ConvGeNBenchmarkResults/data_result/CTAB-GAN/imblearn_mammography.log

///////////////////////////////////////////
// Running CTAB-GAN 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

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 2047, 138
LR fn, tp: 16, 36
LR f1 score: 0.319
LR cohens kappa score: 0.293
LR average precision score: 0.458

-> test with 'GB'
GB tn, fp: 2160, 25
GB fn, tp: 23, 29
GB f1 score: 0.547
GB cohens kappa score: 0.536

-> test with 'KNN'
KNN tn, fp: 1498, 687
KNN fn, tp: 23, 29
KNN f1 score: 0.076
KNN cohens kappa score: 0.034


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

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-> create 8530 synthetic samples
-> 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: 0.459

-> test with 'GB'
GB tn, fp: 2164, 21
GB fn, tp: 24, 28
GB f1 score: 0.554
GB cohens kappa score: 0.544

-> test with 'KNN'
KNN tn, fp: 2158, 27
KNN fn, tp: 24, 28
KNN f1 score: 0.523
KNN cohens kappa score: 0.512


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1995, 190
LR fn, tp: 9, 43
LR f1 score: 0.302
LR cohens kappa score: 0.274
LR average precision score: 0.555

-> test with 'GB'
GB tn, fp: 2168, 17
GB fn, tp: 16, 36
GB f1 score: 0.686
GB cohens kappa score: 0.678

-> test with 'KNN'
KNN tn, fp: 2167, 18
KNN fn, tp: 17, 35
KNN f1 score: 0.667
KNN cohens kappa score: 0.659


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 2070, 115
LR fn, tp: 22, 30
LR f1 score: 0.305
LR cohens kappa score: 0.280
LR average precision score: 0.346

-> test with 'GB'
GB tn, fp: 2172, 13
GB fn, tp: 28, 24
GB f1 score: 0.539
GB cohens kappa score: 0.530

-> test with 'KNN'
KNN tn, fp: 2164, 21
KNN fn, tp: 26, 26
KNN f1 score: 0.525
KNN cohens kappa score: 0.515


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

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-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1889, 294
LR fn, tp: 10, 42
LR f1 score: 0.216
LR cohens kappa score: 0.184
LR average precision score: 0.535

-> test with 'GB'
GB tn, fp: 2162, 21
GB fn, tp: 23, 29
GB f1 score: 0.569
GB cohens kappa score: 0.559

-> test with 'KNN'
KNN tn, fp: 2144, 39
KNN fn, tp: 20, 32
KNN f1 score: 0.520
KNN cohens kappa score: 0.507


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

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

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 2016, 169
LR fn, tp: 15, 37
LR f1 score: 0.287
LR cohens kappa score: 0.259
LR average precision score: 0.478

-> test with 'GB'
GB tn, fp: 2171, 14
GB fn, tp: 21, 31
GB f1 score: 0.639
GB cohens kappa score: 0.631

-> test with 'KNN'
KNN tn, fp: 2157, 28
KNN fn, tp: 15, 37
KNN f1 score: 0.632
KNN cohens kappa score: 0.623


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

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1957, 228
LR fn, tp: 10, 42
LR f1 score: 0.261
LR cohens kappa score: 0.231
LR average precision score: 0.480

-> test with 'GB'
GB tn, fp: 2161, 24
GB fn, tp: 23, 29
GB f1 score: 0.552
GB cohens kappa score: 0.542

-> test with 'KNN'
KNN tn, fp: 2152, 33
KNN fn, tp: 17, 35
KNN f1 score: 0.583
KNN cohens kappa score: 0.572


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 2016, 169
LR fn, tp: 15, 37
LR f1 score: 0.287
LR cohens kappa score: 0.259
LR average precision score: 0.468

-> test with 'GB'
GB tn, fp: 2166, 19
GB fn, tp: 26, 26
GB f1 score: 0.536
GB cohens kappa score: 0.526

-> test with 'KNN'
KNN tn, fp: 2159, 26
KNN fn, tp: 25, 27
KNN f1 score: 0.514
KNN cohens kappa score: 0.503


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 2032, 153
LR fn, tp: 8, 44
LR f1 score: 0.353
LR cohens kappa score: 0.329
LR average precision score: 0.525

-> test with 'GB'
GB tn, fp: 2161, 24
GB fn, tp: 25, 27
GB f1 score: 0.524
GB cohens kappa score: 0.513

-> test with 'KNN'
KNN tn, fp: 2164, 21
KNN fn, tp: 26, 26
KNN f1 score: 0.525
KNN cohens kappa score: 0.515


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

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-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1911, 272
LR fn, tp: 10, 42
LR f1 score: 0.230
LR cohens kappa score: 0.197
LR average precision score: 0.531

