ConvGeNBenchmarkResults/data_result/CTGAN/folding_winequality-red-4.log

///////////////////////////////////////////
// Running CTGAN on folding_winequality-red-4
///////////////////////////////////////////

Load 'folding_winequality-red-4'
from pickle file
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 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 273, 37
LR fn, tp: 10, 1
LR f1 score: 0.041
LR cohens kappa score: -0.013
LR average precision score: 0.043

-> test with 'RF'
RF tn, fp: 298, 12
RF fn, tp: 10, 1
RF f1 score: 0.083
RF cohens kappa score: 0.048

-> test with 'GB'
GB tn, fp: 301, 9
GB fn, tp: 9, 2
GB f1 score: 0.182
GB cohens kappa score: 0.153

-> test with 'KNN'
KNN tn, fp: 287, 23
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.049


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 26
LR fn, tp: 5, 6
LR f1 score: 0.279
LR cohens kappa score: 0.240
LR average precision score: 0.151

-> test with 'RF'
RF tn, fp: 301, 9
RF fn, tp: 8, 3
RF f1 score: 0.261
RF cohens kappa score: 0.233

-> test with 'GB'
GB tn, fp: 301, 9
GB fn, tp: 9, 2
GB f1 score: 0.182
GB cohens kappa score: 0.153

-> test with 'KNN'
KNN tn, fp: 278, 32
KNN fn, tp: 10, 1
KNN f1 score: 0.045
KNN cohens kappa score: -0.006


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 232, 78
LR fn, tp: 7, 4
LR f1 score: 0.086
LR cohens kappa score: 0.027
LR average precision score: 0.044

-> test with 'RF'
RF tn, fp: 301, 9
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.032

-> test with 'GB'
GB tn, fp: 302, 8
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.030

-> test with 'KNN'
KNN tn, fp: 285, 25
KNN fn, tp: 9, 2
KNN f1 score: 0.105
KNN cohens kappa score: 0.059


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 271, 39
LR fn, tp: 8, 3
LR f1 score: 0.113
LR cohens kappa score: 0.062
LR average precision score: 0.108

-> test with 'RF'
RF tn, fp: 297, 13
RF fn, tp: 8, 3
RF f1 score: 0.222
RF cohens kappa score: 0.189

-> test with 'GB'
GB tn, fp: 302, 8
GB fn, tp: 7, 4
GB f1 score: 0.348
GB cohens kappa score: 0.324

-> test with 'KNN'
KNN tn, fp: 289, 21
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.047


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 23
LR fn, tp: 7, 2
LR f1 score: 0.118
LR cohens kappa score: 0.079
LR average precision score: 0.095

-> test with 'RF'
RF tn, fp: 299, 7
RF fn, tp: 8, 1
RF f1 score: 0.118
RF cohens kappa score: 0.093

-> test with 'GB'
GB tn, fp: 299, 7
GB fn, tp: 8, 1
GB f1 score: 0.118
GB cohens kappa score: 0.093

-> test with 'KNN'
KNN tn, fp: 290, 16
KNN fn, tp: 8, 1
KNN f1 score: 0.077
KNN cohens kappa score: 0.041


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 282, 28
LR fn, tp: 7, 4
LR f1 score: 0.186
LR cohens kappa score: 0.142
LR average precision score: 0.130

-> test with 'RF'
RF tn, fp: 299, 11
RF fn, tp: 8, 3
RF f1 score: 0.240
RF cohens kappa score: 0.210

-> test with 'GB'
GB tn, fp: 299, 11
GB fn, tp: 10, 1
GB f1 score: 0.087
GB cohens kappa score: 0.053

-> test with 'KNN'
KNN tn, fp: 288, 22
KNN fn, tp: 9, 2
KNN f1 score: 0.114
KNN cohens kappa score: 0.071


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 251, 59
LR fn, tp: 8, 3
LR f1 score: 0.082
LR cohens kappa score: 0.025
LR average precision score: 0.049

