ConvGeNBenchmarkResults/data_result/convGAN-old-full/folding_winequality-red-4.log

///////////////////////////////////////////
// Running convGAN-full 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: 214, 96
LR fn, tp: 5, 6
LR f1 score: 0.106
LR cohens kappa score: 0.047
LR average precision score: 0.133

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

-> test with 'KNN'
KNN tn, fp: 204, 106
KNN fn, tp: 7, 4
KNN f1 score: 0.066
KNN cohens kappa score: 0.004


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 222, 88
LR fn, tp: 4, 7
LR f1 score: 0.132
LR cohens kappa score: 0.075
LR average precision score: 0.119

-> test with 'GB'
GB tn, fp: 292, 18
GB fn, tp: 7, 4
GB f1 score: 0.242
GB cohens kappa score: 0.206

-> test with 'KNN'
KNN tn, fp: 228, 82
KNN fn, tp: 5, 6
KNN f1 score: 0.121
KNN cohens kappa score: 0.064


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 198, 112
LR fn, tp: 1, 10
LR f1 score: 0.150
LR cohens kappa score: 0.093
LR average precision score: 0.261

-> test with 'GB'
GB tn, fp: 289, 21
GB fn, tp: 6, 5
GB f1 score: 0.270
GB cohens kappa score: 0.233

-> test with 'KNN'
KNN tn, fp: 204, 106
KNN fn, tp: 7, 4
KNN f1 score: 0.066
KNN cohens kappa score: 0.004


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 248, 62
LR fn, tp: 5, 6
LR f1 score: 0.152
LR cohens kappa score: 0.099
LR average precision score: 0.148

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

-> test with 'KNN'
KNN tn, fp: 231, 79
KNN fn, tp: 9, 2
KNN f1 score: 0.043
KNN cohens kappa score: -0.018


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 224, 82
LR fn, tp: 4, 5
LR f1 score: 0.104
LR cohens kappa score: 0.055
LR average precision score: 0.213

-> test with 'GB'
GB tn, fp: 294, 12
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.034

-> test with 'KNN'
KNN tn, fp: 230, 76
KNN fn, tp: 8, 1
KNN f1 score: 0.023
KNN cohens kappa score: -0.029


====== 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: 215, 95
LR fn, tp: 3, 8
LR f1 score: 0.140
LR cohens kappa score: 0.084
LR average precision score: 0.128

-> test with 'GB'
GB tn, fp: 292, 18
GB fn, tp: 8, 3
GB f1 score: 0.187
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 219, 91
KNN fn, tp: 6, 5
KNN f1 score: 0.093
KNN cohens kappa score: 0.034


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 217, 93
LR fn, tp: 3, 8
LR f1 score: 0.143
LR cohens kappa score: 0.086
LR average precision score: 0.132

-> 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: 228, 82
KNN fn, tp: 8, 3
KNN f1 score: 0.062
KNN cohens kappa score: 0.002


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 224, 86
LR fn, tp: 3, 8
LR f1 score: 0.152
LR cohens kappa score: 0.097
LR average precision score: 0.177

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

-> test with 'KNN'
KNN tn, fp: 223, 87
KNN fn, tp: 8, 3
KNN f1 score: 0.059
KNN cohens kappa score: -0.002


------ Step 2/5: 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: 5, 6
LR f1 score: 0.128
LR cohens kappa score: 0.071
LR average precision score: 0.269

-> test with 'GB'
GB tn, fp: 298, 12
GB fn, tp: 9, 2
GB f1 score: 0.160
GB cohens kappa score: 0.126

-> test with 'KNN'
KNN tn, fp: 218, 92
KNN fn, tp: 8, 3
KNN f1 score: 0.057
KNN cohens kappa score: -0.005


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 228, 78
LR fn, tp: 3, 6
LR f1 score: 0.129
LR cohens kappa score: 0.082
LR average precision score: 0.108

-> test with 'GB'
GB tn, fp: 289, 17
GB fn, tp: 7, 2
GB f1 score: 0.143
GB cohens kappa score: 0.108

