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

///////////////////////////////////////////
// Running convGAN-proximary-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: 1976, 209
GAN fn, tp: 3, 49
GAN f1 score: 0.316
GAN cohens kappa score: 0.289

-> test with 'LR'
LR tn, fp: 1990, 195
LR fn, tp: 6, 46
LR f1 score: 0.314
LR cohens kappa score: 0.287
LR average precision score: 0.606

-> test with 'GB'
GB tn, fp: 2117, 68
GB fn, tp: 11, 41
GB f1 score: 0.509
GB cohens kappa score: 0.493

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


------ 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: 2043, 142
GAN fn, tp: 8, 44
GAN f1 score: 0.370
GAN cohens kappa score: 0.346

-> test with 'LR'
LR tn, fp: 1931, 254
LR fn, tp: 7, 45
LR f1 score: 0.256
LR cohens kappa score: 0.226
LR average precision score: 0.592

-> test with 'GB'
GB tn, fp: 2123, 62
GB fn, tp: 9, 43
GB f1 score: 0.548
GB cohens kappa score: 0.533

-> test with 'KNN'
KNN tn, fp: 2125, 60
KNN fn, tp: 10, 42
KNN f1 score: 0.545
KNN cohens kappa score: 0.531


------ 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: 2066, 119
GAN fn, tp: 9, 43
GAN f1 score: 0.402
GAN cohens kappa score: 0.380

-> test with 'LR'
LR tn, fp: 1936, 249
LR fn, tp: 7, 45
LR f1 score: 0.260
LR cohens kappa score: 0.230
LR average precision score: 0.670

-> 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: 1448, 737
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 GAN.predict
GAN tn, fp: 1986, 199
GAN fn, tp: 7, 45
GAN f1 score: 0.304
GAN cohens kappa score: 0.276

-> test with 'LR'
LR tn, fp: 1937, 248
LR fn, tp: 6, 46
LR f1 score: 0.266
LR cohens kappa score: 0.236
LR average precision score: 0.496

-> 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: 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: 2024, 159
GAN fn, tp: 9, 43
GAN f1 score: 0.339
GAN cohens kappa score: 0.313

-> test with 'LR'
LR tn, fp: 1941, 242
LR fn, tp: 6, 46
LR f1 score: 0.271
LR cohens kappa score: 0.241
LR average precision score: 0.585

-> test with 'GB'
GB tn, fp: 2124, 59
GB fn, tp: 7, 45
GB f1 score: 0.577
GB cohens kappa score: 0.563

-> test with 'KNN'
KNN tn, fp: 2118, 65
KNN fn, tp: 12, 40
KNN f1 score: 0.510
KNN cohens kappa score: 0.494


====== 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: 1940, 245
GAN fn, tp: 5, 47
GAN f1 score: 0.273
GAN cohens kappa score: 0.243

-> test with 'LR'
LR tn, fp: 1931, 254
LR fn, tp: 6, 46
LR f1 score: 0.261
LR cohens kappa score: 0.231
LR average precision score: 0.594

-> test with 'GB'
GB tn, fp: 2086, 99
GB fn, tp: 11, 41
GB f1 score: 0.427
GB cohens kappa score: 0.407

-> test with 'KNN'
KNN tn, fp: 2098, 87
KNN fn, tp: 9, 43
KNN f1 score: 0.473
KNN cohens kappa score: 0.454


------ 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: 1983, 202
GAN fn, tp: 7, 45
GAN f1 score: 0.301
GAN cohens kappa score: 0.273

-> 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: 0.590

-> test with 'GB'
GB tn, fp: 2091, 94
GB fn, tp: 9, 43
GB f1 score: 0.455
GB cohens kappa score: 0.436

-> test with 'KNN'
KNN tn, fp: 2094, 91
KNN fn, tp: 9, 43
KNN f1 score: 0.462
KNN cohens kappa score: 0.444


------ 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: 2004, 181
GAN fn, tp: 8, 44
GAN f1 score: 0.318
GAN cohens kappa score: 0.291

-> test with 'LR'
LR tn, fp: 1993, 192
LR fn, tp: 7, 45
LR f1 score: 0.311
LR cohens kappa score: 0.284
LR average precision score: 0.581

