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

///////////////////////////////////////////
// Running convGAN 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: 1899, 286
LR fn, tp: 6, 46
LR f1 score: 0.240
LR cohens kappa score: 0.208
LR average precision score: 0.557

-> test with 'GB'
GB tn, fp: 2124, 61
GB fn, tp: 12, 40
GB f1 score: 0.523
GB cohens kappa score: 0.508

-> test with 'KNN'
KNN tn, fp: 2091, 94
KNN fn, tp: 7, 45
KNN f1 score: 0.471
KNN cohens kappa score: 0.453


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1906, 279
LR fn, tp: 6, 46
LR f1 score: 0.244
LR cohens kappa score: 0.212
LR average precision score: 0.489

-> test with 'GB'
GB tn, fp: 2142, 43
GB fn, tp: 12, 40
GB f1 score: 0.593
GB cohens kappa score: 0.581

-> test with 'KNN'
KNN tn, fp: 2106, 79
KNN fn, tp: 7, 45
KNN f1 score: 0.511
KNN cohens kappa score: 0.495


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1906, 279
LR fn, tp: 6, 46
LR f1 score: 0.244
LR cohens kappa score: 0.212
LR average precision score: 0.606

-> test with 'GB'
GB tn, fp: 2155, 30
GB fn, tp: 15, 37
GB f1 score: 0.622
GB cohens kappa score: 0.612

-> test with 'KNN'
KNN tn, fp: 2106, 79
KNN fn, tp: 7, 45
KNN f1 score: 0.511
KNN cohens kappa score: 0.495


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1891, 294
LR fn, tp: 6, 46
LR f1 score: 0.235
LR cohens kappa score: 0.203
LR average precision score: 0.335

-> test with 'GB'
GB tn, fp: 2136, 49
GB fn, tp: 16, 36
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 2083, 102
KNN fn, tp: 8, 44
KNN f1 score: 0.444
KNN cohens kappa score: 0.425


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1922, 261
LR fn, tp: 6, 46
LR f1 score: 0.256
LR cohens kappa score: 0.225
LR average precision score: 0.555

-> test with 'GB'
GB tn, fp: 2140, 43
GB fn, tp: 12, 40
GB f1 score: 0.593
GB cohens kappa score: 0.581

-> test with 'KNN'
KNN tn, fp: 2096, 87
KNN fn, tp: 12, 40
KNN f1 score: 0.447
KNN cohens kappa score: 0.428


====== 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: 1880, 305
LR fn, tp: 6, 46
LR f1 score: 0.228
LR cohens kappa score: 0.196
LR average precision score: 0.484

-> test with 'GB'
GB tn, fp: 2130, 55
GB fn, tp: 15, 37
GB f1 score: 0.514
GB cohens kappa score: 0.499

-> test with 'KNN'
KNN tn, fp: 2081, 104
KNN fn, tp: 8, 44
KNN f1 score: 0.440
KNN cohens kappa score: 0.420


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1870, 315
LR fn, tp: 7, 45
LR f1 score: 0.218
LR cohens kappa score: 0.185
LR average precision score: 0.461

-> test with 'GB'
GB tn, fp: 2120, 65
GB fn, tp: 12, 40
GB f1 score: 0.510
GB cohens kappa score: 0.494

-> test with 'KNN'
KNN tn, fp: 2074, 111
KNN fn, tp: 6, 46
KNN f1 score: 0.440
KNN cohens kappa score: 0.420


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1920, 265
LR fn, tp: 7, 45
LR f1 score: 0.249
LR cohens kappa score: 0.217
LR average precision score: 0.495

-> test with 'GB'
GB tn, fp: 2145, 40
GB fn, tp: 16, 36
GB f1 score: 0.562
GB cohens kappa score: 0.550

-> test with 'KNN'
KNN tn, fp: 2098, 87
KNN fn, tp: 11, 41
KNN f1 score: 0.456
KNN cohens kappa score: 0.437


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1913, 272
LR fn, tp: 4, 48
LR f1 score: 0.258
LR cohens kappa score: 0.227
LR average precision score: 0.476

-> test with 'GB'
GB tn, fp: 2153, 32
GB fn, tp: 12, 40
GB f1 score: 0.645
GB cohens kappa score: 0.635

-> test with 'KNN'
KNN tn, fp: 2089, 96
KNN fn, tp: 6, 46
KNN f1 score: 0.474
KNN cohens kappa score: 0.456


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1874, 309
LR fn, tp: 8, 44
LR f1 score: 0.217
LR cohens kappa score: 0.184
LR average precision score: 0.553

