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

///////////////////////////////////////////
// Running convGAN-majority-full 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: 301, 192
GAN fn, tp: 1, 14
GAN f1 score: 0.127
GAN cohens kappa score: 0.076

-> test with 'LR'
LR tn, fp: 437, 56
LR fn, tp: 1, 14
LR f1 score: 0.329
LR cohens kappa score: 0.295
LR average precision score: 0.340

-> 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: 386, 107
KNN fn, tp: 10, 5
KNN f1 score: 0.079
KNN cohens kappa score: 0.028


------ 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: 395, 98
GAN fn, tp: 4, 11
GAN f1 score: 0.177
GAN cohens kappa score: 0.132

-> test with 'LR'
LR tn, fp: 435, 58
LR fn, tp: 4, 11
LR f1 score: 0.262
LR cohens kappa score: 0.224
LR average precision score: 0.228

-> test with 'GB'
GB tn, fp: 486, 7
GB fn, tp: 9, 6
GB f1 score: 0.429
GB cohens kappa score: 0.412

-> test with 'KNN'
KNN tn, fp: 423, 70
KNN fn, tp: 10, 5
KNN f1 score: 0.111
KNN cohens kappa score: 0.065


------ 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: 493, 0
GAN fn, tp: 15, 0
GAN f1 score: 0.000
GAN cohens kappa score: 0.000

-> test with 'LR'
LR tn, fp: 439, 54
LR fn, tp: 4, 11
LR f1 score: 0.275
LR cohens kappa score: 0.238
LR average precision score: 0.128

-> test with 'GB'
GB tn, fp: 472, 21
GB fn, tp: 10, 5
GB f1 score: 0.244
GB cohens kappa score: 0.214

-> test with 'KNN'
KNN tn, fp: 390, 103
KNN fn, tp: 7, 8
KNN f1 score: 0.127
KNN cohens kappa score: 0.079


------ 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: 380, 113
GAN fn, tp: 3, 12
GAN f1 score: 0.171
GAN cohens kappa score: 0.125

-> test with 'LR'
LR tn, fp: 436, 57
LR fn, tp: 6, 9
LR f1 score: 0.222
LR cohens kappa score: 0.183
LR average precision score: 0.226

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 9, 6
GB f1 score: 0.353
GB cohens kappa score: 0.331

-> test with 'KNN'
KNN tn, fp: 407, 86
KNN fn, tp: 10, 5
KNN f1 score: 0.094
KNN cohens kappa score: 0.046


------ 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: 302, 189
GAN fn, tp: 1, 12
GAN f1 score: 0.112
GAN cohens kappa score: 0.067

-> test with 'LR'
LR tn, fp: 437, 54
LR fn, tp: 3, 10
LR f1 score: 0.260
LR cohens kappa score: 0.227
LR average precision score: 0.171

-> test with 'GB'
GB tn, fp: 479, 12
GB fn, tp: 11, 2
GB f1 score: 0.148
GB cohens kappa score: 0.125

-> test with 'KNN'
KNN tn, fp: 395, 96
KNN fn, tp: 9, 4
KNN f1 score: 0.071
KNN cohens kappa score: 0.026


====== 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: 306, 187
GAN fn, tp: 2, 13
GAN f1 score: 0.121
GAN cohens kappa score: 0.070

-> test with 'LR'
LR tn, fp: 438, 55
LR fn, tp: 6, 9
LR f1 score: 0.228
LR cohens kappa score: 0.189
LR average precision score: 0.261

-> 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: 406, 87
KNN fn, tp: 8, 7
KNN f1 score: 0.128
KNN cohens kappa score: 0.082


------ 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: 278, 215
GAN fn, tp: 0, 15
GAN f1 score: 0.122
GAN cohens kappa score: 0.071

-> test with 'LR'
LR tn, fp: 443, 50
LR fn, tp: 6, 9
LR f1 score: 0.243
LR cohens kappa score: 0.206
LR average precision score: 0.196

