ConvGeNBenchmarkResults/data_result/ProWRAS/imblearn_mammography.log

///////////////////////////////////////////
// Running ProWRAS 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: 1977, 208
LR fn, tp: 7, 45
LR f1 score: 0.295
LR cohens kappa score: 0.267
LR average precision score: 0.572

-> test with 'GB'
GB tn, fp: 2150, 35
GB fn, tp: 15, 37
GB f1 score: 0.597
GB cohens kappa score: 0.586

-> test with 'KNN'
KNN tn, fp: 2132, 53
KNN fn, tp: 9, 43
KNN f1 score: 0.581
KNN cohens kappa score: 0.568


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1975, 210
LR fn, tp: 7, 45
LR f1 score: 0.293
LR cohens kappa score: 0.265
LR average precision score: 0.483

-> test with 'GB'
GB tn, fp: 2154, 31
GB fn, tp: 15, 37
GB f1 score: 0.617
GB cohens kappa score: 0.606

-> test with 'KNN'
KNN tn, fp: 2151, 34
KNN fn, tp: 13, 39
KNN f1 score: 0.624
KNN cohens kappa score: 0.614


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1966, 219
LR fn, tp: 9, 43
LR f1 score: 0.274
LR cohens kappa score: 0.245
LR average precision score: 0.593

-> test with 'GB'
GB tn, fp: 2146, 39
GB fn, tp: 11, 41
GB f1 score: 0.621
GB cohens kappa score: 0.610

-> test with 'KNN'
KNN tn, fp: 1459, 726
KNN fn, tp: 11, 41
KNN f1 score: 0.100
KNN cohens kappa score: 0.059


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1965, 220
LR fn, tp: 8, 44
LR f1 score: 0.278
LR cohens kappa score: 0.249
LR average precision score: 0.328

-> test with 'GB'
GB tn, fp: 2144, 41
GB fn, tp: 15, 37
GB f1 score: 0.569
GB cohens kappa score: 0.557

-> test with 'KNN'
KNN tn, fp: 2137, 48
KNN fn, tp: 12, 40
KNN f1 score: 0.571
KNN cohens kappa score: 0.559


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1969, 214
LR fn, tp: 8, 44
LR f1 score: 0.284
LR cohens kappa score: 0.255
LR average precision score: 0.563

-> 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: 2147, 36
KNN fn, tp: 15, 37
KNN f1 score: 0.592
KNN cohens kappa score: 0.581


====== 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: 1954, 231
LR fn, tp: 7, 45
LR f1 score: 0.274
LR cohens kappa score: 0.245
LR average precision score: 0.521

-> test with 'GB'
GB tn, fp: 2141, 44
GB fn, tp: 11, 41
GB f1 score: 0.599
GB cohens kappa score: 0.587

-> test with 'KNN'
KNN tn, fp: 2126, 59
KNN fn, tp: 11, 41
KNN f1 score: 0.539
KNN cohens kappa score: 0.525


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1948, 237
LR fn, tp: 9, 43
LR f1 score: 0.259
LR cohens kappa score: 0.229
LR average precision score: 0.468

-> test with 'GB'
GB tn, fp: 2135, 50
GB fn, tp: 15, 37
GB f1 score: 0.532
GB cohens kappa score: 0.518

-> test with 'KNN'
KNN tn, fp: 2138, 47
KNN fn, tp: 9, 43
KNN f1 score: 0.606
KNN cohens kappa score: 0.594


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 2010, 175
LR fn, tp: 8, 44
LR f1 score: 0.325
LR cohens kappa score: 0.298
LR average precision score: 0.536

-> test with 'GB'
GB tn, fp: 2154, 31
GB fn, tp: 16, 36
GB f1 score: 0.605
GB cohens kappa score: 0.594

-> test with 'KNN'
KNN tn, fp: 2142, 43
KNN fn, tp: 13, 39
KNN f1 score: 0.582
KNN cohens kappa score: 0.570


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1967, 218
LR fn, tp: 5, 47
LR f1 score: 0.297
LR cohens kappa score: 0.268
LR average precision score: 0.533

-> test with 'GB'
GB tn, fp: 2152, 33
GB fn, tp: 8, 44
GB f1 score: 0.682
GB cohens kappa score: 0.673

