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

///////////////////////////////////////////
// Running convGAN-majority-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 GAN.predict
GAN tn, fp: 2098, 87
GAN fn, tp: 6, 46
GAN f1 score: 0.497
GAN cohens kappa score: 0.480

-> 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: 0.560

-> test with 'GB'
GB tn, fp: 2130, 55
GB fn, tp: 15, 37
GB f1 score: 0.514
GB cohens kappa score: 0.499

-> test with 'KNN'
KNN tn, fp: 2080, 105
KNN fn, tp: 6, 46
KNN f1 score: 0.453
KNN cohens kappa score: 0.434


------ 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: 2125, 60
GAN fn, tp: 10, 42
GAN f1 score: 0.545
GAN cohens kappa score: 0.531

-> test with 'LR'
LR tn, fp: 1911, 274
LR fn, tp: 6, 46
LR f1 score: 0.247
LR cohens kappa score: 0.216
LR average precision score: 0.479

-> test with 'GB'
GB tn, fp: 2136, 49
GB fn, tp: 12, 40
GB f1 score: 0.567
GB cohens kappa score: 0.554

-> 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 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 2123, 62
GAN fn, tp: 8, 44
GAN f1 score: 0.557
GAN cohens kappa score: 0.543

-> 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: 0.591

-> test with 'GB'
GB tn, fp: 2154, 31
GB fn, tp: 11, 41
GB f1 score: 0.661
GB cohens kappa score: 0.652

-> test with 'KNN'
KNN tn, fp: 2097, 88
KNN fn, tp: 6, 46
KNN f1 score: 0.495
KNN cohens kappa score: 0.477


------ 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: 2114, 71
GAN fn, tp: 10, 42
GAN f1 score: 0.509
GAN cohens kappa score: 0.493

-> test with 'LR'
LR tn, fp: 1916, 269
LR fn, tp: 7, 45
LR f1 score: 0.246
LR cohens kappa score: 0.215
LR average precision score: 0.329

-> test with 'GB'
GB tn, fp: 2134, 51
GB fn, tp: 17, 35
GB f1 score: 0.507
GB cohens kappa score: 0.493

-> test with 'KNN'
KNN tn, fp: 2075, 110
KNN fn, tp: 9, 43
KNN f1 score: 0.420
KNN cohens kappa score: 0.399


------ 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: 2131, 52
GAN fn, tp: 9, 43
GAN f1 score: 0.585
GAN cohens kappa score: 0.572

-> test with 'LR'
LR tn, fp: 1915, 268
LR fn, tp: 6, 46
LR f1 score: 0.251
LR cohens kappa score: 0.220
LR average precision score: 0.563

-> test with 'GB'
GB tn, fp: 2140, 43
GB fn, tp: 13, 39
GB f1 score: 0.582
GB cohens kappa score: 0.570

-> test with 'KNN'
KNN tn, fp: 1484, 699
KNN fn, tp: 10, 42
KNN f1 score: 0.106
KNN cohens kappa score: 0.065


====== 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: 2090, 95
GAN fn, tp: 11, 41
GAN f1 score: 0.436
GAN cohens kappa score: 0.417

-> 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: 0.507

-> test with 'GB'
GB tn, fp: 2114, 71
GB fn, tp: 12, 40
GB f1 score: 0.491
GB cohens kappa score: 0.474

-> test with 'KNN'
KNN tn, fp: 2083, 102
KNN fn, tp: 8, 44
KNN f1 score: 0.444
KNN cohens kappa score: 0.425


------ 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: 2104, 81
GAN fn, tp: 7, 45
GAN f1 score: 0.506
GAN cohens kappa score: 0.489

-> test with 'LR'
LR tn, fp: 1883, 302
LR fn, tp: 7, 45
LR f1 score: 0.226
LR cohens kappa score: 0.193
LR average precision score: 0.419

-> test with 'GB'
GB tn, fp: 2131, 54
GB fn, tp: 12, 40
GB f1 score: 0.548
GB cohens kappa score: 0.534

-> test with 'KNN'
KNN tn, fp: 2067, 118
KNN fn, tp: 5, 47
KNN f1 score: 0.433
KNN cohens kappa score: 0.412


------ 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: 2120, 65
GAN fn, tp: 10, 42
GAN f1 score: 0.528
GAN cohens kappa score: 0.513

