ConvGeNBenchmarkResults/data_result/convGAN-proximary-5/imblearn_mammography.log

///////////////////////////////////////////
// Running convGAN-proximary-5 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: 1180, 1005
GAN fn, tp: 12, 40
GAN f1 score: 0.073
GAN cohens kappa score: 0.030

-> test with 'LR'
LR tn, fp: 1904, 281
LR fn, tp: 6, 46
LR f1 score: 0.243
LR cohens kappa score: 0.211
LR average precision score: 0.568

-> test with 'GB'
GB tn, fp: 2134, 51
GB fn, tp: 17, 35
GB f1 score: 0.507
GB cohens kappa score: 0.493

-> test with 'KNN'
KNN tn, fp: 2086, 99
KNN fn, tp: 7, 45
KNN f1 score: 0.459
KNN cohens kappa score: 0.440


------ 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: 1816, 369
GAN fn, tp: 19, 33
GAN f1 score: 0.145
GAN cohens kappa score: 0.109

-> 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: 0.476

-> test with 'GB'
GB tn, fp: 2132, 53
GB fn, tp: 11, 41
GB f1 score: 0.562
GB cohens kappa score: 0.548

-> test with 'KNN'
KNN tn, fp: 2102, 83
KNN fn, tp: 8, 44
KNN f1 score: 0.492
KNN cohens kappa score: 0.474


------ 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: 853, 1332
GAN fn, tp: 12, 40
GAN f1 score: 0.056
GAN cohens kappa score: 0.012

-> test with 'LR'
LR tn, fp: 1771, 414
LR fn, tp: 5, 47
LR f1 score: 0.183
LR cohens kappa score: 0.148
LR average precision score: 0.602

-> test with 'GB'
GB tn, fp: 2154, 31
GB fn, tp: 12, 40
GB f1 score: 0.650
GB cohens kappa score: 0.641

-> test with 'KNN'
KNN tn, fp: 1432, 753
KNN fn, tp: 9, 43
KNN f1 score: 0.101
KNN cohens kappa score: 0.060


------ 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: 2096, 89
GAN fn, tp: 17, 35
GAN f1 score: 0.398
GAN cohens kappa score: 0.377

-> test with 'LR'
LR tn, fp: 1925, 260
LR fn, tp: 6, 46
LR f1 score: 0.257
LR cohens kappa score: 0.226
LR average precision score: 0.329

-> test with 'GB'
GB tn, fp: 2140, 45
GB fn, tp: 17, 35
GB f1 score: 0.530
GB cohens kappa score: 0.517

-> test with 'KNN'
KNN tn, fp: 2090, 95
KNN fn, tp: 9, 43
KNN f1 score: 0.453
KNN cohens kappa score: 0.434


------ 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: 1857, 326
GAN fn, tp: 18, 34
GAN f1 score: 0.165
GAN cohens kappa score: 0.130

-> test with 'LR'
LR tn, fp: 1898, 285
LR fn, tp: 6, 46
LR f1 score: 0.240
LR cohens kappa score: 0.208
LR average precision score: 0.562

-> test with 'GB'
GB tn, fp: 2143, 40
GB fn, tp: 14, 38
GB f1 score: 0.585
GB cohens kappa score: 0.573

-> test with 'KNN'
KNN tn, fp: 2106, 77
KNN fn, tp: 12, 40
KNN f1 score: 0.473
KNN cohens kappa score: 0.456


====== 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: 2031, 154
GAN fn, tp: 14, 38
GAN f1 score: 0.311
GAN cohens kappa score: 0.285

-> test with 'LR'
LR tn, fp: 1866, 319
LR fn, tp: 6, 46
LR f1 score: 0.221
LR cohens kappa score: 0.188
LR average precision score: 0.511

-> test with 'GB'
GB tn, fp: 2112, 73
GB fn, tp: 11, 41
GB f1 score: 0.494
GB cohens kappa score: 0.477

-> test with 'KNN'
KNN tn, fp: 2088, 97
KNN fn, tp: 8, 44
KNN f1 score: 0.456
KNN cohens kappa score: 0.437


------ 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: 1725, 460
GAN fn, tp: 16, 36
GAN f1 score: 0.131
GAN cohens kappa score: 0.093

-> test with 'LR'
LR tn, fp: 1858, 327
LR fn, tp: 6, 46
LR f1 score: 0.216
LR cohens kappa score: 0.183
LR average precision score: 0.478

