ConvGeNBenchmarkResults/data_result/Repeater/imblearn_ozone_level.log

///////////////////////////////////////////
// Running Repeater on imblearn_ozone_level
///////////////////////////////////////////

Load 'data_input/imblearn_ozone_level'
from imblearn
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 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 424, 69
LR fn, tp: 2, 13
LR f1 score: 0.268
LR cohens kappa score: 0.230
LR average precision score: 0.348

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 8, 7
GB f1 score: 0.400
GB cohens kappa score: 0.379

-> test with 'KNN'
KNN tn, fp: 434, 59
KNN fn, tp: 11, 4
KNN f1 score: 0.103
KNN cohens kappa score: 0.058


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 431, 62
LR fn, tp: 4, 11
LR f1 score: 0.250
LR cohens kappa score: 0.211
LR average precision score: 0.211

-> test with 'GB'
GB tn, fp: 487, 6
GB fn, tp: 7, 8
GB f1 score: 0.552
GB cohens kappa score: 0.539

-> test with 'KNN'
KNN tn, fp: 446, 47
KNN fn, tp: 13, 2
KNN f1 score: 0.062
KNN cohens kappa score: 0.018


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 432, 61
LR fn, tp: 4, 11
LR f1 score: 0.253
LR cohens kappa score: 0.214
LR average precision score: 0.124

-> test with 'GB'
GB tn, fp: 477, 16
GB fn, tp: 9, 6
GB f1 score: 0.324
GB cohens kappa score: 0.300

-> test with 'KNN'
KNN tn, fp: 426, 67
KNN fn, tp: 11, 4
KNN f1 score: 0.093
KNN cohens kappa score: 0.047


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 424, 69
LR fn, tp: 5, 10
LR f1 score: 0.213
LR cohens kappa score: 0.172
LR average precision score: 0.213

-> test with 'GB'
GB tn, fp: 477, 16
GB fn, tp: 8, 7
GB f1 score: 0.368
GB cohens kappa score: 0.345

-> test with 'KNN'
KNN tn, fp: 443, 50
KNN fn, tp: 14, 1
KNN f1 score: 0.030
KNN cohens kappa score: -0.016


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 418, 73
LR fn, tp: 3, 10
LR f1 score: 0.208
LR cohens kappa score: 0.171
LR average precision score: 0.188

-> test with 'GB'
GB tn, fp: 480, 11
GB fn, tp: 7, 6
GB f1 score: 0.400
GB cohens kappa score: 0.382

-> test with 'KNN'
KNN tn, fp: 430, 61
KNN fn, tp: 9, 4
KNN f1 score: 0.103
KNN cohens kappa score: 0.062


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 421, 72
LR fn, tp: 5, 10
LR f1 score: 0.206
LR cohens kappa score: 0.164
LR average precision score: 0.257

-> test with 'GB'
GB tn, fp: 481, 12
GB fn, tp: 12, 3
GB f1 score: 0.200
GB cohens kappa score: 0.176

-> test with 'KNN'
KNN tn, fp: 431, 62
KNN fn, tp: 12, 3
KNN f1 score: 0.075
KNN cohens kappa score: 0.028


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 440, 53
LR fn, tp: 5, 10
LR f1 score: 0.256
LR cohens kappa score: 0.219
LR average precision score: 0.229

-> test with 'GB'
GB tn, fp: 479, 14
GB fn, tp: 6, 9
GB f1 score: 0.474
GB cohens kappa score: 0.454

-> test with 'KNN'
KNN tn, fp: 436, 57
KNN fn, tp: 9, 6
KNN f1 score: 0.154
KNN cohens kappa score: 0.111


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 427, 66
LR fn, tp: 1, 14
LR f1 score: 0.295
LR cohens kappa score: 0.258
LR average precision score: 0.350

-> test with 'GB'
GB tn, fp: 484, 9
GB fn, tp: 9, 6
GB f1 score: 0.400
GB cohens kappa score: 0.382

-> test with 'KNN'
KNN tn, fp: 430, 63
KNN fn, tp: 13, 2
KNN f1 score: 0.050
KNN cohens kappa score: 0.002


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 424, 69
LR fn, tp: 5, 10
LR f1 score: 0.213
LR cohens kappa score: 0.172
LR average precision score: 0.151

-> test with 'GB'
GB tn, fp: 481, 12
GB fn, tp: 11, 4
GB f1 score: 0.258
GB cohens kappa score: 0.235

-> test with 'KNN'
KNN tn, fp: 448, 45
KNN fn, tp: 13, 2
KNN f1 score: 0.065
KNN cohens kappa score: 0.021


