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

///////////////////////////////////////////
// Running ctGAN on folding_winequality-red-4
///////////////////////////////////////////

Load 'data_input/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: 247, 63
LR fn, tp: 9, 2
LR f1 score: 0.053
LR cohens kappa score: -0.006
LR average precision score: 0.039

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

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

-> test with 'KNN'
KNN tn, fp: 290, 20
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.046


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 24
LR fn, tp: 10, 1
LR f1 score: 0.056
LR cohens kappa score: 0.008
LR average precision score: 0.043

-> test with 'RF'
RF tn, fp: 300, 10
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.034

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

-> 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 1/5: Slice 3/5 -------
-> 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: 0.059

-> 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: 303, 7
GB fn, tp: 9, 2
GB f1 score: 0.200
GB cohens kappa score: 0.175

-> 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 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 233, 77
LR fn, tp: 9, 2
LR f1 score: 0.044
LR cohens kappa score: -0.017
LR average precision score: 0.044

-> test with 'RF'
RF tn, fp: 298, 12
RF fn, tp: 8, 3
RF f1 score: 0.231
RF cohens kappa score: 0.199

-> test with 'GB'
GB tn, fp: 299, 11
GB fn, tp: 9, 2
GB f1 score: 0.167
GB cohens kappa score: 0.135

-> test with 'KNN'
KNN tn, fp: 278, 32
KNN fn, tp: 8, 3
KNN f1 score: 0.130
KNN cohens kappa score: 0.083


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 263, 43
LR fn, tp: 5, 4
LR f1 score: 0.143
LR cohens kappa score: 0.100
LR average precision score: 0.102

-> test with 'RF'
RF tn, fp: 302, 4
RF fn, tp: 8, 1
RF f1 score: 0.143
RF cohens kappa score: 0.125

-> test with 'GB'
GB tn, fp: 301, 5
GB fn, tp: 8, 1
GB f1 score: 0.133
GB cohens kappa score: 0.113

-> test with 'KNN'
KNN tn, fp: 297, 9
KNN fn, tp: 8, 1
KNN f1 score: 0.105
KNN cohens kappa score: 0.078


====== 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: 271, 39
LR fn, tp: 8, 3
LR f1 score: 0.113
LR cohens kappa score: 0.062
LR average precision score: 0.103

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

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

-> test with 'KNN'
KNN tn, fp: 288, 22
KNN fn, tp: 10, 1
KNN f1 score: 0.059
KNN cohens kappa score: 0.013


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 242, 68
LR fn, tp: 8, 3
LR f1 score: 0.073
LR cohens kappa score: 0.015
LR average precision score: 0.050

-> test with 'RF'
RF tn, fp: 294, 16
RF fn, tp: 9, 2
RF f1 score: 0.138
RF cohens kappa score: 0.100

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

-> test with 'KNN'
KNN tn, fp: 294, 16
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.042


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 255, 55
LR fn, tp: 4, 7
LR f1 score: 0.192
LR cohens kappa score: 0.142
LR average precision score: 0.148

-> test with 'RF'
RF tn, fp: 303, 7
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.027

-> test with 'GB'
GB tn, fp: 306, 4
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.019

-> test with 'KNN'
KNN tn, fp: 280, 30
KNN fn, tp: 9, 2
KNN f1 score: 0.093
KNN cohens kappa score: 0.044


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 250, 60
LR fn, tp: 5, 6
LR f1 score: 0.156
LR cohens kappa score: 0.103
LR average precision score: 0.101

-> test with 'RF'
RF tn, fp: 298, 12
RF fn, tp: 7, 4
RF f1 score: 0.296
RF cohens kappa score: 0.267

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

-> test with 'KNN'
KNN tn, fp: 290, 20
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.046


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 66
LR fn, tp: 5, 4
LR f1 score: 0.101
LR cohens kappa score: 0.053
LR average precision score: 0.045

-> test with 'RF'
RF tn, fp: 289, 17
RF fn, tp: 8, 1
RF f1 score: 0.074
RF cohens kappa score: 0.037