-> test with 'KNN'
KNN tn, fp: 2136, 49
KNN fn, tp: 10, 42
KNN f1 score: 0.587
KNN cohens kappa score: 0.575


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1994, 189
LR fn, tp: 9, 43
LR f1 score: 0.303
LR cohens kappa score: 0.275
LR average precision score: 0.562

-> test with 'GB'
GB tn, fp: 2162, 21
GB fn, tp: 19, 33
GB f1 score: 0.623
GB cohens kappa score: 0.613

-> test with 'KNN'
KNN tn, fp: 2148, 35
KNN fn, tp: 16, 36
KNN f1 score: 0.585
KNN cohens kappa score: 0.574


====== 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: 1979, 206
LR fn, tp: 8, 44
LR f1 score: 0.291
LR cohens kappa score: 0.263
LR average precision score: 0.588

-> test with 'GB'
GB tn, fp: 2157, 28
GB fn, tp: 9, 43
GB f1 score: 0.699
GB cohens kappa score: 0.691

-> test with 'KNN'
KNN tn, fp: 2136, 49
KNN fn, tp: 10, 42
KNN f1 score: 0.587
KNN cohens kappa score: 0.575


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1970, 215
LR fn, tp: 8, 44
LR f1 score: 0.283
LR cohens kappa score: 0.254
LR average precision score: 0.418

-> test with 'GB'
GB tn, fp: 2155, 30
GB fn, tp: 17, 35
GB f1 score: 0.598
GB cohens kappa score: 0.588

-> test with 'KNN'
KNN tn, fp: 1462, 723
KNN fn, tp: 12, 40
KNN f1 score: 0.098
KNN cohens kappa score: 0.057


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1960, 225
LR fn, tp: 4, 48
LR f1 score: 0.295
LR cohens kappa score: 0.267
LR average precision score: 0.486

-> test with 'GB'
GB tn, fp: 2138, 47
GB fn, tp: 6, 46
GB f1 score: 0.634
GB cohens kappa score: 0.623

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


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1971, 214
LR fn, tp: 11, 41
LR f1 score: 0.267
LR cohens kappa score: 0.238
LR average precision score: 0.519

-> test with 'GB'
GB tn, fp: 2149, 36
GB fn, tp: 17, 35
GB f1 score: 0.569
GB cohens kappa score: 0.557

-> test with 'KNN'
KNN tn, fp: 2145, 40
KNN fn, tp: 15, 37
KNN f1 score: 0.574
KNN cohens kappa score: 0.561


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1958, 225
LR fn, tp: 7, 45
LR f1 score: 0.280
LR cohens kappa score: 0.250
LR average precision score: 0.586

-> test with 'GB'
GB tn, fp: 2148, 35
GB fn, tp: 17, 35
GB f1 score: 0.574
GB cohens kappa score: 0.562

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


====== 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: 1972, 213
LR fn, tp: 8, 44
LR f1 score: 0.285
LR cohens kappa score: 0.256
LR average precision score: 0.570

-> test with 'GB'
GB tn, fp: 2152, 33
GB fn, tp: 19, 33
GB f1 score: 0.559
GB cohens kappa score: 0.548

-> test with 'KNN'
KNN tn, fp: 2152, 33
KNN fn, tp: 13, 39
KNN f1 score: 0.629
KNN cohens kappa score: 0.619


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1968, 217
LR fn, tp: 8, 44
LR f1 score: 0.281
LR cohens kappa score: 0.252
LR average precision score: 0.424

-> test with 'GB'
GB tn, fp: 2146, 39
GB fn, tp: 13, 39
GB f1 score: 0.600
GB cohens kappa score: 0.589

-> test with 'KNN'
KNN tn, fp: 2141, 44
KNN fn, tp: 11, 41
KNN f1 score: 0.599
KNN cohens kappa score: 0.587


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1980, 205
LR fn, tp: 7, 45
LR f1 score: 0.298
LR cohens kappa score: 0.270
LR average precision score: 0.507

-> test with 'GB'
GB tn, fp: 2152, 33
GB fn, tp: 9, 43
GB f1 score: 0.672
GB cohens kappa score: 0.663

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


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1974, 211
LR fn, tp: 10, 42
LR f1 score: 0.275
LR cohens kappa score: 0.246
LR average precision score: 0.481

