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

///////////////////////////////////////////
// Running convGAN-proxymary-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: 2002, 183
GAN fn, tp: 13, 39
GAN f1 score: 0.285
GAN cohens kappa score: 0.257

-> 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: 0.573

-> 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: 2093, 92
KNN fn, tp: 8, 44
KNN f1 score: 0.468
KNN cohens kappa score: 0.450


------ 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: 1357, 828
GAN fn, tp: 19, 33
GAN f1 score: 0.072
GAN cohens kappa score: 0.030

-> 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: 0.482

-> test with 'GB'
GB tn, fp: 2138, 47
GB fn, tp: 12, 40
GB f1 score: 0.576
GB cohens kappa score: 0.563

-> test with 'KNN'
KNN tn, fp: 2091, 94
KNN fn, tp: 8, 44
KNN f1 score: 0.463
KNN cohens kappa score: 0.444


------ 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: 1015, 1170
GAN fn, tp: 12, 40
GAN f1 score: 0.063
GAN cohens kappa score: 0.020

-> test with 'LR'
LR tn, fp: 1903, 282
LR fn, tp: 6, 46
LR f1 score: 0.242
LR cohens kappa score: 0.210
LR average precision score: 0.603

-> test with 'GB'
GB tn, fp: 2156, 29
GB fn, tp: 12, 40
GB f1 score: 0.661
GB cohens kappa score: 0.652

-> test with 'KNN'
KNN tn, fp: 2105, 80
KNN fn, tp: 8, 44
KNN f1 score: 0.500
KNN cohens kappa score: 0.483


------ 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: 2093, 92
GAN fn, tp: 16, 36
GAN f1 score: 0.400
GAN cohens kappa score: 0.379

-> test with 'LR'
LR tn, fp: 1928, 257
LR fn, tp: 6, 46
LR f1 score: 0.259
LR cohens kappa score: 0.229
LR average precision score: 0.340

-> 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: 2081, 104
KNN fn, tp: 9, 43
KNN f1 score: 0.432
KNN cohens kappa score: 0.412


------ 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: 1957, 226
GAN fn, tp: 16, 36
GAN f1 score: 0.229
GAN cohens kappa score: 0.198

-> test with 'LR'
LR tn, fp: 1913, 270
LR fn, tp: 6, 46
LR f1 score: 0.250
LR cohens kappa score: 0.219
LR average precision score: 0.563

-> test with 'GB'
GB tn, fp: 2145, 38
GB fn, tp: 11, 41
GB f1 score: 0.626
GB cohens kappa score: 0.615

-> 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 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: 1945, 240
GAN fn, tp: 10, 42
GAN f1 score: 0.251
GAN cohens kappa score: 0.221

-> test with 'LR'
LR tn, fp: 1880, 305
LR fn, tp: 6, 46
LR f1 score: 0.228
LR cohens kappa score: 0.196
LR average precision score: 0.486

-> test with 'GB'
GB tn, fp: 2116, 69
GB fn, tp: 11, 41
GB f1 score: 0.506
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 2066, 119
KNN fn, tp: 8, 44
KNN f1 score: 0.409
KNN cohens kappa score: 0.388


------ 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: 1752, 433
GAN fn, tp: 18, 34
GAN f1 score: 0.131
GAN cohens kappa score: 0.093

-> 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: 0.432

-> 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: 2071, 114
KNN fn, tp: 8, 44
KNN f1 score: 0.419
KNN cohens kappa score: 0.398


------ 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: 1817, 368
GAN fn, tp: 21, 31
GAN f1 score: 0.137
GAN cohens kappa score: 0.100

-> test with 'LR'
LR tn, fp: 1934, 251
LR fn, tp: 8, 44
LR f1 score: 0.254
LR cohens kappa score: 0.223
LR average precision score: 0.511

-> test with 'GB'
GB tn, fp: 2143, 42
GB fn, tp: 17, 35
GB f1 score: 0.543
GB cohens kappa score: 0.530

