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

///////////////////////////////////////////
// Running ProWRAS 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: 227, 83
LR fn, tp: 6, 5
LR f1 score: 0.101
LR cohens kappa score: 0.043
LR average precision score: 0.130

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

-> test with 'KNN'
KNN tn, fp: 264, 46
KNN fn, tp: 10, 1
KNN f1 score: 0.034
KNN cohens kappa score: -0.022


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 239, 71
LR fn, tp: 4, 7
LR f1 score: 0.157
LR cohens kappa score: 0.103
LR average precision score: 0.121

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

-> test with 'KNN'
KNN tn, fp: 273, 37
KNN fn, tp: 7, 4
KNN f1 score: 0.154
KNN cohens kappa score: 0.106


------ Step 1/5: 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: 0.262

-> 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: 253, 57
KNN fn, tp: 9, 2
KNN f1 score: 0.057
KNN cohens kappa score: -0.001


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 258, 52
LR fn, tp: 6, 5
LR f1 score: 0.147
LR cohens kappa score: 0.095
LR average precision score: 0.154

-> 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: 266, 44
KNN fn, tp: 10, 1
KNN f1 score: 0.036
KNN cohens kappa score: -0.020


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 233, 73
LR fn, tp: 4, 5
LR f1 score: 0.115
LR cohens kappa score: 0.067
LR average precision score: 0.211

-> 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: 269, 37
KNN fn, tp: 8, 1
KNN f1 score: 0.043
KNN cohens kappa score: -0.004


====== 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: 230, 80
LR fn, tp: 3, 8
LR f1 score: 0.162
LR cohens kappa score: 0.107
LR average precision score: 0.137

-> 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: 258, 52
KNN fn, tp: 9, 2
KNN f1 score: 0.062
KNN cohens kappa score: 0.005


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 233, 77
LR fn, tp: 3, 8
LR f1 score: 0.167
LR cohens kappa score: 0.113
LR average precision score: 0.140

-> 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: 269, 41
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.057


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 241, 69
LR fn, tp: 3, 8
LR f1 score: 0.182
LR cohens kappa score: 0.130
LR average precision score: 0.159

-> 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: 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: 232, 78
LR fn, tp: 6, 5
LR f1 score: 0.106
LR cohens kappa score: 0.049
LR average precision score: 0.259

-> 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: 270, 40
KNN fn, tp: 8, 3
KNN f1 score: 0.111
KNN cohens kappa score: 0.060


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 236, 70
LR fn, tp: 3, 6
LR f1 score: 0.141
LR cohens kappa score: 0.095
LR average precision score: 0.153

-> 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: 270, 36
KNN fn, tp: 6, 3
KNN f1 score: 0.125
KNN cohens kappa score: 0.082


====== 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: 238, 72
LR fn, tp: 5, 6
LR f1 score: 0.135
LR cohens kappa score: 0.080
LR average precision score: 0.177

-> 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: 277, 33
KNN fn, tp: 9, 2
KNN f1 score: 0.087
KNN cohens kappa score: 0.037


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 225, 85
LR fn, tp: 2, 9
LR f1 score: 0.171
LR cohens kappa score: 0.117
LR average precision score: 0.178

-> 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: 275, 35
KNN fn, tp: 10, 1
KNN f1 score: 0.043
KNN cohens kappa score: -0.010


------ Step 3/5: Slice 3/5 -------
-> 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: 0.063

-> 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: 274, 36
KNN fn, tp: 8, 3
KNN f1 score: 0.120
KNN cohens kappa score: 0.070


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 219, 91
LR fn, tp: 2, 9
LR f1 score: 0.162
LR cohens kappa score: 0.107
LR average precision score: 0.193

-> 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: 265, 45
KNN fn, tp: 7, 4
KNN f1 score: 0.133
KNN cohens kappa score: 0.082


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 241, 65
LR fn, tp: 3, 6
LR f1 score: 0.150
LR cohens kappa score: 0.105
LR average precision score: 0.117

-> test with 'GB'
GB tn, fp: 299, 7
GB fn, tp: 6, 3
GB f1 score: 0.316
GB cohens kappa score: 0.295

-> test with 'KNN'
KNN tn, fp: 259, 47
KNN fn, tp: 8, 1
KNN f1 score: 0.035
KNN cohens kappa score: -0.014


====== 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: 229, 81
LR fn, tp: 3, 8
LR f1 score: 0.160
LR cohens kappa score: 0.105
LR average precision score: 0.394

-> 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: 266, 44
KNN fn, tp: 10, 1
KNN f1 score: 0.036
KNN cohens kappa score: -0.020