-> test with 'RF'
RF tn, fp: 300, 10
RF fn, tp: 10, 1
RF f1 score: 0.091
RF cohens kappa score: 0.059

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011

-> test with 'KNN'
KNN tn, fp: 279, 31
KNN fn, tp: 10, 1
KNN f1 score: 0.047
KNN cohens kappa score: -0.005


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 290, 20
LR fn, tp: 10, 1
LR f1 score: 0.062
LR cohens kappa score: 0.018
LR average precision score: 0.077

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 10, 1
GB f1 score: 0.133
GB cohens kappa score: 0.117

-> test with 'KNN'
KNN tn, fp: 298, 12
KNN fn, tp: 10, 1
KNN f1 score: 0.083
KNN cohens kappa score: 0.048


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 271, 39
LR fn, tp: 8, 3
LR f1 score: 0.113
LR cohens kappa score: 0.062
LR average precision score: 0.157

-> test with 'RF'
RF tn, fp: 300, 10
RF fn, tp: 9, 2
RF f1 score: 0.174
RF cohens kappa score: 0.143

-> test with 'GB'
GB tn, fp: 301, 9
GB fn, tp: 10, 1
GB f1 score: 0.095
GB cohens kappa score: 0.065

-> test with 'KNN'
KNN tn, fp: 272, 38
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.056


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 32
LR fn, tp: 6, 3
LR f1 score: 0.136
LR cohens kappa score: 0.095
LR average precision score: 0.187

-> test with 'RF'
RF tn, fp: 297, 9
RF fn, tp: 7, 2
RF f1 score: 0.200
RF cohens kappa score: 0.174

-> test with 'GB'
GB tn, fp: 299, 7
GB fn, tp: 8, 1
GB f1 score: 0.118
GB cohens kappa score: 0.093

-> test with 'KNN'
KNN tn, fp: 281, 25
KNN fn, tp: 8, 1
KNN f1 score: 0.057
KNN cohens kappa score: 0.015


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 261, 49
LR fn, tp: 8, 3
LR f1 score: 0.095
LR cohens kappa score: 0.041
LR average precision score: 0.065

-> test with 'RF'
RF tn, fp: 301, 9
RF fn, tp: 8, 3
RF f1 score: 0.261
RF cohens kappa score: 0.233

-> test with 'GB'
GB tn, fp: 302, 8
GB fn, tp: 10, 1
GB f1 score: 0.100
GB cohens kappa score: 0.071

-> test with 'KNN'
KNN tn, fp: 296, 14
KNN fn, tp: 10, 1
KNN f1 score: 0.077
KNN cohens kappa score: 0.039


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 255, 55
LR fn, tp: 8, 3
LR f1 score: 0.087
LR cohens kappa score: 0.031
LR average precision score: 0.071

-> test with 'RF'
RF tn, fp: 294, 16
RF fn, tp: 10, 1
RF f1 score: 0.071
RF cohens kappa score: 0.031

-> test with 'GB'
GB tn, fp: 298, 12
GB fn, tp: 10, 1
GB f1 score: 0.083
GB cohens kappa score: 0.048

-> test with 'KNN'
KNN tn, fp: 266, 44
KNN fn, tp: 10, 1
KNN f1 score: 0.036
KNN cohens kappa score: -0.020


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 291, 19
LR fn, tp: 8, 3
LR f1 score: 0.182
LR cohens kappa score: 0.143
LR average precision score: 0.074

-> test with 'RF'
RF tn, fp: 303, 7
RF fn, tp: 10, 1
RF f1 score: 0.105
RF cohens kappa score: 0.079

-> test with 'GB'
GB tn, fp: 302, 8
GB fn, tp: 10, 1
GB f1 score: 0.100
GB cohens kappa score: 0.071

-> test with 'KNN'
KNN tn, fp: 292, 18
KNN fn, tp: 9, 2
KNN f1 score: 0.129
KNN cohens kappa score: 0.089


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 41
LR fn, tp: 7, 4
LR f1 score: 0.143
LR cohens kappa score: 0.093
LR average precision score: 0.133

