ConvGeNBenchmarkResults/data_result/convGAN-old-full/imblearn_mammography.log

///////////////////////////////////////////
// Running convGAN-full on imblearn_mammography
///////////////////////////////////////////

Load 'data_input/imblearn_mammography'
from imblearn
non empty cut in data_input/imblearn_mammography! (7 points)
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 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1904, 281
LR fn, tp: 6, 46
LR f1 score: 0.243
LR cohens kappa score: 0.211
LR average precision score: 0.560

-> test with 'GB'
GB tn, fp: 2129, 56
GB fn, tp: 12, 40
GB f1 score: 0.541
GB cohens kappa score: 0.526

-> test with 'KNN'
KNN tn, fp: 1429, 756
KNN fn, tp: 6, 46
KNN f1 score: 0.108
KNN cohens kappa score: 0.067


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1915, 270
LR fn, tp: 6, 46
LR f1 score: 0.250
LR cohens kappa score: 0.219
LR average precision score: 0.481

-> test with 'GB'
GB tn, fp: 2141, 44
GB fn, tp: 12, 40
GB f1 score: 0.588
GB cohens kappa score: 0.576

-> test with 'KNN'
KNN tn, fp: 2093, 92
KNN fn, tp: 9, 43
KNN f1 score: 0.460
KNN cohens kappa score: 0.441


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1916, 269
LR fn, tp: 6, 46
LR f1 score: 0.251
LR cohens kappa score: 0.220
LR average precision score: 0.606

-> test with 'GB'
GB tn, fp: 2153, 32
GB fn, tp: 12, 40
GB f1 score: 0.645
GB cohens kappa score: 0.635

-> test with 'KNN'
KNN tn, fp: 1408, 777
KNN fn, tp: 6, 46
KNN f1 score: 0.105
KNN cohens kappa score: 0.064


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1918, 267
LR fn, tp: 7, 45
LR f1 score: 0.247
LR cohens kappa score: 0.216
LR average precision score: 0.326

-> test with 'GB'
GB tn, fp: 2119, 66
GB fn, tp: 13, 39
GB f1 score: 0.497
GB cohens kappa score: 0.481

-> test with 'KNN'
KNN tn, fp: 2092, 93
KNN fn, tp: 8, 44
KNN f1 score: 0.466
KNN cohens kappa score: 0.447


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1917, 266
LR fn, tp: 5, 47
LR f1 score: 0.258
LR cohens kappa score: 0.227
LR average precision score: 0.569

-> test with 'GB'
GB tn, fp: 2139, 44
GB fn, tp: 9, 43
GB f1 score: 0.619
GB cohens kappa score: 0.607

-> test with 'KNN'
KNN tn, fp: 1484, 699
KNN fn, tp: 9, 43
KNN f1 score: 0.108
KNN cohens kappa score: 0.068


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1879, 306
LR fn, tp: 6, 46
LR f1 score: 0.228
LR cohens kappa score: 0.195
LR average precision score: 0.487

-> test with 'GB'
GB tn, fp: 2114, 71
GB fn, tp: 10, 42
GB f1 score: 0.509
GB cohens kappa score: 0.493

-> test with 'KNN'
KNN tn, fp: 2078, 107
KNN fn, tp: 8, 44
KNN f1 score: 0.433
KNN cohens kappa score: 0.413


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1881, 304
LR fn, tp: 7, 45
LR f1 score: 0.224
LR cohens kappa score: 0.192
LR average precision score: 0.410

-> test with 'GB'
GB tn, fp: 2126, 59
GB fn, tp: 10, 42
GB f1 score: 0.549
GB cohens kappa score: 0.535

-> test with 'KNN'
KNN tn, fp: 2069, 116
KNN fn, tp: 7, 45
KNN f1 score: 0.423
KNN cohens kappa score: 0.402


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1936, 249
LR fn, tp: 7, 45
LR f1 score: 0.260
LR cohens kappa score: 0.230
LR average precision score: 0.513

-> test with 'GB'
GB tn, fp: 2135, 50
GB fn, tp: 13, 39
GB f1 score: 0.553
GB cohens kappa score: 0.540

-> test with 'KNN'
KNN tn, fp: 2084, 101
KNN fn, tp: 10, 42
KNN f1 score: 0.431
KNN cohens kappa score: 0.411


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1911, 274
LR fn, tp: 4, 48
LR f1 score: 0.257
LR cohens kappa score: 0.226
LR average precision score: 0.490

-> test with 'GB'
GB tn, fp: 2140, 45
GB fn, tp: 9, 43
GB f1 score: 0.614
GB cohens kappa score: 0.603

-> test with 'KNN'
KNN tn, fp: 2079, 106
KNN fn, tp: 6, 46
KNN f1 score: 0.451
KNN cohens kappa score: 0.431