-> test with 'GB'
GB tn, fp: 2163, 20
GB fn, tp: 25, 27
GB f1 score: 0.545
GB cohens kappa score: 0.535

-> test with 'KNN'
KNN tn, fp: 2161, 22
KNN fn, tp: 26, 26
KNN f1 score: 0.520
KNN cohens kappa score: 0.509


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1883, 302
LR fn, tp: 11, 41
LR f1 score: 0.208
LR cohens kappa score: 0.174
LR average precision score: 0.550

-> test with 'GB'
GB tn, fp: 2166, 19
GB fn, tp: 18, 34
GB f1 score: 0.648
GB cohens kappa score: 0.639

-> test with 'KNN'
KNN tn, fp: 2154, 31
KNN fn, tp: 18, 34
KNN f1 score: 0.581
KNN cohens kappa score: 0.570


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1947, 238
LR fn, tp: 9, 43
LR f1 score: 0.258
LR cohens kappa score: 0.228
LR average precision score: 0.420

-> test with 'GB'
GB tn, fp: 2165, 20
GB fn, tp: 30, 22
GB f1 score: 0.468
GB cohens kappa score: 0.457

-> test with 'KNN'
KNN tn, fp: 1483, 702
KNN fn, tp: 26, 26
KNN f1 score: 0.067
KNN cohens kappa score: 0.024


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 2066, 119
LR fn, tp: 12, 40
LR f1 score: 0.379
LR cohens kappa score: 0.357
LR average precision score: 0.513

-> test with 'GB'
GB tn, fp: 2174, 11
GB fn, tp: 21, 31
GB f1 score: 0.660
GB cohens kappa score: 0.652

-> test with 'KNN'
KNN tn, fp: 2171, 14
KNN fn, tp: 26, 26
KNN f1 score: 0.565
KNN cohens kappa score: 0.556


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 2119, 66
LR fn, tp: 25, 27
LR f1 score: 0.372
LR cohens kappa score: 0.353
LR average precision score: 0.474

-> test with 'GB'
GB tn, fp: 2177, 8
GB fn, tp: 23, 29
GB f1 score: 0.652
GB cohens kappa score: 0.645

-> test with 'KNN'
KNN tn, fp: 2175, 10
KNN fn, tp: 25, 27
KNN f1 score: 0.607
KNN cohens kappa score: 0.599


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:09<00:00,  1.08it/s]
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 2074, 109
LR fn, tp: 13, 39
LR f1 score: 0.390
LR cohens kappa score: 0.368
LR average precision score: 0.526

-> test with 'GB'
GB tn, fp: 2173, 10
GB fn, tp: 22, 30
GB f1 score: 0.652
GB cohens kappa score: 0.645

-> test with 'KNN'
KNN tn, fp: 2169, 14
KNN fn, tp: 23, 29
KNN f1 score: 0.611
KNN cohens kappa score: 0.602


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 2048, 137
LR fn, tp: 17, 35
LR f1 score: 0.312
LR cohens kappa score: 0.287
LR average precision score: 0.532

-> test with 'GB'
GB tn, fp: 2172, 13
GB fn, tp: 23, 29
GB f1 score: 0.617
GB cohens kappa score: 0.609

-> test with 'KNN'
KNN tn, fp: 2165, 20
KNN fn, tp: 22, 30
KNN f1 score: 0.588
KNN cohens kappa score: 0.579


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:07<00:00,  1.26it/s]
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 2096, 89
LR fn, tp: 19, 33
LR f1 score: 0.379
LR cohens kappa score: 0.358
LR average precision score: 0.469

-> test with 'GB'
GB tn, fp: 2172, 13
GB fn, tp: 23, 29
GB f1 score: 0.617
GB cohens kappa score: 0.609

-> test with 'KNN'
KNN tn, fp: 2172, 13
KNN fn, tp: 23, 29
KNN f1 score: 0.617
KNN cohens kappa score: 0.609


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1957, 228
LR fn, tp: 10, 42
LR f1 score: 0.261
LR cohens kappa score: 0.231
LR average precision score: 0.506

-> test with 'GB'
GB tn, fp: 2167, 18
GB fn, tp: 21, 31
GB f1 score: 0.614
GB cohens kappa score: 0.605

-> test with 'KNN'
KNN tn, fp: 2163, 22
KNN fn, tp: 24, 28
KNN f1 score: 0.549
KNN cohens kappa score: 0.539


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1988, 197
LR fn, tp: 17, 35
LR f1 score: 0.246
LR cohens kappa score: 0.217
LR average precision score: 0.389

-> test with 'GB'
GB tn, fp: 2165, 20
GB fn, tp: 26, 26
GB f1 score: 0.531
GB cohens kappa score: 0.520

