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

///////////////////////////////////////////
// Running convGAN-majority-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: 56164, 699
GAN fn, tp: 36, 63
GAN f1 score: 0.146
GAN cohens kappa score: 0.144

-> test with 'LR'
LR tn, fp: 53960, 2903
LR fn, tp: 16, 83
LR f1 score: 0.054
LR cohens kappa score: 0.051
LR average precision score: 0.568

-> test with 'GB'
GB tn, fp: 56550, 313
GB fn, tp: 19, 80
GB f1 score: 0.325
GB cohens kappa score: 0.323

-> test with 'KNN'
KNN tn, fp: 56654, 209
KNN fn, tp: 78, 21
KNN f1 score: 0.128
KNN cohens kappa score: 0.126


------ 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: 801, 56062
GAN fn, tp: 0, 99
GAN f1 score: 0.004
GAN cohens kappa score: 0.000

-> test with 'LR'
LR tn, fp: 53519, 3344
LR fn, tp: 6, 93
LR f1 score: 0.053
LR cohens kappa score: 0.049
LR average precision score: 0.711

-> test with 'GB'
GB tn, fp: 56592, 271
GB fn, tp: 10, 89
GB f1 score: 0.388
GB cohens kappa score: 0.386

-> test with 'KNN'
KNN tn, fp: 56676, 187
KNN fn, tp: 81, 18
KNN f1 score: 0.118
KNN cohens kappa score: 0.116


------ 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: 39860, 17003
GAN fn, tp: 3, 96
GAN f1 score: 0.011
GAN cohens kappa score: 0.008

-> test with 'LR'
LR tn, fp: 55010, 1853
LR fn, tp: 9, 90
LR f1 score: 0.088
LR cohens kappa score: 0.085
LR average precision score: 0.683

-> 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: 56536, 327
KNN fn, tp: 76, 23
KNN f1 score: 0.102
KNN cohens kappa score: 0.100


------ 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: 56745, 118
GAN fn, tp: 43, 56
GAN f1 score: 0.410
GAN cohens kappa score: 0.409

-> test with 'LR'
LR tn, fp: 55193, 1670
LR fn, tp: 6, 93
LR f1 score: 0.100
LR cohens kappa score: 0.097
LR average precision score: 0.752

-> test with 'GB'
GB tn, fp: 56514, 349
GB fn, tp: 8, 91
GB f1 score: 0.338
GB cohens kappa score: 0.336

-> test with 'KNN'
KNN tn, fp: 56516, 347
KNN fn, tp: 71, 28
KNN f1 score: 0.118
KNN cohens kappa score: 0.116


------ 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: 991, 55872
GAN fn, tp: 0, 96
GAN f1 score: 0.003
GAN cohens kappa score: 0.000

-> test with 'LR'
LR tn, fp: 55266, 1597
LR fn, tp: 7, 89
LR f1 score: 0.100
LR cohens kappa score: 0.097
LR average precision score: 0.854

-> test with 'GB'
GB tn, fp: 56509, 354
GB fn, tp: 10, 86
GB f1 score: 0.321
GB cohens kappa score: 0.319

-> test with 'KNN'
KNN tn, fp: 56483, 380
KNN fn, tp: 71, 25
KNN f1 score: 0.100
KNN cohens kappa score: 0.097


====== 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: 27129, 29734
GAN fn, tp: 1, 98
GAN f1 score: 0.007
GAN cohens kappa score: 0.003

-> test with 'LR'
LR tn, fp: 54102, 2761
LR fn, tp: 8, 91
LR f1 score: 0.062
LR cohens kappa score: 0.059
LR average precision score: 0.719

-> test with 'GB'
GB tn, fp: 56469, 394
GB fn, tp: 10, 89
GB f1 score: 0.306
GB cohens kappa score: 0.304

-> test with 'KNN'
KNN tn, fp: 56476, 387
KNN fn, tp: 75, 24
KNN f1 score: 0.094
KNN cohens kappa score: 0.092


------ 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: 55309, 1554
GAN fn, tp: 13, 86
GAN f1 score: 0.099
GAN cohens kappa score: 0.096

-> test with 'LR'
LR tn, fp: 54067, 2796
LR fn, tp: 12, 87
LR f1 score: 0.058
LR cohens kappa score: 0.055
LR average precision score: 0.611

-> test with 'GB'
GB tn, fp: 56443, 420
GB fn, tp: 11, 88
GB f1 score: 0.290
GB cohens kappa score: 0.288

-> test with 'KNN'
KNN tn, fp: 56541, 322
KNN fn, tp: 71, 28
KNN f1 score: 0.125
KNN cohens kappa score: 0.122


------ 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: 56670, 193
GAN fn, tp: 49, 50
GAN f1 score: 0.292
GAN cohens kappa score: 0.291

