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

///////////////////////////////////////////
// Running convGAN-majority-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: 53161, 3702
GAN fn, tp: 18, 81
GAN f1 score: 0.042
GAN cohens kappa score: 0.038

-> test with 'LR'
LR tn, fp: 53086, 3777
LR fn, tp: 20, 79
LR f1 score: 0.040
LR cohens kappa score: 0.037
LR average precision score: 0.518

-> test with 'GB'
GB tn, fp: 56581, 282
GB fn, tp: 19, 80
GB f1 score: 0.347
GB cohens kappa score: 0.345

-> test with 'KNN'
KNN tn, fp: 55211, 1652
KNN fn, tp: 71, 28
KNN f1 score: 0.031
KNN cohens kappa score: 0.028


------ 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: 43088, 13775
GAN fn, tp: 2, 97
GAN f1 score: 0.014
GAN cohens kappa score: 0.010

-> test with 'LR'
LR tn, fp: 53787, 3076
LR fn, tp: 6, 93
LR f1 score: 0.057
LR cohens kappa score: 0.054
LR average precision score: 0.729

-> test with 'GB'
GB tn, fp: 56521, 342
GB fn, tp: 10, 89
GB f1 score: 0.336
GB cohens kappa score: 0.334

-> test with 'KNN'
KNN tn, fp: 55842, 1021
KNN fn, tp: 71, 28
KNN f1 score: 0.049
KNN cohens kappa score: 0.046


------ 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: 8231, 48632
GAN fn, tp: 2, 97
GAN f1 score: 0.004
GAN cohens kappa score: 0.001

-> test with 'LR'
LR tn, fp: 54646, 2217
LR fn, tp: 6, 93
LR f1 score: 0.077
LR cohens kappa score: 0.074
LR average precision score: 0.707

-> test with 'GB'
GB tn, fp: 56483, 380
GB fn, tp: 11, 88
GB f1 score: 0.310
GB cohens kappa score: 0.308

-> test with 'KNN'
KNN tn, fp: 56049, 814
KNN fn, tp: 71, 28
KNN f1 score: 0.060
KNN cohens kappa score: 0.057


------ 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: 38275, 18588
GAN fn, tp: 0, 99
GAN f1 score: 0.011
GAN cohens kappa score: 0.007

-> test with 'LR'
LR tn, fp: 55145, 1718
LR fn, tp: 6, 93
LR f1 score: 0.097
LR cohens kappa score: 0.094
LR average precision score: 0.764

-> test with 'GB'
GB tn, fp: 56487, 376
GB fn, tp: 8, 91
GB f1 score: 0.322
GB cohens kappa score: 0.320

-> test with 'KNN'
KNN tn, fp: 55182, 1681
KNN fn, tp: 59, 40
KNN f1 score: 0.044
KNN cohens kappa score: 0.041


------ 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: 56659, 204
GAN fn, tp: 18, 78
GAN f1 score: 0.413
GAN cohens kappa score: 0.411

-> test with 'LR'
LR tn, fp: 54854, 2009
LR fn, tp: 7, 89
LR f1 score: 0.081
LR cohens kappa score: 0.078
LR average precision score: 0.838

-> test with 'GB'
GB tn, fp: 56424, 439
GB fn, tp: 9, 87
GB f1 score: 0.280
GB cohens kappa score: 0.278

-> test with 'KNN'
KNN tn, fp: 55912, 951
KNN fn, tp: 62, 34
KNN f1 score: 0.063
KNN cohens kappa score: 0.060


====== 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: 56413, 450
GAN fn, tp: 21, 78
GAN f1 score: 0.249
GAN cohens kappa score: 0.247

-> test with 'LR'
LR tn, fp: 52754, 4109
LR fn, tp: 11, 88
LR f1 score: 0.041
LR cohens kappa score: 0.038
LR average precision score: 0.695

-> test with 'GB'
GB tn, fp: 56439, 424
GB fn, tp: 10, 89
GB f1 score: 0.291
GB cohens kappa score: 0.289

-> test with 'KNN'
KNN tn, fp: 55796, 1067
KNN fn, tp: 69, 30
KNN f1 score: 0.050
KNN cohens kappa score: 0.047


------ 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: 52914, 3949
GAN fn, tp: 8, 91
GAN f1 score: 0.044
GAN cohens kappa score: 0.041

-> test with 'LR'
LR tn, fp: 54662, 2201
LR fn, tp: 8, 91
LR f1 score: 0.076
LR cohens kappa score: 0.073
LR average precision score: 0.667

-> test with 'GB'
GB tn, fp: 56466, 397
GB fn, tp: 11, 88
GB f1 score: 0.301
GB cohens kappa score: 0.299