-> test with 'KNN'
KNN tn, fp: 2098, 87
KNN fn, tp: 8, 44
KNN f1 score: 0.481
KNN cohens kappa score: 0.463


------ 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: 2036, 149
GAN fn, tp: 24, 28
GAN f1 score: 0.245
GAN cohens kappa score: 0.216

-> test with 'LR'
LR tn, fp: 1903, 282
LR fn, tp: 5, 47
LR f1 score: 0.247
LR cohens kappa score: 0.215
LR average precision score: 0.493

-> test with 'GB'
GB tn, fp: 2143, 42
GB fn, tp: 13, 39
GB f1 score: 0.586
GB cohens kappa score: 0.574

-> 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 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with GAN.predict
GAN tn, fp: 1791, 392
GAN fn, tp: 17, 35
GAN f1 score: 0.146
GAN cohens kappa score: 0.109

-> test with 'LR'
LR tn, fp: 1915, 268
LR fn, tp: 8, 44
LR f1 score: 0.242
LR cohens kappa score: 0.210
LR average precision score: 0.531

-> test with 'GB'
GB tn, fp: 2155, 28
GB fn, tp: 15, 37
GB f1 score: 0.632
GB cohens kappa score: 0.623

-> test with 'KNN'
KNN tn, fp: 2109, 74
KNN fn, tp: 11, 41
KNN f1 score: 0.491
KNN cohens kappa score: 0.474


====== 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: 1120, 1065
GAN fn, tp: 10, 42
GAN f1 score: 0.072
GAN cohens kappa score: 0.029

-> 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: 0.595

-> test with 'GB'
GB tn, fp: 2147, 38
GB fn, tp: 13, 39
GB f1 score: 0.605
GB cohens kappa score: 0.593

-> test with 'KNN'
KNN tn, fp: 2091, 94
KNN fn, tp: 7, 45
KNN f1 score: 0.471
KNN cohens kappa score: 0.453


------ 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: 1313, 872
GAN fn, tp: 18, 34
GAN f1 score: 0.071
GAN cohens kappa score: 0.028

-> test with 'LR'
LR tn, fp: 1921, 264
LR fn, tp: 7, 45
LR f1 score: 0.249
LR cohens kappa score: 0.218
LR average precision score: 0.393

-> test with 'GB'
GB tn, fp: 2145, 40
GB fn, tp: 18, 34
GB f1 score: 0.540
GB cohens kappa score: 0.527

-> test with 'KNN'
KNN tn, fp: 2088, 97
KNN fn, tp: 10, 42
KNN f1 score: 0.440
KNN cohens kappa score: 0.420


------ 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: 1190, 995
GAN fn, tp: 10, 42
GAN f1 score: 0.077
GAN cohens kappa score: 0.034

-> test with 'LR'
LR tn, fp: 1904, 281
LR fn, tp: 3, 49
LR f1 score: 0.257
LR cohens kappa score: 0.225
LR average precision score: 0.449

-> test with 'GB'
GB tn, fp: 2134, 51
GB fn, tp: 8, 44
GB f1 score: 0.599
GB cohens kappa score: 0.586

-> test with 'KNN'
KNN tn, fp: 1425, 760
KNN fn, tp: 4, 48
KNN f1 score: 0.112
KNN cohens kappa score: 0.071


------ 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: 1461, 724
GAN fn, tp: 19, 33
GAN f1 score: 0.082
GAN cohens kappa score: 0.040

-> test with 'LR'
LR tn, fp: 1918, 267
LR fn, tp: 9, 43
LR f1 score: 0.238
LR cohens kappa score: 0.206
LR average precision score: 0.484

-> 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: 2095, 90
KNN fn, tp: 11, 41
KNN f1 score: 0.448
KNN cohens kappa score: 0.429