-> 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: 273, 33
KNN fn, tp: 8, 1
KNN f1 score: 0.047
KNN cohens kappa score: 0.001


====== 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: 248, 62
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.062
LR average precision score: 0.026

-> test with 'RF'
RF tn, fp: 304, 6
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.025

-> 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: 295, 15
KNN fn, tp: 10, 1
KNN f1 score: 0.074
KNN cohens kappa score: 0.035


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 237, 73
LR fn, tp: 3, 8
LR f1 score: 0.174
LR cohens kappa score: 0.121
LR average precision score: 0.104

-> test with 'RF'
RF tn, fp: 297, 13
RF fn, tp: 9, 2
RF f1 score: 0.154
RF cohens kappa score: 0.119

-> 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: 286, 24
KNN fn, tp: 9, 2
KNN f1 score: 0.108
KNN cohens kappa score: 0.063


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 219, 91
LR fn, tp: 9, 2
LR f1 score: 0.038
LR cohens kappa score: -0.024
LR average precision score: 0.037

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

-> test with 'GB'
GB tn, fp: 300, 10
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.034

-> 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 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 263, 47
LR fn, tp: 7, 4
LR f1 score: 0.129
LR cohens kappa score: 0.077
LR average precision score: 0.111

-> 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: 303, 7
GB fn, tp: 10, 1
GB f1 score: 0.105
GB cohens kappa score: 0.079

-> test with 'KNN'
KNN tn, fp: 284, 26
KNN fn, tp: 10, 1
KNN f1 score: 0.053
KNN cohens kappa score: 0.004


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 215, 91
LR fn, tp: 3, 6
LR f1 score: 0.113
LR cohens kappa score: 0.064
LR average precision score: 0.085

-> test with 'RF'
RF tn, fp: 301, 5
RF fn, tp: 7, 2
RF f1 score: 0.250
RF cohens kappa score: 0.231

-> test with 'GB'
GB tn, fp: 300, 6
GB fn, tp: 8, 1
GB f1 score: 0.125
GB cohens kappa score: 0.103

-> test with 'KNN'
KNN tn, fp: 285, 21
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.042


====== 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: 300, 10
LR fn, tp: 7, 4
LR f1 score: 0.320
LR cohens kappa score: 0.293
LR average precision score: 0.305

-> 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: 306, 4
GB fn, tp: 10, 1
GB f1 score: 0.125
GB cohens kappa score: 0.106

-> test with 'KNN'
KNN tn, fp: 279, 31
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.053


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 43
LR fn, tp: 8, 3
LR f1 score: 0.105
LR cohens kappa score: 0.053
LR average precision score: 0.061

-> test with 'RF'
RF tn, fp: 306, 4
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.019

-> test with 'GB'
GB tn, fp: 303, 7
GB fn, tp: 9, 2
GB f1 score: 0.200
GB cohens kappa score: 0.175

-> test with 'KNN'
KNN tn, fp: 286, 24
KNN fn, tp: 9, 2
KNN f1 score: 0.108
KNN cohens kappa score: 0.063


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 300, 10
LR fn, tp: 10, 1
LR f1 score: 0.091
LR cohens kappa score: 0.059
LR average precision score: 0.168

-> test with 'RF'
RF tn, fp: 303, 7
RF fn, tp: 9, 2
RF f1 score: 0.200
RF cohens kappa score: 0.175

-> 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: 290, 20
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.046


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 70
LR fn, tp: 3, 8
LR f1 score: 0.180
LR cohens kappa score: 0.127
LR average precision score: 0.146

-> test with 'RF'
RF tn, fp: 295, 15
RF fn, tp: 9, 2
RF f1 score: 0.143
RF cohens kappa score: 0.106

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

-> test with 'KNN'
KNN tn, fp: 295, 15
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.041


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 39
LR fn, tp: 7, 2
LR f1 score: 0.080
LR cohens kappa score: 0.035
LR average precision score: 0.060

-> test with 'RF'
RF tn, fp: 287, 19
RF fn, tp: 7, 2
RF f1 score: 0.133
RF cohens kappa score: 0.097

