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

///////////////////////////////////////////
// Running convGAN-majority-5 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 GAN.predict
GAN tn, fp: 2126, 59
GAN fn, tp: 7, 45
GAN f1 score: 0.577
GAN cohens kappa score: 0.563

-> test with 'LR'
LR tn, fp: 1831, 354
LR fn, tp: 6, 46
LR f1 score: 0.204
LR cohens kappa score: 0.169
LR average precision score: 0.560

-> test with 'GB'
GB tn, fp: 2123, 62
GB fn, tp: 16, 36
GB f1 score: 0.480
GB cohens kappa score: 0.464

-> test with 'KNN'
KNN tn, fp: 2092, 93
KNN fn, tp: 6, 46
KNN f1 score: 0.482
KNN cohens kappa score: 0.464


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2142, 43
GAN fn, tp: 13, 39
GAN f1 score: 0.582
GAN cohens kappa score: 0.570

-> test with 'LR'
LR tn, fp: 1909, 276
LR fn, tp: 6, 46
LR f1 score: 0.246
LR cohens kappa score: 0.215
LR average precision score: 0.489

-> test with 'GB'
GB tn, fp: 2140, 45
GB fn, tp: 12, 40
GB f1 score: 0.584
GB cohens kappa score: 0.572

-> test with 'KNN'
KNN tn, fp: 2109, 76
KNN fn, tp: 8, 44
KNN f1 score: 0.512
KNN cohens kappa score: 0.495


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2128, 57
GAN fn, tp: 7, 45
GAN f1 score: 0.584
GAN cohens kappa score: 0.571

-> test with 'LR'
LR tn, fp: 1907, 278
LR fn, tp: 6, 46
LR f1 score: 0.245
LR cohens kappa score: 0.213
LR average precision score: 0.590

-> test with 'GB'
GB tn, fp: 2131, 54
GB fn, tp: 9, 43
GB f1 score: 0.577
GB cohens kappa score: 0.564

-> test with 'KNN'
KNN tn, fp: 2105, 80
KNN fn, tp: 7, 45
KNN f1 score: 0.508
KNN cohens kappa score: 0.492


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2085, 100
GAN fn, tp: 10, 42
GAN f1 score: 0.433
GAN cohens kappa score: 0.413

-> test with 'LR'
LR tn, fp: 1887, 298
LR fn, tp: 6, 46
LR f1 score: 0.232
LR cohens kappa score: 0.200
LR average precision score: 0.327

-> test with 'GB'
GB tn, fp: 2142, 43
GB fn, tp: 17, 35
GB f1 score: 0.538
GB cohens kappa score: 0.525

-> test with 'KNN'
KNN tn, fp: 2089, 96
KNN fn, tp: 9, 43
KNN f1 score: 0.450
KNN cohens kappa score: 0.431


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2111, 72
GAN fn, tp: 9, 43
GAN f1 score: 0.515
GAN cohens kappa score: 0.499

-> test with 'LR'
LR tn, fp: 1914, 269
LR fn, tp: 5, 47
LR f1 score: 0.255
LR cohens kappa score: 0.224
LR average precision score: 0.568

-> test with 'GB'
GB tn, fp: 2139, 44
GB fn, tp: 13, 39
GB f1 score: 0.578
GB cohens kappa score: 0.565

-> test with 'KNN'
KNN tn, fp: 2093, 90
KNN fn, tp: 11, 41
KNN f1 score: 0.448
KNN cohens kappa score: 0.429


====== 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 GAN.predict
GAN tn, fp: 2097, 88
GAN fn, tp: 10, 42
GAN f1 score: 0.462
GAN cohens kappa score: 0.443

-> 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: 2115, 70
GB fn, tp: 12, 40
GB f1 score: 0.494
GB cohens kappa score: 0.477

-> 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 GAN.predict
GAN tn, fp: 2077, 108
GAN fn, tp: 3, 49
GAN f1 score: 0.469
GAN cohens kappa score: 0.450

-> test with 'LR'
LR tn, fp: 1878, 307
LR fn, tp: 8, 44
LR f1 score: 0.218
LR cohens kappa score: 0.185
LR average precision score: 0.454

-> test with 'GB'
GB tn, fp: 2125, 60
GB fn, tp: 13, 39
GB f1 score: 0.517
GB cohens kappa score: 0.501

-> test with 'KNN'
KNN tn, fp: 2079, 106
KNN fn, tp: 7, 45
KNN f1 score: 0.443
KNN cohens kappa score: 0.423


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2149, 36
GAN fn, tp: 12, 40
GAN f1 score: 0.625
GAN cohens kappa score: 0.614

