
LR f1 score: 0.067
LR cohens kappa score: 0.064
LR average precision score: 0.751

-> test with 'GB'
GB tn, fp: 56419, 444
GB fn, tp: 11, 88
GB f1 score: 0.279
GB cohens kappa score: 0.277

-> test with 'KNN'
KNN tn, fp: 56198, 665
KNN fn, tp: 64, 35
KNN f1 score: 0.088
KNN cohens kappa score: 0.085


------ 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: 55163, 1700
GAN fn, tp: 13, 83
GAN f1 score: 0.088
GAN cohens kappa score: 0.085

-> test with 'LR'
LR tn, fp: 54890, 1973
LR fn, tp: 11, 85
LR f1 score: 0.079
LR cohens kappa score: 0.076
LR average precision score: 0.720

-> test with 'GB'
GB tn, fp: 56487, 376
GB fn, tp: 14, 82
GB f1 score: 0.296
GB cohens kappa score: 0.294

-> test with 'KNN'
KNN tn, fp: 56260, 603
KNN fn, tp: 70, 26
KNN f1 score: 0.072
KNN cohens kappa score: 0.069


====== 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: 52566, 4297
GAN fn, tp: 12, 87
GAN f1 score: 0.039
GAN cohens kappa score: 0.036

-> test with 'LR'
LR tn, fp: 54385, 2478
LR fn, tp: 11, 88
LR f1 score: 0.066
LR cohens kappa score: 0.063
LR average precision score: 0.630

-> test with 'GB'
GB tn, fp: 56585, 278
GB fn, tp: 16, 83
GB f1 score: 0.361
GB cohens kappa score: 0.359

-> test with 'KNN'
KNN tn, fp: 56156, 707
KNN fn, tp: 75, 24
KNN f1 score: 0.058
KNN cohens kappa score: 0.055


------ 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: 51554, 5309
GAN fn, tp: 3, 96
GAN f1 score: 0.035
GAN cohens kappa score: 0.032

-> test with 'LR'
LR tn, fp: 54103, 2760
LR fn, tp: 5, 94
LR f1 score: 0.064
LR cohens kappa score: 0.061
LR average precision score: 0.773

-> test with 'GB'
GB tn, fp: 56491, 372
GB fn, tp: 7, 92
GB f1 score: 0.327
GB cohens kappa score: 0.325

-> test with 'KNN'
KNN tn, fp: 56163, 700
KNN fn, tp: 71, 28
KNN f1 score: 0.068
KNN cohens kappa score: 0.065


------ 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: 55413, 1450
GAN fn, tp: 14, 85
GAN f1 score: 0.104
GAN cohens kappa score: 0.101

-> test with 'LR'
LR tn, fp: 54887, 1976
LR fn, tp: 11, 88
LR f1 score: 0.081
LR cohens kappa score: 0.078
LR average precision score: 0.688

-> test with 'GB'
GB tn, fp: 56491, 372
GB fn, tp: 13, 86
GB f1 score: 0.309
GB cohens kappa score: 0.307

-> test with 'KNN'
KNN tn, fp: 56159, 704
KNN fn, tp: 74, 25
KNN f1 score: 0.060
KNN cohens kappa score: 0.058


------ 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: 56099, 764
GAN fn, tp: 11, 88
GAN f1 score: 0.185
GAN cohens kappa score: 0.183

-> test with 'LR'
LR tn, fp: 54530, 2333
LR fn, tp: 7, 92
LR f1 score: 0.073
LR cohens kappa score: 0.070
LR average precision score: 0.767

-> test with 'GB'
GB tn, fp: 56486, 377
GB fn, tp: 9, 90
GB f1 score: 0.318
GB cohens kappa score: 0.316

-> test with 'KNN'
KNN tn, fp: 55906, 957
KNN fn, tp: 66, 33
KNN f1 score: 0.061
KNN cohens kappa score: 0.058


------ 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: 55476, 1387
GAN fn, tp: 12, 84
GAN f1 score: 0.107
GAN cohens kappa score: 0.104

-> test with 'LR'
LR tn, fp: 54225, 2638
LR fn, tp: 9, 87
LR f1 score: 0.062
LR cohens kappa score: 0.059
LR average precision score: 0.653

-> test with 'GB'
GB tn, fp: 56478, 385
GB fn, tp: 9, 87
GB f1 score: 0.306
GB cohens kappa score: 0.304

-> test with 'KNN'
KNN tn, fp: 56508, 355
KNN fn, tp: 73, 23
KNN f1 score: 0.097
KNN cohens kappa score: 0.095

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 55008, 4041
LR fn, tp: 16, 96
LR f1 score: 0.091
LR cohens kappa score: 0.088
LR average precision score: 0.793


average:
LR tn, fp: 54365.56, 2497.44
LR fn, tp: 8.96, 89.44
LR f1 score: 0.069
LR cohens kappa score: 0.066
LR average precision score: 0.706


minimum:
LR tn, fp: 52822, 1855
LR fn, tp: 3, 83
LR f1 score: 0.041
LR cohens kappa score: 0.038
LR average precision score: 0.563


-----[ GB ]-----
maximum:
GB tn, fp: 56611, 472
GB fn, tp: 19, 92
GB f1 score: 0.371
GB cohens kappa score: 0.370


average:
GB tn, fp: 56487.36, 375.64
GB fn, tp: 11.28, 87.12
GB f1 score: 0.313
GB cohens kappa score: 0.311


minimum:
GB tn, fp: 56391, 252
GB fn, tp: 7, 80
GB f1 score: 0.266
GB cohens kappa score: 0.264


-----[ KNN ]-----
maximum:
KNN tn, fp: 56552, 1437
KNN fn, tp: 78, 35
KNN f1 score: 0.116
KNN cohens kappa score: 0.114


average:
KNN tn, fp: 56106.24, 756.76
KNN fn, tp: 72.0, 26.4
KNN f1 score: 0.065
KNN cohens kappa score: 0.062


minimum:
KNN tn, fp: 55426, 311
KNN fn, tp: 64, 20
KNN f1 score: 0.032
KNN cohens kappa score: 0.029


-----[ GAN ]-----
maximum:
GAN tn, fp: 56570, 44358
GAN fn, tp: 22, 98
GAN f1 score: 0.328
GAN cohens kappa score: 0.327


average:
GAN tn, fp: 50556.52, 6306.48
GAN fn, tp: 9.8, 88.6
GAN f1 score: 0.098
GAN cohens kappa score: 0.095


minimum:
GAN tn, fp: 12505, 293
GAN fn, tp: 1, 77
GAN f1 score: 0.004
GAN cohens kappa score: 0.001