-> test with 'GB'
GB tn, fp: 2127, 58
GB fn, tp: 12, 40
GB f1 score: 0.533
GB cohens kappa score: 0.519

-> test with 'KNN'
KNN tn, fp: 2119, 66
KNN fn, tp: 11, 41
KNN f1 score: 0.516
KNN cohens kappa score: 0.500


------ 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: 2013, 172
GAN fn, tp: 6, 46
GAN f1 score: 0.341
GAN cohens kappa score: 0.315

-> test with 'LR'
LR tn, fp: 1938, 247
LR fn, tp: 4, 48
LR f1 score: 0.277
LR cohens kappa score: 0.247
LR average precision score: 0.630

-> test with 'GB'
GB tn, fp: 2113, 72
GB fn, tp: 6, 46
GB f1 score: 0.541
GB cohens kappa score: 0.526

-> test with 'KNN'
KNN tn, fp: 2106, 79
KNN fn, tp: 5, 47
KNN f1 score: 0.528
KNN cohens kappa score: 0.512


------ 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: 2057, 126
GAN fn, tp: 10, 42
GAN f1 score: 0.382
GAN cohens kappa score: 0.359

-> test with 'LR'
LR tn, fp: 1969, 214
LR fn, tp: 9, 43
LR f1 score: 0.278
LR cohens kappa score: 0.249
LR average precision score: 0.596

-> test with 'GB'
GB tn, fp: 2129, 54
GB fn, tp: 13, 39
GB f1 score: 0.538
GB cohens kappa score: 0.524

-> test with 'KNN'
KNN tn, fp: 2137, 46
KNN fn, tp: 12, 40
KNN f1 score: 0.580
KNN cohens kappa score: 0.567


====== 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: 1992, 193
GAN fn, tp: 8, 44
GAN f1 score: 0.304
GAN cohens kappa score: 0.277

-> test with 'LR'
LR tn, fp: 1933, 252
LR fn, tp: 5, 47
LR f1 score: 0.268
LR cohens kappa score: 0.238
LR average precision score: 0.688

-> test with 'GB'
GB tn, fp: 2114, 71
GB fn, tp: 6, 46
GB f1 score: 0.544
GB cohens kappa score: 0.529

-> test with 'KNN'
KNN tn, fp: 2126, 59
KNN fn, tp: 7, 45
KNN f1 score: 0.577
KNN cohens kappa score: 0.563


------ 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: 1997, 188
GAN fn, tp: 5, 47
GAN f1 score: 0.328
GAN cohens kappa score: 0.301

-> test with 'LR'
LR tn, fp: 1956, 229
LR fn, tp: 6, 46
LR f1 score: 0.281
LR cohens kappa score: 0.252
LR average precision score: 0.502

-> test with 'GB'
GB tn, fp: 2122, 63
GB fn, tp: 13, 39
GB f1 score: 0.506
GB cohens kappa score: 0.491

-> test with 'KNN'
KNN tn, fp: 2131, 54
KNN fn, tp: 10, 42
KNN f1 score: 0.568
KNN cohens kappa score: 0.554


------ 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: 1995, 190
GAN fn, tp: 5, 47
GAN f1 score: 0.325
GAN cohens kappa score: 0.299

-> test with 'LR'
LR tn, fp: 1935, 250
LR fn, tp: 2, 50
LR f1 score: 0.284
LR cohens kappa score: 0.255
LR average precision score: 0.583

-> test with 'GB'
GB tn, fp: 2104, 81
GB fn, tp: 4, 48
GB f1 score: 0.530
GB cohens kappa score: 0.514

-> test with 'KNN'
KNN tn, fp: 2119, 66
KNN fn, tp: 6, 46
KNN f1 score: 0.561
KNN cohens kappa score: 0.547


------ 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: 1925, 260
GAN fn, tp: 11, 41
GAN f1 score: 0.232
GAN cohens kappa score: 0.201

-> test with 'LR'
LR tn, fp: 1984, 201
LR fn, tp: 11, 41
LR f1 score: 0.279
LR cohens kappa score: 0.250
LR average precision score: 0.556

-> test with 'GB'
GB tn, fp: 2101, 84
GB fn, tp: 12, 40
GB f1 score: 0.455
GB cohens kappa score: 0.436

-> test with 'KNN'
KNN tn, fp: 2097, 88
KNN fn, tp: 11, 41
KNN f1 score: 0.453
KNN cohens kappa score: 0.434


