ConvGeNBenchmarkResults/data_result/Repeater/kaggle_creditcard.log

///////////////////////////////////////////
// Running Repeater 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: 53336, 3527
LR fn, tp: 16, 83
LR f1 score: 0.045
LR cohens kappa score: 0.042
LR average precision score: 0.562

-> test with 'GB'
GB tn, fp: 56582, 281
GB fn, tp: 19, 80
GB f1 score: 0.348
GB cohens kappa score: 0.346

-> test with 'KNN'
KNN tn, fp: 56663, 200
KNN fn, tp: 79, 20
KNN f1 score: 0.125
KNN cohens kappa score: 0.123


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54088, 2775
LR fn, tp: 6, 93
LR f1 score: 0.063
LR cohens kappa score: 0.060
LR average precision score: 0.738

-> test with 'GB'
GB tn, fp: 56485, 378
GB fn, tp: 8, 91
GB f1 score: 0.320
GB cohens kappa score: 0.318

-> test with 'KNN'
KNN tn, fp: 56680, 183
KNN fn, tp: 81, 18
KNN f1 score: 0.120
KNN cohens kappa score: 0.118


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54602, 2261
LR fn, tp: 8, 91
LR f1 score: 0.074
LR cohens kappa score: 0.071
LR average precision score: 0.685

-> test with 'GB'
GB tn, fp: 56497, 366
GB fn, tp: 11, 88
GB f1 score: 0.318
GB cohens kappa score: 0.316

-> test with 'KNN'
KNN tn, fp: 56697, 166
KNN fn, tp: 78, 21
KNN f1 score: 0.147
KNN cohens kappa score: 0.145


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54722, 2141
LR fn, tp: 6, 93
LR f1 score: 0.080
LR cohens kappa score: 0.077
LR average precision score: 0.754

-> test with 'GB'
GB tn, fp: 56421, 442
GB fn, tp: 6, 93
GB f1 score: 0.293
GB cohens kappa score: 0.291

-> test with 'KNN'
KNN tn, fp: 56708, 155
KNN fn, tp: 73, 26
KNN f1 score: 0.186
KNN cohens kappa score: 0.184


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 54488, 2375
LR fn, tp: 9, 87
LR f1 score: 0.068
LR cohens kappa score: 0.065
LR average precision score: 0.794

-> test with 'GB'
GB tn, fp: 56372, 491
GB fn, tp: 9, 87
GB f1 score: 0.258
GB cohens kappa score: 0.256

-> test with 'KNN'
KNN tn, fp: 56693, 170
KNN fn, tp: 73, 23
KNN f1 score: 0.159
KNN cohens kappa score: 0.157


====== 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: 53491, 3372
LR fn, tp: 6, 93
LR f1 score: 0.052
LR cohens kappa score: 0.049
LR average precision score: 0.737

-> test with 'GB'
GB tn, fp: 56455, 408
GB fn, tp: 10, 89
GB f1 score: 0.299
GB cohens kappa score: 0.297

-> test with 'KNN'
KNN tn, fp: 56652, 211
KNN fn, tp: 78, 21
KNN f1 score: 0.127
KNN cohens kappa score: 0.125


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 53916, 2947
LR fn, tp: 9, 90
LR f1 score: 0.057
LR cohens kappa score: 0.054
LR average precision score: 0.642

-> test with 'GB'
GB tn, fp: 56360, 503
GB fn, tp: 9, 90
GB f1 score: 0.260
GB cohens kappa score: 0.258

-> test with 'KNN'
KNN tn, fp: 56682, 181
KNN fn, tp: 74, 25
KNN f1 score: 0.164
KNN cohens kappa score: 0.162


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54424, 2439
LR fn, tp: 9, 90
LR f1 score: 0.068
LR cohens kappa score: 0.065
LR average precision score: 0.717

-> test with 'GB'
GB tn, fp: 56506, 357
GB fn, tp: 11, 88
GB f1 score: 0.324
GB cohens kappa score: 0.322

-> test with 'KNN'
KNN tn, fp: 56698, 165
KNN fn, tp: 69, 30
KNN f1 score: 0.204
KNN cohens kappa score: 0.202


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54803, 2060
LR fn, tp: 8, 91
LR f1 score: 0.081
LR cohens kappa score: 0.078
LR average precision score: 0.735

-> test with 'GB'
GB tn, fp: 56506, 357
GB fn, tp: 11, 88
GB f1 score: 0.324
GB cohens kappa score: 0.322