-> test with 'GB'
GB tn, fp: 2132, 53
GB fn, tp: 16, 36
GB f1 score: 0.511
GB cohens kappa score: 0.496

-> test with 'KNN'
KNN tn, fp: 1421, 764
KNN fn, tp: 5, 47
KNN f1 score: 0.109
KNN cohens kappa score: 0.068


------ 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: 1824, 361
GAN fn, tp: 20, 32
GAN f1 score: 0.144
GAN cohens kappa score: 0.107

-> test with 'LR'
LR tn, fp: 1918, 267
LR fn, tp: 8, 44
LR f1 score: 0.242
LR cohens kappa score: 0.211
LR average precision score: 0.509

-> test with 'GB'
GB tn, fp: 2140, 45
GB fn, tp: 17, 35
GB f1 score: 0.530
GB cohens kappa score: 0.517

-> test with 'KNN'
KNN tn, fp: 2102, 83
KNN fn, tp: 11, 41
KNN f1 score: 0.466
KNN cohens kappa score: 0.448


------ 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: 1777, 408
GAN fn, tp: 19, 33
GAN f1 score: 0.134
GAN cohens kappa score: 0.096

-> test with 'LR'
LR tn, fp: 1906, 279
LR fn, tp: 4, 48
LR f1 score: 0.253
LR cohens kappa score: 0.222
LR average precision score: 0.485

-> test with 'GB'
GB tn, fp: 2146, 39
GB fn, tp: 12, 40
GB f1 score: 0.611
GB cohens kappa score: 0.599

-> test with 'KNN'
KNN tn, fp: 2090, 95
KNN fn, tp: 6, 46
KNN f1 score: 0.477
KNN cohens kappa score: 0.458


------ 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: 859, 1324
GAN fn, tp: 18, 34
GAN f1 score: 0.048
GAN cohens kappa score: 0.004

-> test with 'LR'
LR tn, fp: 1769, 414
LR fn, tp: 8, 44
LR f1 score: 0.173
LR cohens kappa score: 0.136
LR average precision score: 0.534

-> test with 'GB'
GB tn, fp: 2156, 27
GB fn, tp: 15, 37
GB f1 score: 0.638
GB cohens kappa score: 0.628

-> test with 'KNN'
KNN tn, fp: 2117, 66
KNN fn, tp: 11, 41
KNN f1 score: 0.516
KNN cohens kappa score: 0.500


====== 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: 1235, 950
GAN fn, tp: 8, 44
GAN f1 score: 0.084
GAN cohens kappa score: 0.042

-> test with 'LR'
LR tn, fp: 1913, 272
LR fn, tp: 6, 46
LR f1 score: 0.249
LR cohens kappa score: 0.217
LR average precision score: 0.549

-> test with 'GB'
GB tn, fp: 2148, 37
GB fn, tp: 10, 42
GB f1 score: 0.641
GB cohens kappa score: 0.631

-> test with 'KNN'
KNN tn, fp: 2100, 85
KNN fn, tp: 6, 46
KNN f1 score: 0.503
KNN cohens kappa score: 0.486


------ 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: 1223, 962
GAN fn, tp: 19, 33
GAN f1 score: 0.063
GAN cohens kappa score: 0.020

-> 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: 0.398

-> 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: 2104, 81
KNN fn, tp: 9, 43
KNN f1 score: 0.489
KNN cohens kappa score: 0.471


------ 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: 1211, 974
GAN fn, tp: 13, 39
GAN f1 score: 0.073
GAN cohens kappa score: 0.030

-> test with 'LR'
LR tn, fp: 1913, 272
LR fn, tp: 3, 49
LR f1 score: 0.263
LR cohens kappa score: 0.232
LR average precision score: 0.447

-> test with 'GB'
GB tn, fp: 2140, 45
GB fn, tp: 8, 44
GB f1 score: 0.624
GB cohens kappa score: 0.613

-> test with 'KNN'
KNN tn, fp: 1428, 757
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: 1386, 799
GAN fn, tp: 18, 34
GAN f1 score: 0.077
GAN cohens kappa score: 0.035

-> test with 'LR'
LR tn, fp: 1919, 266
LR fn, tp: 9, 43
LR f1 score: 0.238
LR cohens kappa score: 0.207
LR average precision score: 0.486

-> 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: 2093, 92
KNN fn, tp: 12, 40
KNN f1 score: 0.435
KNN cohens kappa score: 0.415


------ 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: 1226, 957
GAN fn, tp: 18, 34
GAN f1 score: 0.065
GAN cohens kappa score: 0.022

