ConvGeNBenchmarkResults/data_result/convGAN-majority-5/imblearn_ozone_level.log

///////////////////////////////////////////
// Running convGAN-majority-5 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 GAN.predict
GAN tn, fp: 475, 18
GAN fn, tp: 8, 7
GAN f1 score: 0.350
GAN cohens kappa score: 0.325

-> test with 'LR'
LR tn, fp: 422, 71
LR fn, tp: 2, 13
LR f1 score: 0.263
LR cohens kappa score: 0.224
LR average precision score: 0.331

-> 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: 405, 88
KNN fn, tp: 9, 6
KNN f1 score: 0.110
KNN cohens kappa score: 0.062


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 247, 246
GAN fn, tp: 2, 13
GAN f1 score: 0.095
GAN cohens kappa score: 0.041

-> test with 'LR'
LR tn, fp: 433, 60
LR fn, tp: 5, 10
LR f1 score: 0.235
LR cohens kappa score: 0.196
LR average precision score: 0.219

-> test with 'GB'
GB tn, fp: 483, 10
GB fn, tp: 8, 7
GB f1 score: 0.437
GB cohens kappa score: 0.419

-> test with 'KNN'
KNN tn, fp: 407, 86
KNN fn, tp: 7, 8
KNN f1 score: 0.147
KNN cohens kappa score: 0.101


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 412, 81
GAN fn, tp: 3, 12
GAN f1 score: 0.222
GAN cohens kappa score: 0.181

-> test with 'LR'
LR tn, fp: 433, 60
LR fn, tp: 4, 11
LR f1 score: 0.256
LR cohens kappa score: 0.218
LR average precision score: 0.123

-> test with 'GB'
GB tn, fp: 476, 17
GB fn, tp: 8, 7
GB f1 score: 0.359
GB cohens kappa score: 0.335

-> 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 GAN.predict
GAN tn, fp: 338, 155
GAN fn, tp: 3, 12
GAN f1 score: 0.132
GAN cohens kappa score: 0.082

-> test with 'LR'
LR tn, fp: 432, 61
LR fn, tp: 5, 10
LR f1 score: 0.233
LR cohens kappa score: 0.193
LR average precision score: 0.208

-> 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: 406, 87
KNN fn, tp: 9, 6
KNN f1 score: 0.111
KNN cohens kappa score: 0.063


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 466, 25
GAN fn, tp: 8, 5
GAN f1 score: 0.233
GAN cohens kappa score: 0.204

-> test with 'LR'
LR tn, fp: 431, 60
LR fn, tp: 3, 10
LR f1 score: 0.241
LR cohens kappa score: 0.206
LR average precision score: 0.134

-> test with 'GB'
GB tn, fp: 479, 12
GB fn, tp: 10, 3
GB f1 score: 0.214
GB cohens kappa score: 0.192

-> test with 'KNN'
KNN tn, fp: 382, 109
KNN fn, tp: 8, 5
KNN f1 score: 0.079
KNN cohens kappa score: 0.034


====== 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 GAN.predict
GAN tn, fp: 433, 60
GAN fn, tp: 6, 9
GAN f1 score: 0.214
GAN cohens kappa score: 0.174

-> test with 'LR'
LR tn, fp: 422, 71
LR fn, tp: 5, 10
LR f1 score: 0.208
LR cohens kappa score: 0.167
LR average precision score: 0.273

-> test with 'GB'
GB tn, fp: 482, 11
GB fn, tp: 12, 3
GB f1 score: 0.207
GB cohens kappa score: 0.184

-> test with 'KNN'
KNN tn, fp: 402, 91
KNN fn, tp: 9, 6
KNN f1 score: 0.107
KNN cohens kappa score: 0.059


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 416, 77
GAN fn, tp: 4, 11
GAN f1 score: 0.214
GAN cohens kappa score: 0.172

-> 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: 0.237

-> 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: 366, 127
KNN fn, tp: 4, 11
KNN f1 score: 0.144
KNN cohens kappa score: 0.096


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 349, 144
GAN fn, tp: 2, 13
GAN f1 score: 0.151
GAN cohens kappa score: 0.103