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 9, 6
GB f1 score: 0.353
GB cohens kappa score: 0.331

-> test with 'KNN'
KNN tn, fp: 402, 91
KNN fn, tp: 7, 8
KNN f1 score: 0.140
KNN cohens kappa score: 0.094


------ 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: 339, 154
GAN fn, tp: 1, 14
GAN f1 score: 0.153
GAN cohens kappa score: 0.104

-> test with 'LR'
LR tn, fp: 437, 56
LR fn, tp: 1, 14
LR f1 score: 0.329
LR cohens kappa score: 0.295
LR average precision score: 0.468

-> 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: 390, 103
KNN fn, tp: 13, 2
KNN f1 score: 0.033
KNN cohens kappa score: -0.019


------ 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: 399, 94
GAN fn, tp: 6, 9
GAN f1 score: 0.153
GAN cohens kappa score: 0.106

-> 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: 0.165

-> test with 'GB'
GB tn, fp: 479, 14
GB fn, tp: 9, 6
GB f1 score: 0.343
GB cohens kappa score: 0.320

-> test with 'KNN'
KNN tn, fp: 415, 78
KNN fn, tp: 10, 5
KNN f1 score: 0.102
KNN cohens kappa score: 0.055


------ 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: 484, 7
GAN fn, tp: 10, 3
GAN f1 score: 0.261
GAN cohens kappa score: 0.244

-> test with 'LR'
LR tn, fp: 426, 65
LR fn, tp: 3, 10
LR f1 score: 0.227
LR cohens kappa score: 0.192
LR average precision score: 0.214

-> test with 'GB'
GB tn, fp: 481, 10
GB fn, tp: 9, 4
GB f1 score: 0.296
GB cohens kappa score: 0.277

-> test with 'KNN'
KNN tn, fp: 402, 89
KNN fn, tp: 7, 6
KNN f1 score: 0.111
KNN cohens kappa score: 0.069


====== 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: 458, 35
GAN fn, tp: 9, 6
GAN f1 score: 0.214
GAN cohens kappa score: 0.179

-> 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: 0.319

-> 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: 411, 82
KNN fn, tp: 12, 3
KNN f1 score: 0.060
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 GAN.predict
GAN tn, fp: 449, 44
GAN fn, tp: 6, 9
GAN f1 score: 0.265
GAN cohens kappa score: 0.229

-> test with 'LR'
LR tn, fp: 437, 56
LR fn, tp: 5, 10
LR f1 score: 0.247
LR cohens kappa score: 0.209
LR average precision score: 0.135

-> test with 'GB'
GB tn, fp: 485, 8
GB fn, tp: 9, 6
GB f1 score: 0.414
GB cohens kappa score: 0.397

-> test with 'KNN'
KNN tn, fp: 411, 82
KNN fn, tp: 10, 5
KNN f1 score: 0.098
KNN cohens kappa score: 0.050


------ 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: 463, 30
GAN fn, tp: 8, 7
GAN f1 score: 0.269
GAN cohens kappa score: 0.237

-> 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: 0.179

-> test with 'GB'
GB tn, fp: 476, 17
GB fn, tp: 7, 8
GB f1 score: 0.400
GB cohens kappa score: 0.377

-> test with 'KNN'
KNN tn, fp: 393, 100
KNN fn, tp: 6, 9
KNN f1 score: 0.145
KNN cohens kappa score: 0.098


------ 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: 328, 165
GAN fn, tp: 1, 14
GAN f1 score: 0.144
GAN cohens kappa score: 0.095

-> test with 'LR'
LR tn, fp: 441, 52
LR fn, tp: 5, 10
LR f1 score: 0.260
LR cohens kappa score: 0.223
LR average precision score: 0.187

-> 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: 11, 4
KNN f1 score: 0.076
KNN cohens kappa score: 0.027


------ 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: 460, 31
GAN fn, tp: 7, 6
GAN f1 score: 0.240
GAN cohens kappa score: 0.210