-> test with 'KNN'
KNN tn, fp: 55755, 1108
KNN fn, tp: 66, 33
KNN f1 score: 0.053
KNN cohens kappa score: 0.050


------ 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: 56556, 307
GAN fn, tp: 24, 75
GAN f1 score: 0.312
GAN cohens kappa score: 0.310

-> test with 'LR'
LR tn, fp: 54718, 2145
LR fn, tp: 8, 91
LR f1 score: 0.078
LR cohens kappa score: 0.075
LR average precision score: 0.715

-> 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: 55500, 1363
KNN fn, tp: 60, 39
KNN f1 score: 0.052
KNN cohens kappa score: 0.049


------ 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: 3888, 52975
GAN fn, tp: 1, 98
GAN f1 score: 0.004
GAN cohens kappa score: 0.000

-> test with 'LR'
LR tn, fp: 54534, 2329
LR fn, tp: 12, 87
LR f1 score: 0.069
LR cohens kappa score: 0.066
LR average precision score: 0.713

-> test with 'GB'
GB tn, fp: 56507, 356
GB fn, tp: 11, 88
GB f1 score: 0.324
GB cohens kappa score: 0.322

-> test with 'KNN'
KNN tn, fp: 56368, 495
KNN fn, tp: 72, 27
KNN f1 score: 0.087
KNN cohens kappa score: 0.084


------ 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: 54173, 2690
GAN fn, tp: 10, 86
GAN f1 score: 0.060
GAN cohens kappa score: 0.057

-> test with 'LR'
LR tn, fp: 54782, 2081
LR fn, tp: 12, 84
LR f1 score: 0.074
LR cohens kappa score: 0.071
LR average precision score: 0.759

-> test with 'GB'
GB tn, fp: 56481, 382
GB fn, tp: 15, 81
GB f1 score: 0.290
GB cohens kappa score: 0.288

-> test with 'KNN'
KNN tn, fp: 55998, 865
KNN fn, tp: 74, 22
KNN f1 score: 0.045
KNN cohens kappa score: 0.042


====== 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: 56008, 855
GAN fn, tp: 16, 83
GAN f1 score: 0.160
GAN cohens kappa score: 0.157

-> test with 'LR'
LR tn, fp: 53786, 3077
LR fn, tp: 8, 91
LR f1 score: 0.056
LR cohens kappa score: 0.053
LR average precision score: 0.659

-> test with 'GB'
GB tn, fp: 56468, 395
GB fn, tp: 13, 86
GB f1 score: 0.297
GB cohens kappa score: 0.295

-> test with 'KNN'
KNN tn, fp: 56174, 689
KNN fn, tp: 73, 26
KNN f1 score: 0.064
KNN cohens kappa score: 0.061


------ 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: 54949, 1914
GAN fn, tp: 10, 89
GAN f1 score: 0.085
GAN cohens kappa score: 0.082

-> test with 'LR'
LR tn, fp: 54319, 2544
LR fn, tp: 7, 92
LR f1 score: 0.067
LR cohens kappa score: 0.064
LR average precision score: 0.632

-> test with 'GB'
GB tn, fp: 56480, 383
GB fn, tp: 9, 90
GB f1 score: 0.315
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 55640, 1223
KNN fn, tp: 63, 36
KNN f1 score: 0.053
KNN cohens kappa score: 0.050


------ 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: 56510, 353
GAN fn, tp: 19, 80
GAN f1 score: 0.301
GAN cohens kappa score: 0.299

-> test with 'LR'
LR tn, fp: 54720, 2143
LR fn, tp: 10, 89
LR f1 score: 0.076
LR cohens kappa score: 0.073
LR average precision score: 0.705

-> test with 'GB'
GB tn, fp: 56498, 365
GB fn, tp: 13, 86
GB f1 score: 0.313
GB cohens kappa score: 0.311

-> test with 'KNN'
KNN tn, fp: 56393, 470
KNN fn, tp: 75, 24
KNN f1 score: 0.081
KNN cohens kappa score: 0.078


------ 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: 53475, 3388
GAN fn, tp: 6, 93
GAN f1 score: 0.052
GAN cohens kappa score: 0.049

-> test with 'LR'
LR tn, fp: 54682, 2181
LR fn, tp: 8, 91
LR f1 score: 0.077
LR cohens kappa score: 0.074
LR average precision score: 0.783

-> test with 'GB'
GB tn, fp: 56451, 412
GB fn, tp: 9, 90
GB f1 score: 0.300
GB cohens kappa score: 0.297

-> test with 'KNN'
KNN tn, fp: 56008, 855
KNN fn, tp: 72, 27
KNN f1 score: 0.055
KNN cohens kappa score: 0.052