-> test with 'LR'
LR tn, fp: 423, 70
LR fn, tp: 1, 14
LR f1 score: 0.283
LR cohens kappa score: 0.245
LR average precision score: 0.423

-> test with 'GB'
GB tn, fp: 484, 9
GB fn, tp: 7, 8
GB f1 score: 0.500
GB cohens kappa score: 0.484

-> test with 'KNN'
KNN tn, fp: 435, 58
KNN fn, tp: 13, 2
KNN f1 score: 0.053
KNN cohens kappa score: 0.006


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 213, 280
GAN fn, tp: 2, 13
GAN f1 score: 0.084
GAN cohens kappa score: 0.030

-> test with 'LR'
LR tn, fp: 433, 60
LR fn, tp: 5, 10
LR f1 score: 0.235
LR cohens kappa score: 0.196
LR average precision score: 0.174

-> test with 'GB'
GB tn, fp: 475, 18
GB fn, tp: 9, 6
GB f1 score: 0.308
GB cohens kappa score: 0.282

-> test with 'KNN'
KNN tn, fp: 414, 79
KNN fn, tp: 10, 5
KNN f1 score: 0.101
KNN cohens kappa score: 0.054


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 233, 258
GAN fn, tp: 0, 13
GAN f1 score: 0.092
GAN cohens kappa score: 0.045

-> test with 'LR'
LR tn, fp: 416, 75
LR fn, tp: 3, 10
LR f1 score: 0.204
LR cohens kappa score: 0.167
LR average precision score: 0.205

-> test with 'GB'
GB tn, fp: 479, 12
GB fn, tp: 7, 6
GB f1 score: 0.387
GB cohens kappa score: 0.368

-> 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 GAN.predict
GAN tn, fp: 324, 169
GAN fn, tp: 3, 12
GAN f1 score: 0.122
GAN cohens kappa score: 0.072

-> 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: 0.336

-> test with 'GB'
GB tn, fp: 472, 21
GB fn, tp: 9, 6
GB f1 score: 0.286
GB cohens kappa score: 0.258

-> test with 'KNN'
KNN tn, fp: 418, 75
KNN fn, tp: 12, 3
KNN f1 score: 0.065
KNN cohens kappa score: 0.016


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 369, 124
GAN fn, tp: 3, 12
GAN f1 score: 0.159
GAN cohens kappa score: 0.112

-> test with 'LR'
LR tn, fp: 428, 65
LR fn, tp: 3, 12
LR f1 score: 0.261
LR cohens kappa score: 0.222
LR average precision score: 0.131

-> test with 'GB'
GB tn, fp: 477, 16
GB fn, tp: 7, 8
GB f1 score: 0.410
GB cohens kappa score: 0.388

-> test with 'KNN'
KNN tn, fp: 413, 80
KNN fn, tp: 10, 5
KNN f1 score: 0.100
KNN cohens kappa score: 0.052


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 475, 18
GAN fn, tp: 9, 6
GAN f1 score: 0.308
GAN cohens kappa score: 0.282

-> test with 'LR'
LR tn, fp: 446, 47
LR fn, tp: 4, 11
LR f1 score: 0.301
LR cohens kappa score: 0.267
LR average precision score: 0.181

-> test with 'GB'
GB tn, fp: 481, 12
GB fn, tp: 8, 7
GB f1 score: 0.412
GB cohens kappa score: 0.392

-> test with 'KNN'
KNN tn, fp: 396, 97
KNN fn, tp: 9, 6
KNN f1 score: 0.102
KNN cohens kappa score: 0.053


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 287, 206
GAN fn, tp: 2, 13
GAN f1 score: 0.111
GAN cohens kappa score: 0.059

-> test with 'LR'
LR tn, fp: 428, 65
LR fn, tp: 5, 10
LR f1 score: 0.222
LR cohens kappa score: 0.182
LR average precision score: 0.161

-> 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: 400, 93
KNN fn, tp: 9, 6
KNN f1 score: 0.105
KNN cohens kappa score: 0.057


