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: 2122, 63
GAN fn, tp: 12, 40
GAN f1 score: 0.516
GAN cohens kappa score: 0.501

-> test with 'LR'
LR tn, fp: 1916, 269
LR fn, tp: 5, 47
LR f1 score: 0.255
LR cohens kappa score: 0.224
LR average precision score: 0.611

-> test with 'GB'
GB tn, fp: 2118, 67
GB fn, tp: 10, 42
GB f1 score: 0.522
GB cohens kappa score: 0.506

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


------ 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: 2137, 48
GAN fn, tp: 13, 39
GAN f1 score: 0.561
GAN cohens kappa score: 0.548

-> test with 'LR'
LR tn, fp: 1943, 242
LR fn, tp: 7, 45
LR f1 score: 0.265
LR cohens kappa score: 0.235
LR average precision score: 0.591

-> 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: 2122, 63
KNN fn, tp: 10, 42
KNN f1 score: 0.535
KNN cohens kappa score: 0.520


------ 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: 2136, 49
GAN fn, tp: 11, 41
GAN f1 score: 0.577
GAN cohens kappa score: 0.565

-> test with 'LR'
LR tn, fp: 1928, 257
LR fn, tp: 7, 45
LR f1 score: 0.254
LR cohens kappa score: 0.223
LR average precision score: 0.664

-> test with 'GB'
GB tn, fp: 2123, 62
GB fn, tp: 8, 44
GB f1 score: 0.557
GB cohens kappa score: 0.543

-> test with 'KNN'
KNN tn, fp: 2128, 57
KNN fn, tp: 9, 43
KNN f1 score: 0.566
KNN cohens kappa score: 0.552


------ 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: 2128, 57
GAN fn, tp: 8, 44
GAN f1 score: 0.575
GAN cohens kappa score: 0.562

-> test with 'LR'
LR tn, fp: 1940, 245
LR fn, tp: 6, 46
LR f1 score: 0.268
LR cohens kappa score: 0.238
LR average precision score: 0.504

-> test with 'GB'
GB tn, fp: 2121, 64
GB fn, tp: 10, 42
GB f1 score: 0.532
GB cohens kappa score: 0.517

-> test with 'KNN'
KNN tn, fp: 2112, 73
KNN fn, tp: 9, 43
KNN f1 score: 0.512
KNN cohens kappa score: 0.496


------ 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: 2129, 54
GAN fn, tp: 9, 43
GAN f1 score: 0.577
GAN cohens kappa score: 0.564

-> test with 'LR'
LR tn, fp: 1989, 194
LR fn, tp: 7, 45
LR f1 score: 0.309
LR cohens kappa score: 0.282
LR average precision score: 0.575

-> test with 'GB'
GB tn, fp: 2128, 55
GB fn, tp: 10, 42
GB f1 score: 0.564
GB cohens kappa score: 0.550

-> test with 'KNN'
KNN tn, fp: 1504, 679
KNN fn, tp: 11, 41
KNN f1 score: 0.106
KNN cohens kappa score: 0.066


====== 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: 2107, 78
GAN fn, tp: 11, 41
GAN f1 score: 0.480
GAN cohens kappa score: 0.462

-> test with 'LR'
LR tn, fp: 1905, 280
LR fn, tp: 6, 46
LR f1 score: 0.243
LR cohens kappa score: 0.212
LR average precision score: 0.587

-> test with 'GB'
GB tn, fp: 2105, 80
GB fn, tp: 11, 41
GB f1 score: 0.474
GB cohens kappa score: 0.456

-> test with 'KNN'
KNN tn, fp: 2106, 79
KNN fn, tp: 9, 43
KNN f1 score: 0.494
KNN cohens kappa score: 0.477


------ 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: 2123, 62
GAN fn, tp: 10, 42
GAN f1 score: 0.538
GAN cohens kappa score: 0.524

-> test with 'LR'
LR tn, fp: 1918, 267
LR fn, tp: 5, 47
LR f1 score: 0.257
LR cohens kappa score: 0.226
LR average precision score: 0.568

-> test with 'GB'
GB tn, fp: 2096, 89
GB fn, tp: 8, 44
GB f1 score: 0.476
GB cohens kappa score: 0.458

-> test with 'KNN'
KNN tn, fp: 2100, 85
KNN fn, tp: 9, 43
KNN f1 score: 0.478
KNN cohens kappa score: 0.460