-> test with 'LR'
LR tn, fp: 436, 55
LR fn, tp: 3, 10
LR f1 score: 0.256
LR cohens kappa score: 0.223
LR average precision score: 0.335

-> test with 'GB'
GB tn, fp: 475, 16
GB fn, tp: 7, 6
GB f1 score: 0.343
GB cohens kappa score: 0.321

-> 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 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: 357, 136
GAN fn, tp: 1, 14
GAN f1 score: 0.170
GAN cohens kappa score: 0.123

-> 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: 0.293

-> 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: 393, 100
KNN fn, tp: 11, 4
KNN f1 score: 0.067
KNN cohens kappa score: 0.016


------ 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: 464, 29
GAN fn, tp: 8, 7
GAN f1 score: 0.275
GAN cohens kappa score: 0.243

-> test with 'LR'
LR tn, fp: 442, 51
LR fn, tp: 3, 12
LR f1 score: 0.308
LR cohens kappa score: 0.273
LR average precision score: 0.226

-> 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: 421, 72
KNN fn, tp: 7, 8
KNN f1 score: 0.168
KNN cohens kappa score: 0.125


------ 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: 413, 80
GAN fn, tp: 4, 11
GAN f1 score: 0.208
GAN cohens kappa score: 0.165

-> test with 'LR'
LR tn, fp: 446, 47
LR fn, tp: 3, 12
LR f1 score: 0.324
LR cohens kappa score: 0.291
LR average precision score: 0.197

-> 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: 409, 84
KNN fn, tp: 9, 6
KNN f1 score: 0.114
KNN cohens kappa score: 0.067


------ 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: 313, 180
GAN fn, tp: 3, 12
GAN f1 score: 0.116
GAN cohens kappa score: 0.065

-> test with 'LR'
LR tn, fp: 432, 61
LR fn, tp: 3, 12
LR f1 score: 0.273
LR cohens kappa score: 0.235
LR average precision score: 0.288

-> test with 'GB'
GB tn, fp: 486, 7
GB fn, tp: 8, 7
GB f1 score: 0.483
GB cohens kappa score: 0.468

-> test with 'KNN'
KNN tn, fp: 392, 101
KNN fn, tp: 9, 6
KNN f1 score: 0.098
KNN cohens kappa score: 0.049


------ 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: 361, 130
GAN fn, tp: 2, 11
GAN f1 score: 0.143
GAN cohens kappa score: 0.100

-> test with 'LR'
LR tn, fp: 430, 61
LR fn, tp: 4, 9
LR f1 score: 0.217
LR cohens kappa score: 0.181
LR average precision score: 0.177

-> test with 'GB'
GB tn, fp: 477, 14
GB fn, tp: 7, 6
GB f1 score: 0.364
GB cohens kappa score: 0.343

-> test with 'KNN'
KNN tn, fp: 388, 103
KNN fn, tp: 6, 7
KNN f1 score: 0.114
KNN cohens kappa score: 0.071


====== 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: 451, 42
GAN fn, tp: 6, 9
GAN f1 score: 0.273
GAN cohens kappa score: 0.238

-> test with 'LR'
LR tn, fp: 439, 54
LR fn, tp: 2, 13
LR f1 score: 0.317
LR cohens kappa score: 0.282
LR average precision score: 0.293

-> test with 'GB'
GB tn, fp: 483, 10
GB fn, tp: 6, 9
GB f1 score: 0.529
GB cohens kappa score: 0.513

-> test with 'KNN'
KNN tn, fp: 404, 89
KNN fn, tp: 10, 5
KNN f1 score: 0.092
KNN cohens kappa score: 0.043


------ 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: 415, 78
GAN fn, tp: 3, 12
GAN f1 score: 0.229
GAN cohens kappa score: 0.187

-> 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: 0.181

-> 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: 401, 92
KNN fn, tp: 11, 4
KNN f1 score: 0.072
KNN cohens kappa score: 0.022