-> test with 'KNN'
KNN tn, fp: 2161, 24
KNN fn, tp: 24, 28
KNN f1 score: 0.538
KNN cohens kappa score: 0.527


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1976, 207
LR fn, tp: 8, 44
LR f1 score: 0.290
LR cohens kappa score: 0.262
LR average precision score: 0.513

-> test with 'GB'
GB tn, fp: 2158, 25
GB fn, tp: 25, 27
GB f1 score: 0.519
GB cohens kappa score: 0.508

-> test with 'KNN'
KNN tn, fp: 2151, 32
KNN fn, tp: 16, 36
KNN f1 score: 0.600
KNN cohens kappa score: 0.589


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

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

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-> create 8530 synthetic samples
-> 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: 0.484

-> test with 'GB'
GB tn, fp: 2163, 22
GB fn, tp: 22, 30
GB f1 score: 0.577
GB cohens kappa score: 0.567

-> test with 'KNN'
KNN tn, fp: 1499, 686
KNN fn, tp: 17, 35
KNN f1 score: 0.091
KNN cohens kappa score: 0.049


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

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 2027, 158
LR fn, tp: 16, 36
LR f1 score: 0.293
LR cohens kappa score: 0.266
LR average precision score: 0.455

-> test with 'GB'
GB tn, fp: 2168, 17
GB fn, tp: 25, 27
GB f1 score: 0.562
GB cohens kappa score: 0.553

-> test with 'KNN'
KNN tn, fp: 2158, 27
KNN fn, tp: 24, 28
KNN f1 score: 0.523
KNN cohens kappa score: 0.512


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 2048, 137
LR fn, tp: 17, 35
LR f1 score: 0.312
LR cohens kappa score: 0.287
LR average precision score: 0.435

-> test with 'GB'
GB tn, fp: 2173, 12
GB fn, tp: 28, 24
GB f1 score: 0.545
GB cohens kappa score: 0.537

-> test with 'KNN'
KNN tn, fp: 2163, 22
KNN fn, tp: 26, 26
KNN f1 score: 0.520
KNN cohens kappa score: 0.509


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1990, 195
LR fn, tp: 9, 43
LR f1 score: 0.297
LR cohens kappa score: 0.269
LR average precision score: 0.482

-> test with 'GB'
GB tn, fp: 2165, 20
GB fn, tp: 19, 33
GB f1 score: 0.629
GB cohens kappa score: 0.620

-> test with 'KNN'
KNN tn, fp: 2165, 20
KNN fn, tp: 15, 37
KNN f1 score: 0.679
KNN cohens kappa score: 0.671


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 2109, 74
LR fn, tp: 21, 31
LR f1 score: 0.395
LR cohens kappa score: 0.375
LR average precision score: 0.501

-> test with 'GB'
GB tn, fp: 2177, 6
GB fn, tp: 24, 28
GB f1 score: 0.651
GB cohens kappa score: 0.645

-> test with 'KNN'
KNN tn, fp: 2170, 13
KNN fn, tp: 27, 25
KNN f1 score: 0.556
KNN cohens kappa score: 0.547

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 2119, 328
LR fn, tp: 25, 46
LR f1 score: 0.395
LR cohens kappa score: 0.375
LR average precision score: 0.555


average:
LR tn, fp: 2001.08, 183.52
LR fn, tp: 13.32, 38.68
LR f1 score: 0.296
LR cohens kappa score: 0.269
LR average precision score: 0.483


minimum:
LR tn, fp: 1857, 66
LR fn, tp: 6, 27
LR f1 score: 0.208
LR cohens kappa score: 0.174
LR average precision score: 0.346


-----[ GB ]-----
maximum:
GB tn, fp: 2177, 25
GB fn, tp: 30, 36
GB f1 score: 0.686
GB cohens kappa score: 0.678


average:
GB tn, fp: 2167.32, 17.28
GB fn, tp: 23.36, 28.64
GB f1 score: 0.585
GB cohens kappa score: 0.576


minimum:
GB tn, fp: 2158, 6
GB fn, tp: 16, 22
GB f1 score: 0.468
GB cohens kappa score: 0.457


-----[ KNN ]-----
maximum:
KNN tn, fp: 2175, 702
KNN fn, tp: 27, 37
KNN f1 score: 0.679
KNN cohens kappa score: 0.671


average:
KNN tn, fp: 2081.72, 102.88
KNN fn, tp: 22.2, 29.8
KNN f1 score: 0.511
KNN cohens kappa score: 0.497


minimum:
KNN tn, fp: 1483, 10
KNN fn, tp: 15, 25
KNN f1 score: 0.067
KNN cohens kappa score: 0.024