-> test with 'LR'
LR tn, fp: 54617, 2246
LR fn, tp: 10, 89
LR f1 score: 0.073
LR cohens kappa score: 0.070
LR average precision score: 0.653

-> test with 'GB'
GB tn, fp: 56525, 338
GB fn, tp: 12, 87
GB f1 score: 0.332
GB cohens kappa score: 0.330

-> test with 'KNN'
KNN tn, fp: 56435, 428
KNN fn, tp: 66, 33
KNN f1 score: 0.118
KNN cohens kappa score: 0.115


------ 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: 56631, 232
GAN fn, tp: 29, 70
GAN f1 score: 0.349
GAN cohens kappa score: 0.347

-> test with 'LR'
LR tn, fp: 55314, 1549
LR fn, tp: 7, 92
LR f1 score: 0.106
LR cohens kappa score: 0.103
LR average precision score: 0.719

-> test with 'GB'
GB tn, fp: 56564, 299
GB fn, tp: 12, 87
GB f1 score: 0.359
GB cohens kappa score: 0.357

-> test with 'KNN'
KNN tn, fp: 56575, 288
KNN fn, tp: 78, 21
KNN f1 score: 0.103
KNN cohens kappa score: 0.101


------ 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: 56719, 144
GAN fn, tp: 40, 56
GAN f1 score: 0.378
GAN cohens kappa score: 0.377

-> test with 'LR'
LR tn, fp: 54929, 1934
LR fn, tp: 11, 85
LR f1 score: 0.080
LR cohens kappa score: 0.077
LR average precision score: 0.753

-> test with 'GB'
GB tn, fp: 56466, 397
GB fn, tp: 14, 82
GB f1 score: 0.285
GB cohens kappa score: 0.283

-> test with 'KNN'
KNN tn, fp: 56696, 167
KNN fn, tp: 76, 20
KNN f1 score: 0.141
KNN cohens kappa score: 0.139


====== 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: 55199, 1664
GAN fn, tp: 14, 85
GAN f1 score: 0.092
GAN cohens kappa score: 0.089

-> test with 'LR'
LR tn, fp: 54138, 2725
LR fn, tp: 14, 85
LR f1 score: 0.058
LR cohens kappa score: 0.055
LR average precision score: 0.617

-> test with 'GB'
GB tn, fp: 56598, 265
GB fn, tp: 15, 84
GB f1 score: 0.375
GB cohens kappa score: 0.373

-> test with 'KNN'
KNN tn, fp: 56453, 410
KNN fn, tp: 73, 26
KNN f1 score: 0.097
KNN cohens kappa score: 0.095


------ 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: 56534, 329
GAN fn, tp: 42, 57
GAN f1 score: 0.235
GAN cohens kappa score: 0.233

-> test with 'LR'
LR tn, fp: 54318, 2545
LR fn, tp: 10, 89
LR f1 score: 0.065
LR cohens kappa score: 0.062
LR average precision score: 0.600

-> test with 'GB'
GB tn, fp: 56555, 308
GB fn, tp: 11, 88
GB f1 score: 0.356
GB cohens kappa score: 0.354

-> test with 'KNN'
KNN tn, fp: 56453, 410
KNN fn, tp: 71, 28
KNN f1 score: 0.104
KNN cohens kappa score: 0.102


------ 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: 56114, 749
GAN fn, tp: 15, 84
GAN f1 score: 0.180
GAN cohens kappa score: 0.178

-> test with 'LR'
LR tn, fp: 55387, 1476
LR fn, tp: 11, 88
LR f1 score: 0.106
LR cohens kappa score: 0.103
LR average precision score: 0.721

-> test with 'GB'
GB tn, fp: 56653, 210
GB fn, tp: 14, 85
GB f1 score: 0.431
GB cohens kappa score: 0.430

-> test with 'KNN'
KNN tn, fp: 56285, 578
KNN fn, tp: 71, 28
KNN f1 score: 0.079
KNN cohens kappa score: 0.077


------ 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: 50741, 6122
GAN fn, tp: 6, 93
GAN f1 score: 0.029
GAN cohens kappa score: 0.026

-> test with 'LR'
LR tn, fp: 55113, 1750
LR fn, tp: 9, 90
LR f1 score: 0.093
LR cohens kappa score: 0.090
LR average precision score: 0.796

-> test with 'GB'
GB tn, fp: 56531, 332
GB fn, tp: 9, 90
GB f1 score: 0.345
GB cohens kappa score: 0.344

-> test with 'KNN'
KNN tn, fp: 56470, 393
KNN fn, tp: 76, 23
KNN f1 score: 0.089
KNN cohens kappa score: 0.087