-> test with 'GB'
GB tn, fp: 2144, 39
GB fn, tp: 16, 36
GB f1 score: 0.567
GB cohens kappa score: 0.555

-> test with 'KNN'
KNN tn, fp: 2115, 68
KNN fn, tp: 10, 42
KNN f1 score: 0.519
KNN cohens kappa score: 0.503


====== 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: 1909, 276
LR fn, tp: 6, 46
LR f1 score: 0.246
LR cohens kappa score: 0.215
LR average precision score: 0.541

-> 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: 2101, 84
KNN fn, tp: 6, 46
KNN f1 score: 0.505
KNN cohens kappa score: 0.489


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1890, 295
LR fn, tp: 6, 46
LR f1 score: 0.234
LR cohens kappa score: 0.202
LR average precision score: 0.413

-> test with 'GB'
GB tn, fp: 2136, 49
GB fn, tp: 14, 38
GB f1 score: 0.547
GB cohens kappa score: 0.533

-> test with 'KNN'
KNN tn, fp: 2084, 101
KNN fn, tp: 11, 41
KNN f1 score: 0.423
KNN cohens kappa score: 0.402


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1869, 316
LR fn, tp: 2, 50
LR f1 score: 0.239
LR cohens kappa score: 0.207
LR average precision score: 0.464

-> test with 'GB'
GB tn, fp: 2133, 52
GB fn, tp: 9, 43
GB f1 score: 0.585
GB cohens kappa score: 0.572

-> test with 'KNN'
KNN tn, fp: 2105, 80
KNN fn, tp: 5, 47
KNN f1 score: 0.525
KNN cohens kappa score: 0.509


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1912, 273
LR fn, tp: 9, 43
LR f1 score: 0.234
LR cohens kappa score: 0.202
LR average precision score: 0.489

-> test with 'GB'
GB tn, fp: 2138, 47
GB fn, tp: 18, 34
GB f1 score: 0.511
GB cohens kappa score: 0.497

-> test with 'KNN'
KNN tn, fp: 2101, 84
KNN fn, tp: 11, 41
KNN f1 score: 0.463
KNN cohens kappa score: 0.445


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1911, 272
LR fn, tp: 7, 45
LR f1 score: 0.244
LR cohens kappa score: 0.212
LR average precision score: 0.570

-> test with 'GB'
GB tn, fp: 2145, 38
GB fn, tp: 17, 35
GB f1 score: 0.560
GB cohens kappa score: 0.548

-> test with 'KNN'
KNN tn, fp: 2098, 85
KNN fn, tp: 10, 42
KNN f1 score: 0.469
KNN cohens kappa score: 0.451


====== 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: 1927, 258
LR fn, tp: 7, 45
LR f1 score: 0.254
LR cohens kappa score: 0.223
LR average precision score: 0.551

-> test with 'GB'
GB tn, fp: 2143, 42
GB fn, tp: 19, 33
GB f1 score: 0.520
GB cohens kappa score: 0.506

-> test with 'KNN'
KNN tn, fp: 2123, 62
KNN fn, tp: 11, 41
KNN f1 score: 0.529
KNN cohens kappa score: 0.514


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1899, 286
LR fn, tp: 5, 47
LR f1 score: 0.244
LR cohens kappa score: 0.212
LR average precision score: 0.413

-> test with 'GB'
GB tn, fp: 2146, 39
GB fn, tp: 15, 37
GB f1 score: 0.578
GB cohens kappa score: 0.566

-> test with 'KNN'
KNN tn, fp: 2104, 81
KNN fn, tp: 8, 44
KNN f1 score: 0.497
KNN cohens kappa score: 0.480


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1915, 270
LR fn, tp: 7, 45
LR f1 score: 0.245
LR cohens kappa score: 0.214
LR average precision score: 0.458

-> test with 'GB'
GB tn, fp: 2150, 35
GB fn, tp: 11, 41
GB f1 score: 0.641
GB cohens kappa score: 0.630

-> test with 'KNN'
KNN tn, fp: 2093, 92
KNN fn, tp: 9, 43
KNN f1 score: 0.460
KNN cohens kappa score: 0.441


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1916, 269
LR fn, tp: 9, 43
LR f1 score: 0.236
LR cohens kappa score: 0.205
LR average precision score: 0.487

-> test with 'GB'
GB tn, fp: 2133, 52
GB fn, tp: 12, 40
GB f1 score: 0.556
GB cohens kappa score: 0.542

-> test with 'KNN'
KNN tn, fp: 2085, 100
KNN fn, tp: 9, 43
KNN f1 score: 0.441
KNN cohens kappa score: 0.421