------ 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: 1367, 816
GAN fn, tp: 18, 34
GAN f1 score: 0.075
GAN cohens kappa score: 0.033

-> test with 'LR'
LR tn, fp: 1899, 284
LR fn, tp: 7, 45
LR f1 score: 0.236
LR cohens kappa score: 0.204
LR average precision score: 0.567

-> 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: 2094, 89
KNN fn, tp: 10, 42
KNN f1 score: 0.459
KNN cohens kappa score: 0.440


====== 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: 1152, 1033
GAN fn, tp: 14, 38
GAN f1 score: 0.068
GAN cohens kappa score: 0.024

-> test with 'LR'
LR tn, fp: 1919, 266
LR fn, tp: 7, 45
LR f1 score: 0.248
LR cohens kappa score: 0.217
LR average precision score: 0.561

-> 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: 2118, 67
KNN fn, tp: 11, 41
KNN f1 score: 0.513
KNN cohens kappa score: 0.497


------ 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: 2040, 145
GAN fn, tp: 14, 38
GAN f1 score: 0.323
GAN cohens kappa score: 0.298

-> test with 'LR'
LR tn, fp: 1899, 286
LR fn, tp: 5, 47
LR f1 score: 0.244
LR cohens kappa score: 0.212
LR average precision score: 0.419

-> 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: 2098, 87
KNN fn, tp: 7, 45
KNN f1 score: 0.489
KNN cohens kappa score: 0.472


------ 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: 1976, 209
GAN fn, tp: 20, 32
GAN f1 score: 0.218
GAN cohens kappa score: 0.187

-> test with 'LR'
LR tn, fp: 1922, 263
LR fn, tp: 7, 45
LR f1 score: 0.250
LR cohens kappa score: 0.219
LR average precision score: 0.458

-> 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: 2091, 94
KNN fn, tp: 8, 44
KNN f1 score: 0.463
KNN cohens kappa score: 0.444


------ 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: 1218, 967
GAN fn, tp: 15, 37
GAN f1 score: 0.070
GAN cohens kappa score: 0.027

-> 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: 0.483

-> test with 'GB'
GB tn, fp: 2142, 43
GB fn, tp: 14, 38
GB f1 score: 0.571
GB cohens kappa score: 0.559

-> test with 'KNN'
KNN tn, fp: 2080, 105
KNN fn, tp: 8, 44
KNN f1 score: 0.438
KNN cohens kappa score: 0.418


------ 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: 1959, 224
GAN fn, tp: 18, 34
GAN f1 score: 0.219
GAN cohens kappa score: 0.188

-> test with 'LR'
LR tn, fp: 1880, 303
LR fn, tp: 1, 51
LR f1 score: 0.251
LR cohens kappa score: 0.220
LR average precision score: 0.494

-> test with 'GB'
GB tn, fp: 2141, 42
GB fn, tp: 11, 41
GB f1 score: 0.607
GB cohens kappa score: 0.596

-> test with 'KNN'
KNN tn, fp: 2083, 100
KNN fn, tp: 7, 45
KNN f1 score: 0.457
KNN cohens kappa score: 0.438


====== 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: 1408, 777
GAN fn, tp: 18, 34
GAN f1 score: 0.079
GAN cohens kappa score: 0.037

-> test with 'LR'
LR tn, fp: 1898, 287
LR fn, tp: 3, 49
LR f1 score: 0.253
LR cohens kappa score: 0.221
LR average precision score: 0.473

-> test with 'GB'
GB tn, fp: 2135, 50
GB fn, tp: 10, 42
GB f1 score: 0.583
GB cohens kappa score: 0.571

-> test with 'KNN'
KNN tn, fp: 2098, 87
KNN fn, tp: 6, 46
KNN f1 score: 0.497
KNN cohens kappa score: 0.480


------ 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: 1119, 1066
GAN fn, tp: 12, 40
GAN f1 score: 0.069
GAN cohens kappa score: 0.026