-> test with 'GB'
GB tn, fp: 289, 17
GB fn, tp: 8, 1
GB f1 score: 0.074
GB cohens kappa score: 0.037

-> test with 'KNN'
KNN tn, fp: 282, 24
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.043


====== 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: 270, 40
LR fn, tp: 8, 3
LR f1 score: 0.111
LR cohens kappa score: 0.060
LR average precision score: 0.164

-> test with 'RF'
RF tn, fp: 295, 15
RF fn, tp: 10, 1
RF f1 score: 0.074
RF cohens kappa score: 0.035

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

-> test with 'KNN'
KNN tn, fp: 293, 17
KNN fn, tp: 9, 2
KNN f1 score: 0.133
KNN cohens kappa score: 0.094


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 291, 19
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.045
LR average precision score: 0.060

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

-> test with 'GB'
GB tn, fp: 306, 4
GB fn, tp: 10, 1
GB f1 score: 0.125
GB cohens kappa score: 0.106

-> 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 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 43
LR fn, tp: 10, 1
LR f1 score: 0.036
LR cohens kappa score: -0.020
LR average precision score: 0.057

-> test with 'RF'
RF tn, fp: 294, 16
RF fn, tp: 8, 3
RF f1 score: 0.200
RF cohens kappa score: 0.164

-> 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: 288, 22
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.048


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 40
LR fn, tp: 8, 3
LR f1 score: 0.111
LR cohens kappa score: 0.060
LR average precision score: 0.076

-> test with 'RF'
RF tn, fp: 294, 16
RF fn, tp: 7, 4
RF f1 score: 0.258
RF cohens kappa score: 0.224

-> test with 'GB'
GB tn, fp: 298, 12
GB fn, tp: 8, 3
GB f1 score: 0.231
GB cohens kappa score: 0.199

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 290, 16
LR fn, tp: 6, 3
LR f1 score: 0.214
LR cohens kappa score: 0.183
LR average precision score: 0.134

-> 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: 302, 4
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.018

-> test with 'KNN'
KNN tn, fp: 291, 15
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.037

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 300, 91
LR fn, tp: 11, 8
LR f1 score: 0.320
LR cohens kappa score: 0.293
LR average precision score: 0.305


average:
LR tn, fp: 259.68, 49.52
LR fn, tp: 7.28, 3.32
LR f1 score: 0.109
LR cohens kappa score: 0.059
LR average precision score: 0.093


minimum:
LR tn, fp: 215, 10
LR fn, tp: 3, 0
LR f1 score: 0.000
LR cohens kappa score: -0.062
LR average precision score: 0.026


-----[ RF ]-----
maximum:
RF tn, fp: 306, 19
RF fn, tp: 11, 4
RF f1 score: 0.296
RF cohens kappa score: 0.267


average:
RF tn, fp: 298.56, 10.64
RF fn, tp: 9.04, 1.56
RF f1 score: 0.132
RF cohens kappa score: 0.102


minimum:
RF tn, fp: 287, 4
RF fn, tp: 7, 0
RF f1 score: 0.000
RF cohens kappa score: -0.034


-----[ GB ]-----
maximum:
GB tn, fp: 308, 17
GB fn, tp: 11, 3
GB f1 score: 0.261
GB cohens kappa score: 0.233


average:
GB tn, fp: 301.28, 7.92
GB fn, tp: 9.44, 1.16
GB f1 score: 0.112
GB cohens kappa score: 0.086


minimum:
GB tn, fp: 289, 2
GB fn, tp: 8, 0
GB f1 score: 0.000
GB cohens kappa score: -0.034


-----[ KNN ]-----
maximum:
KNN tn, fp: 297, 33
KNN fn, tp: 11, 3
KNN f1 score: 0.133
KNN cohens kappa score: 0.094


average:
KNN tn, fp: 287.24, 21.96
KNN fn, tp: 9.8, 0.8
KNN f1 score: 0.046
KNN cohens kappa score: 0.002


minimum:
KNN tn, fp: 273, 9
KNN fn, tp: 8, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.053