-> test with 'RF'
RF tn, fp: 304, 6
RF fn, tp: 9, 2
RF f1 score: 0.211
RF cohens kappa score: 0.187

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 9, 2
GB f1 score: 0.222
GB cohens kappa score: 0.201

-> test with 'KNN'
KNN tn, fp: 278, 32
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.054


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 265, 41
LR fn, tp: 7, 2
LR f1 score: 0.077
LR cohens kappa score: 0.031
LR average precision score: 0.060

-> test with 'RF'
RF tn, fp: 291, 15
RF fn, tp: 7, 2
RF f1 score: 0.154
RF cohens kappa score: 0.121

-> test with 'GB'
GB tn, fp: 298, 8
GB fn, tp: 8, 1
GB f1 score: 0.111
GB cohens kappa score: 0.085

-> test with 'KNN'
KNN tn, fp: 283, 23
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.043


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 295, 15
LR fn, tp: 8, 3
LR f1 score: 0.207
LR cohens kappa score: 0.172
LR average precision score: 0.226

-> test with 'RF'
RF tn, fp: 305, 5
RF fn, tp: 9, 2
RF f1 score: 0.222
RF cohens kappa score: 0.201

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 9, 2
GB f1 score: 0.267
GB cohens kappa score: 0.253

-> test with 'KNN'
KNN tn, fp: 278, 32
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.054


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 276, 34
LR fn, tp: 6, 5
LR f1 score: 0.200
LR cohens kappa score: 0.155
LR average precision score: 0.163

-> test with 'RF'
RF tn, fp: 296, 14
RF fn, tp: 9, 2
RF f1 score: 0.148
RF cohens kappa score: 0.112

-> test with 'GB'
GB tn, fp: 302, 8
GB fn, tp: 9, 2
GB f1 score: 0.190
GB cohens kappa score: 0.163

-> test with 'KNN'
KNN tn, fp: 287, 23
KNN fn, tp: 9, 2
KNN f1 score: 0.111
KNN cohens kappa score: 0.067


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 44
LR fn, tp: 7, 4
LR f1 score: 0.136
LR cohens kappa score: 0.085
LR average precision score: 0.045

-> test with 'RF'
RF tn, fp: 292, 18
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.044

-> test with 'GB'
GB tn, fp: 298, 12
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.037

-> test with 'KNN'
KNN tn, fp: 287, 23
KNN fn, tp: 10, 1
KNN f1 score: 0.057
KNN cohens kappa score: 0.011


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 21
LR fn, tp: 8, 3
LR f1 score: 0.171
LR cohens kappa score: 0.131
LR average precision score: 0.145

-> test with 'RF'
RF tn, fp: 302, 8
RF fn, tp: 8, 3
RF f1 score: 0.273
RF cohens kappa score: 0.247

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 9, 2
GB f1 score: 0.222
GB cohens kappa score: 0.201

-> test with 'KNN'
KNN tn, fp: 288, 22
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.048


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 37
LR fn, tp: 8, 1
LR f1 score: 0.043
LR cohens kappa score: -0.004
LR average precision score: 0.036

-> test with 'RF'
RF tn, fp: 297, 9
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.029

-> test with 'GB'
GB tn, fp: 299, 7
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.026

-> test with 'KNN'
KNN tn, fp: 278, 28
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.045


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 44
LR fn, tp: 8, 3
LR f1 score: 0.103
LR cohens kappa score: 0.051
LR average precision score: 0.127

-> test with 'RF'
RF tn, fp: 304, 6
RF fn, tp: 10, 1
RF f1 score: 0.111
RF cohens kappa score: 0.087

-> test with 'GB'
GB tn, fp: 302, 8
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.030

-> test with 'KNN'
KNN tn, fp: 292, 18
KNN fn, tp: 9, 2
KNN f1 score: 0.129
KNN cohens kappa score: 0.089


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 282, 28
LR fn, tp: 7, 4
LR f1 score: 0.186
LR cohens kappa score: 0.142
LR average precision score: 0.185