------ 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: 445, 48
GAN fn, tp: 7, 8
GAN f1 score: 0.225
GAN cohens kappa score: 0.188

-> test with 'LR'
LR tn, fp: 460, 33
LR fn, tp: 5, 10
LR f1 score: 0.345
LR cohens kappa score: 0.315
LR average precision score: 0.196

-> test with 'GB'
GB tn, fp: 486, 7
GB fn, tp: 11, 4
GB f1 score: 0.308
GB cohens kappa score: 0.290

-> test with 'KNN'
KNN tn, fp: 405, 88
KNN fn, tp: 10, 5
KNN f1 score: 0.093
KNN cohens kappa score: 0.044


------ 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: 379, 114
GAN fn, tp: 2, 13
GAN f1 score: 0.183
GAN cohens kappa score: 0.138

-> test with 'LR'
LR tn, fp: 426, 67
LR fn, tp: 2, 13
LR f1 score: 0.274
LR cohens kappa score: 0.236
LR average precision score: 0.237

-> 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: 398, 95
KNN fn, tp: 7, 8
KNN f1 score: 0.136
KNN cohens kappa score: 0.089


------ 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: 380, 111
GAN fn, tp: 4, 9
GAN f1 score: 0.135
GAN cohens kappa score: 0.093

-> test with 'LR'
LR tn, fp: 428, 63
LR fn, tp: 3, 10
LR f1 score: 0.233
LR cohens kappa score: 0.197
LR average precision score: 0.235

-> test with 'GB'
GB tn, fp: 481, 10
GB fn, tp: 9, 4
GB f1 score: 0.296
GB cohens kappa score: 0.277

-> test with 'KNN'
KNN tn, fp: 402, 89
KNN fn, tp: 8, 5
KNN f1 score: 0.093
KNN cohens kappa score: 0.050

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 460, 67
LR fn, tp: 6, 14
LR f1 score: 0.345
LR cohens kappa score: 0.315
LR average precision score: 0.468


average:
LR tn, fp: 437.16, 55.44
LR fn, tp: 3.64, 10.96
LR f1 score: 0.272
LR cohens kappa score: 0.236
LR average precision score: 0.235


minimum:
LR tn, fp: 426, 33
LR fn, tp: 1, 9
LR f1 score: 0.217
LR cohens kappa score: 0.181
LR average precision score: 0.128


-----[ GB ]-----
maximum:
GB tn, fp: 487, 21
GB fn, tp: 12, 9
GB f1 score: 0.529
GB cohens kappa score: 0.513


average:
GB tn, fp: 480.44, 12.16
GB fn, tp: 8.84, 5.76
GB f1 score: 0.352
GB cohens kappa score: 0.331


minimum:
GB tn, fp: 472, 6
GB fn, tp: 6, 2
GB f1 score: 0.148
GB cohens kappa score: 0.125


-----[ KNN ]-----
maximum:
KNN tn, fp: 423, 109
KNN fn, tp: 13, 9
KNN f1 score: 0.168
KNN cohens kappa score: 0.125


average:
KNN tn, fp: 401.32, 91.28
KNN fn, tp: 9.04, 5.56
KNN f1 score: 0.100
KNN cohens kappa score: 0.053


minimum:
KNN tn, fp: 382, 70
KNN fn, tp: 6, 2
KNN f1 score: 0.033
KNN cohens kappa score: -0.019


-----[ GAN ]-----
maximum:
GAN tn, fp: 493, 215
GAN fn, tp: 15, 15
GAN f1 score: 0.275
GAN cohens kappa score: 0.244


average:
GAN tn, fp: 392.52, 100.08
GAN fn, tp: 4.56, 10.04
GAN f1 score: 0.179
GAN cohens kappa score: 0.139


minimum:
GAN tn, fp: 278, 0
GAN fn, tp: 0, 0
GAN f1 score: 0.000
GAN cohens kappa score: 0.000