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

///////////////////////////////////////////
// Running convGAN 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: 200, 110
LR fn, tp: 5, 6
LR f1 score: 0.094
LR cohens kappa score: 0.034
LR average precision score: 0.094

-> 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: 237, 73
KNN fn, tp: 7, 4
KNN f1 score: 0.091
KNN cohens kappa score: 0.033


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 223, 87
LR fn, tp: 3, 8
LR f1 score: 0.151
LR cohens kappa score: 0.095
LR average precision score: 0.110

-> 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: 229, 81
KNN fn, tp: 6, 5
KNN f1 score: 0.103
KNN cohens kappa score: 0.045


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 131
LR fn, tp: 1, 10
LR f1 score: 0.132
LR cohens kappa score: 0.073
LR average precision score: 0.234

-> test with 'GB'
GB tn, fp: 290, 20
GB fn, tp: 6, 5
GB f1 score: 0.278
GB cohens kappa score: 0.242

-> test with 'KNN'
KNN tn, fp: 211, 99
KNN fn, tp: 7, 4
KNN f1 score: 0.070
KNN cohens kappa score: 0.009


------ 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: 6, 5
LR f1 score: 0.108
LR cohens kappa score: 0.050
LR average precision score: 0.129

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

-> test with 'KNN'
KNN tn, fp: 235, 75
KNN fn, tp: 6, 5
KNN f1 score: 0.110
KNN cohens kappa score: 0.053


------ Step 1/5: 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: 4, 5
LR f1 score: 0.118
LR cohens kappa score: 0.070
LR average precision score: 0.212

-> 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: 232, 74
KNN fn, tp: 5, 4
KNN f1 score: 0.092
KNN cohens kappa score: 0.043


====== 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: 207, 103
LR fn, tp: 2, 9
LR f1 score: 0.146
LR cohens kappa score: 0.090
LR average precision score: 0.136

-> test with 'GB'
GB tn, fp: 289, 21
GB fn, tp: 7, 4
GB f1 score: 0.222
GB cohens kappa score: 0.183

-> test with 'KNN'
KNN tn, fp: 214, 96
KNN fn, tp: 8, 3
KNN f1 score: 0.055
KNN cohens kappa score: -0.008


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 209, 101
LR fn, tp: 3, 8
LR f1 score: 0.133
LR cohens kappa score: 0.076
LR average precision score: 0.128

-> 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: 229, 81
KNN fn, tp: 9, 2
KNN f1 score: 0.043
KNN cohens kappa score: -0.019


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 230, 80
LR fn, tp: 4, 7
LR f1 score: 0.143
LR cohens kappa score: 0.087
LR average precision score: 0.120

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

-> test with 'KNN'
KNN tn, fp: 241, 69
KNN fn, tp: 9, 2
KNN f1 score: 0.049
KNN cohens kappa score: -0.011


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 215, 95
LR fn, tp: 5, 6
LR f1 score: 0.107
LR cohens kappa score: 0.048
LR average precision score: 0.299

-> 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: 229, 81
KNN fn, tp: 6, 5
KNN f1 score: 0.103
KNN cohens kappa score: 0.045


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 226, 80
LR fn, tp: 3, 6
LR f1 score: 0.126
LR cohens kappa score: 0.079
LR average precision score: 0.136

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

-> test with 'KNN'
KNN tn, fp: 225, 81
KNN fn, tp: 6, 3
KNN f1 score: 0.065
KNN cohens kappa score: 0.014


====== 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: 230, 80
LR fn, tp: 4, 7
LR f1 score: 0.143
LR cohens kappa score: 0.087
LR average precision score: 0.152

-> 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: 222, 88
KNN fn, tp: 9, 2
KNN f1 score: 0.040
KNN cohens kappa score: -0.023


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 204, 106
LR fn, tp: 3, 8
LR f1 score: 0.128
LR cohens kappa score: 0.070
LR average precision score: 0.254

-> test with 'GB'
GB tn, fp: 291, 19
GB fn, tp: 8, 3
GB f1 score: 0.182
GB cohens kappa score: 0.143

-> test with 'KNN'
KNN tn, fp: 230, 80
KNN fn, tp: 9, 2
KNN f1 score: 0.043
KNN cohens kappa score: -0.019


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 238, 72
LR fn, tp: 5, 6
LR f1 score: 0.135
LR cohens kappa score: 0.080
LR average precision score: 0.070

-> test with 'GB'
GB tn, fp: 286, 24
GB fn, tp: 8, 3
GB f1 score: 0.158
GB cohens kappa score: 0.115

-> test with 'KNN'
KNN tn, fp: 230, 80
KNN fn, tp: 5, 6
KNN f1 score: 0.124
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: 201, 109
LR fn, tp: 2, 9
LR f1 score: 0.140
LR cohens kappa score: 0.082
LR average precision score: 0.182

-> test with 'GB'
GB tn, fp: 285, 25
GB fn, tp: 8, 3
GB f1 score: 0.154
GB cohens kappa score: 0.110

-> test with 'KNN'
KNN tn, fp: 230, 80
KNN fn, tp: 4, 7
KNN f1 score: 0.143
KNN cohens kappa score: 0.087


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 226, 80
LR fn, tp: 2, 7
LR f1 score: 0.146
LR cohens kappa score: 0.099
LR average precision score: 0.151

