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: 283, 27
LR fn, tp: 6, 5
LR f1 score: 0.233
LR cohens kappa score: 0.191
LR average precision score: 0.097

-> test with 'GB'
GB tn, fp: 299, 11
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.035

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


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 36
LR fn, tp: 7, 4
LR f1 score: 0.157
LR cohens kappa score: 0.109
LR average precision score: 0.156

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

-> test with 'KNN'
KNN tn, fp: 293, 17
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.043


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 218, 92
LR fn, tp: 7, 4
LR f1 score: 0.075
LR cohens kappa score: 0.014
LR average precision score: 0.052

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

-> 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 1/5: 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: 0.083

-> 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: 7, 4
KNN f1 score: 0.170
KNN cohens kappa score: 0.124


------ Step 1/5: 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: 0.048

-> test with 'GB'
GB tn, fp: 304, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.155

-> test with 'KNN'
KNN tn, fp: 286, 20
KNN fn, tp: 9, 0
KNN f1 score: 0.000
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: 267, 43
LR fn, tp: 6, 5
LR f1 score: 0.169
LR cohens kappa score: 0.120
LR average precision score: 0.125

-> 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: 297, 13
KNN fn, tp: 10, 1
KNN f1 score: 0.080
KNN cohens kappa score: 0.043


------ Step 2/5: 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: 9, 2
LR f1 score: 0.125
LR cohens kappa score: 0.084
LR average precision score: 0.055

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 10, 1
GB f1 score: 0.143
GB cohens kappa score: 0.130

-> test with 'KNN'
KNN tn, fp: 293, 17
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.043


------ 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: 0.066

-> 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: 284, 26
KNN fn, tp: 10, 1
KNN f1 score: 0.053
KNN cohens kappa score: 0.004


------ Step 2/5: 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: 0.109

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

-> 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 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 40
LR fn, tp: 8, 1
LR f1 score: 0.040
LR cohens kappa score: -0.007
LR average precision score: 0.037

-> 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: 280, 26
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.044


====== 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: 275, 35
LR fn, tp: 10, 1
LR f1 score: 0.043
LR cohens kappa score: -0.010
LR average precision score: 0.036

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

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


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 294, 16
LR fn, tp: 8, 3
LR f1 score: 0.200
LR cohens kappa score: 0.164
LR average precision score: 0.155

-> 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: 10, 1
KNN f1 score: 0.056
KNN cohens kappa score: 0.008


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 36
LR fn, tp: 8, 3
LR f1 score: 0.120
LR cohens kappa score: 0.070
LR average precision score: 0.073

-> 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: 281, 29
KNN fn, tp: 9, 2
KNN f1 score: 0.095
KNN cohens kappa score: 0.047


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 214, 96
LR fn, tp: 7, 4
LR f1 score: 0.072
LR cohens kappa score: 0.011
LR average precision score: 0.045

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


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 264, 42
LR fn, tp: 7, 2
LR f1 score: 0.075
LR cohens kappa score: 0.029
LR average precision score: 0.042

-> 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: 290, 16
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.038


====== 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: 276, 34
LR fn, tp: 6, 5
LR f1 score: 0.200
LR cohens kappa score: 0.155
LR average precision score: 0.174

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

-> test with 'KNN'
KNN tn, fp: 283, 27
KNN fn, tp: 10, 1
KNN f1 score: 0.051
KNN cohens kappa score: 0.002


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 27
LR fn, tp: 10, 1
LR f1 score: 0.051
LR cohens kappa score: 0.002
LR average precision score: 0.056

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 10, 1
GB f1 score: 0.118
GB cohens kappa score: 0.096

-> test with 'KNN'
KNN tn, fp: 280, 30
KNN fn, tp: 8, 3
KNN f1 score: 0.136
KNN cohens kappa score: 0.090


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 265, 45
LR fn, tp: 8, 3
LR f1 score: 0.102
LR cohens kappa score: 0.049
LR average precision score: 0.062

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

-> test with 'KNN'
KNN tn, fp: 293, 17
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.043


------ Step 4/5: 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: 9, 2
LR f1 score: 0.074
LR cohens kappa score: 0.021
LR average precision score: 0.061

-> 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: 302, 8
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.030


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 235, 71
LR fn, tp: 8, 1
LR f1 score: 0.025
LR cohens kappa score: -0.027
LR average precision score: 0.033

-> test with 'GB'
GB tn, fp: 293, 13
GB fn, tp: 8, 1
GB f1 score: 0.087
GB cohens kappa score: 0.054

-> 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: 235, 75
LR fn, tp: 7, 4
LR f1 score: 0.089
LR cohens kappa score: 0.031
LR average precision score: 0.046

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

-> test with 'KNN'
KNN tn, fp: 289, 21
KNN fn, tp: 8, 3
KNN f1 score: 0.171
KNN cohens kappa score: 0.131


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 22
LR fn, tp: 10, 1
LR f1 score: 0.059
LR cohens kappa score: 0.013
LR average precision score: 0.058

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.022

-> test with 'KNN'
KNN tn, fp: 290, 20
KNN fn, tp: 9, 2
KNN f1 score: 0.121
KNN cohens kappa score: 0.079


------ Step 5/5: 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: 4, 7
LR f1 score: 0.209
LR cohens kappa score: 0.161
LR average precision score: 0.203

-> test with 'GB'
GB tn, fp: 299, 11
GB fn, tp: 7, 4
GB f1 score: 0.308
GB cohens kappa score: 0.279

-> 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 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 128
LR fn, tp: 4, 7
LR f1 score: 0.096
LR cohens kappa score: 0.035
LR average precision score: 0.106

-> 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: 282, 28
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: 247, 59
LR fn, tp: 8, 1
LR f1 score: 0.029
LR cohens kappa score: -0.022
LR average precision score: 0.035

-> test with 'GB'
GB tn, fp: 301, 5
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.021

-> test with 'KNN'
KNN tn, fp: 289, 17
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.039

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 294, 128
LR fn, tp: 10, 7
LR f1 score: 0.233
LR cohens kappa score: 0.191
LR average precision score: 0.203


average:
LR tn, fp: 263.04, 46.16
LR fn, tp: 7.56, 3.04
LR f1 score: 0.108
LR cohens kappa score: 0.059
LR average precision score: 0.080


minimum:
LR tn, fp: 182, 16
LR fn, tp: 4, 1
LR f1 score: 0.025
LR cohens kappa score: -0.027
LR average precision score: 0.033


-----[ GB ]-----
maximum:
GB tn, fp: 308, 14
GB fn, tp: 11, 4
GB f1 score: 0.308
GB cohens kappa score: 0.279


average:
GB tn, fp: 302.28, 6.92
GB fn, tp: 9.56, 1.04
GB f1 score: 0.106
GB cohens kappa score: 0.081


minimum:
GB tn, fp: 293, 2
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.035


-----[ KNN ]-----
maximum:
KNN tn, fp: 302, 32
KNN fn, tp: 11, 4
KNN f1 score: 0.171
KNN cohens kappa score: 0.131


average:
KNN tn, fp: 288.76, 20.44
KNN fn, tp: 9.8, 0.8
KNN f1 score: 0.043
KNN cohens kappa score: 0.000


minimum:
KNN tn, fp: 278, 8
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.052