ConvGeNBenchmarkResults/data_result/Repeater/imblearn_mammography.log

///////////////////////////////////////////
// Running Repeater 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: 1897, 288
LR fn, tp: 7, 45
LR f1 score: 0.234
LR cohens kappa score: 0.202
LR average precision score: 0.558

-> test with 'GB'
GB tn, fp: 2110, 75
GB fn, tp: 10, 42
GB f1 score: 0.497
GB cohens kappa score: 0.480

-> test with 'KNN'
KNN tn, fp: 1452, 733
KNN fn, tp: 9, 43
KNN f1 score: 0.104
KNN cohens kappa score: 0.063


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> 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: 0.506

-> test with 'GB'
GB tn, fp: 2119, 66
GB fn, tp: 10, 42
GB f1 score: 0.525
GB cohens kappa score: 0.510

-> test with 'KNN'
KNN tn, fp: 1432, 753
KNN fn, tp: 8, 44
KNN f1 score: 0.104
KNN cohens kappa score: 0.063


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1893, 292
LR fn, tp: 5, 47
LR f1 score: 0.240
LR cohens kappa score: 0.209
LR average precision score: 0.601

-> test with 'GB'
GB tn, fp: 2116, 69
GB fn, tp: 9, 43
GB f1 score: 0.524
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 1450, 735
KNN fn, tp: 8, 44
KNN f1 score: 0.106
KNN cohens kappa score: 0.065


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1905, 280
LR fn, tp: 8, 44
LR f1 score: 0.234
LR cohens kappa score: 0.202
LR average precision score: 0.373

-> test with 'GB'
GB tn, fp: 2106, 79
GB fn, tp: 10, 42
GB f1 score: 0.486
GB cohens kappa score: 0.468

-> test with 'KNN'
KNN tn, fp: 1430, 755
KNN fn, tp: 7, 45
KNN f1 score: 0.106
KNN cohens kappa score: 0.065


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1895, 288
LR fn, tp: 6, 46
LR f1 score: 0.238
LR cohens kappa score: 0.206
LR average precision score: 0.555

-> test with 'GB'
GB tn, fp: 2111, 72
GB fn, tp: 6, 46
GB f1 score: 0.541
GB cohens kappa score: 0.526

-> test with 'KNN'
KNN tn, fp: 1508, 675
KNN fn, tp: 12, 40
KNN f1 score: 0.104
KNN cohens kappa score: 0.064


====== 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: 1877, 308
LR fn, tp: 7, 45
LR f1 score: 0.222
LR cohens kappa score: 0.189
LR average precision score: 0.530

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

-> test with 'KNN'
KNN tn, fp: 1459, 726
KNN fn, tp: 7, 45
KNN f1 score: 0.109
KNN cohens kappa score: 0.069


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1880, 305
LR fn, tp: 8, 44
LR f1 score: 0.219
LR cohens kappa score: 0.187
LR average precision score: 0.499

-> test with 'GB'
GB tn, fp: 2107, 78
GB fn, tp: 6, 46
GB f1 score: 0.523
GB cohens kappa score: 0.507

-> test with 'KNN'
KNN tn, fp: 1442, 743
KNN fn, tp: 8, 44
KNN f1 score: 0.105
KNN cohens kappa score: 0.064


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1911, 274
LR fn, tp: 8, 44
LR f1 score: 0.238
LR cohens kappa score: 0.206
LR average precision score: 0.521

-> test with 'GB'
GB tn, fp: 2129, 56
GB fn, tp: 9, 43
GB f1 score: 0.570
GB cohens kappa score: 0.556

-> test with 'KNN'
KNN tn, fp: 1467, 718
KNN fn, tp: 10, 42
KNN f1 score: 0.103
KNN cohens kappa score: 0.063


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1912, 273
LR fn, tp: 4, 48
LR f1 score: 0.257
LR cohens kappa score: 0.226
LR average precision score: 0.533

-> test with 'GB'
GB tn, fp: 2116, 69
GB fn, tp: 4, 48
GB f1 score: 0.568
GB cohens kappa score: 0.554

-> test with 'KNN'
KNN tn, fp: 1454, 731
KNN fn, tp: 5, 47
KNN f1 score: 0.113
KNN cohens kappa score: 0.073


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1906, 277
LR fn, tp: 8, 44
LR f1 score: 0.236
LR cohens kappa score: 0.204
LR average precision score: 0.564