-> test with 'KNN'
KNN tn, fp: 56681, 182
KNN fn, tp: 78, 21
KNN f1 score: 0.139
KNN cohens kappa score: 0.137


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 54675, 2188
LR fn, tp: 9, 87
LR f1 score: 0.073
LR cohens kappa score: 0.070
LR average precision score: 0.744

-> test with 'GB'
GB tn, fp: 56426, 437
GB fn, tp: 15, 81
GB f1 score: 0.264
GB cohens kappa score: 0.262

-> 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 'LR'
LR tn, fp: 53273, 3590
LR fn, tp: 8, 91
LR f1 score: 0.048
LR cohens kappa score: 0.045
LR average precision score: 0.673

-> test with 'GB'
GB tn, fp: 56427, 436
GB fn, tp: 12, 87
GB f1 score: 0.280
GB cohens kappa score: 0.278

-> test with 'KNN'
KNN tn, fp: 56662, 201
KNN fn, tp: 77, 22
KNN f1 score: 0.137
KNN cohens kappa score: 0.135


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54063, 2800
LR fn, tp: 7, 92
LR f1 score: 0.062
LR cohens kappa score: 0.058
LR average precision score: 0.644

-> test with 'GB'
GB tn, fp: 56441, 422
GB fn, tp: 11, 88
GB f1 score: 0.289
GB cohens kappa score: 0.287

-> test with 'KNN'
KNN tn, fp: 56691, 172
KNN fn, tp: 78, 21
KNN f1 score: 0.144
KNN cohens kappa score: 0.142


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54655, 2208
LR fn, tp: 10, 89
LR f1 score: 0.074
LR cohens kappa score: 0.071
LR average precision score: 0.709

-> test with 'GB'
GB tn, fp: 56473, 390
GB fn, tp: 13, 86
GB f1 score: 0.299
GB cohens kappa score: 0.297

-> test with 'KNN'
KNN tn, fp: 56727, 136
KNN fn, tp: 80, 19
KNN f1 score: 0.150
KNN cohens kappa score: 0.148


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54008, 2855
LR fn, tp: 8, 91
LR f1 score: 0.060
LR cohens kappa score: 0.057
LR average precision score: 0.745

-> test with 'GB'
GB tn, fp: 56442, 421
GB fn, tp: 8, 91
GB f1 score: 0.298
GB cohens kappa score: 0.296

-> 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 'LR'
LR tn, fp: 54921, 1942
LR fn, tp: 7, 89
LR f1 score: 0.084
LR cohens kappa score: 0.081
LR average precision score: 0.750

-> test with 'GB'
GB tn, fp: 56501, 362
GB fn, tp: 12, 84
GB f1 score: 0.310
GB cohens kappa score: 0.308

-> test with 'KNN'
KNN tn, fp: 56701, 162
KNN fn, tp: 70, 26
KNN f1 score: 0.183
KNN cohens kappa score: 0.181


====== 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: 53074, 3789
LR fn, tp: 5, 94
LR f1 score: 0.047
LR cohens kappa score: 0.044
LR average precision score: 0.672

-> test with 'GB'
GB tn, fp: 56468, 395
GB fn, tp: 7, 92
GB f1 score: 0.314
GB cohens kappa score: 0.312

-> test with 'KNN'
KNN tn, fp: 56696, 167
KNN fn, tp: 81, 18
KNN f1 score: 0.127
KNN cohens kappa score: 0.125


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54055, 2808
LR fn, tp: 11, 88
LR f1 score: 0.059
LR cohens kappa score: 0.056
LR average precision score: 0.647

-> test with 'GB'
GB tn, fp: 56513, 350
GB fn, tp: 13, 86
GB f1 score: 0.321
GB cohens kappa score: 0.320

-> test with 'KNN'
KNN tn, fp: 56692, 171
KNN fn, tp: 84, 15
KNN f1 score: 0.105
KNN cohens kappa score: 0.103


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 54609, 2254
LR fn, tp: 10, 89
LR f1 score: 0.073
LR cohens kappa score: 0.070
LR average precision score: 0.720

-> 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: 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: 54164, 2699
LR fn, tp: 9, 90
LR f1 score: 0.062
LR cohens kappa score: 0.059
LR average precision score: 0.758

-> test with 'GB'
GB tn, fp: 56367, 496
GB fn, tp: 11, 88
GB f1 score: 0.258
GB cohens kappa score: 0.255