-> test with 'LR'
LR tn, fp: 1926, 259
LR fn, tp: 7, 45
LR f1 score: 0.253
LR cohens kappa score: 0.222
LR average precision score: 0.507

-> 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: 1438, 747
KNN fn, tp: 10, 42
KNN f1 score: 0.100
KNN cohens kappa score: 0.059


------ 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: 2125, 60
GAN fn, tp: 8, 44
GAN f1 score: 0.564
GAN cohens kappa score: 0.550

-> test with 'LR'
LR tn, fp: 1911, 274
LR fn, tp: 5, 47
LR f1 score: 0.252
LR cohens kappa score: 0.221
LR average precision score: 0.476

-> test with 'GB'
GB tn, fp: 2134, 51
GB fn, tp: 9, 43
GB f1 score: 0.589
GB cohens kappa score: 0.576

-> test with 'KNN'
KNN tn, fp: 1414, 771
KNN fn, tp: 4, 48
KNN f1 score: 0.110
KNN cohens kappa score: 0.070


------ 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: 2125, 58
GAN fn, tp: 11, 41
GAN f1 score: 0.543
GAN cohens kappa score: 0.529

-> test with 'LR'
LR tn, fp: 1933, 250
LR fn, tp: 8, 44
LR f1 score: 0.254
LR cohens kappa score: 0.224
LR average precision score: 0.518

-> test with 'GB'
GB tn, fp: 2147, 36
GB fn, tp: 15, 37
GB f1 score: 0.592
GB cohens kappa score: 0.581

-> test with 'KNN'
KNN tn, fp: 2117, 66
KNN fn, tp: 9, 43
KNN f1 score: 0.534
KNN cohens kappa score: 0.519


====== 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: 2121, 64
GAN fn, tp: 8, 44
GAN f1 score: 0.550
GAN cohens kappa score: 0.535

-> 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: 0.565

-> test with 'GB'
GB tn, fp: 2138, 47
GB fn, tp: 10, 42
GB f1 score: 0.596
GB cohens kappa score: 0.584

-> test with 'KNN'
KNN tn, fp: 2090, 95
KNN fn, tp: 6, 46
KNN f1 score: 0.477
KNN cohens kappa score: 0.458


------ 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: 2099, 86
GAN fn, tp: 8, 44
GAN f1 score: 0.484
GAN cohens kappa score: 0.466

-> test with 'LR'
LR tn, fp: 1917, 268
LR fn, tp: 6, 46
LR f1 score: 0.251
LR cohens kappa score: 0.220
LR average precision score: 0.465

-> test with 'GB'
GB tn, fp: 2145, 40
GB fn, tp: 17, 35
GB f1 score: 0.551
GB cohens kappa score: 0.539

-> test with 'KNN'
KNN tn, fp: 2105, 80
KNN fn, tp: 10, 42
KNN f1 score: 0.483
KNN cohens kappa score: 0.465


------ 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: 2089, 96
GAN fn, tp: 3, 49
GAN f1 score: 0.497
GAN cohens kappa score: 0.480

-> test with 'LR'
LR tn, fp: 1803, 382
LR fn, tp: 2, 50
LR f1 score: 0.207
LR cohens kappa score: 0.172
LR average precision score: 0.500

-> test with 'GB'
GB tn, fp: 2128, 57
GB fn, tp: 3, 49
GB f1 score: 0.620
GB cohens kappa score: 0.608

-> test with 'KNN'
KNN tn, fp: 1412, 773
KNN fn, tp: 5, 47
KNN f1 score: 0.108
KNN cohens kappa score: 0.067


------ 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: 13, 39
GAN f1 score: 0.513
GAN cohens kappa score: 0.498

-> 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: 0.487

-> test with 'GB'
GB tn, fp: 2134, 51
GB fn, tp: 14, 38
GB f1 score: 0.539
GB cohens kappa score: 0.525

-> test with 'KNN'
KNN tn, fp: 2092, 93
KNN fn, tp: 12, 40
KNN f1 score: 0.432
KNN cohens kappa score: 0.413


------ 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: 2122, 61
GAN fn, tp: 13, 39
GAN f1 score: 0.513
GAN cohens kappa score: 0.498

-> test with 'LR'
LR tn, fp: 1908, 275
LR fn, tp: 7, 45
LR f1 score: 0.242
LR cohens kappa score: 0.210
LR average precision score: 0.564