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 415, 76
LR fn, tp: 3, 10
LR f1 score: 0.202
LR cohens kappa score: 0.165
LR average precision score: 0.203

-> test with 'GB'
GB tn, fp: 481, 10
GB fn, tp: 7, 6
GB f1 score: 0.414
GB cohens kappa score: 0.397

-> test with 'KNN'
KNN tn, fp: 431, 60
KNN fn, tp: 7, 6
KNN f1 score: 0.152
KNN cohens kappa score: 0.114


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 427, 66
LR fn, tp: 3, 12
LR f1 score: 0.258
LR cohens kappa score: 0.219
LR average precision score: 0.311

-> test with 'GB'
GB tn, fp: 478, 15
GB fn, tp: 7, 8
GB f1 score: 0.421
GB cohens kappa score: 0.400

-> test with 'KNN'
KNN tn, fp: 439, 54
KNN fn, tp: 13, 2
KNN f1 score: 0.056
KNN cohens kappa score: 0.010


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 424, 69
LR fn, tp: 4, 11
LR f1 score: 0.232
LR cohens kappa score: 0.191
LR average precision score: 0.138

-> test with 'GB'
GB tn, fp: 482, 11
GB fn, tp: 5, 10
GB f1 score: 0.556
GB cohens kappa score: 0.540

-> test with 'KNN'
KNN tn, fp: 439, 54
KNN fn, tp: 13, 2
KNN f1 score: 0.056
KNN cohens kappa score: 0.010


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 445, 48
LR fn, tp: 4, 11
LR f1 score: 0.297
LR cohens kappa score: 0.263
LR average precision score: 0.177

-> test with 'GB'
GB tn, fp: 484, 9
GB fn, tp: 9, 6
GB f1 score: 0.400
GB cohens kappa score: 0.382

-> test with 'KNN'
KNN tn, fp: 429, 64
KNN fn, tp: 12, 3
KNN f1 score: 0.073
KNN cohens kappa score: 0.026


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 427, 66
LR fn, tp: 5, 10
LR f1 score: 0.220
LR cohens kappa score: 0.179
LR average precision score: 0.171

-> test with 'GB'
GB tn, fp: 482, 11
GB fn, tp: 8, 7
GB f1 score: 0.424
GB cohens kappa score: 0.405

-> test with 'KNN'
KNN tn, fp: 436, 57
KNN fn, tp: 13, 2
KNN f1 score: 0.054
KNN cohens kappa score: 0.007


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 418, 73
LR fn, tp: 3, 10
LR f1 score: 0.208
LR cohens kappa score: 0.171
LR average precision score: 0.345

-> test with 'GB'
GB tn, fp: 476, 15
GB fn, tp: 8, 5
GB f1 score: 0.303
GB cohens kappa score: 0.281

-> test with 'KNN'
KNN tn, fp: 425, 66
KNN fn, tp: 9, 4
KNN f1 score: 0.096
KNN cohens kappa score: 0.055


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 420, 73
LR fn, tp: 3, 12
LR f1 score: 0.240
LR cohens kappa score: 0.200
LR average precision score: 0.276

-> test with 'GB'
GB tn, fp: 477, 16
GB fn, tp: 6, 9
GB f1 score: 0.450
GB cohens kappa score: 0.429

-> test with 'KNN'
KNN tn, fp: 419, 74
KNN fn, tp: 11, 4
KNN f1 score: 0.086
KNN cohens kappa score: 0.038


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 434, 59
LR fn, tp: 4, 11
LR f1 score: 0.259
LR cohens kappa score: 0.221
LR average precision score: 0.230

-> test with 'GB'
GB tn, fp: 483, 10
GB fn, tp: 9, 6
GB f1 score: 0.387
GB cohens kappa score: 0.368

-> test with 'KNN'
KNN tn, fp: 444, 49
KNN fn, tp: 11, 4
KNN f1 score: 0.118
KNN cohens kappa score: 0.075


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 436, 57
LR fn, tp: 3, 12
LR f1 score: 0.286
LR cohens kappa score: 0.249
LR average precision score: 0.211

-> test with 'GB'
GB tn, fp: 484, 9
GB fn, tp: 6, 9
GB f1 score: 0.545
GB cohens kappa score: 0.530

-> test with 'KNN'
KNN tn, fp: 449, 44
KNN fn, tp: 14, 1
KNN f1 score: 0.033
KNN cohens kappa score: -0.011


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 418, 75
LR fn, tp: 3, 12
LR f1 score: 0.235
LR cohens kappa score: 0.195
LR average precision score: 0.277