------ 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: 56592, 271
GAN fn, tp: 28, 68
GAN f1 score: 0.313
GAN cohens kappa score: 0.311

-> test with 'LR'
LR tn, fp: 55327, 1536
LR fn, tp: 10, 86
LR f1 score: 0.100
LR cohens kappa score: 0.097
LR average precision score: 0.760

-> test with 'GB'
GB tn, fp: 56555, 308
GB fn, tp: 12, 84
GB f1 score: 0.344
GB cohens kappa score: 0.342

-> test with 'KNN'
KNN tn, fp: 56420, 443
KNN fn, tp: 66, 30
KNN f1 score: 0.105
KNN cohens kappa score: 0.103


====== 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: 23078, 33785
GAN fn, tp: 3, 96
GAN f1 score: 0.006
GAN cohens kappa score: 0.002

-> test with 'LR'
LR tn, fp: 54806, 2057
LR fn, tp: 5, 94
LR f1 score: 0.084
LR cohens kappa score: 0.081
LR average precision score: 0.702

-> test with 'GB'
GB tn, fp: 56518, 345
GB fn, tp: 8, 91
GB f1 score: 0.340
GB cohens kappa score: 0.338

-> test with 'KNN'
KNN tn, fp: 56571, 292
KNN fn, tp: 77, 22
KNN f1 score: 0.107
KNN cohens kappa score: 0.104


------ 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: 53630, 3233
GAN fn, tp: 11, 88
GAN f1 score: 0.051
GAN cohens kappa score: 0.048

-> test with 'LR'
LR tn, fp: 54935, 1928
LR fn, tp: 12, 87
LR f1 score: 0.082
LR cohens kappa score: 0.079
LR average precision score: 0.651

-> test with 'GB'
GB tn, fp: 56553, 310
GB fn, tp: 12, 87
GB f1 score: 0.351
GB cohens kappa score: 0.349

-> test with 'KNN'
KNN tn, fp: 56392, 471
KNN fn, tp: 80, 19
KNN f1 score: 0.065
KNN cohens kappa score: 0.062


------ 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: 56154, 709
GAN fn, tp: 19, 80
GAN f1 score: 0.180
GAN cohens kappa score: 0.178

-> test with 'LR'
LR tn, fp: 54382, 2481
LR fn, tp: 15, 84
LR f1 score: 0.063
LR cohens kappa score: 0.060
LR average precision score: 0.684

-> test with 'GB'
GB tn, fp: 56479, 384
GB fn, tp: 12, 87
GB f1 score: 0.305
GB cohens kappa score: 0.303

-> test with 'KNN'
KNN tn, fp: 56490, 373
KNN fn, tp: 69, 30
KNN f1 score: 0.120
KNN cohens kappa score: 0.117


------ 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: 41568, 15295
GAN fn, tp: 2, 97
GAN f1 score: 0.013
GAN cohens kappa score: 0.009

-> test with 'LR'
LR tn, fp: 54841, 2022
LR fn, tp: 7, 92
LR f1 score: 0.083
LR cohens kappa score: 0.080
LR average precision score: 0.762

-> test with 'GB'
GB tn, fp: 56487, 376
GB fn, tp: 11, 88
GB f1 score: 0.313
GB cohens kappa score: 0.311

-> test with 'KNN'
KNN tn, fp: 56549, 314
KNN fn, tp: 66, 33
KNN f1 score: 0.148
KNN cohens kappa score: 0.146


------ 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: 54292, 2571
GAN fn, tp: 10, 86
GAN f1 score: 0.062
GAN cohens kappa score: 0.059

-> test with 'LR'
LR tn, fp: 54314, 2549
LR fn, tp: 7, 89
LR f1 score: 0.065
LR cohens kappa score: 0.062
LR average precision score: 0.737

-> test with 'GB'
GB tn, fp: 56610, 253
GB fn, tp: 15, 81
GB f1 score: 0.377
GB cohens kappa score: 0.375

-> test with 'KNN'
KNN tn, fp: 56559, 304
KNN fn, tp: 71, 25
KNN f1 score: 0.118
KNN cohens kappa score: 0.115


====== 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: 56153, 710
GAN fn, tp: 24, 75
GAN f1 score: 0.170
GAN cohens kappa score: 0.167

-> test with 'LR'
LR tn, fp: 53916, 2947
LR fn, tp: 16, 83
LR f1 score: 0.053
LR cohens kappa score: 0.050
LR average precision score: 0.612

-> test with 'GB'
GB tn, fp: 56623, 240
GB fn, tp: 18, 81
GB f1 score: 0.386
GB cohens kappa score: 0.384

-> 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 GAN.predict
GAN tn, fp: 55608, 1255
GAN fn, tp: 10, 89
GAN f1 score: 0.123
GAN cohens kappa score: 0.121