-> test with 'KNN'
KNN tn, fp: 222, 84
KNN fn, tp: 6, 3
KNN f1 score: 0.062
KNN cohens kappa score: 0.011


====== 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: 233, 77
LR fn, tp: 5, 6
LR f1 score: 0.128
LR cohens kappa score: 0.071
LR average precision score: 0.171

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

-> test with 'KNN'
KNN tn, fp: 216, 94
KNN fn, tp: 7, 4
KNN f1 score: 0.073
KNN cohens kappa score: 0.013


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 214, 96
LR fn, tp: 2, 9
LR f1 score: 0.155
LR cohens kappa score: 0.099
LR average precision score: 0.254

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

-> test with 'KNN'
KNN tn, fp: 230, 80
KNN fn, tp: 8, 3
KNN f1 score: 0.064
KNN cohens kappa score: 0.004


------ 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: 5, 6
LR f1 score: 0.111
LR cohens kappa score: 0.053
LR average precision score: 0.069

-> test with 'GB'
GB tn, fp: 289, 21
GB fn, tp: 10, 1
GB f1 score: 0.061
GB cohens kappa score: 0.016

-> test with 'KNN'
KNN tn, fp: 211, 99
KNN fn, tp: 6, 5
KNN f1 score: 0.087
KNN cohens kappa score: 0.027


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 212, 98
LR fn, tp: 2, 9
LR f1 score: 0.153
LR cohens kappa score: 0.096
LR average precision score: 0.193

-> test with 'GB'
GB tn, fp: 289, 21
GB fn, tp: 10, 1
GB f1 score: 0.061
GB cohens kappa score: 0.016

-> test with 'KNN'
KNN tn, fp: 228, 82
KNN fn, tp: 5, 6
KNN f1 score: 0.121
KNN cohens kappa score: 0.064


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 231, 75
LR fn, tp: 3, 6
LR f1 score: 0.133
LR cohens kappa score: 0.086
LR average precision score: 0.093

-> test with 'GB'
GB tn, fp: 293, 13
GB fn, tp: 7, 2
GB f1 score: 0.167
GB cohens kappa score: 0.136

-> test with 'KNN'
KNN tn, fp: 229, 77
KNN fn, tp: 7, 2
KNN f1 score: 0.045
KNN cohens kappa score: -0.006


====== 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: 225, 85
LR fn, tp: 3, 8
LR f1 score: 0.154
LR cohens kappa score: 0.099
LR average precision score: 0.380

-> 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: 236, 74
KNN fn, tp: 9, 2
KNN f1 score: 0.046
KNN cohens kappa score: -0.015


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 215, 95
LR fn, tp: 3, 8
LR f1 score: 0.140
LR cohens kappa score: 0.084
LR average precision score: 0.174

-> test with 'GB'
GB tn, fp: 291, 19
GB fn, tp: 10, 1
GB f1 score: 0.065
GB cohens kappa score: 0.021

-> test with 'KNN'
KNN tn, fp: 215, 95
KNN fn, tp: 9, 2
KNN f1 score: 0.037
KNN cohens kappa score: -0.026


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 229, 81
LR fn, tp: 5, 6
LR f1 score: 0.122
LR cohens kappa score: 0.066
LR average precision score: 0.109

-> test with 'GB'
GB tn, fp: 291, 19
GB fn, tp: 9, 2
GB f1 score: 0.125
GB cohens kappa score: 0.084

-> test with 'KNN'
KNN tn, fp: 231, 79
KNN fn, tp: 8, 3
KNN f1 score: 0.065
KNN cohens kappa score: 0.004


------ Step 4/5: 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: 1, 10
LR f1 score: 0.171
LR cohens kappa score: 0.116
LR average precision score: 0.152

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

-> test with 'KNN'
KNN tn, fp: 215, 95
KNN fn, tp: 8, 3
KNN f1 score: 0.055
KNN cohens kappa score: -0.007