-> test with 'GB'
GB tn, fp: 2126, 57
GB fn, tp: 11, 41
GB f1 score: 0.547
GB cohens kappa score: 0.532

-> test with 'KNN'
KNN tn, fp: 1446, 737
KNN fn, tp: 12, 40
KNN f1 score: 0.097
KNN cohens kappa score: 0.055


====== 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: 1888, 297
LR fn, tp: 8, 44
LR f1 score: 0.224
LR cohens kappa score: 0.191
LR average precision score: 0.604

-> test with 'GB'
GB tn, fp: 2106, 79
GB fn, tp: 8, 44
GB f1 score: 0.503
GB cohens kappa score: 0.486

-> test with 'KNN'
KNN tn, fp: 1475, 710
KNN fn, tp: 5, 47
KNN f1 score: 0.116
KNN cohens kappa score: 0.076


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1907, 278
LR fn, tp: 7, 45
LR f1 score: 0.240
LR cohens kappa score: 0.208
LR average precision score: 0.420

-> test with 'GB'
GB tn, fp: 2123, 62
GB fn, tp: 12, 40
GB f1 score: 0.519
GB cohens kappa score: 0.504

-> test with 'KNN'
KNN tn, fp: 1446, 739
KNN fn, tp: 10, 42
KNN f1 score: 0.101
KNN cohens kappa score: 0.060


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1886, 299
LR fn, tp: 2, 50
LR f1 score: 0.249
LR cohens kappa score: 0.218
LR average precision score: 0.506

-> test with 'GB'
GB tn, fp: 2110, 75
GB fn, tp: 4, 48
GB f1 score: 0.549
GB cohens kappa score: 0.533

-> test with 'KNN'
KNN tn, fp: 1447, 738
KNN fn, tp: 7, 45
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 'LR'
LR tn, fp: 1902, 283
LR fn, tp: 9, 43
LR f1 score: 0.228
LR cohens kappa score: 0.195
LR average precision score: 0.501

-> test with 'GB'
GB tn, fp: 2113, 72
GB fn, tp: 10, 42
GB f1 score: 0.506
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 1470, 715
KNN fn, tp: 11, 41
KNN f1 score: 0.101
KNN cohens kappa score: 0.061


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1894, 289
LR fn, tp: 7, 45
LR f1 score: 0.233
LR cohens kappa score: 0.201
LR average precision score: 0.609

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

-> test with 'KNN'
KNN tn, fp: 1448, 735
KNN fn, tp: 9, 43
KNN f1 score: 0.104
KNN cohens kappa score: 0.063


====== 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: 1917, 268
LR fn, tp: 8, 44
LR f1 score: 0.242
LR cohens kappa score: 0.210
LR average precision score: 0.589

-> test with 'GB'
GB tn, fp: 2124, 61
GB fn, tp: 17, 35
GB f1 score: 0.473
GB cohens kappa score: 0.457

-> test with 'KNN'
KNN tn, fp: 1491, 694
KNN fn, tp: 12, 40
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: 1899, 286
LR fn, tp: 6, 46
LR f1 score: 0.240
LR cohens kappa score: 0.208
LR average precision score: 0.442

-> test with 'GB'
GB tn, fp: 2116, 69
GB fn, tp: 8, 44
GB f1 score: 0.533
GB cohens kappa score: 0.518

-> test with 'KNN'
KNN tn, fp: 1500, 685
KNN fn, tp: 7, 45
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 'LR'
LR tn, fp: 1902, 283
LR fn, tp: 7, 45
LR f1 score: 0.237
LR cohens kappa score: 0.205
LR average precision score: 0.569

-> test with 'GB'
GB tn, fp: 2110, 75
GB fn, tp: 7, 45
GB f1 score: 0.523
GB cohens kappa score: 0.507

-> test with 'KNN'
KNN tn, fp: 1417, 768
KNN fn, tp: 7, 45
KNN f1 score: 0.104
KNN cohens kappa score: 0.063


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1901, 284
LR fn, tp: 9, 43
LR f1 score: 0.227
LR cohens kappa score: 0.195
LR average precision score: 0.477

-> test with 'GB'
GB tn, fp: 2115, 70
GB fn, tp: 9, 43
GB f1 score: 0.521
GB cohens kappa score: 0.505

-> test with 'KNN'
KNN tn, fp: 1413, 772
KNN fn, tp: 10, 42
KNN f1 score: 0.097
KNN cohens kappa score: 0.056