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 234, 76
LR fn, tp: 3, 8
LR f1 score: 0.168
LR cohens kappa score: 0.115
LR average precision score: 0.175

-> test with 'GB'
GB tn, fp: 306, 4
GB fn, tp: 8, 3
GB f1 score: 0.333
GB cohens kappa score: 0.315

-> test with 'KNN'
KNN tn, fp: 258, 52
KNN fn, tp: 10, 1
KNN f1 score: 0.031
KNN cohens kappa score: -0.027


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 248, 62
LR fn, tp: 8, 3
LR f1 score: 0.079
LR cohens kappa score: 0.022
LR average precision score: 0.083

-> 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: 267, 43
KNN fn, tp: 8, 3
KNN f1 score: 0.105
KNN cohens kappa score: 0.053


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 225, 85
LR fn, tp: 2, 9
LR f1 score: 0.171
LR cohens kappa score: 0.117
LR average precision score: 0.134

-> 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: 262, 48
KNN fn, tp: 9, 2
KNN f1 score: 0.066
KNN cohens kappa score: 0.010


------ Step 4/5: 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: 6, 3
LR f1 score: 0.065
LR cohens kappa score: 0.014
LR average precision score: 0.075

-> test with 'GB'
GB tn, fp: 290, 16
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.038

-> test with 'KNN'
KNN tn, fp: 266, 40
KNN fn, tp: 8, 1
KNN f1 score: 0.040
KNN cohens kappa score: -0.007


====== 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: 246, 64
LR fn, tp: 5, 6
LR f1 score: 0.148
LR cohens kappa score: 0.095
LR average precision score: 0.084

-> 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: 254, 56
KNN fn, tp: 9, 2
KNN f1 score: 0.058
KNN cohens kappa score: 0.000


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 241, 69
LR fn, tp: 6, 5
LR f1 score: 0.118
LR cohens kappa score: 0.062
LR average precision score: 0.089

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

-> test with 'KNN'
KNN tn, fp: 259, 51
KNN fn, tp: 10, 1
KNN f1 score: 0.032
KNN cohens kappa score: -0.026


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 215, 95
LR fn, tp: 0, 11
LR f1 score: 0.188
LR cohens kappa score: 0.134
LR average precision score: 0.298

-> test with 'GB'
GB tn, fp: 297, 13
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.039

-> test with 'KNN'
KNN tn, fp: 275, 35
KNN fn, tp: 7, 4
KNN f1 score: 0.160
KNN cohens kappa score: 0.113


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 227, 83
LR fn, tp: 5, 6
LR f1 score: 0.120
LR cohens kappa score: 0.063
LR average precision score: 0.189

-> 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: 267, 43
KNN fn, tp: 8, 3
KNN f1 score: 0.105
KNN cohens kappa score: 0.053


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 258, 48
LR fn, tp: 4, 5
LR f1 score: 0.161
LR cohens kappa score: 0.118
LR average precision score: 0.143

-> test with 'GB'
GB tn, fp: 298, 8
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.028

-> test with 'KNN'
KNN tn, fp: 271, 35
KNN fn, tp: 7, 2
KNN f1 score: 0.087
KNN cohens kappa score: 0.043

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 258, 107
LR fn, tp: 8, 11
LR f1 score: 0.188
LR cohens kappa score: 0.134
LR average precision score: 0.394


average:
LR tn, fp: 233.92, 75.28
LR fn, tp: 3.92, 6.68
LR f1 score: 0.143
LR cohens kappa score: 0.090
LR average precision score: 0.165


minimum:
LR tn, fp: 203, 48
LR fn, tp: 0, 3
LR f1 score: 0.065
LR cohens kappa score: 0.014
LR average precision score: 0.063


-----[ GB ]-----
maximum:
GB tn, fp: 306, 16
GB fn, tp: 11, 3
GB f1 score: 0.333
GB cohens kappa score: 0.315


average:
GB tn, fp: 301.36, 7.84
GB fn, tp: 9.64, 0.96
GB f1 score: 0.096
GB cohens kappa score: 0.070


minimum:
GB tn, fp: 290, 3
GB fn, tp: 6, 0
GB f1 score: 0.000
GB cohens kappa score: -0.041


-----[ KNN ]-----
maximum:
KNN tn, fp: 284, 57
KNN fn, tp: 11, 4
KNN f1 score: 0.160
KNN cohens kappa score: 0.113


average:
KNN tn, fp: 266.84, 42.36
KNN fn, tp: 8.64, 1.96
KNN f1 score: 0.072
KNN cohens kappa score: 0.020


minimum:
KNN tn, fp: 253, 26
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.057