-> test with 'KNN'
KNN tn, fp: 56690, 173
KNN fn, tp: 67, 32
KNN f1 score: 0.211
KNN cohens kappa score: 0.209


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 54833, 2030
LR fn, tp: 8, 88
LR f1 score: 0.079
LR cohens kappa score: 0.077
LR average precision score: 0.692

-> test with 'GB'
GB tn, fp: 56490, 373
GB fn, tp: 15, 81
GB f1 score: 0.295
GB cohens kappa score: 0.293

-> test with 'KNN'
KNN tn, fp: 56695, 168
KNN fn, tp: 74, 22
KNN f1 score: 0.154
KNN cohens kappa score: 0.152


====== 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: 54233, 2630
LR fn, tp: 10, 89
LR f1 score: 0.063
LR cohens kappa score: 0.060
LR average precision score: 0.630

-> test with 'GB'
GB tn, fp: 56542, 321
GB fn, tp: 17, 82
GB f1 score: 0.327
GB cohens kappa score: 0.325

-> 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: 54380, 2483
LR fn, tp: 4, 95
LR f1 score: 0.071
LR cohens kappa score: 0.068
LR average precision score: 0.767

-> test with 'GB'
GB tn, fp: 56450, 413
GB fn, tp: 7, 92
GB f1 score: 0.305
GB cohens kappa score: 0.303

-> test with 'KNN'
KNN tn, fp: 56690, 173
KNN fn, tp: 75, 24
KNN f1 score: 0.162
KNN cohens kappa score: 0.160


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227059 synthetic samples
-> 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: 0.673

-> test with 'GB'
GB tn, fp: 56462, 401
GB fn, tp: 12, 87
GB f1 score: 0.296
GB cohens kappa score: 0.294

-> 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: 54436, 2427
LR fn, tp: 6, 93
LR f1 score: 0.071
LR cohens kappa score: 0.068
LR average precision score: 0.751

-> test with 'GB'
GB tn, fp: 56433, 430
GB fn, tp: 10, 89
GB f1 score: 0.288
GB cohens kappa score: 0.286

-> test with 'KNN'
KNN tn, fp: 56676, 187
KNN fn, tp: 78, 21
KNN f1 score: 0.137
KNN cohens kappa score: 0.135


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 54576, 2287
LR fn, tp: 5, 91
LR f1 score: 0.074
LR cohens kappa score: 0.071
LR average precision score: 0.650

-> test with 'GB'
GB tn, fp: 56468, 395
GB fn, tp: 9, 87
GB f1 score: 0.301
GB cohens kappa score: 0.299

-> test with 'KNN'
KNN tn, fp: 56695, 168
KNN fn, tp: 73, 23
KNN f1 score: 0.160
KNN cohens kappa score: 0.158

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 54921, 3789
LR fn, tp: 16, 95
LR f1 score: 0.084
LR cohens kappa score: 0.081
LR average precision score: 0.794


average:
LR tn, fp: 54248.4, 2614.6
LR fn, tp: 8.2, 90.2
LR f1 score: 0.066
LR cohens kappa score: 0.063
LR average precision score: 0.704


minimum:
LR tn, fp: 53074, 1942
LR fn, tp: 4, 83
LR f1 score: 0.045
LR cohens kappa score: 0.042
LR average precision score: 0.562


-----[ GB ]-----
maximum:
GB tn, fp: 56582, 503
GB fn, tp: 19, 93
GB f1 score: 0.348
GB cohens kappa score: 0.346


average:
GB tn, fp: 56464.48, 398.52
GB fn, tp: 11.12, 87.28
GB f1 score: 0.301
GB cohens kappa score: 0.299


minimum:
GB tn, fp: 56360, 281
GB fn, tp: 6, 80
GB f1 score: 0.258
GB cohens kappa score: 0.255


-----[ KNN ]-----
maximum:
KNN tn, fp: 56727, 211
KNN fn, tp: 84, 32
KNN f1 score: 0.211
KNN cohens kappa score: 0.209


average:
KNN tn, fp: 56690.6, 172.4
KNN fn, tp: 76.2, 22.2
KNN f1 score: 0.152
KNN cohens kappa score: 0.150


minimum:
KNN tn, fp: 56652, 136
KNN fn, tp: 67, 15
KNN f1 score: 0.105
KNN cohens kappa score: 0.103