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1850, 333
LR fn, tp: 1, 51
LR f1 score: 0.234
LR cohens kappa score: 0.201
LR average precision score: 0.510

-> test with 'GB'
GB tn, fp: 2129, 54
GB fn, tp: 8, 44
GB f1 score: 0.587
GB cohens kappa score: 0.574

-> test with 'KNN'
KNN tn, fp: 1447, 736
KNN fn, tp: 9, 43
KNN f1 score: 0.103
KNN cohens kappa score: 0.063


====== 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: 1784, 401
LR fn, tp: 1, 51
LR f1 score: 0.202
LR cohens kappa score: 0.168
LR average precision score: 0.510

-> test with 'GB'
GB tn, fp: 2153, 32
GB fn, tp: 15, 37
GB f1 score: 0.612
GB cohens kappa score: 0.601

-> test with 'KNN'
KNN tn, fp: 2115, 70
KNN fn, tp: 8, 44
KNN f1 score: 0.530
KNN cohens kappa score: 0.515


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1905, 280
LR fn, tp: 7, 45
LR f1 score: 0.239
LR cohens kappa score: 0.207
LR average precision score: 0.436

-> test with 'GB'
GB tn, fp: 2142, 43
GB fn, tp: 12, 40
GB f1 score: 0.593
GB cohens kappa score: 0.581

-> test with 'KNN'
KNN tn, fp: 2078, 107
KNN fn, tp: 7, 45
KNN f1 score: 0.441
KNN cohens kappa score: 0.421


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1909, 276
LR fn, tp: 8, 44
LR f1 score: 0.237
LR cohens kappa score: 0.205
LR average precision score: 0.510

-> test with 'GB'
GB tn, fp: 2143, 42
GB fn, tp: 19, 33
GB f1 score: 0.520
GB cohens kappa score: 0.506

-> test with 'KNN'
KNN tn, fp: 2100, 85
KNN fn, tp: 10, 42
KNN f1 score: 0.469
KNN cohens kappa score: 0.451


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8530 synthetic samples
-> test with 'LR'
LR tn, fp: 1709, 476
LR fn, tp: 4, 48
LR f1 score: 0.167
LR cohens kappa score: 0.130
LR average precision score: 0.525

-> test with 'GB'
GB tn, fp: 2136, 49
GB fn, tp: 12, 40
GB f1 score: 0.567
GB cohens kappa score: 0.554

-> test with 'KNN'
KNN tn, fp: 2095, 90
KNN fn, tp: 9, 43
KNN f1 score: 0.465
KNN cohens kappa score: 0.446


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 8532 synthetic samples
-> test with 'LR'
LR tn, fp: 1832, 351
LR fn, tp: 6, 46
LR f1 score: 0.205
LR cohens kappa score: 0.171
LR average precision score: 0.589

-> test with 'GB'
GB tn, fp: 2137, 46
GB fn, tp: 16, 36
GB f1 score: 0.537
GB cohens kappa score: 0.524

-> test with 'KNN'
KNN tn, fp: 2098, 85
KNN fn, tp: 10, 42
KNN f1 score: 0.469
KNN cohens kappa score: 0.451

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 1927, 476
LR fn, tp: 9, 51
LR f1 score: 0.258
LR cohens kappa score: 0.227
LR average precision score: 0.606


average:
LR tn, fp: 1884.72, 299.88
LR fn, tp: 5.88, 46.12
LR f1 score: 0.234
LR cohens kappa score: 0.202
LR average precision score: 0.499


minimum:
LR tn, fp: 1709, 258
LR fn, tp: 1, 43
LR f1 score: 0.167
LR cohens kappa score: 0.130
LR average precision score: 0.335


-----[ GB ]-----
maximum:
GB tn, fp: 2155, 65
GB fn, tp: 19, 44
GB f1 score: 0.650
GB cohens kappa score: 0.641


average:
GB tn, fp: 2140.28, 44.32
GB fn, tp: 13.88, 38.12
GB f1 score: 0.569
GB cohens kappa score: 0.556


minimum:
GB tn, fp: 2120, 30
GB fn, tp: 8, 33
GB f1 score: 0.510
GB cohens kappa score: 0.494


-----[ KNN ]-----
maximum:
KNN tn, fp: 2123, 736
KNN fn, tp: 12, 47
KNN f1 score: 0.530
KNN cohens kappa score: 0.515


average:
KNN tn, fp: 2070.64, 113.96
KNN fn, tp: 8.6, 43.4
KNN f1 score: 0.460
KNN cohens kappa score: 0.441


minimum:
KNN tn, fp: 1447, 62
KNN fn, tp: 5, 40
KNN f1 score: 0.103
KNN cohens kappa score: 0.063