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 7, 8
GB f1 score: 0.444
GB cohens kappa score: 0.425

-> test with 'KNN'
KNN tn, fp: 425, 68
KNN fn, tp: 11, 4
KNN f1 score: 0.092
KNN cohens kappa score: 0.045


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 419, 72
LR fn, tp: 3, 10
LR f1 score: 0.211
LR cohens kappa score: 0.174
LR average precision score: 0.214

-> test with 'GB'
GB tn, fp: 480, 11
GB fn, tp: 8, 5
GB f1 score: 0.345
GB cohens kappa score: 0.326

-> test with 'KNN'
KNN tn, fp: 426, 65
KNN fn, tp: 12, 1
KNN f1 score: 0.025
KNN cohens kappa score: -0.019


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 430, 63
LR fn, tp: 2, 13
LR f1 score: 0.286
LR cohens kappa score: 0.249
LR average precision score: 0.276

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 6, 9
GB f1 score: 0.486
GB cohens kappa score: 0.468

-> test with 'KNN'
KNN tn, fp: 421, 72
KNN fn, tp: 12, 3
KNN f1 score: 0.067
KNN cohens kappa score: 0.018


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 418, 75
LR fn, tp: 3, 12
LR f1 score: 0.235
LR cohens kappa score: 0.195
LR average precision score: 0.167

-> test with 'GB'
GB tn, fp: 482, 11
GB fn, tp: 9, 6
GB f1 score: 0.375
GB cohens kappa score: 0.355

-> test with 'KNN'
KNN tn, fp: 429, 64
KNN fn, tp: 12, 3
KNN f1 score: 0.073
KNN cohens kappa score: 0.026


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 449, 44
LR fn, tp: 5, 10
LR f1 score: 0.290
LR cohens kappa score: 0.255
LR average precision score: 0.187

-> test with 'GB'
GB tn, fp: 483, 10
GB fn, tp: 10, 5
GB f1 score: 0.333
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 433, 60
KNN fn, tp: 11, 4
KNN f1 score: 0.101
KNN cohens kappa score: 0.056


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 425, 68
LR fn, tp: 3, 12
LR f1 score: 0.253
LR cohens kappa score: 0.214
LR average precision score: 0.233

-> test with 'GB'
GB tn, fp: 479, 14
GB fn, tp: 8, 7
GB f1 score: 0.389
GB cohens kappa score: 0.367

-> test with 'KNN'
KNN tn, fp: 447, 46
KNN fn, tp: 11, 4
KNN f1 score: 0.123
KNN cohens kappa score: 0.081


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 410, 81
LR fn, tp: 2, 11
LR f1 score: 0.210
LR cohens kappa score: 0.172
LR average precision score: 0.261

-> test with 'GB'
GB tn, fp: 482, 9
GB fn, tp: 8, 5
GB f1 score: 0.370
GB cohens kappa score: 0.353

-> test with 'KNN'
KNN tn, fp: 421, 70
KNN fn, tp: 10, 3
KNN f1 score: 0.070
KNN cohens kappa score: 0.027

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 449, 81
LR fn, tp: 5, 14
LR f1 score: 0.297
LR cohens kappa score: 0.263
LR average precision score: 0.350


average:
LR tn, fp: 426.24, 66.36
LR fn, tp: 3.48, 11.12
LR f1 score: 0.243
LR cohens kappa score: 0.205
LR average precision score: 0.230


minimum:
LR tn, fp: 410, 44
LR fn, tp: 1, 10
LR f1 score: 0.202
LR cohens kappa score: 0.164
LR average precision score: 0.124


-----[ GB ]-----
maximum:
GB tn, fp: 487, 16
GB fn, tp: 12, 10
GB f1 score: 0.556
GB cohens kappa score: 0.540


average:
GB tn, fp: 480.76, 11.84
GB fn, tp: 7.92, 6.68
GB f1 score: 0.401
GB cohens kappa score: 0.381


minimum:
GB tn, fp: 476, 6
GB fn, tp: 5, 3
GB f1 score: 0.200
GB cohens kappa score: 0.176


-----[ KNN ]-----
maximum:
KNN tn, fp: 449, 74
KNN fn, tp: 14, 6
KNN f1 score: 0.154
KNN cohens kappa score: 0.114


average:
KNN tn, fp: 433.48, 59.12
KNN fn, tp: 11.48, 3.12
KNN f1 score: 0.080
KNN cohens kappa score: 0.036


minimum:
KNN tn, fp: 419, 44
KNN fn, tp: 7, 1
KNN f1 score: 0.025
KNN cohens kappa score: -0.019