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 474, 17
GAN fn, tp: 8, 5
GAN f1 score: 0.286
GAN cohens kappa score: 0.262

-> test with 'LR'
LR tn, fp: 421, 70
LR fn, tp: 3, 10
LR f1 score: 0.215
LR cohens kappa score: 0.179
LR average precision score: 0.362

-> test with 'GB'
GB tn, fp: 478, 13
GB fn, tp: 8, 5
GB f1 score: 0.323
GB cohens kappa score: 0.302

-> 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 GAN.predict
GAN tn, fp: 208, 285
GAN fn, tp: 1, 14
GAN f1 score: 0.089
GAN cohens kappa score: 0.035

-> test with 'LR'
LR tn, fp: 423, 70
LR fn, tp: 4, 11
LR f1 score: 0.229
LR cohens kappa score: 0.189
LR average precision score: 0.276

-> test with 'GB'
GB tn, fp: 476, 17
GB fn, tp: 9, 6
GB f1 score: 0.316
GB cohens kappa score: 0.290

-> test with 'KNN'
KNN tn, fp: 417, 76
KNN fn, tp: 11, 4
KNN f1 score: 0.084
KNN cohens kappa score: 0.036


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 343, 150
GAN fn, tp: 2, 13
GAN f1 score: 0.146
GAN cohens kappa score: 0.097

-> test with 'LR'
LR tn, fp: 435, 58
LR fn, tp: 3, 12
LR f1 score: 0.282
LR cohens kappa score: 0.246
LR average precision score: 0.237

-> test with 'GB'
GB tn, fp: 485, 8
GB fn, tp: 10, 5
GB f1 score: 0.357
GB cohens kappa score: 0.339

-> 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 GAN.predict
GAN tn, fp: 271, 222
GAN fn, tp: 2, 13
GAN f1 score: 0.104
GAN cohens kappa score: 0.051

-> test with 'LR'
LR tn, fp: 435, 58
LR fn, tp: 3, 12
LR f1 score: 0.282
LR cohens kappa score: 0.246
LR average precision score: 0.197

-> test with 'GB'
GB tn, fp: 481, 12
GB fn, tp: 8, 7
GB f1 score: 0.412
GB cohens kappa score: 0.392

-> 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 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 333, 160
GAN fn, tp: 4, 11
GAN f1 score: 0.118
GAN cohens kappa score: 0.068

-> test with 'LR'
LR tn, fp: 426, 67
LR fn, tp: 3, 12
LR f1 score: 0.255
LR cohens kappa score: 0.216
LR average precision score: 0.280

-> test with 'GB'
GB tn, fp: 479, 14
GB fn, tp: 7, 8
GB f1 score: 0.432
GB cohens kappa score: 0.412

-> test with 'KNN'
KNN tn, fp: 383, 110
KNN fn, tp: 9, 6
KNN f1 score: 0.092
KNN cohens kappa score: 0.041


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 418, 73
GAN fn, tp: 5, 8
GAN f1 score: 0.170
GAN cohens kappa score: 0.132

-> test with 'LR'
LR tn, fp: 423, 68
LR fn, tp: 4, 9
LR f1 score: 0.200
LR cohens kappa score: 0.163
LR average precision score: 0.183

-> test with 'GB'
GB tn, fp: 471, 20
GB fn, tp: 7, 6
GB f1 score: 0.308
GB cohens kappa score: 0.283

-> test with 'KNN'
KNN tn, fp: 385, 106
KNN fn, tp: 6, 7
KNN f1 score: 0.111
KNN cohens kappa score: 0.068


====== 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 GAN.predict
GAN tn, fp: 310, 183
GAN fn, tp: 0, 15
GAN f1 score: 0.141
GAN cohens kappa score: 0.091

-> test with 'LR'
LR tn, fp: 438, 55
LR fn, tp: 2, 13
LR f1 score: 0.313
LR cohens kappa score: 0.278
LR average precision score: 0.269

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 5, 10
GB f1 score: 0.526
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 398, 95
KNN fn, tp: 10, 5
KNN f1 score: 0.087
KNN cohens kappa score: 0.038