------ 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: 2148, 37
GAN fn, tp: 12, 40
GAN f1 score: 0.620
GAN cohens kappa score: 0.609

-> test with 'LR'
LR tn, fp: 1965, 220
LR fn, tp: 7, 45
LR f1 score: 0.284
LR cohens kappa score: 0.255
LR average precision score: 0.582

-> test with 'GB'
GB tn, fp: 2124, 61
GB fn, tp: 10, 42
GB f1 score: 0.542
GB cohens kappa score: 0.527

-> test with 'KNN'
KNN tn, fp: 2120, 65
KNN fn, tp: 11, 41
KNN f1 score: 0.519
KNN cohens kappa score: 0.504


------ 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: 2135, 50
GAN fn, tp: 9, 43
GAN f1 score: 0.593
GAN cohens kappa score: 0.581

-> test with 'LR'
LR tn, fp: 1934, 251
LR fn, tp: 4, 48
LR f1 score: 0.274
LR cohens kappa score: 0.244
LR average precision score: 0.635

-> test with 'GB'
GB tn, fp: 2123, 62
GB fn, tp: 6, 46
GB f1 score: 0.575
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 2111, 74
KNN fn, tp: 5, 47
KNN f1 score: 0.543
KNN cohens kappa score: 0.528


------ 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: 2142, 41
GAN fn, tp: 16, 36
GAN f1 score: 0.558
GAN cohens kappa score: 0.546

-> test with 'LR'
LR tn, fp: 1964, 219
LR fn, tp: 9, 43
LR f1 score: 0.274
LR cohens kappa score: 0.245
LR average precision score: 0.599

-> test with 'GB'
GB tn, fp: 2130, 53
GB fn, tp: 12, 40
GB f1 score: 0.552
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 2136, 47
KNN fn, tp: 13, 39
KNN f1 score: 0.565
KNN cohens kappa score: 0.552


====== 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: 2133, 52
GAN fn, tp: 11, 41
GAN f1 score: 0.566
GAN cohens kappa score: 0.552

-> test with 'LR'
LR tn, fp: 1988, 197
LR fn, tp: 5, 47
LR f1 score: 0.318
LR cohens kappa score: 0.290
LR average precision score: 0.688

-> test with 'GB'
GB tn, fp: 2121, 64
GB fn, tp: 8, 44
GB f1 score: 0.550
GB cohens kappa score: 0.535

-> test with 'KNN'
KNN tn, fp: 2127, 58
KNN fn, tp: 7, 45
KNN f1 score: 0.581
KNN cohens kappa score: 0.567


------ 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: 2139, 46
GAN fn, tp: 12, 40
GAN f1 score: 0.580
GAN cohens kappa score: 0.567

-> test with 'LR'
LR tn, fp: 1938, 247
LR fn, tp: 6, 46
LR f1 score: 0.267
LR cohens kappa score: 0.237
LR average precision score: 0.505

-> test with 'GB'
GB tn, fp: 2125, 60
GB fn, tp: 12, 40
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 1448, 737
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 GAN.predict
GAN tn, fp: 2148, 37
GAN fn, tp: 6, 46
GAN f1 score: 0.681
GAN cohens kappa score: 0.672

-> test with 'LR'
LR tn, fp: 1948, 237
LR fn, tp: 3, 49
LR f1 score: 0.290
LR cohens kappa score: 0.261
LR average precision score: 0.608

-> test with 'GB'
GB tn, fp: 2109, 76
GB fn, tp: 3, 49
GB f1 score: 0.554
GB cohens kappa score: 0.539

-> test with 'KNN'
KNN tn, fp: 2121, 64
KNN fn, tp: 6, 46
KNN f1 score: 0.568
KNN cohens kappa score: 0.554


------ 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: 2129, 56
GAN fn, tp: 13, 39
GAN f1 score: 0.531
GAN cohens kappa score: 0.516

-> test with 'LR'
LR tn, fp: 1950, 235
LR fn, tp: 10, 42
LR f1 score: 0.255
LR cohens kappa score: 0.225
LR average precision score: 0.566

-> test with 'GB'
GB tn, fp: 2114, 71
GB fn, tp: 13, 39
GB f1 score: 0.481
GB cohens kappa score: 0.465

-> test with 'KNN'
KNN tn, fp: 2111, 74
KNN fn, tp: 11, 41
KNN f1 score: 0.491
KNN cohens kappa score: 0.474