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1868, 315
LR fn, tp: 3, 49
LR f1 score: 0.236
LR cohens kappa score: 0.203
LR average precision score: 0.538

-> test with 'GB'
GB tn, fp: 2118, 65
GB fn, tp: 6, 46
GB f1 score: 0.564
GB cohens kappa score: 0.550

-> test with 'KNN'
KNN tn, fp: 1464, 719
KNN fn, tp: 10, 42
KNN f1 score: 0.103
KNN cohens kappa score: 0.062


====== 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: 1890, 295
LR fn, tp: 3, 49
LR f1 score: 0.247
LR cohens kappa score: 0.216
LR average precision score: 0.533

-> test with 'GB'
GB tn, fp: 2121, 64
GB fn, tp: 4, 48
GB f1 score: 0.585
GB cohens kappa score: 0.572

-> test with 'KNN'
KNN tn, fp: 1469, 716
KNN fn, tp: 9, 43
KNN f1 score: 0.106
KNN cohens kappa score: 0.065


------ Step 5/5: Slice 2/5 -------
-> 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: 0.482

-> test with 'GB'
GB tn, fp: 2107, 78
GB fn, tp: 7, 45
GB f1 score: 0.514
GB cohens kappa score: 0.498

-> test with 'KNN'
KNN tn, fp: 1448, 737
KNN fn, tp: 7, 45
KNN f1 score: 0.108
KNN cohens kappa score: 0.067


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1899, 286
LR fn, tp: 10, 42
LR f1 score: 0.221
LR cohens kappa score: 0.188
LR average precision score: 0.529

-> test with 'GB'
GB tn, fp: 2114, 71
GB fn, tp: 11, 41
GB f1 score: 0.500
GB cohens kappa score: 0.484

-> test with 'KNN'
KNN tn, fp: 1464, 721
KNN fn, tp: 11, 41
KNN f1 score: 0.101
KNN cohens kappa score: 0.060


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1888, 297
LR fn, tp: 4, 48
LR f1 score: 0.242
LR cohens kappa score: 0.210
LR average precision score: 0.521

-> test with 'GB'
GB tn, fp: 2109, 76
GB fn, tp: 9, 43
GB f1 score: 0.503
GB cohens kappa score: 0.486

-> test with 'KNN'
KNN tn, fp: 1470, 715
KNN fn, tp: 7, 45
KNN f1 score: 0.111
KNN cohens kappa score: 0.070


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1900, 283
LR fn, tp: 9, 43
LR f1 score: 0.228
LR cohens kappa score: 0.195
LR average precision score: 0.598

-> test with 'GB'
GB tn, fp: 2115, 68
GB fn, tp: 10, 42
GB f1 score: 0.519
GB cohens kappa score: 0.503

-> test with 'KNN'
KNN tn, fp: 1432, 751
KNN fn, tp: 12, 40
KNN f1 score: 0.095
KNN cohens kappa score: 0.054

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 1917, 315
LR fn, tp: 10, 50
LR f1 score: 0.257
LR cohens kappa score: 0.226
LR average precision score: 0.609


average:
LR tn, fp: 1896.36, 288.24
LR fn, tp: 6.6, 45.4
LR f1 score: 0.236
LR cohens kappa score: 0.203
LR average precision score: 0.526


minimum:
LR tn, fp: 1868, 268
LR fn, tp: 2, 42
LR f1 score: 0.219
LR cohens kappa score: 0.187
LR average precision score: 0.373


-----[ GB ]-----
maximum:
GB tn, fp: 2129, 85
GB fn, tp: 17, 48
GB f1 score: 0.585
GB cohens kappa score: 0.572


average:
GB tn, fp: 2114.32, 70.28
GB fn, tp: 8.64, 43.36
GB f1 score: 0.524
GB cohens kappa score: 0.508


minimum:
GB tn, fp: 2100, 56
GB fn, tp: 4, 35
GB f1 score: 0.469
GB cohens kappa score: 0.451


-----[ KNN ]-----
maximum:
KNN tn, fp: 1508, 772
KNN fn, tp: 12, 47
KNN f1 score: 0.116
KNN cohens kappa score: 0.076


average:
KNN tn, fp: 1455.76, 728.84
KNN fn, tp: 8.8, 43.2
KNN f1 score: 0.105
KNN cohens kappa score: 0.064


minimum:
KNN tn, fp: 1413, 675
KNN fn, tp: 5, 40
KNN f1 score: 0.095
KNN cohens kappa score: 0.054