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1927, 256
LR fn, tp: 7, 45
LR f1 score: 0.255
LR cohens kappa score: 0.224
LR average precision score: 0.529

-> test with 'GB'
GB tn, fp: 2161, 22
GB fn, tp: 14, 38
GB f1 score: 0.679
GB cohens kappa score: 0.670

-> test with 'KNN'
KNN tn, fp: 2110, 73
KNN fn, tp: 10, 42
KNN f1 score: 0.503
KNN cohens kappa score: 0.487


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1920, 265
LR fn, tp: 6, 46
LR f1 score: 0.253
LR cohens kappa score: 0.222
LR average precision score: 0.579

-> test with 'GB'
GB tn, fp: 2141, 44
GB fn, tp: 10, 42
GB f1 score: 0.609
GB cohens kappa score: 0.597

-> test with 'KNN'
KNN tn, fp: 2083, 102
KNN fn, tp: 7, 45
KNN f1 score: 0.452
KNN cohens kappa score: 0.433


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1874, 311
LR fn, tp: 6, 46
LR f1 score: 0.225
LR cohens kappa score: 0.192
LR average precision score: 0.434

-> test with 'GB'
GB tn, fp: 2139, 46
GB fn, tp: 15, 37
GB f1 score: 0.548
GB cohens kappa score: 0.535

-> test with 'KNN'
KNN tn, fp: 2100, 85
KNN fn, tp: 10, 42
KNN f1 score: 0.469
KNN cohens kappa score: 0.451


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1907, 278
LR fn, tp: 3, 49
LR f1 score: 0.259
LR cohens kappa score: 0.228
LR average precision score: 0.462

-> test with 'GB'
GB tn, fp: 2133, 52
GB fn, tp: 9, 43
GB f1 score: 0.585
GB cohens kappa score: 0.572

-> test with 'KNN'
KNN tn, fp: 1417, 768
KNN fn, tp: 4, 48
KNN f1 score: 0.111
KNN cohens kappa score: 0.070


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1913, 272
LR fn, tp: 9, 43
LR f1 score: 0.234
LR cohens kappa score: 0.203
LR average precision score: 0.489

-> test with 'GB'
GB tn, fp: 2141, 44
GB fn, tp: 15, 37
GB f1 score: 0.556
GB cohens kappa score: 0.543

-> test with 'KNN'
KNN tn, fp: 2095, 90
KNN fn, tp: 10, 42
KNN f1 score: 0.457
KNN cohens kappa score: 0.438


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1909, 274
LR fn, tp: 7, 45
LR f1 score: 0.243
LR cohens kappa score: 0.211
LR average precision score: 0.567

-> test with 'GB'
GB tn, fp: 2150, 33
GB fn, tp: 16, 36
GB f1 score: 0.595
GB cohens kappa score: 0.584

-> test with 'KNN'
KNN tn, fp: 2096, 87
KNN fn, tp: 9, 43
KNN f1 score: 0.473
KNN cohens kappa score: 0.454


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1912, 273
LR fn, tp: 7, 45
LR f1 score: 0.243
LR cohens kappa score: 0.212
LR average precision score: 0.565

-> test with 'GB'
GB tn, fp: 2137, 48
GB fn, tp: 18, 34
GB f1 score: 0.507
GB cohens kappa score: 0.493

-> test with 'KNN'
KNN tn, fp: 1456, 729
KNN fn, tp: 10, 42
KNN f1 score: 0.102
KNN cohens kappa score: 0.061


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1894, 291
LR fn, tp: 5, 47
LR f1 score: 0.241
LR cohens kappa score: 0.209
LR average precision score: 0.400

-> test with 'GB'
GB tn, fp: 2134, 51
GB fn, tp: 16, 36
GB f1 score: 0.518
GB cohens kappa score: 0.504

-> test with 'KNN'
KNN tn, fp: 1460, 725
KNN fn, tp: 5, 47
KNN f1 score: 0.114
KNN cohens kappa score: 0.074


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1920, 265
LR fn, tp: 7, 45
LR f1 score: 0.249
LR cohens kappa score: 0.217
LR average precision score: 0.474

-> test with 'GB'
GB tn, fp: 2138, 47
GB fn, tp: 8, 44
GB f1 score: 0.615
GB cohens kappa score: 0.604

-> test with 'KNN'
KNN tn, fp: 2090, 95
KNN fn, tp: 8, 44
KNN f1 score: 0.461
KNN cohens kappa score: 0.442


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1915, 270
LR fn, tp: 9, 43
LR f1 score: 0.236
LR cohens kappa score: 0.204
LR average precision score: 0.483

-> test with 'GB'
GB tn, fp: 2134, 51
GB fn, tp: 11, 41
GB f1 score: 0.569
GB cohens kappa score: 0.556