-> test with 'LR'
LR tn, fp: 1805, 380
LR fn, tp: 7, 45
LR f1 score: 0.189
LR cohens kappa score: 0.154
LR average precision score: 0.512

-> test with 'GB'
GB tn, fp: 2143, 42
GB fn, tp: 12, 40
GB f1 score: 0.597
GB cohens kappa score: 0.585

-> test with 'KNN'
KNN tn, fp: 2104, 81
KNN fn, tp: 10, 42
KNN f1 score: 0.480
KNN cohens kappa score: 0.462


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2094, 91
GAN fn, tp: 5, 47
GAN f1 score: 0.495
GAN cohens kappa score: 0.477

-> test with 'LR'
LR tn, fp: 1816, 369
LR fn, tp: 3, 49
LR f1 score: 0.209
LR cohens kappa score: 0.174
LR average precision score: 0.505

-> test with 'GB'
GB tn, fp: 2133, 52
GB fn, tp: 11, 41
GB f1 score: 0.566
GB cohens kappa score: 0.552

-> test with 'KNN'
KNN tn, fp: 2088, 97
KNN fn, tp: 6, 46
KNN f1 score: 0.472
KNN cohens kappa score: 0.453


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2135, 48
GAN fn, tp: 11, 41
GAN f1 score: 0.582
GAN cohens kappa score: 0.569

-> test with 'LR'
LR tn, fp: 1925, 258
LR fn, tp: 8, 44
LR f1 score: 0.249
LR cohens kappa score: 0.218
LR average precision score: 0.504

-> test with 'GB'
GB tn, fp: 2156, 27
GB fn, tp: 16, 36
GB f1 score: 0.626
GB cohens kappa score: 0.616

-> test with 'KNN'
KNN tn, fp: 2113, 70
KNN fn, tp: 11, 41
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 GAN.predict
GAN tn, fp: 2111, 74
GAN fn, tp: 5, 47
GAN f1 score: 0.543
GAN cohens kappa score: 0.528

-> test with 'LR'
LR tn, fp: 1888, 297
LR fn, tp: 6, 46
LR f1 score: 0.233
LR cohens kappa score: 0.201
LR average precision score: 0.543

-> test with 'GB'
GB tn, fp: 2149, 36
GB fn, tp: 10, 42
GB f1 score: 0.646
GB cohens kappa score: 0.636

-> test with 'KNN'
KNN tn, fp: 2102, 83
KNN fn, tp: 6, 46
KNN f1 score: 0.508
KNN cohens kappa score: 0.491


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2063, 122
GAN fn, tp: 8, 44
GAN f1 score: 0.404
GAN cohens kappa score: 0.382

-> test with 'LR'
LR tn, fp: 1906, 279
LR fn, tp: 7, 45
LR f1 score: 0.239
LR cohens kappa score: 0.208
LR average precision score: 0.412

-> test with 'GB'
GB tn, fp: 2140, 45
GB fn, tp: 17, 35
GB f1 score: 0.530
GB cohens kappa score: 0.517

-> test with 'KNN'
KNN tn, fp: 2092, 93
KNN fn, tp: 10, 42
KNN f1 score: 0.449
KNN cohens kappa score: 0.430


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2104, 81
GAN fn, tp: 3, 49
GAN f1 score: 0.538
GAN cohens kappa score: 0.523

-> test with 'LR'
LR tn, fp: 1889, 296
LR fn, tp: 3, 49
LR f1 score: 0.247
LR cohens kappa score: 0.215
LR average precision score: 0.488

-> test with 'GB'
GB tn, fp: 2134, 51
GB fn, tp: 10, 42
GB f1 score: 0.579
GB cohens kappa score: 0.566

-> test with 'KNN'
KNN tn, fp: 2101, 84
KNN fn, tp: 4, 48
KNN f1 score: 0.522
KNN cohens kappa score: 0.505


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2124, 61
GAN fn, tp: 12, 40
GAN f1 score: 0.523
GAN cohens kappa score: 0.508

-> test with 'LR'
LR tn, fp: 1892, 293
LR fn, tp: 9, 43
LR f1 score: 0.222
LR cohens kappa score: 0.189
LR average precision score: 0.479

-> test with 'GB'
GB tn, fp: 2143, 42
GB fn, tp: 16, 36
GB f1 score: 0.554
GB cohens kappa score: 0.541

-> test with 'KNN'
KNN tn, fp: 2101, 84
KNN fn, tp: 14, 38
KNN f1 score: 0.437
KNN cohens kappa score: 0.418