------ 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: 55179, 1684
GAN fn, tp: 10, 86
GAN f1 score: 0.092
GAN cohens kappa score: 0.089

-> test with 'LR'
LR tn, fp: 55002, 1861
LR fn, tp: 5, 91
LR f1 score: 0.089
LR cohens kappa score: 0.086
LR average precision score: 0.720

-> test with 'GB'
GB tn, fp: 56544, 319
GB fn, tp: 12, 84
GB f1 score: 0.337
GB cohens kappa score: 0.335

-> test with 'KNN'
KNN tn, fp: 55746, 1117
KNN fn, tp: 63, 33
KNN f1 score: 0.053
KNN cohens kappa score: 0.050


====== 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: 56586, 277
GAN fn, tp: 30, 69
GAN f1 score: 0.310
GAN cohens kappa score: 0.308

-> test with 'LR'
LR tn, fp: 54089, 2774
LR fn, tp: 3, 96
LR f1 score: 0.065
LR cohens kappa score: 0.062
LR average precision score: 0.678

-> test with 'GB'
GB tn, fp: 56499, 364
GB fn, tp: 7, 92
GB f1 score: 0.332
GB cohens kappa score: 0.330

-> test with 'KNN'
KNN tn, fp: 56138, 725
KNN fn, tp: 76, 23
KNN f1 score: 0.054
KNN cohens kappa score: 0.051


------ 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: 56474, 389
GAN fn, tp: 22, 77
GAN f1 score: 0.273
GAN cohens kappa score: 0.270

-> test with 'LR'
LR tn, fp: 54363, 2500
LR fn, tp: 12, 87
LR f1 score: 0.065
LR cohens kappa score: 0.062
LR average precision score: 0.646

-> test with 'GB'
GB tn, fp: 56536, 327
GB fn, tp: 12, 87
GB f1 score: 0.339
GB cohens kappa score: 0.337

-> test with 'KNN'
KNN tn, fp: 55879, 984
KNN fn, tp: 78, 21
KNN f1 score: 0.038
KNN cohens kappa score: 0.035


------ 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: 54791, 2072
GAN fn, tp: 11, 88
GAN f1 score: 0.078
GAN cohens kappa score: 0.075

-> test with 'LR'
LR tn, fp: 54784, 2079
LR fn, tp: 10, 89
LR f1 score: 0.079
LR cohens kappa score: 0.075
LR average precision score: 0.733

-> test with 'GB'
GB tn, fp: 56502, 361
GB fn, tp: 12, 87
GB f1 score: 0.318
GB cohens kappa score: 0.316

-> test with 'KNN'
KNN tn, fp: 55465, 1398
KNN fn, tp: 66, 33
KNN f1 score: 0.043
KNN cohens kappa score: 0.040


------ 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: 54843, 2020
GAN fn, tp: 8, 91
GAN f1 score: 0.082
GAN cohens kappa score: 0.079

-> test with 'LR'
LR tn, fp: 54175, 2688
LR fn, tp: 9, 90
LR f1 score: 0.063
LR cohens kappa score: 0.059
LR average precision score: 0.741

-> test with 'GB'
GB tn, fp: 56422, 441
GB fn, tp: 10, 89
GB f1 score: 0.283
GB cohens kappa score: 0.281

-> test with 'KNN'
KNN tn, fp: 55767, 1096
KNN fn, tp: 62, 37
KNN f1 score: 0.060
KNN cohens kappa score: 0.057


------ 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: 32504, 24359
GAN fn, tp: 0, 96
GAN f1 score: 0.008
GAN cohens kappa score: 0.004

-> test with 'LR'
LR tn, fp: 54932, 1931
LR fn, tp: 11, 85
LR f1 score: 0.080
LR cohens kappa score: 0.078
LR average precision score: 0.718

-> test with 'GB'
GB tn, fp: 56523, 340
GB fn, tp: 15, 81
GB f1 score: 0.313
GB cohens kappa score: 0.311

-> test with 'KNN'
KNN tn, fp: 56062, 801
KNN fn, tp: 71, 25
KNN f1 score: 0.054
KNN cohens kappa score: 0.051


====== 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: 56001, 862
GAN fn, tp: 20, 79
GAN f1 score: 0.152
GAN cohens kappa score: 0.149

-> test with 'LR'
LR tn, fp: 53935, 2928
LR fn, tp: 17, 82
LR f1 score: 0.053
LR cohens kappa score: 0.050
LR average precision score: 0.616

-> test with 'GB'
GB tn, fp: 56593, 270
GB fn, tp: 16, 83
GB f1 score: 0.367
GB cohens kappa score: 0.366

-> test with 'KNN'
KNN tn, fp: 55419, 1444
KNN fn, tp: 66, 33
KNN f1 score: 0.042
KNN cohens kappa score: 0.039