------ 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: 1982, 201
GAN fn, tp: 13, 39
GAN f1 score: 0.267
GAN cohens kappa score: 0.238

-> test with 'LR'
LR tn, fp: 1941, 242
LR fn, tp: 7, 45
LR f1 score: 0.265
LR cohens kappa score: 0.235
LR average precision score: 0.651

-> test with 'GB'
GB tn, fp: 2118, 65
GB fn, tp: 10, 42
GB f1 score: 0.528
GB cohens kappa score: 0.513

-> 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 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: 2054, 131
GAN fn, tp: 11, 41
GAN f1 score: 0.366
GAN cohens kappa score: 0.343

-> test with 'LR'
LR tn, fp: 1996, 189
LR fn, tp: 8, 44
LR f1 score: 0.309
LR cohens kappa score: 0.281
LR average precision score: 0.637

-> test with 'GB'
GB tn, fp: 2123, 62
GB fn, tp: 17, 35
GB f1 score: 0.470
GB cohens kappa score: 0.453

-> test with 'KNN'
KNN tn, fp: 2126, 59
KNN fn, tp: 12, 40
KNN f1 score: 0.530
KNN cohens kappa score: 0.515


------ 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: 2038, 147
GAN fn, tp: 7, 45
GAN f1 score: 0.369
GAN cohens kappa score: 0.345

-> test with 'LR'
LR tn, fp: 1967, 218
LR fn, tp: 6, 46
LR f1 score: 0.291
LR cohens kappa score: 0.262
LR average precision score: 0.574

-> 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: 2123, 62
KNN fn, tp: 8, 44
KNN f1 score: 0.557
KNN cohens kappa score: 0.543


------ 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: 2057, 128
GAN fn, tp: 7, 45
GAN f1 score: 0.400
GAN cohens kappa score: 0.378

-> test with 'LR'
LR tn, fp: 1956, 229
LR fn, tp: 7, 45
LR f1 score: 0.276
LR cohens kappa score: 0.247
LR average precision score: 0.691

-> test with 'GB'
GB tn, fp: 2119, 66
GB fn, tp: 8, 44
GB f1 score: 0.543
GB cohens kappa score: 0.528

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


------ 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: 2015, 170
GAN fn, tp: 7, 45
GAN f1 score: 0.337
GAN cohens kappa score: 0.311

-> test with 'LR'
LR tn, fp: 1934, 251
LR fn, tp: 9, 43
LR f1 score: 0.249
LR cohens kappa score: 0.218
LR average precision score: 0.502

-> 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: 2114, 71
KNN fn, tp: 12, 40
KNN f1 score: 0.491
KNN cohens kappa score: 0.474


------ 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: 2003, 180
GAN fn, tp: 4, 48
GAN f1 score: 0.343
GAN cohens kappa score: 0.317

-> test with 'LR'
LR tn, fp: 1946, 237
LR fn, tp: 2, 50
LR f1 score: 0.295
LR cohens kappa score: 0.266
LR average precision score: 0.592

-> test with 'GB'
GB tn, fp: 2111, 72
GB fn, tp: 5, 47
GB f1 score: 0.550
GB cohens kappa score: 0.535

-> test with 'KNN'
KNN tn, fp: 2099, 84
KNN fn, tp: 7, 45
KNN f1 score: 0.497
KNN cohens kappa score: 0.480


====== 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: 2024, 161
GAN fn, tp: 8, 44
GAN f1 score: 0.342
GAN cohens kappa score: 0.317

-> test with 'LR'
LR tn, fp: 1971, 214
LR fn, tp: 4, 48
LR f1 score: 0.306
LR cohens kappa score: 0.278
LR average precision score: 0.663

-> test with 'GB'
GB tn, fp: 2124, 61
GB fn, tp: 7, 45
GB f1 score: 0.570
GB cohens kappa score: 0.556

-> test with 'KNN'
KNN tn, fp: 1457, 728
KNN fn, tp: 9, 43
KNN f1 score: 0.104
KNN cohens kappa score: 0.064


------ 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: 1878, 307
GAN fn, tp: 4, 48
GAN f1 score: 0.236
GAN cohens kappa score: 0.204