-> test with 'LR'
LR tn, fp: 1904, 281
LR fn, tp: 7, 45
LR f1 score: 0.238
LR cohens kappa score: 0.206
LR average precision score: 0.448

-> 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: 2065, 120
KNN fn, tp: 7, 45
KNN f1 score: 0.415
KNN cohens kappa score: 0.393


------ 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: 1400, 785
GAN fn, tp: 18, 34
GAN f1 score: 0.078
GAN cohens kappa score: 0.036

-> test with 'LR'
LR tn, fp: 1916, 269
LR fn, tp: 8, 44
LR f1 score: 0.241
LR cohens kappa score: 0.210
LR average precision score: 0.515

-> test with 'GB'
GB tn, fp: 2139, 46
GB fn, tp: 19, 33
GB f1 score: 0.504
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 2087, 98
KNN fn, tp: 11, 41
KNN f1 score: 0.429
KNN cohens kappa score: 0.409


------ 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: 1294, 891
GAN fn, tp: 13, 39
GAN f1 score: 0.079
GAN cohens kappa score: 0.037

-> 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: 0.459

-> 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: 1031, 1152
GAN fn, tp: 17, 35
GAN f1 score: 0.056
GAN cohens kappa score: 0.012

-> test with 'LR'
LR tn, fp: 1935, 248
LR fn, tp: 8, 44
LR f1 score: 0.256
LR cohens kappa score: 0.225
LR average precision score: 0.578

-> test with 'GB'
GB tn, fp: 2132, 51
GB fn, tp: 16, 36
GB f1 score: 0.518
GB cohens kappa score: 0.504

-> test with 'KNN'
KNN tn, fp: 2093, 90
KNN fn, tp: 10, 42
KNN f1 score: 0.457
KNN cohens kappa score: 0.438

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 1935, 305
LR fn, tp: 9, 51
LR f1 score: 0.259
LR cohens kappa score: 0.229
LR average precision score: 0.603


average:
LR tn, fp: 1908.12, 276.48
LR fn, tp: 6.2, 45.8
LR f1 score: 0.245
LR cohens kappa score: 0.213
LR average precision score: 0.496


minimum:
LR tn, fp: 1880, 248
LR fn, tp: 1, 43
LR f1 score: 0.224
LR cohens kappa score: 0.192
LR average precision score: 0.340


-----[ GB ]-----
maximum:
GB tn, fp: 2156, 69
GB fn, tp: 19, 44
GB f1 score: 0.661
GB cohens kappa score: 0.652


average:
GB tn, fp: 2138.12, 46.48
GB fn, tp: 13.24, 38.76
GB f1 score: 0.567
GB cohens kappa score: 0.554


minimum:
GB tn, fp: 2116, 28
GB fn, tp: 8, 33
GB f1 score: 0.504
GB cohens kappa score: 0.490


-----[ KNN ]-----
maximum:
KNN tn, fp: 2118, 760
KNN fn, tp: 11, 48
KNN f1 score: 0.513
KNN cohens kappa score: 0.497


average:
KNN tn, fp: 2063.28, 121.32
KNN fn, tp: 8.32, 43.68
KNN f1 score: 0.445
KNN cohens kappa score: 0.425


minimum:
KNN tn, fp: 1425, 67
KNN fn, tp: 4, 41
KNN f1 score: 0.112
KNN cohens kappa score: 0.071


-----[ GAN ]-----
maximum:
GAN tn, fp: 2093, 1170
GAN fn, tp: 24, 42
GAN f1 score: 0.400
GAN cohens kappa score: 0.379


average:
GAN tn, fp: 1552.52, 632.08
GAN fn, tp: 16.0, 36.0
GAN f1 score: 0.144
GAN cohens kappa score: 0.106


minimum:
GAN tn, fp: 1015, 92
GAN fn, tp: 10, 28
GAN f1 score: 0.056
GAN cohens kappa score: 0.012