-> test with 'GB'
GB tn, fp: 2146, 37
GB fn, tp: 17, 35
GB f1 score: 0.565
GB cohens kappa score: 0.552

-> test with 'KNN'
KNN tn, fp: 2096, 87
KNN fn, tp: 10, 42
KNN f1 score: 0.464
KNN cohens kappa score: 0.446


====== 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: 2117, 68
GAN fn, tp: 9, 43
GAN f1 score: 0.528
GAN cohens kappa score: 0.512

-> test with 'LR'
LR tn, fp: 1912, 273
LR fn, tp: 8, 44
LR f1 score: 0.238
LR cohens kappa score: 0.207
LR average precision score: 0.562

-> test with 'GB'
GB tn, fp: 2144, 41
GB fn, tp: 20, 32
GB f1 score: 0.512
GB cohens kappa score: 0.498

-> test with 'KNN'
KNN tn, fp: 2113, 72
KNN fn, tp: 11, 41
KNN f1 score: 0.497
KNN cohens kappa score: 0.480


------ 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: 2118, 67
GAN fn, tp: 9, 43
GAN f1 score: 0.531
GAN cohens kappa score: 0.516

-> test with 'LR'
LR tn, fp: 1895, 290
LR fn, tp: 5, 47
LR f1 score: 0.242
LR cohens kappa score: 0.210
LR average precision score: 0.412

-> test with 'GB'
GB tn, fp: 2139, 46
GB fn, tp: 11, 41
GB f1 score: 0.590
GB cohens kappa score: 0.578

-> test with 'KNN'
KNN tn, fp: 2104, 81
KNN fn, tp: 9, 43
KNN f1 score: 0.489
KNN cohens kappa score: 0.471


------ 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: 2114, 71
GAN fn, tp: 8, 44
GAN f1 score: 0.527
GAN cohens kappa score: 0.511

-> 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: 0.476

-> test with 'GB'
GB tn, fp: 2130, 55
GB fn, tp: 10, 42
GB f1 score: 0.564
GB cohens kappa score: 0.550

-> test with 'KNN'
KNN tn, fp: 2091, 94
KNN fn, tp: 9, 43
KNN f1 score: 0.455
KNN cohens kappa score: 0.436


------ 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: 2087, 98
GAN fn, tp: 6, 46
GAN f1 score: 0.469
GAN cohens kappa score: 0.451

-> test with 'LR'
LR tn, fp: 1921, 264
LR fn, tp: 9, 43
LR f1 score: 0.240
LR cohens kappa score: 0.208
LR average precision score: 0.486

-> test with 'GB'
GB tn, fp: 2145, 40
GB fn, tp: 13, 39
GB f1 score: 0.595
GB cohens kappa score: 0.584

-> test with 'KNN'
KNN tn, fp: 2087, 98
KNN fn, tp: 7, 45
KNN f1 score: 0.462
KNN cohens kappa score: 0.443


------ 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: 2121, 62
GAN fn, tp: 10, 42
GAN f1 score: 0.538
GAN cohens kappa score: 0.524

-> test with 'LR'
LR tn, fp: 1881, 302
LR fn, tp: 1, 51
LR f1 score: 0.252
LR cohens kappa score: 0.220
LR average precision score: 0.482

-> test with 'GB'
GB tn, fp: 2125, 58
GB fn, tp: 8, 44
GB f1 score: 0.571
GB cohens kappa score: 0.558

-> test with 'KNN'
KNN tn, fp: 2068, 115
KNN fn, tp: 7, 45
KNN f1 score: 0.425
KNN cohens kappa score: 0.404


====== 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: 2151, 34
GAN fn, tp: 12, 40
GAN f1 score: 0.635
GAN cohens kappa score: 0.625

-> test with 'LR'
LR tn, fp: 1901, 284
LR fn, tp: 3, 49
LR f1 score: 0.255
LR cohens kappa score: 0.223
LR average precision score: 0.485

-> test with 'GB'
GB tn, fp: 2156, 29
GB fn, tp: 9, 43
GB f1 score: 0.694
GB cohens kappa score: 0.685

-> test with 'KNN'
KNN tn, fp: 2095, 90
KNN fn, tp: 7, 45
KNN f1 score: 0.481
KNN cohens kappa score: 0.463


------ 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: 2081, 104
GAN fn, tp: 5, 47
GAN f1 score: 0.463
GAN cohens kappa score: 0.444