-> test with 'LR'
LR tn, fp: 2005, 180
LR fn, tp: 8, 44
LR f1 score: 0.319
LR cohens kappa score: 0.292
LR average precision score: 0.554

-> test with 'GB'
GB tn, fp: 2114, 71
GB fn, tp: 6, 46
GB f1 score: 0.544
GB cohens kappa score: 0.529

-> test with 'KNN'
KNN tn, fp: 2107, 78
KNN fn, tp: 8, 44
KNN f1 score: 0.506
KNN cohens kappa score: 0.489


------ 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: 1973, 212
GAN fn, tp: 10, 42
GAN f1 score: 0.275
GAN cohens kappa score: 0.245

-> test with 'LR'
LR tn, fp: 1997, 188
LR fn, tp: 10, 42
LR f1 score: 0.298
LR cohens kappa score: 0.270
LR average precision score: 0.576

-> test with 'GB'
GB tn, fp: 2120, 65
GB fn, tp: 14, 38
GB f1 score: 0.490
GB cohens kappa score: 0.474

-> test with 'KNN'
KNN tn, fp: 2109, 76
KNN fn, tp: 13, 39
KNN f1 score: 0.467
KNN cohens kappa score: 0.449


------ 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: 1982, 203
GAN fn, tp: 7, 45
GAN f1 score: 0.300
GAN cohens kappa score: 0.272

-> test with 'LR'
LR tn, fp: 2003, 182
LR fn, tp: 6, 46
LR f1 score: 0.329
LR cohens kappa score: 0.302
LR average precision score: 0.619

-> test with 'GB'
GB tn, fp: 2123, 62
GB fn, tp: 10, 42
GB f1 score: 0.538
GB cohens kappa score: 0.524

-> 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: 2013, 170
GAN fn, tp: 11, 41
GAN f1 score: 0.312
GAN cohens kappa score: 0.285

-> test with 'LR'
LR tn, fp: 1977, 206
LR fn, tp: 9, 43
LR f1 score: 0.286
LR cohens kappa score: 0.257
LR average precision score: 0.621

-> test with 'GB'
GB tn, fp: 2118, 65
GB fn, tp: 14, 38
GB f1 score: 0.490
GB cohens kappa score: 0.474

-> 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: 2005, 254
LR fn, tp: 11, 50
LR f1 score: 0.329
LR cohens kappa score: 0.302
LR average precision score: 0.691


average:
LR tn, fp: 1960.28, 224.32
LR fn, tp: 6.56, 45.44
LR f1 score: 0.284
LR cohens kappa score: 0.255
LR average precision score: 0.598


minimum:
LR tn, fp: 1931, 180
LR fn, tp: 2, 41
LR f1 score: 0.249
LR cohens kappa score: 0.218
LR average precision score: 0.496


-----[ GB ]-----
maximum:
GB tn, fp: 2129, 99
GB fn, tp: 17, 48
GB f1 score: 0.577
GB cohens kappa score: 0.563


average:
GB tn, fp: 2115.12, 69.48
GB fn, tp: 9.76, 42.24
GB f1 score: 0.517
GB cohens kappa score: 0.501


minimum:
GB tn, fp: 2086, 54
GB fn, tp: 4, 35
GB f1 score: 0.427
GB cohens kappa score: 0.407


-----[ KNN ]-----
maximum:
KNN tn, fp: 2137, 737
KNN fn, tp: 13, 47
KNN f1 score: 0.580
KNN cohens kappa score: 0.567


average:
KNN tn, fp: 2061.4, 123.2
KNN fn, tp: 9.52, 42.48
KNN f1 score: 0.484
KNN cohens kappa score: 0.466


minimum:
KNN tn, fp: 1448, 46
KNN fn, tp: 5, 39
KNN f1 score: 0.104
KNN cohens kappa score: 0.064


-----[ GAN ]-----
maximum:
GAN tn, fp: 2066, 307
GAN fn, tp: 13, 49
GAN f1 score: 0.402
GAN cohens kappa score: 0.380


average:
GAN tn, fp: 2000.8, 183.8
GAN fn, tp: 7.6, 44.4
GAN f1 score: 0.323
GAN cohens kappa score: 0.297


minimum:
GAN tn, fp: 1878, 119
GAN fn, tp: 3, 39
GAN f1 score: 0.232
GAN cohens kappa score: 0.201