------ 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: 56691, 172
GAN fn, tp: 30, 69
GAN f1 score: 0.406
GAN cohens kappa score: 0.404

-> test with 'LR'
LR tn, fp: 54246, 2617
LR fn, tp: 5, 94
LR f1 score: 0.067
LR cohens kappa score: 0.064
LR average precision score: 0.746

-> test with 'GB'
GB tn, fp: 56516, 347
GB fn, tp: 8, 91
GB f1 score: 0.339
GB cohens kappa score: 0.337

-> test with 'KNN'
KNN tn, fp: 55656, 1207
KNN fn, tp: 68, 31
KNN f1 score: 0.046
KNN cohens kappa score: 0.043


------ 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: 53258, 3605
GAN fn, tp: 13, 86
GAN f1 score: 0.045
GAN cohens kappa score: 0.042

-> test with 'LR'
LR tn, fp: 54527, 2336
LR fn, tp: 11, 88
LR f1 score: 0.070
LR cohens kappa score: 0.067
LR average precision score: 0.671

-> 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: 56334, 529
KNN fn, tp: 74, 25
KNN f1 score: 0.077
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 GAN.predict
GAN tn, fp: 55530, 1333
GAN fn, tp: 9, 90
GAN f1 score: 0.118
GAN cohens kappa score: 0.115

-> test with 'LR'
LR tn, fp: 54654, 2209
LR fn, tp: 7, 92
LR f1 score: 0.077
LR cohens kappa score: 0.074
LR average precision score: 0.769

-> test with 'GB'
GB tn, fp: 56451, 412
GB fn, tp: 10, 89
GB f1 score: 0.297
GB cohens kappa score: 0.295

-> test with 'KNN'
KNN tn, fp: 55919, 944
KNN fn, tp: 67, 32
KNN f1 score: 0.060
KNN cohens kappa score: 0.057


------ 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: 56221, 642
GAN fn, tp: 13, 83
GAN f1 score: 0.202
GAN cohens kappa score: 0.200

-> test with 'LR'
LR tn, fp: 54469, 2394
LR fn, tp: 11, 85
LR f1 score: 0.066
LR cohens kappa score: 0.063
LR average precision score: 0.659

-> test with 'GB'
GB tn, fp: 56476, 387
GB fn, tp: 9, 87
GB f1 score: 0.305
GB cohens kappa score: 0.303

-> test with 'KNN'
KNN tn, fp: 55591, 1272
KNN fn, tp: 62, 34
KNN f1 score: 0.049
KNN cohens kappa score: 0.046

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 55145, 4109
LR fn, tp: 20, 96
LR f1 score: 0.097
LR cohens kappa score: 0.094
LR average precision score: 0.838


average:
LR tn, fp: 54386.04, 2476.96
LR fn, tp: 9.2, 89.2
LR f1 score: 0.070
LR cohens kappa score: 0.066
LR average precision score: 0.703


minimum:
LR tn, fp: 52754, 1718
LR fn, tp: 3, 79
LR f1 score: 0.040
LR cohens kappa score: 0.037
LR average precision score: 0.518


-----[ GB ]-----
maximum:
GB tn, fp: 56593, 441
GB fn, tp: 19, 92
GB f1 score: 0.367
GB cohens kappa score: 0.366


average:
GB tn, fp: 56494.16, 368.84
GB fn, tp: 11.36, 87.04
GB f1 score: 0.316
GB cohens kappa score: 0.314


minimum:
GB tn, fp: 56422, 270
GB fn, tp: 7, 80
GB f1 score: 0.280
GB cohens kappa score: 0.278


-----[ KNN ]-----
maximum:
KNN tn, fp: 56393, 1681
KNN fn, tp: 78, 40
KNN f1 score: 0.087
KNN cohens kappa score: 0.084


average:
KNN tn, fp: 55832.16, 1030.84
KNN fn, tp: 68.44, 29.96
KNN f1 score: 0.054
KNN cohens kappa score: 0.052


minimum:
KNN tn, fp: 55182, 470
KNN fn, tp: 59, 21
KNN f1 score: 0.031
KNN cohens kappa score: 0.028


-----[ GAN ]-----
maximum:
GAN tn, fp: 56691, 52975
GAN fn, tp: 30, 99
GAN f1 score: 0.413
GAN cohens kappa score: 0.411


average:
GAN tn, fp: 49295.12, 7567.88
GAN fn, tp: 12.84, 85.56
GAN f1 score: 0.141
GAN cohens kappa score: 0.138


minimum:
GAN tn, fp: 3888, 172
GAN fn, tp: 0, 69
GAN f1 score: 0.004
GAN cohens kappa score: 0.000