-> test with 'RF'
RF tn, fp: 306, 4
RF fn, tp: 9, 2
RF f1 score: 0.235
RF cohens kappa score: 0.216

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 8, 3
GB f1 score: 0.316
GB cohens kappa score: 0.295

-> test with 'KNN'
KNN tn, fp: 288, 22
KNN fn, tp: 9, 2
KNN f1 score: 0.114
KNN cohens kappa score: 0.071


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 242, 68
LR fn, tp: 9, 2
LR f1 score: 0.049
LR cohens kappa score: -0.010
LR average precision score: 0.045

-> test with 'RF'
RF tn, fp: 300, 10
RF fn, tp: 10, 1
RF f1 score: 0.091
RF cohens kappa score: 0.059

-> test with 'GB'
GB tn, fp: 299, 11
GB fn, tp: 10, 1
GB f1 score: 0.087
GB cohens kappa score: 0.053

-> test with 'KNN'
KNN tn, fp: 276, 34
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.055


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 44
LR fn, tp: 6, 5
LR f1 score: 0.167
LR cohens kappa score: 0.117
LR average precision score: 0.161

-> test with 'RF'
RF tn, fp: 301, 9
RF fn, tp: 7, 4
RF f1 score: 0.333
RF cohens kappa score: 0.308

-> test with 'GB'
GB tn, fp: 303, 7
GB fn, tp: 8, 3
GB f1 score: 0.286
GB cohens kappa score: 0.262

-> test with 'KNN'
KNN tn, fp: 281, 29
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.052


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 253, 53
LR fn, tp: 5, 4
LR f1 score: 0.121
LR cohens kappa score: 0.076
LR average precision score: 0.118

-> test with 'RF'
RF tn, fp: 299, 7
RF fn, tp: 7, 2
RF f1 score: 0.222
RF cohens kappa score: 0.199

-> test with 'GB'
GB tn, fp: 303, 3
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.014

-> test with 'KNN'
KNN tn, fp: 288, 18
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.040

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 295, 78
LR fn, tp: 10, 6
LR f1 score: 0.279
LR cohens kappa score: 0.240
LR average precision score: 0.226


average:
LR tn, fp: 270.24, 38.96
LR fn, tp: 7.44, 3.16
LR f1 score: 0.127
LR cohens kappa score: 0.080
LR average precision score: 0.108


minimum:
LR tn, fp: 232, 15
LR fn, tp: 5, 1
LR f1 score: 0.041
LR cohens kappa score: -0.013
LR average precision score: 0.036


-----[ RF ]-----
maximum:
RF tn, fp: 309, 18
RF fn, tp: 11, 4
RF f1 score: 0.333
RF cohens kappa score: 0.308


average:
RF tn, fp: 299.84, 9.36
RF fn, tp: 8.92, 1.68
RF f1 score: 0.153
RF cohens kappa score: 0.125


minimum:
RF tn, fp: 291, 1
RF fn, tp: 7, 0
RF f1 score: 0.000
RF cohens kappa score: -0.044


-----[ GB ]-----
maximum:
GB tn, fp: 308, 12
GB fn, tp: 11, 4
GB f1 score: 0.348
GB cohens kappa score: 0.324


average:
GB tn, fp: 301.92, 7.28
GB fn, tp: 9.32, 1.28
GB f1 score: 0.130
GB cohens kappa score: 0.104


minimum:
GB tn, fp: 298, 2
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.037


-----[ KNN ]-----
maximum:
KNN tn, fp: 298, 44
KNN fn, tp: 11, 2
KNN f1 score: 0.129
KNN cohens kappa score: 0.089


average:
KNN tn, fp: 284.2, 25.0
KNN fn, tp: 9.8, 0.8
KNN f1 score: 0.047
KNN cohens kappa score: 0.001


minimum:
KNN tn, fp: 266, 12
KNN fn, tp: 8, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.056

  wall time: 00:06:38s, process time: 00:48:37s