-> test with 'LR'
LR tn, fp: 1903, 282
LR fn, tp: 7, 45
LR f1 score: 0.237
LR cohens kappa score: 0.206
LR average precision score: 0.437

-> test with 'GB'
GB tn, fp: 2137, 48
GB fn, tp: 9, 43
GB f1 score: 0.601
GB cohens kappa score: 0.589

-> test with 'KNN'
KNN tn, fp: 2069, 116
KNN fn, tp: 5, 47
KNN f1 score: 0.437
KNN cohens kappa score: 0.417


------ 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: 2120, 65
GAN fn, tp: 12, 40
GAN f1 score: 0.510
GAN cohens kappa score: 0.494

-> test with 'LR'
LR tn, fp: 1914, 271
LR fn, tp: 8, 44
LR f1 score: 0.240
LR cohens kappa score: 0.208
LR average precision score: 0.506

-> test with 'GB'
GB tn, fp: 2137, 48
GB fn, tp: 15, 37
GB f1 score: 0.540
GB cohens kappa score: 0.526

-> test with 'KNN'
KNN tn, fp: 2092, 93
KNN fn, tp: 11, 41
KNN f1 score: 0.441
KNN cohens kappa score: 0.421


------ 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: 2106, 79
GAN fn, tp: 9, 43
GAN f1 score: 0.494
GAN cohens kappa score: 0.477

-> test with 'LR'
LR tn, fp: 1901, 284
LR fn, tp: 4, 48
LR f1 score: 0.250
LR cohens kappa score: 0.219
LR average precision score: 0.474

-> 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: 2099, 86
KNN fn, tp: 6, 46
KNN f1 score: 0.500
KNN cohens kappa score: 0.483


------ 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: 2076, 107
GAN fn, tp: 10, 42
GAN f1 score: 0.418
GAN cohens kappa score: 0.397

-> 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: 0.572

-> test with 'GB'
GB tn, fp: 2138, 45
GB fn, tp: 14, 38
GB f1 score: 0.563
GB cohens kappa score: 0.550

-> test with 'KNN'
KNN tn, fp: 2085, 98
KNN fn, tp: 10, 42
KNN f1 score: 0.438
KNN cohens kappa score: 0.418

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 1933, 382
LR fn, tp: 9, 51
LR f1 score: 0.255
LR cohens kappa score: 0.224
LR average precision score: 0.591


average:
LR tn, fp: 1903.52, 281.08
LR fn, tp: 6.16, 45.84
LR f1 score: 0.243
LR cohens kappa score: 0.211
LR average precision score: 0.497


minimum:
LR tn, fp: 1803, 250
LR fn, tp: 1, 43
LR f1 score: 0.207
LR cohens kappa score: 0.172
LR average precision score: 0.329


-----[ GB ]-----
maximum:
GB tn, fp: 2156, 71
GB fn, tp: 20, 49
GB f1 score: 0.694
GB cohens kappa score: 0.685


average:
GB tn, fp: 2137.6, 47.0
GB fn, tp: 12.6, 39.4
GB f1 score: 0.570
GB cohens kappa score: 0.557


minimum:
GB tn, fp: 2114, 29
GB fn, tp: 3, 32
GB f1 score: 0.491
GB cohens kappa score: 0.474


-----[ KNN ]-----
maximum:
KNN tn, fp: 2117, 773
KNN fn, tp: 12, 48
KNN f1 score: 0.534
KNN cohens kappa score: 0.519


average:
KNN tn, fp: 1985.88, 198.72
KNN fn, tp: 7.96, 44.04
KNN f1 score: 0.406
KNN cohens kappa score: 0.384


minimum:
KNN tn, fp: 1412, 66
KNN fn, tp: 4, 40
KNN f1 score: 0.100
KNN cohens kappa score: 0.059


-----[ GAN ]-----
maximum:
GAN tn, fp: 2151, 107
GAN fn, tp: 13, 49
GAN f1 score: 0.635
GAN cohens kappa score: 0.625


average:
GAN tn, fp: 2112.04, 72.56
GAN fn, tp: 9.0, 43.0
GAN f1 score: 0.518
GAN cohens kappa score: 0.502


minimum:
GAN tn, fp: 2076, 34
GAN fn, tp: 3, 39
GAN f1 score: 0.418
GAN cohens kappa score: 0.397