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 212, 94
LR fn, tp: 6, 3
LR f1 score: 0.057
LR cohens kappa score: 0.005
LR average precision score: 0.070

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

-> test with 'KNN'
KNN tn, fp: 238, 68
KNN fn, tp: 7, 2
KNN f1 score: 0.051
KNN cohens kappa score: 0.000


====== 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: 232, 78
LR fn, tp: 5, 6
LR f1 score: 0.126
LR cohens kappa score: 0.070
LR average precision score: 0.073

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

-> test with 'KNN'
KNN tn, fp: 227, 83
KNN fn, tp: 7, 4
KNN f1 score: 0.082
KNN cohens kappa score: 0.022


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 228, 82
LR fn, tp: 6, 5
LR f1 score: 0.102
LR cohens kappa score: 0.044
LR average precision score: 0.106

-> test with 'GB'
GB tn, fp: 289, 21
GB fn, tp: 9, 2
GB f1 score: 0.118
GB cohens kappa score: 0.075

-> test with 'KNN'
KNN tn, fp: 228, 82
KNN fn, tp: 7, 4
KNN f1 score: 0.082
KNN cohens kappa score: 0.023


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 203, 107
LR fn, tp: 0, 11
LR f1 score: 0.171
LR cohens kappa score: 0.115
LR average precision score: 0.275

-> test with 'GB'
GB tn, fp: 281, 29
GB fn, tp: 9, 2
GB f1 score: 0.095
GB cohens kappa score: 0.047

-> test with 'KNN'
KNN tn, fp: 220, 90
KNN fn, tp: 7, 4
KNN f1 score: 0.076
KNN cohens kappa score: 0.016


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 221, 89
LR fn, tp: 4, 7
LR f1 score: 0.131
LR cohens kappa score: 0.074
LR average precision score: 0.193

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

-> test with 'KNN'
KNN tn, fp: 234, 76
KNN fn, tp: 7, 4
KNN f1 score: 0.088
KNN cohens kappa score: 0.029


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 248, 58
LR fn, tp: 4, 5
LR f1 score: 0.139
LR cohens kappa score: 0.094
LR average precision score: 0.142

-> test with 'GB'
GB tn, fp: 296, 10
GB fn, tp: 8, 1
GB f1 score: 0.100
GB cohens kappa score: 0.071

-> test with 'KNN'
KNN tn, fp: 227, 79
KNN fn, tp: 7, 2
KNN f1 score: 0.044
KNN cohens kappa score: -0.007

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 248, 112
LR fn, tp: 6, 11
LR f1 score: 0.171
LR cohens kappa score: 0.116
LR average precision score: 0.380


average:
LR tn, fp: 222.36, 86.84
LR fn, tp: 3.6, 7.0
LR f1 score: 0.133
LR cohens kappa score: 0.078
LR average precision score: 0.166


minimum:
LR tn, fp: 198, 58
LR fn, tp: 0, 3
LR f1 score: 0.057
LR cohens kappa score: 0.005
LR average precision score: 0.069


-----[ GB ]-----
maximum:
GB tn, fp: 298, 29
GB fn, tp: 11, 5
GB f1 score: 0.270
GB cohens kappa score: 0.233


average:
GB tn, fp: 291.6, 17.6
GB fn, tp: 8.8, 1.8
GB f1 score: 0.118
GB cohens kappa score: 0.079


minimum:
GB tn, fp: 281, 10
GB fn, tp: 6, 0
GB f1 score: 0.000
GB cohens kappa score: -0.044


-----[ KNN ]-----
maximum:
KNN tn, fp: 238, 106
KNN fn, tp: 9, 6
KNN f1 score: 0.121
KNN cohens kappa score: 0.064


average:
KNN tn, fp: 223.68, 85.52
KNN fn, tp: 7.24, 3.36
KNN f1 score: 0.067
KNN cohens kappa score: 0.008


minimum:
KNN tn, fp: 204, 68
KNN fn, tp: 5, 1
KNN f1 score: 0.023
KNN cohens kappa score: -0.029