-> test with 'KNN'
KNN tn, fp: 2085, 100
KNN fn, tp: 8, 44
KNN f1 score: 0.449
KNN cohens kappa score: 0.429


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1888, 295
LR fn, tp: 1, 51
LR f1 score: 0.256
LR cohens kappa score: 0.225
LR average precision score: 0.494

-> test with 'GB'
GB tn, fp: 2133, 50
GB fn, tp: 10, 42
GB f1 score: 0.583
GB cohens kappa score: 0.571

-> test with 'KNN'
KNN tn, fp: 2073, 110
KNN fn, tp: 8, 44
KNN f1 score: 0.427
KNN cohens kappa score: 0.407


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1896, 289
LR fn, tp: 3, 49
LR f1 score: 0.251
LR cohens kappa score: 0.220
LR average precision score: 0.467

-> test with 'GB'
GB tn, fp: 2145, 40
GB fn, tp: 12, 40
GB f1 score: 0.606
GB cohens kappa score: 0.595

-> test with 'KNN'
KNN tn, fp: 2100, 85
KNN fn, tp: 9, 43
KNN f1 score: 0.478
KNN cohens kappa score: 0.460


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1876, 309
LR fn, tp: 6, 46
LR f1 score: 0.226
LR cohens kappa score: 0.193
LR average precision score: 0.478

-> test with 'GB'
GB tn, fp: 2128, 57
GB fn, tp: 9, 43
GB f1 score: 0.566
GB cohens kappa score: 0.552

-> test with 'KNN'
KNN tn, fp: 1399, 786
KNN fn, tp: 5, 47
KNN f1 score: 0.106
KNN cohens kappa score: 0.065


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1907, 278
LR fn, tp: 9, 43
LR f1 score: 0.231
LR cohens kappa score: 0.198
LR average precision score: 0.512

-> test with 'GB'
GB tn, fp: 2144, 41
GB fn, tp: 18, 34
GB f1 score: 0.535
GB cohens kappa score: 0.522

-> test with 'KNN'
KNN tn, fp: 2083, 102
KNN fn, tp: 10, 42
KNN f1 score: 0.429
KNN cohens kappa score: 0.408


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1783, 402
LR fn, tp: 4, 48
LR f1 score: 0.191
LR cohens kappa score: 0.156
LR average precision score: 0.494

-> test with 'GB'
GB tn, fp: 2136, 49
GB fn, tp: 11, 41
GB f1 score: 0.577
GB cohens kappa score: 0.565

-> test with 'KNN'
KNN tn, fp: 2094, 91
KNN fn, tp: 7, 45
KNN f1 score: 0.479
KNN cohens kappa score: 0.461


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1853, 330
LR fn, tp: 7, 45
LR f1 score: 0.211
LR cohens kappa score: 0.177
LR average precision score: 0.589

-> test with 'GB'
GB tn, fp: 2126, 57
GB fn, tp: 13, 39
GB f1 score: 0.527
GB cohens kappa score: 0.512

-> test with 'KNN'
KNN tn, fp: 2099, 84
KNN fn, tp: 10, 42
KNN f1 score: 0.472
KNN cohens kappa score: 0.454

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 1936, 402
LR fn, tp: 9, 51
LR f1 score: 0.260
LR cohens kappa score: 0.230
LR average precision score: 0.606


average:
LR tn, fp: 1898.84, 285.76
LR fn, tp: 6.0, 46.0
LR f1 score: 0.241
LR cohens kappa score: 0.209
LR average precision score: 0.498


minimum:
LR tn, fp: 1783, 249
LR fn, tp: 1, 43
LR f1 score: 0.191
LR cohens kappa score: 0.156
LR average precision score: 0.326


-----[ GB ]-----
maximum:
GB tn, fp: 2161, 71
GB fn, tp: 18, 44
GB f1 score: 0.679
GB cohens kappa score: 0.670


average:
GB tn, fp: 2136.64, 47.96
GB fn, tp: 12.2, 39.8
GB f1 score: 0.572
GB cohens kappa score: 0.559


minimum:
GB tn, fp: 2114, 22
GB fn, tp: 8, 34
GB f1 score: 0.497
GB cohens kappa score: 0.481


-----[ KNN ]-----
maximum:
KNN tn, fp: 2110, 786
KNN fn, tp: 10, 48
KNN f1 score: 0.503
KNN cohens kappa score: 0.487


average:
KNN tn, fp: 1906.24, 278.36
KNN fn, tp: 7.96, 44.04
KNN f1 score: 0.359
KNN cohens kappa score: 0.333


minimum:
KNN tn, fp: 1399, 73
KNN fn, tp: 4, 42
KNN f1 score: 0.102
KNN cohens kappa score: 0.061