------ 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: 2130, 53
GAN fn, tp: 17, 35
GAN f1 score: 0.500
GAN cohens kappa score: 0.485

-> test with 'LR'
LR tn, fp: 1934, 249
LR fn, tp: 7, 45
LR f1 score: 0.260
LR cohens kappa score: 0.230
LR average precision score: 0.649

-> test with 'GB'
GB tn, fp: 2121, 62
GB fn, tp: 10, 42
GB f1 score: 0.538
GB cohens kappa score: 0.524

-> test with 'KNN'
KNN tn, fp: 2115, 68
KNN fn, tp: 11, 41
KNN f1 score: 0.509
KNN cohens kappa score: 0.493


====== 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: 2144, 41
GAN fn, tp: 14, 38
GAN f1 score: 0.580
GAN cohens kappa score: 0.568

-> test with 'LR'
LR tn, fp: 1957, 228
LR fn, tp: 8, 44
LR f1 score: 0.272
LR cohens kappa score: 0.242
LR average precision score: 0.631

-> test with 'GB'
GB tn, fp: 2129, 56
GB fn, tp: 15, 37
GB f1 score: 0.510
GB cohens kappa score: 0.495

-> test with 'KNN'
KNN tn, fp: 1493, 692
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 GAN.predict
GAN tn, fp: 2138, 47
GAN fn, tp: 11, 41
GAN f1 score: 0.586
GAN cohens kappa score: 0.573

-> test with 'LR'
LR tn, fp: 1935, 250
LR fn, tp: 5, 47
LR f1 score: 0.269
LR cohens kappa score: 0.239
LR average precision score: 0.561

-> test with 'GB'
GB tn, fp: 2108, 77
GB fn, tp: 7, 45
GB f1 score: 0.517
GB cohens kappa score: 0.501

-> 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 GAN.predict
GAN tn, fp: 2138, 47
GAN fn, tp: 10, 42
GAN f1 score: 0.596
GAN cohens kappa score: 0.584

-> test with 'LR'
LR tn, fp: 1955, 230
LR fn, tp: 7, 45
LR f1 score: 0.275
LR cohens kappa score: 0.246
LR average precision score: 0.687

-> test with 'GB'
GB tn, fp: 2119, 66
GB fn, tp: 7, 45
GB f1 score: 0.552
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 2114, 71
KNN fn, tp: 9, 43
KNN f1 score: 0.518
KNN cohens kappa score: 0.502


------ 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: 2137, 48
GAN fn, tp: 10, 42
GAN f1 score: 0.592
GAN cohens kappa score: 0.579

-> test with 'LR'
LR tn, fp: 1931, 254
LR fn, tp: 9, 43
LR f1 score: 0.246
LR cohens kappa score: 0.215
LR average precision score: 0.501

-> test with 'GB'
GB tn, fp: 2117, 68
GB fn, tp: 10, 42
GB f1 score: 0.519
GB cohens kappa score: 0.503

-> test with 'KNN'
KNN tn, fp: 2117, 68
KNN fn, tp: 12, 40
KNN f1 score: 0.500
KNN cohens kappa score: 0.484


------ 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: 2134, 49
GAN fn, tp: 9, 43
GAN f1 score: 0.597
GAN cohens kappa score: 0.585

-> test with 'LR'
LR tn, fp: 1909, 274
LR fn, tp: 2, 50
LR f1 score: 0.266
LR cohens kappa score: 0.235
LR average precision score: 0.598

-> test with 'GB'
GB tn, fp: 2119, 64
GB fn, tp: 6, 46
GB f1 score: 0.568
GB cohens kappa score: 0.554

-> test with 'KNN'
KNN tn, fp: 2111, 72
KNN fn, tp: 10, 42
KNN f1 score: 0.506
KNN cohens kappa score: 0.490


====== 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: 2141, 44
GAN fn, tp: 13, 39
GAN f1 score: 0.578
GAN cohens kappa score: 0.565

-> test with 'LR'
LR tn, fp: 1918, 267
LR fn, tp: 4, 48
LR f1 score: 0.262
LR cohens kappa score: 0.231
LR average precision score: 0.644

-> test with 'GB'
GB tn, fp: 2127, 58
GB fn, tp: 5, 47
GB f1 score: 0.599
GB cohens kappa score: 0.586

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


------ 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: 2116, 69
GAN fn, tp: 6, 46
GAN f1 score: 0.551
GAN cohens kappa score: 0.536