-> test with 'LR'
LR tn, fp: 1901, 282
LR fn, tp: 7, 45
LR f1 score: 0.237
LR cohens kappa score: 0.206
LR average precision score: 0.564

-> test with 'GB'
GB tn, fp: 2143, 40
GB fn, tp: 17, 35
GB f1 score: 0.551
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 2103, 80
KNN fn, tp: 10, 42
KNN f1 score: 0.483
KNN cohens kappa score: 0.465


====== 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: 1846, 339
GAN fn, tp: 15, 37
GAN f1 score: 0.173
GAN cohens kappa score: 0.138

-> test with 'LR'
LR tn, fp: 1920, 265
LR fn, tp: 8, 44
LR f1 score: 0.244
LR cohens kappa score: 0.212
LR average precision score: 0.578

-> test with 'GB'
GB tn, fp: 2148, 37
GB fn, tp: 17, 35
GB f1 score: 0.565
GB cohens kappa score: 0.552

-> test with 'KNN'
KNN tn, fp: 2114, 71
KNN fn, tp: 11, 41
KNN f1 score: 0.500
KNN cohens kappa score: 0.484


------ 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: 1964, 221
GAN fn, tp: 16, 36
GAN f1 score: 0.233
GAN cohens kappa score: 0.202

-> test with 'LR'
LR tn, fp: 1828, 357
LR fn, tp: 6, 46
LR f1 score: 0.202
LR cohens kappa score: 0.168
LR average precision score: 0.425

-> 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: 1480, 705
KNN fn, tp: 6, 46
KNN f1 score: 0.115
KNN cohens kappa score: 0.074


------ 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: 1286, 899
GAN fn, tp: 18, 34
GAN f1 score: 0.069
GAN cohens kappa score: 0.026

-> test with 'LR'
LR tn, fp: 1917, 268
LR fn, tp: 7, 45
LR f1 score: 0.247
LR cohens kappa score: 0.215
LR average precision score: 0.453

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

-> test with 'KNN'
KNN tn, fp: 2102, 83
KNN fn, tp: 8, 44
KNN f1 score: 0.492
KNN cohens kappa score: 0.474


------ 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: 1665, 520
GAN fn, tp: 18, 34
GAN f1 score: 0.112
GAN cohens kappa score: 0.073

-> test with 'LR'
LR tn, fp: 1859, 326
LR fn, tp: 8, 44
LR f1 score: 0.209
LR cohens kappa score: 0.175
LR average precision score: 0.479

-> test with 'GB'
GB tn, fp: 2136, 49
GB fn, tp: 13, 39
GB f1 score: 0.557
GB cohens kappa score: 0.544

-> test with 'KNN'
KNN tn, fp: 2086, 99
KNN fn, tp: 10, 42
KNN f1 score: 0.435
KNN cohens kappa score: 0.415


------ 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: 2040, 143
GAN fn, tp: 17, 35
GAN f1 score: 0.304
GAN cohens kappa score: 0.278

-> test with 'LR'
LR tn, fp: 1886, 297
LR fn, tp: 1, 51
LR f1 score: 0.255
LR cohens kappa score: 0.224
LR average precision score: 0.480

-> test with 'GB'
GB tn, fp: 2129, 54
GB fn, tp: 10, 42
GB f1 score: 0.568
GB cohens kappa score: 0.554

-> test with 'KNN'
KNN tn, fp: 2086, 97
KNN fn, tp: 10, 42
KNN f1 score: 0.440
KNN cohens kappa score: 0.420


====== 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: 1969, 216
GAN fn, tp: 17, 35
GAN f1 score: 0.231
GAN cohens kappa score: 0.200

-> test with 'LR'
LR tn, fp: 1854, 331
LR fn, tp: 1, 51
LR f1 score: 0.235
LR cohens kappa score: 0.202
LR average precision score: 0.496

-> test with 'GB'
GB tn, fp: 2145, 40
GB fn, tp: 12, 40
GB f1 score: 0.606
GB cohens kappa score: 0.595

-> 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 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with GAN.predict
GAN tn, fp: 959, 1226
GAN fn, tp: 15, 37
GAN f1 score: 0.056
GAN cohens kappa score: 0.012

-> test with 'LR'
LR tn, fp: 1906, 279
LR fn, tp: 7, 45
LR f1 score: 0.239
LR cohens kappa score: 0.208
LR average precision score: 0.428