-> test with 'GB'
GB tn, fp: 2145, 40
GB fn, tp: 14, 38
GB f1 score: 0.585
GB cohens kappa score: 0.573

-> test with 'KNN'
KNN tn, fp: 2135, 50
KNN fn, tp: 14, 38
KNN f1 score: 0.543
KNN cohens kappa score: 0.529


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1954, 229
LR fn, tp: 5, 47
LR f1 score: 0.287
LR cohens kappa score: 0.258
LR average precision score: 0.509

-> test with 'GB'
GB tn, fp: 2144, 39
GB fn, tp: 10, 42
GB f1 score: 0.632
GB cohens kappa score: 0.621

-> test with 'KNN'
KNN tn, fp: 2134, 49
KNN fn, tp: 12, 40
KNN f1 score: 0.567
KNN cohens kappa score: 0.554


====== 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: 1973, 212
LR fn, tp: 4, 48
LR f1 score: 0.308
LR cohens kappa score: 0.280
LR average precision score: 0.542

-> test with 'GB'
GB tn, fp: 2149, 36
GB fn, tp: 12, 40
GB f1 score: 0.625
GB cohens kappa score: 0.614

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


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1970, 215
LR fn, tp: 8, 44
LR f1 score: 0.283
LR cohens kappa score: 0.254
LR average precision score: 0.446

-> test with 'GB'
GB tn, fp: 2146, 39
GB fn, tp: 16, 36
GB f1 score: 0.567
GB cohens kappa score: 0.555

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


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1980, 205
LR fn, tp: 11, 41
LR f1 score: 0.275
LR cohens kappa score: 0.246
LR average precision score: 0.540

-> test with 'GB'
GB tn, fp: 2154, 31
GB fn, tp: 16, 36
GB f1 score: 0.605
GB cohens kappa score: 0.594

-> test with 'KNN'
KNN tn, fp: 2139, 46
KNN fn, tp: 18, 34
KNN f1 score: 0.515
KNN cohens kappa score: 0.501


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1960, 225
LR fn, tp: 4, 48
LR f1 score: 0.295
LR cohens kappa score: 0.267
LR average precision score: 0.525

-> 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: 2141, 44
KNN fn, tp: 9, 43
KNN f1 score: 0.619
KNN cohens kappa score: 0.607


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1987, 196
LR fn, tp: 9, 43
LR f1 score: 0.296
LR cohens kappa score: 0.268
LR average precision score: 0.600

-> test with 'GB'
GB tn, fp: 2153, 30
GB fn, tp: 17, 35
GB f1 score: 0.598
GB cohens kappa score: 0.588

-> test with 'KNN'
KNN tn, fp: 2141, 42
KNN fn, tp: 13, 39
KNN f1 score: 0.586
KNN cohens kappa score: 0.574

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 2010, 237
LR fn, tp: 11, 48
LR f1 score: 0.325
LR cohens kappa score: 0.298
LR average precision score: 0.600


average:
LR tn, fp: 1971.24, 213.36
LR fn, tp: 7.56, 44.44
LR f1 score: 0.287
LR cohens kappa score: 0.259
LR average precision score: 0.516


minimum:
LR tn, fp: 1948, 175
LR fn, tp: 4, 41
LR f1 score: 0.259
LR cohens kappa score: 0.229
LR average precision score: 0.328


-----[ GB ]-----
maximum:
GB tn, fp: 2162, 50
GB fn, tp: 19, 46
GB f1 score: 0.699
GB cohens kappa score: 0.691


average:
GB tn, fp: 2148.76, 35.84
GB fn, tp: 13.76, 38.24
GB f1 score: 0.607
GB cohens kappa score: 0.596


minimum:
GB tn, fp: 2135, 21
GB fn, tp: 6, 33
GB f1 score: 0.532
GB cohens kappa score: 0.518


-----[ KNN ]-----
maximum:
KNN tn, fp: 2152, 726
KNN fn, tp: 18, 44
KNN f1 score: 0.646
KNN cohens kappa score: 0.636


average:
KNN tn, fp: 2085.68, 98.92
KNN fn, tp: 11.8, 40.2
KNN f1 score: 0.549
KNN cohens kappa score: 0.535


minimum:
KNN tn, fp: 1459, 33
KNN fn, tp: 8, 34
KNN f1 score: 0.098
KNN cohens kappa score: 0.057