-> test with 'GB'
GB tn, fp: 293, 13
GB fn, tp: 6, 3
GB f1 score: 0.240
GB cohens kappa score: 0.211

-> test with 'KNN'
KNN tn, fp: 221, 85
KNN fn, tp: 5, 4
KNN f1 score: 0.082
KNN cohens kappa score: 0.031


====== 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: 224, 86
LR fn, tp: 3, 8
LR f1 score: 0.152
LR cohens kappa score: 0.097
LR average precision score: 0.378

-> 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: 234, 76
KNN fn, tp: 9, 2
KNN f1 score: 0.045
KNN cohens kappa score: -0.016


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 212, 98
LR fn, tp: 3, 8
LR f1 score: 0.137
LR cohens kappa score: 0.080
LR average precision score: 0.201

-> test with 'GB'
GB tn, fp: 285, 25
GB fn, tp: 8, 3
GB f1 score: 0.154
GB cohens kappa score: 0.110

-> test with 'KNN'
KNN tn, fp: 233, 77
KNN fn, tp: 8, 3
KNN f1 score: 0.066
KNN cohens kappa score: 0.006


------ Step 4/5: 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: 5, 6
LR f1 score: 0.117
LR cohens kappa score: 0.059
LR average precision score: 0.104

-> 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: 226, 84
KNN fn, tp: 8, 3
KNN f1 score: 0.061
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 216, 94
LR fn, tp: 1, 10
LR f1 score: 0.174
LR cohens kappa score: 0.119
LR average precision score: 0.141

-> test with 'GB'
GB tn, fp: 293, 17
GB fn, tp: 6, 5
GB f1 score: 0.303
GB cohens kappa score: 0.270

-> 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 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 209, 97
LR fn, tp: 5, 4
LR f1 score: 0.073
LR cohens kappa score: 0.021
LR average precision score: 0.058

-> test with 'GB'
GB tn, fp: 275, 31
GB fn, tp: 8, 1
GB f1 score: 0.049
GB cohens kappa score: 0.004

-> test with 'KNN'
KNN tn, fp: 230, 76
KNN fn, tp: 7, 2
KNN f1 score: 0.046
KNN cohens kappa score: -0.006


====== 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: 244, 66
LR fn, tp: 5, 6
LR f1 score: 0.145
LR cohens kappa score: 0.091
LR average precision score: 0.104

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

-> 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 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 230, 80
LR fn, tp: 6, 5
LR f1 score: 0.104
LR cohens kappa score: 0.046
LR average precision score: 0.097

-> 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: 216, 94
KNN fn, tp: 6, 5
KNN f1 score: 0.091
KNN cohens kappa score: 0.031


------ 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: 1, 10
LR f1 score: 0.156
LR cohens kappa score: 0.100
LR average precision score: 0.229

-> test with 'GB'
GB tn, fp: 282, 28
GB fn, tp: 7, 4
GB f1 score: 0.186
GB cohens kappa score: 0.142

-> test with 'KNN'
KNN tn, fp: 251, 59
KNN fn, tp: 6, 5
KNN f1 score: 0.133
KNN cohens kappa score: 0.079


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 235, 75
LR fn, tp: 5, 6
LR f1 score: 0.130
LR cohens kappa score: 0.075
LR average precision score: 0.192

-> 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: 238, 72
KNN fn, tp: 6, 5
KNN f1 score: 0.114
KNN cohens kappa score: 0.057


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 242, 64
LR fn, tp: 3, 6
LR f1 score: 0.152
LR cohens kappa score: 0.107
LR average precision score: 0.160

-> test with 'GB'
GB tn, fp: 288, 18
GB fn, tp: 7, 2
GB f1 score: 0.138
GB cohens kappa score: 0.103

-> test with 'KNN'
KNN tn, fp: 234, 72
KNN fn, tp: 8, 1
KNN f1 score: 0.024
KNN cohens kappa score: -0.028

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 244, 131
LR fn, tp: 6, 10
LR f1 score: 0.174
LR cohens kappa score: 0.119
LR average precision score: 0.378


average:
LR tn, fp: 219.8, 89.4
LR fn, tp: 3.56, 7.04
LR f1 score: 0.132
LR cohens kappa score: 0.077
LR average precision score: 0.163


minimum:
LR tn, fp: 179, 64
LR fn, tp: 1, 4
LR f1 score: 0.073
LR cohens kappa score: 0.021
LR average precision score: 0.058


-----[ GB ]-----
maximum:
GB tn, fp: 301, 31
GB fn, tp: 10, 5
GB f1 score: 0.303
GB cohens kappa score: 0.270


average:
GB tn, fp: 291.0, 18.2
GB fn, tp: 8.2, 2.4
GB f1 score: 0.150
GB cohens kappa score: 0.112


minimum:
GB tn, fp: 275, 9
GB fn, tp: 6, 0
GB f1 score: 0.000
GB cohens kappa score: -0.034


-----[ KNN ]-----
maximum:
KNN tn, fp: 251, 99
KNN fn, tp: 9, 7
KNN f1 score: 0.143
KNN cohens kappa score: 0.087


average:
KNN tn, fp: 229.52, 79.68
KNN fn, tp: 6.92, 3.68
KNN f1 score: 0.078
KNN cohens kappa score: 0.021


minimum:
KNN tn, fp: 211, 59
KNN fn, tp: 4, 1
KNN f1 score: 0.024
KNN cohens kappa score: -0.028