ConvGeNBenchmarkResults/data_result/ProWRAS/imblearn_ozone_level.log

///////////////////////////////////////////
// Running ProWRAS 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: 459, 34
LR fn, tp: 2, 13
LR f1 score: 0.419
LR cohens kappa score: 0.392
LR average precision score: 0.394

-> test with 'GB'
GB tn, fp: 482, 11
GB fn, tp: 11, 4
GB f1 score: 0.267
GB cohens kappa score: 0.244

-> test with 'KNN'
KNN tn, fp: 424, 69
KNN fn, tp: 11, 4
KNN f1 score: 0.091
KNN cohens kappa score: 0.044


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 452, 41
LR fn, tp: 5, 10
LR f1 score: 0.303
LR cohens kappa score: 0.270
LR average precision score: 0.237

-> test with 'GB'
GB tn, fp: 490, 3
GB fn, tp: 11, 4
GB f1 score: 0.364
GB cohens kappa score: 0.351

-> test with 'KNN'
KNN tn, fp: 426, 67
KNN fn, tp: 10, 5
KNN f1 score: 0.115
KNN cohens kappa score: 0.069


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 451, 42
LR fn, tp: 5, 10
LR f1 score: 0.299
LR cohens kappa score: 0.265
LR average precision score: 0.168

-> test with 'GB'
GB tn, fp: 485, 8
GB fn, tp: 11, 4
GB f1 score: 0.296
GB cohens kappa score: 0.277

-> test with 'KNN'
KNN tn, fp: 421, 72
KNN fn, tp: 9, 6
KNN f1 score: 0.129
KNN cohens kappa score: 0.084


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 444, 49
LR fn, tp: 7, 8
LR f1 score: 0.222
LR cohens kappa score: 0.184
LR average precision score: 0.220

-> test with 'GB'
GB tn, fp: 487, 6
GB fn, tp: 12, 3
GB f1 score: 0.250
GB cohens kappa score: 0.233

-> test with 'KNN'
KNN tn, fp: 449, 44
KNN fn, tp: 8, 7
KNN f1 score: 0.212
KNN cohens kappa score: 0.174


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 443, 48
LR fn, tp: 3, 10
LR f1 score: 0.282
LR cohens kappa score: 0.250
LR average precision score: 0.144

-> test with 'GB'
GB tn, fp: 484, 7
GB fn, tp: 10, 3
GB f1 score: 0.261
GB cohens kappa score: 0.244

-> 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 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: 453, 40
LR fn, tp: 8, 7
LR f1 score: 0.226
LR cohens kappa score: 0.190
LR average precision score: 0.203

-> test with 'GB'
GB tn, fp: 484, 9
GB fn, tp: 12, 3
GB f1 score: 0.222
GB cohens kappa score: 0.201

-> test with 'KNN'
KNN tn, fp: 428, 65
KNN fn, tp: 11, 4
KNN f1 score: 0.095
KNN cohens kappa score: 0.049


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 461, 32
LR fn, tp: 5, 10
LR f1 score: 0.351
LR cohens kappa score: 0.321
LR average precision score: 0.180

-> test with 'GB'
GB tn, fp: 485, 8
GB fn, tp: 13, 2
GB f1 score: 0.160
GB cohens kappa score: 0.140

-> test with 'KNN'
KNN tn, fp: 428, 65
KNN fn, tp: 6, 9
KNN f1 score: 0.202
KNN cohens kappa score: 0.161


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 454, 39
LR fn, tp: 2, 13
LR f1 score: 0.388
LR cohens kappa score: 0.359
LR average precision score: 0.404

-> test with 'GB'
GB tn, fp: 487, 6
GB fn, tp: 11, 4
GB f1 score: 0.320
GB cohens kappa score: 0.304

-> test with 'KNN'
KNN tn, fp: 427, 66
KNN fn, tp: 10, 5
KNN f1 score: 0.116
KNN cohens kappa score: 0.071


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 458, 35
LR fn, tp: 5, 10
LR f1 score: 0.333
LR cohens kappa score: 0.302
LR average precision score: 0.165

-> test with 'GB'
GB tn, fp: 485, 8
GB fn, tp: 12, 3
GB f1 score: 0.231
GB cohens kappa score: 0.211

-> test with 'KNN'
KNN tn, fp: 441, 52
KNN fn, tp: 9, 6
KNN f1 score: 0.164
KNN cohens kappa score: 0.123


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 442, 49
LR fn, tp: 5, 8
LR f1 score: 0.229
LR cohens kappa score: 0.195
LR average precision score: 0.195