-> test with 'GB'
GB tn, fp: 2140, 45
GB fn, tp: 10, 42
GB f1 score: 0.604
GB cohens kappa score: 0.592

-> test with 'KNN'
KNN tn, fp: 2074, 111
KNN fn, tp: 7, 45
KNN f1 score: 0.433
KNN cohens kappa score: 0.412


------ 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: 1254, 931
GAN fn, tp: 18, 34
GAN f1 score: 0.067
GAN cohens kappa score: 0.024

-> test with 'LR'
LR tn, fp: 1914, 271
LR fn, tp: 8, 44
LR f1 score: 0.240
LR cohens kappa score: 0.208
LR average precision score: 0.510

-> test with 'GB'
GB tn, fp: 2123, 62
GB fn, tp: 14, 38
GB f1 score: 0.500
GB cohens kappa score: 0.484

-> test with 'KNN'
KNN tn, fp: 2091, 94
KNN fn, tp: 11, 41
KNN f1 score: 0.439
KNN cohens kappa score: 0.419


------ 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: 1191, 994
GAN fn, tp: 11, 41
GAN f1 score: 0.075
GAN cohens kappa score: 0.033

-> test with 'LR'
LR tn, fp: 1885, 300
LR fn, tp: 4, 48
LR f1 score: 0.240
LR cohens kappa score: 0.208
LR average precision score: 0.487

-> test with 'GB'
GB tn, fp: 2126, 59
GB fn, tp: 10, 42
GB f1 score: 0.549
GB cohens kappa score: 0.535

-> test with 'KNN'
KNN tn, fp: 2099, 86
KNN fn, tp: 8, 44
KNN f1 score: 0.484
KNN cohens kappa score: 0.466


------ 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: 1008, 1175
GAN fn, tp: 19, 33
GAN f1 score: 0.052
GAN cohens kappa score: 0.008

-> test with 'LR'
LR tn, fp: 1868, 315
LR fn, tp: 8, 44
LR f1 score: 0.214
LR cohens kappa score: 0.181
LR average precision score: 0.582

-> test with 'GB'
GB tn, fp: 2132, 51
GB fn, tp: 15, 37
GB f1 score: 0.529
GB cohens kappa score: 0.514

-> test with 'KNN'
KNN tn, fp: 2104, 79
KNN fn, tp: 10, 42
KNN f1 score: 0.486
KNN cohens kappa score: 0.468

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 1925, 414
LR fn, tp: 9, 51
LR f1 score: 0.263
LR cohens kappa score: 0.232
LR average precision score: 0.602


average:
LR tn, fp: 1885.0, 299.6
LR fn, tp: 6.08, 45.92
LR f1 score: 0.233
LR cohens kappa score: 0.201
LR average precision score: 0.497


minimum:
LR tn, fp: 1769, 260
LR fn, tp: 1, 43
LR f1 score: 0.173
LR cohens kappa score: 0.136
LR average precision score: 0.329


-----[ GB ]-----
maximum:
GB tn, fp: 2156, 73
GB fn, tp: 17, 44
GB f1 score: 0.650
GB cohens kappa score: 0.641


average:
GB tn, fp: 2138.6, 46.0
GB fn, tp: 13.36, 38.64
GB f1 score: 0.568
GB cohens kappa score: 0.555


minimum:
GB tn, fp: 2112, 27
GB fn, tp: 8, 35
GB f1 score: 0.494
GB cohens kappa score: 0.477


-----[ KNN ]-----
maximum:
KNN tn, fp: 2117, 764
KNN fn, tp: 12, 48
KNN f1 score: 0.516
KNN cohens kappa score: 0.500


average:
KNN tn, fp: 1991.84, 192.76
KNN fn, tp: 8.6, 43.4
KNN f1 score: 0.413
KNN cohens kappa score: 0.392


minimum:
KNN tn, fp: 1421, 66
KNN fn, tp: 4, 40
KNN f1 score: 0.101
KNN cohens kappa score: 0.060


-----[ GAN ]-----
maximum:
GAN tn, fp: 2096, 1332
GAN fn, tp: 20, 44
GAN f1 score: 0.398
GAN cohens kappa score: 0.377


average:
GAN tn, fp: 1499.24, 685.36
GAN fn, tp: 16.2, 35.8
GAN f1 score: 0.134
GAN cohens kappa score: 0.095


minimum:
GAN tn, fp: 853, 89
GAN fn, tp: 8, 32
GAN f1 score: 0.048
GAN cohens kappa score: 0.004