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2111, 72
GAN fn, tp: 14, 38
GAN f1 score: 0.469
GAN cohens kappa score: 0.452

-> test with 'LR'
LR tn, fp: 1906, 277
LR fn, tp: 7, 45
LR f1 score: 0.241
LR cohens kappa score: 0.209
LR average precision score: 0.567

-> test with 'GB'
GB tn, fp: 2144, 39
GB fn, tp: 15, 37
GB f1 score: 0.578
GB cohens kappa score: 0.566

-> test with 'KNN'
KNN tn, fp: 2106, 77
KNN fn, tp: 11, 41
KNN f1 score: 0.482
KNN cohens kappa score: 0.465


====== 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 GAN.predict
GAN tn, fp: 2104, 81
GAN fn, tp: 10, 42
GAN f1 score: 0.480
GAN cohens kappa score: 0.462

-> 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: 0.564

-> test with 'GB'
GB tn, fp: 2142, 43
GB fn, tp: 18, 34
GB f1 score: 0.527
GB cohens kappa score: 0.514

-> test with 'KNN'
KNN tn, fp: 2120, 65
KNN fn, tp: 10, 42
KNN f1 score: 0.528
KNN cohens kappa score: 0.513


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2104, 81
GAN fn, tp: 6, 46
GAN f1 score: 0.514
GAN cohens kappa score: 0.497

-> test with 'LR'
LR tn, fp: 1898, 287
LR fn, tp: 5, 47
LR f1 score: 0.244
LR cohens kappa score: 0.212
LR average precision score: 0.401

-> test with 'GB'
GB tn, fp: 2139, 46
GB fn, tp: 17, 35
GB f1 score: 0.526
GB cohens kappa score: 0.513

-> test with 'KNN'
KNN tn, fp: 1466, 719
KNN fn, tp: 5, 47
KNN f1 score: 0.115
KNN cohens kappa score: 0.075


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2120, 65
GAN fn, tp: 9, 43
GAN f1 score: 0.537
GAN cohens kappa score: 0.523

-> test with 'LR'
LR tn, fp: 1901, 284
LR fn, tp: 7, 45
LR f1 score: 0.236
LR cohens kappa score: 0.204
LR average precision score: 0.468

-> test with 'GB'
GB tn, fp: 2135, 50
GB fn, tp: 8, 44
GB f1 score: 0.603
GB cohens kappa score: 0.590

-> test with 'KNN'
KNN tn, fp: 2095, 90
KNN fn, tp: 8, 44
KNN f1 score: 0.473
KNN cohens kappa score: 0.455


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2081, 104
GAN fn, tp: 6, 46
GAN f1 score: 0.455
GAN cohens kappa score: 0.436

-> test with 'LR'
LR tn, fp: 1917, 268
LR fn, tp: 9, 43
LR f1 score: 0.237
LR cohens kappa score: 0.205
LR average precision score: 0.484

-> test with 'GB'
GB tn, fp: 2133, 52
GB fn, tp: 12, 40
GB f1 score: 0.556
GB cohens kappa score: 0.542

-> test with 'KNN'
KNN tn, fp: 2084, 101
KNN fn, tp: 7, 45
KNN f1 score: 0.455
KNN cohens kappa score: 0.435


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2125, 58
GAN fn, tp: 9, 43
GAN f1 score: 0.562
GAN cohens kappa score: 0.548

-> test with 'LR'
LR tn, fp: 1889, 294
LR fn, tp: 1, 51
LR f1 score: 0.257
LR cohens kappa score: 0.226
LR average precision score: 0.465

-> test with 'GB'
GB tn, fp: 2143, 40
GB fn, tp: 12, 40
GB f1 score: 0.606
GB cohens kappa score: 0.595

-> test with 'KNN'
KNN tn, fp: 2087, 96
KNN fn, tp: 9, 43
KNN f1 score: 0.450
KNN cohens kappa score: 0.431


====== 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 GAN.predict
GAN tn, fp: 2121, 64
GAN fn, tp: 6, 46
GAN f1 score: 0.568
GAN cohens kappa score: 0.554

-> test with 'LR'
LR tn, fp: 1890, 295
LR fn, tp: 4, 48
LR f1 score: 0.243
LR cohens kappa score: 0.211
LR average precision score: 0.493

-> 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: 1444, 741
KNN fn, tp: 7, 45
KNN f1 score: 0.107
KNN cohens kappa score: 0.067


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2111, 74
GAN fn, tp: 8, 44
GAN f1 score: 0.518
GAN cohens kappa score: 0.502