-> test with 'GB'
GB tn, fp: 488, 3
GB fn, tp: 11, 2
GB f1 score: 0.222
GB cohens kappa score: 0.211

-> test with 'KNN'
KNN tn, fp: 413, 78
KNN fn, tp: 7, 6
KNN f1 score: 0.124
KNN cohens kappa score: 0.083


====== 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: 450, 43
LR fn, tp: 4, 11
LR f1 score: 0.319
LR cohens kappa score: 0.286
LR average precision score: 0.236

-> test with 'GB'
GB tn, fp: 485, 8
GB fn, tp: 14, 1
GB f1 score: 0.083
GB cohens kappa score: 0.063

-> test with 'KNN'
KNN tn, fp: 445, 48
KNN fn, tp: 11, 4
KNN f1 score: 0.119
KNN cohens kappa score: 0.077


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 448, 45
LR fn, tp: 5, 10
LR f1 score: 0.286
LR cohens kappa score: 0.251
LR average precision score: 0.168

-> test with 'GB'
GB tn, fp: 482, 11
GB fn, tp: 10, 5
GB f1 score: 0.323
GB cohens kappa score: 0.301

-> test with 'KNN'
KNN tn, fp: 443, 50
KNN fn, tp: 11, 4
KNN f1 score: 0.116
KNN cohens kappa score: 0.073


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 453, 40
LR fn, tp: 5, 10
LR f1 score: 0.308
LR cohens kappa score: 0.275
LR average precision score: 0.251

-> test with 'GB'
GB tn, fp: 485, 8
GB fn, tp: 12, 3
GB f1 score: 0.231
GB cohens kappa score: 0.211

-> test with 'KNN'
KNN tn, fp: 433, 60
KNN fn, tp: 10, 5
KNN f1 score: 0.125
KNN cohens kappa score: 0.081


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 463, 30
LR fn, tp: 7, 8
LR f1 score: 0.302
LR cohens kappa score: 0.271
LR average precision score: 0.183

-> test with 'GB'
GB tn, fp: 490, 3
GB fn, tp: 12, 3
GB f1 score: 0.286
GB cohens kappa score: 0.273

-> test with 'KNN'
KNN tn, fp: 425, 68
KNN fn, tp: 9, 6
KNN f1 score: 0.135
KNN cohens kappa score: 0.090


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 443, 48
LR fn, tp: 4, 9
LR f1 score: 0.257
LR cohens kappa score: 0.225
LR average precision score: 0.347

-> test with 'GB'
GB tn, fp: 485, 6
GB fn, tp: 8, 5
GB f1 score: 0.417
GB cohens kappa score: 0.403

-> test with 'KNN'
KNN tn, fp: 409, 82
KNN fn, tp: 7, 6
KNN f1 score: 0.119
KNN cohens kappa score: 0.077


====== 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: 449, 44
LR fn, tp: 4, 11
LR f1 score: 0.314
LR cohens kappa score: 0.281
LR average precision score: 0.273

-> test with 'GB'
GB tn, fp: 485, 8
GB fn, tp: 12, 3
GB f1 score: 0.231
GB cohens kappa score: 0.211

-> test with 'KNN'
KNN tn, fp: 426, 67
KNN fn, tp: 12, 3
KNN f1 score: 0.071
KNN cohens kappa score: 0.023


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 457, 36
LR fn, tp: 4, 11
LR f1 score: 0.355
LR cohens kappa score: 0.325
LR average precision score: 0.239

-> test with 'GB'
GB tn, fp: 487, 6
GB fn, tp: 12, 3
GB f1 score: 0.250
GB cohens kappa score: 0.233

-> 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 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 461, 32
LR fn, tp: 7, 8
LR f1 score: 0.291
LR cohens kappa score: 0.259
LR average precision score: 0.201

-> test with 'GB'
GB tn, fp: 491, 2
GB fn, tp: 10, 5
GB f1 score: 0.455
GB cohens kappa score: 0.444

-> test with 'KNN'
KNN tn, fp: 428, 65
KNN fn, tp: 11, 4
KNN f1 score: 0.095
KNN cohens kappa score: 0.049


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 447, 46
LR fn, tp: 3, 12
LR f1 score: 0.329
LR cohens kappa score: 0.296
LR average precision score: 0.318

-> test with 'GB'
GB tn, fp: 489, 4
GB fn, tp: 11, 4
GB f1 score: 0.348
GB cohens kappa score: 0.334