-> test with 'LR'
LR tn, fp: 55396, 1467
LR fn, tp: 6, 93
LR f1 score: 0.112
LR cohens kappa score: 0.109
LR average precision score: 0.781

-> test with 'GB'
GB tn, fp: 56562, 301
GB fn, tp: 7, 92
GB f1 score: 0.374
GB cohens kappa score: 0.372

-> test with 'KNN'
KNN tn, fp: 56379, 484
KNN fn, tp: 68, 31
KNN f1 score: 0.101
KNN cohens kappa score: 0.098


------ 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: 53055, 3808
GAN fn, tp: 12, 87
GAN f1 score: 0.044
GAN cohens kappa score: 0.040

-> test with 'LR'
LR tn, fp: 55211, 1652
LR fn, tp: 12, 87
LR f1 score: 0.095
LR cohens kappa score: 0.092
LR average precision score: 0.656

-> test with 'GB'
GB tn, fp: 56539, 324
GB fn, tp: 12, 87
GB f1 score: 0.341
GB cohens kappa score: 0.339

-> test with 'KNN'
KNN tn, fp: 56461, 402
KNN fn, tp: 74, 25
KNN f1 score: 0.095
KNN cohens kappa score: 0.092


------ 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: 32505, 24358
GAN fn, tp: 1, 98
GAN f1 score: 0.008
GAN cohens kappa score: 0.005

-> test with 'LR'
LR tn, fp: 54241, 2622
LR fn, tp: 11, 88
LR f1 score: 0.063
LR cohens kappa score: 0.060
LR average precision score: 0.745

-> test with 'GB'
GB tn, fp: 56438, 425
GB fn, tp: 9, 90
GB f1 score: 0.293
GB cohens kappa score: 0.291

-> test with 'KNN'
KNN tn, fp: 56668, 195
KNN fn, tp: 78, 21
KNN f1 score: 0.133
KNN cohens kappa score: 0.131


------ 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: 56149, 714
GAN fn, tp: 14, 82
GAN f1 score: 0.184
GAN cohens kappa score: 0.181

-> test with 'LR'
LR tn, fp: 54754, 2109
LR fn, tp: 7, 89
LR f1 score: 0.078
LR cohens kappa score: 0.075
LR average precision score: 0.658

-> test with 'GB'
GB tn, fp: 56468, 395
GB fn, tp: 8, 88
GB f1 score: 0.304
GB cohens kappa score: 0.302

-> test with 'KNN'
KNN tn, fp: 56594, 269
KNN fn, tp: 72, 24
KNN f1 score: 0.123
KNN cohens kappa score: 0.121

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 55396, 3344
LR fn, tp: 16, 94
LR f1 score: 0.112
LR cohens kappa score: 0.109
LR average precision score: 0.854


average:
LR tn, fp: 54682.24, 2180.76
LR fn, tp: 9.76, 88.64
LR f1 score: 0.079
LR cohens kappa score: 0.076
LR average precision score: 0.700


minimum:
LR tn, fp: 53519, 1467
LR fn, tp: 5, 83
LR f1 score: 0.053
LR cohens kappa score: 0.049
LR average precision score: 0.568


-----[ GB ]-----
maximum:
GB tn, fp: 56653, 425
GB fn, tp: 19, 92
GB f1 score: 0.431
GB cohens kappa score: 0.430


average:
GB tn, fp: 56534.52, 328.48
GB fn, tp: 11.68, 86.72
GB f1 score: 0.341
GB cohens kappa score: 0.339


minimum:
GB tn, fp: 56438, 210
GB fn, tp: 7, 80
GB f1 score: 0.285
GB cohens kappa score: 0.283


-----[ KNN ]-----
maximum:
KNN tn, fp: 56702, 578
KNN fn, tp: 81, 33
KNN f1 score: 0.156
KNN cohens kappa score: 0.154


average:
KNN tn, fp: 56521.36, 341.64
KNN fn, tp: 73.28, 25.12
KNN f1 score: 0.112
KNN cohens kappa score: 0.109


minimum:
KNN tn, fp: 56285, 161
KNN fn, tp: 66, 18
KNN f1 score: 0.065
KNN cohens kappa score: 0.062


-----[ GAN ]-----
maximum:
GAN tn, fp: 56745, 56062
GAN fn, tp: 49, 99
GAN f1 score: 0.410
GAN cohens kappa score: 0.409


average:
GAN tn, fp: 46575.64, 10287.36
GAN fn, tp: 17.0, 81.4
GAN f1 score: 0.136
GAN cohens kappa score: 0.133


minimum:
GAN tn, fp: 801, 118
GAN fn, tp: 0, 50
GAN f1 score: 0.003
GAN cohens kappa score: 0.000