-> test with 'LR'
LR tn, fp: 1926, 259
LR fn, tp: 5, 47
LR f1 score: 0.263
LR cohens kappa score: 0.232
LR average precision score: 0.550

-> test with 'GB'
GB tn, fp: 2112, 73
GB fn, tp: 6, 46
GB f1 score: 0.538
GB cohens kappa score: 0.523

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


------ 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: 2131, 54
GAN fn, tp: 16, 36
GAN f1 score: 0.507
GAN cohens kappa score: 0.492

-> test with 'LR'
LR tn, fp: 1958, 227
LR fn, tp: 9, 43
LR f1 score: 0.267
LR cohens kappa score: 0.237
LR average precision score: 0.577

-> test with 'GB'
GB tn, fp: 2126, 59
GB fn, tp: 13, 39
GB f1 score: 0.520
GB cohens kappa score: 0.505

-> test with 'KNN'
KNN tn, fp: 2117, 68
KNN fn, tp: 13, 39
KNN f1 score: 0.491
KNN cohens kappa score: 0.474


------ 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: 2135, 50
GAN fn, tp: 10, 42
GAN f1 score: 0.583
GAN cohens kappa score: 0.571

-> test with 'LR'
LR tn, fp: 1911, 274
LR fn, tp: 4, 48
LR f1 score: 0.257
LR cohens kappa score: 0.226
LR average precision score: 0.610

-> test with 'GB'
GB tn, fp: 2109, 76
GB fn, tp: 8, 44
GB f1 score: 0.512
GB cohens kappa score: 0.495

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


------ 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: 2134, 49
GAN fn, tp: 13, 39
GAN f1 score: 0.557
GAN cohens kappa score: 0.544

-> test with 'LR'
LR tn, fp: 1948, 235
LR fn, tp: 8, 44
LR f1 score: 0.266
LR cohens kappa score: 0.236
LR average precision score: 0.624

-> test with 'GB'
GB tn, fp: 2119, 64
GB fn, tp: 15, 37
GB f1 score: 0.484
GB cohens kappa score: 0.467

-> test with 'KNN'
KNN tn, fp: 2124, 59
KNN fn, tp: 12, 40
KNN f1 score: 0.530
KNN cohens kappa score: 0.515

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 1989, 280
LR fn, tp: 10, 50
LR f1 score: 0.318
LR cohens kappa score: 0.290
LR average precision score: 0.688


average:
LR tn, fp: 1940.32, 244.28
LR fn, tp: 6.2, 45.8
LR f1 score: 0.269
LR cohens kappa score: 0.239
LR average precision score: 0.597


minimum:
LR tn, fp: 1905, 194
LR fn, tp: 2, 42
LR f1 score: 0.243
LR cohens kappa score: 0.212
LR average precision score: 0.501


-----[ GB ]-----
maximum:
GB tn, fp: 2130, 89
GB fn, tp: 15, 49
GB f1 score: 0.599
GB cohens kappa score: 0.586


average:
GB tn, fp: 2118.48, 66.12
GB fn, tp: 9.32, 42.68
GB f1 score: 0.531
GB cohens kappa score: 0.516


minimum:
GB tn, fp: 2096, 53
GB fn, tp: 3, 37
GB f1 score: 0.474
GB cohens kappa score: 0.456


-----[ KNN ]-----
maximum:
KNN tn, fp: 2136, 737
KNN fn, tp: 13, 47
KNN f1 score: 0.581
KNN cohens kappa score: 0.567


average:
KNN tn, fp: 2016.64, 167.96
KNN fn, tp: 9.68, 42.32
KNN f1 score: 0.459
KNN cohens kappa score: 0.440


minimum:
KNN tn, fp: 1448, 47
KNN fn, tp: 5, 39
KNN f1 score: 0.101
KNN cohens kappa score: 0.060


-----[ GAN ]-----
maximum:
GAN tn, fp: 2148, 78
GAN fn, tp: 17, 46
GAN f1 score: 0.681
GAN cohens kappa score: 0.672


average:
GAN tn, fp: 2133.36, 51.24
GAN fn, tp: 11.28, 40.72
GAN f1 score: 0.567
GAN cohens kappa score: 0.554


minimum:
GAN tn, fp: 2107, 37
GAN fn, tp: 6, 35
GAN f1 score: 0.480
GAN cohens kappa score: 0.462