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 459, 34
GAN fn, tp: 7, 8
GAN f1 score: 0.281
GAN cohens kappa score: 0.248

-> test with 'LR'
LR tn, fp: 430, 63
LR fn, tp: 3, 12
LR f1 score: 0.267
LR cohens kappa score: 0.229
LR average precision score: 0.158

-> test with 'GB'
GB tn, fp: 479, 14
GB fn, tp: 10, 5
GB f1 score: 0.294
GB cohens kappa score: 0.270

-> test with 'KNN'
KNN tn, fp: 401, 92
KNN fn, tp: 12, 3
KNN f1 score: 0.055
KNN cohens kappa score: 0.004


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 408, 85
GAN fn, tp: 5, 10
GAN f1 score: 0.182
GAN cohens kappa score: 0.138

-> test with 'LR'
LR tn, fp: 455, 38
LR fn, tp: 7, 8
LR f1 score: 0.262
LR cohens kappa score: 0.228
LR average precision score: 0.179

-> 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: 374, 119
KNN fn, tp: 6, 9
KNN f1 score: 0.126
KNN cohens kappa score: 0.077


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 490, 3
GAN fn, tp: 14, 1
GAN f1 score: 0.105
GAN cohens kappa score: 0.094

-> test with 'LR'
LR tn, fp: 422, 71
LR fn, tp: 2, 13
LR f1 score: 0.263
LR cohens kappa score: 0.224
LR average precision score: 0.232

-> 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: 432, 61
KNN fn, tp: 10, 5
KNN f1 score: 0.123
KNN cohens kappa score: 0.079


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 245, 246
GAN fn, tp: 1, 12
GAN f1 score: 0.089
GAN cohens kappa score: 0.041

-> test with 'LR'
LR tn, fp: 419, 72
LR fn, tp: 2, 11
LR f1 score: 0.229
LR cohens kappa score: 0.193
LR average precision score: 0.270

-> 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: 369, 122
KNN fn, tp: 8, 5
KNN f1 score: 0.071
KNN cohens kappa score: 0.026

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 455, 75
LR fn, tp: 7, 14
LR f1 score: 0.313
LR cohens kappa score: 0.278
LR average precision score: 0.423


average:
LR tn, fp: 429.88, 62.72
LR fn, tp: 3.56, 11.04
LR f1 score: 0.251
LR cohens kappa score: 0.214
LR average precision score: 0.231


minimum:
LR tn, fp: 416, 38
LR fn, tp: 1, 8
LR f1 score: 0.200
LR cohens kappa score: 0.163
LR average precision score: 0.123


-----[ GB ]-----
maximum:
GB tn, fp: 485, 21
GB fn, tp: 12, 10
GB f1 score: 0.526
GB cohens kappa score: 0.509


average:
GB tn, fp: 479.0, 13.6
GB fn, tp: 8.36, 6.24
GB f1 score: 0.360
GB cohens kappa score: 0.338


minimum:
GB tn, fp: 471, 8
GB fn, tp: 5, 3
GB f1 score: 0.207
GB cohens kappa score: 0.184


-----[ KNN ]-----
maximum:
KNN tn, fp: 444, 127
KNN fn, tp: 13, 11
KNN f1 score: 0.152
KNN cohens kappa score: 0.114


average:
KNN tn, fp: 406.12, 86.48
KNN fn, tp: 9.2, 5.4
KNN f1 score: 0.101
KNN cohens kappa score: 0.054


minimum:
KNN tn, fp: 366, 49
KNN fn, tp: 4, 2
KNN f1 score: 0.053
KNN cohens kappa score: 0.004


-----[ GAN ]-----
maximum:
GAN tn, fp: 490, 285
GAN fn, tp: 14, 15
GAN f1 score: 0.350
GAN cohens kappa score: 0.325


average:
GAN tn, fp: 359.84, 132.76
GAN fn, tp: 4.16, 10.44
GAN f1 score: 0.168
GAN cohens kappa score: 0.125


minimum:
GAN tn, fp: 208, 3
GAN fn, tp: 0, 1
GAN f1 score: 0.084
GAN cohens kappa score: 0.030