-> test with 'LR'
LR tn, fp: 1898, 287
LR fn, tp: 6, 46
LR f1 score: 0.239
LR cohens kappa score: 0.207
LR average precision score: 0.422

-> test with 'GB'
GB tn, fp: 2137, 48
GB fn, tp: 14, 38
GB f1 score: 0.551
GB cohens kappa score: 0.537

-> test with 'KNN'
KNN tn, fp: 1417, 768
KNN fn, tp: 6, 46
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 GAN.predict
GAN tn, fp: 2130, 55
GAN fn, tp: 13, 39
GAN f1 score: 0.534
GAN cohens kappa score: 0.520

-> test with 'LR'
LR tn, fp: 1857, 328
LR fn, tp: 8, 44
LR f1 score: 0.208
LR cohens kappa score: 0.174
LR average precision score: 0.531

-> test with 'GB'
GB tn, fp: 2133, 52
GB fn, tp: 19, 33
GB f1 score: 0.482
GB cohens kappa score: 0.466

-> test with 'KNN'
KNN tn, fp: 2096, 89
KNN fn, tp: 11, 41
KNN f1 score: 0.451
KNN cohens kappa score: 0.432


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2118, 67
GAN fn, tp: 8, 44
GAN f1 score: 0.540
GAN cohens kappa score: 0.525

-> test with 'LR'
LR tn, fp: 1891, 294
LR fn, tp: 4, 48
LR f1 score: 0.244
LR cohens kappa score: 0.212
LR average precision score: 0.477

-> test with 'GB'
GB tn, fp: 2130, 55
GB fn, tp: 11, 41
GB f1 score: 0.554
GB cohens kappa score: 0.540

-> test with 'KNN'
KNN tn, fp: 2095, 90
KNN fn, tp: 8, 44
KNN f1 score: 0.473
KNN cohens kappa score: 0.455


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2056, 127
GAN fn, tp: 9, 43
GAN f1 score: 0.387
GAN cohens kappa score: 0.365

-> test with 'LR'
LR tn, fp: 1930, 253
LR fn, tp: 8, 44
LR f1 score: 0.252
LR cohens kappa score: 0.221
LR average precision score: 0.576

-> test with 'GB'
GB tn, fp: 2134, 49
GB fn, tp: 14, 38
GB f1 score: 0.547
GB cohens kappa score: 0.533

-> test with 'KNN'
KNN tn, fp: 2094, 89
KNN fn, tp: 11, 41
KNN f1 score: 0.451
KNN cohens kappa score: 0.432

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 1930, 380
LR fn, tp: 9, 51
LR f1 score: 0.257
LR cohens kappa score: 0.226
LR average precision score: 0.590


average:
LR tn, fp: 1888.84, 295.76
LR fn, tp: 6.08, 45.92
LR f1 score: 0.234
LR cohens kappa score: 0.202
LR average precision score: 0.495


minimum:
LR tn, fp: 1805, 253
LR fn, tp: 1, 43
LR f1 score: 0.189
LR cohens kappa score: 0.154
LR average precision score: 0.327


-----[ GB ]-----
maximum:
GB tn, fp: 2156, 70
GB fn, tp: 19, 44
GB f1 score: 0.646
GB cohens kappa score: 0.636


average:
GB tn, fp: 2137.12, 47.48
GB fn, tp: 13.44, 38.56
GB f1 score: 0.560
GB cohens kappa score: 0.547


minimum:
GB tn, fp: 2115, 27
GB fn, tp: 8, 33
GB f1 score: 0.480
GB cohens kappa score: 0.464


-----[ KNN ]-----
maximum:
KNN tn, fp: 2120, 768
KNN fn, tp: 14, 48
KNN f1 score: 0.528
KNN cohens kappa score: 0.513


average:
KNN tn, fp: 2018.0, 166.6
KNN fn, tp: 8.4, 43.6
KNN f1 score: 0.430
KNN cohens kappa score: 0.409


minimum:
KNN tn, fp: 1417, 65
KNN fn, tp: 4, 38
KNN f1 score: 0.106
KNN cohens kappa score: 0.065


-----[ GAN ]-----
maximum:
GAN tn, fp: 2149, 127
GAN fn, tp: 14, 49
GAN f1 score: 0.625
GAN cohens kappa score: 0.614


average:
GAN tn, fp: 2109.08, 75.52
GAN fn, tp: 8.52, 43.48
GAN f1 score: 0.516
GAN cohens kappa score: 0.500


minimum:
GAN tn, fp: 2056, 36
GAN fn, tp: 3, 38
GAN f1 score: 0.387
GAN cohens kappa score: 0.365