-> test with 'KNN'
KNN tn, fp: 424, 69
KNN fn, tp: 10, 5
KNN f1 score: 0.112
KNN cohens kappa score: 0.067


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 456, 35
LR fn, tp: 4, 9
LR f1 score: 0.316
LR cohens kappa score: 0.287
LR average precision score: 0.203

-> test with 'GB'
GB tn, fp: 484, 7
GB fn, tp: 10, 3
GB f1 score: 0.261
GB cohens kappa score: 0.244

-> test with 'KNN'
KNN tn, fp: 421, 70
KNN fn, tp: 7, 6
KNN f1 score: 0.135
KNN cohens kappa score: 0.095


====== 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: 461, 32
LR fn, tp: 4, 11
LR f1 score: 0.379
LR cohens kappa score: 0.351
LR average precision score: 0.263

-> test with 'GB'
GB tn, fp: 486, 7
GB fn, tp: 6, 9
GB f1 score: 0.581
GB cohens kappa score: 0.567

-> test with 'KNN'
KNN tn, fp: 419, 74
KNN fn, tp: 10, 5
KNN f1 score: 0.106
KNN cohens kappa score: 0.060


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 443, 50
LR fn, tp: 5, 10
LR f1 score: 0.267
LR cohens kappa score: 0.230
LR average precision score: 0.206

-> test with 'GB'
GB tn, fp: 483, 10
GB fn, tp: 12, 3
GB f1 score: 0.214
GB cohens kappa score: 0.192

-> 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 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 470, 23
LR fn, tp: 8, 7
LR f1 score: 0.311
LR cohens kappa score: 0.283
LR average precision score: 0.179

-> test with 'GB'
GB tn, fp: 487, 6
GB fn, tp: 13, 2
GB f1 score: 0.174
GB cohens kappa score: 0.157

-> test with 'KNN'
KNN tn, fp: 419, 74
KNN fn, tp: 7, 8
KNN f1 score: 0.165
KNN cohens kappa score: 0.121


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 444, 49
LR fn, tp: 3, 12
LR f1 score: 0.316
LR cohens kappa score: 0.282
LR average precision score: 0.226

-> test with 'GB'
GB tn, fp: 487, 6
GB fn, tp: 9, 6
GB f1 score: 0.444
GB cohens kappa score: 0.429

-> test with 'KNN'
KNN tn, fp: 438, 55
KNN fn, tp: 9, 6
KNN f1 score: 0.158
KNN cohens kappa score: 0.116


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 442, 49
LR fn, tp: 3, 10
LR f1 score: 0.278
LR cohens kappa score: 0.246
LR average precision score: 0.230

-> test with 'GB'
GB tn, fp: 484, 7
GB fn, tp: 9, 4
GB f1 score: 0.333
GB cohens kappa score: 0.317

-> test with 'KNN'
KNN tn, fp: 423, 68
KNN fn, tp: 10, 3
KNN f1 score: 0.071
KNN cohens kappa score: 0.029

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 470, 50
LR fn, tp: 8, 13
LR f1 score: 0.419
LR cohens kappa score: 0.392
LR average precision score: 0.404


average:
LR tn, fp: 452.16, 40.44
LR fn, tp: 4.68, 9.92
LR f1 score: 0.307
LR cohens kappa score: 0.275
LR average precision score: 0.233


minimum:
LR tn, fp: 442, 23
LR fn, tp: 2, 7
LR f1 score: 0.222
LR cohens kappa score: 0.184
LR average precision score: 0.144


-----[ GB ]-----
maximum:
GB tn, fp: 491, 11
GB fn, tp: 14, 9
GB f1 score: 0.581
GB cohens kappa score: 0.567


average:
GB tn, fp: 485.88, 6.72
GB fn, tp: 10.96, 3.64
GB f1 score: 0.289
GB cohens kappa score: 0.272


minimum:
GB tn, fp: 482, 2
GB fn, tp: 6, 1
GB f1 score: 0.083
GB cohens kappa score: 0.063


-----[ KNN ]-----
maximum:
KNN tn, fp: 449, 82
KNN fn, tp: 12, 9
KNN f1 score: 0.212
KNN cohens kappa score: 0.174


average:
KNN tn, fp: 427.88, 64.72
KNN fn, tp: 9.36, 5.24
KNN f1 score: 0.125
KNN cohens kappa score: 0.081


minimum:
KNN tn, fp: 409, 44
KNN fn, tp: 6, 3
KNN f1 score: 0.071
KNN cohens kappa score: 0.023