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

///////////////////////////////////////////
// Running Repeater on folding_winequality-red-4
///////////////////////////////////////////

Load '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: 186, 124
LR fn, tp: 1, 10
LR f1 score: 0.138
LR cohens kappa score: 0.080
LR average precision score: 0.093

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> 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: 261, 49
KNN fn, tp: 10, 1
KNN f1 score: 0.033
KNN cohens kappa score: -0.025


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 110
LR fn, tp: 3, 8
LR f1 score: 0.124
LR cohens kappa score: 0.065
LR average precision score: 0.111

-> test with 'RF'
RF tn, fp: 307, 3
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.015

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

-> test with 'KNN'
KNN tn, fp: 266, 44
KNN fn, tp: 7, 4
KNN f1 score: 0.136
KNN cohens kappa score: 0.085


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 168, 142
LR fn, tp: 1, 10
LR f1 score: 0.123
LR cohens kappa score: 0.063
LR average precision score: 0.148

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

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

-> test with 'KNN'
KNN tn, fp: 264, 46
KNN fn, tp: 8, 3
KNN f1 score: 0.100
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: 216, 94
LR fn, tp: 6, 5
LR f1 score: 0.091
LR cohens kappa score: 0.031
LR average precision score: 0.119

-> test with 'RF'
RF tn, fp: 307, 3
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.015

-> 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: 265, 45
KNN fn, tp: 8, 3
KNN f1 score: 0.102
KNN cohens kappa score: 0.049


------ Step 1/5: 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: 4, 5
LR f1 score: 0.090
LR cohens kappa score: 0.040
LR average precision score: 0.226

-> test with 'RF'
RF tn, fp: 306, 0
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> 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: 267, 39
KNN fn, tp: 8, 1
KNN f1 score: 0.041
KNN cohens kappa score: -0.006


====== 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: 185, 125
LR fn, tp: 2, 9
LR f1 score: 0.124
LR cohens kappa score: 0.065
LR average precision score: 0.130

-> test with 'RF'
RF tn, fp: 307, 3
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.015

-> 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: 268, 42
KNN fn, tp: 10, 1
KNN f1 score: 0.037
KNN cohens kappa score: -0.019


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 191, 119
LR fn, tp: 3, 8
LR f1 score: 0.116
LR cohens kappa score: 0.056
LR average precision score: 0.141

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> 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: 270, 40
KNN fn, tp: 10, 1
KNN f1 score: 0.038
KNN cohens kappa score: -0.016


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 192, 118
LR fn, tp: 3, 8
LR f1 score: 0.117
LR cohens kappa score: 0.057
LR average precision score: 0.164

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> 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: 263, 47
KNN fn, tp: 10, 1
KNN f1 score: 0.034
KNN cohens kappa score: -0.023


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 210, 100
LR fn, tp: 5, 6
LR f1 score: 0.103
LR cohens kappa score: 0.043
LR average precision score: 0.277

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

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

-> test with 'KNN'
KNN tn, fp: 271, 39
KNN fn, tp: 7, 4
KNN f1 score: 0.148
KNN cohens kappa score: 0.099


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 206, 100
LR fn, tp: 3, 6
LR f1 score: 0.104
LR cohens kappa score: 0.055
LR average precision score: 0.090

-> test with 'RF'
RF tn, fp: 306, 0
RF fn, tp: 8, 1
RF f1 score: 0.200
RF cohens kappa score: 0.195

-> test with 'GB'
GB tn, fp: 296, 10
GB fn, tp: 7, 2
GB f1 score: 0.190
GB cohens kappa score: 0.163

-> test with 'KNN'
KNN tn, fp: 262, 44
KNN fn, tp: 7, 2
KNN f1 score: 0.073
KNN cohens kappa score: 0.026


====== 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: 215, 95
LR fn, tp: 5, 6
LR f1 score: 0.107
LR cohens kappa score: 0.048
LR average precision score: 0.118

-> test with 'RF'
RF tn, fp: 308, 2
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.011

-> 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: 275, 35
KNN fn, tp: 9, 2
KNN f1 score: 0.083
KNN cohens kappa score: 0.032


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 193, 117
LR fn, tp: 2, 9
LR f1 score: 0.131
LR cohens kappa score: 0.073
LR average precision score: 0.224

-> test with 'RF'
RF tn, fp: 308, 2
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.011

-> 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: 267, 43
KNN fn, tp: 10, 1
KNN f1 score: 0.036
KNN cohens kappa score: -0.020


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 108
LR fn, tp: 6, 5
LR f1 score: 0.081
LR cohens kappa score: 0.019
LR average precision score: 0.077

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> 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: 263, 47
KNN fn, tp: 8, 3
KNN f1 score: 0.098
KNN cohens kappa score: 0.045


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 193, 117
LR fn, tp: 2, 9
LR f1 score: 0.131
LR cohens kappa score: 0.073
LR average precision score: 0.149

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> 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: 273, 37
KNN fn, tp: 9, 2
KNN f1 score: 0.080
KNN cohens kappa score: 0.028


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 198, 108
LR fn, tp: 2, 7
LR f1 score: 0.113
LR cohens kappa score: 0.063
LR average precision score: 0.108

-> test with 'RF'
RF tn, fp: 305, 1
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> 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: 258, 48
KNN fn, tp: 7, 2
KNN f1 score: 0.068
KNN cohens kappa score: 0.020


====== 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: 197, 113
LR fn, tp: 2, 9
LR f1 score: 0.135
LR cohens kappa score: 0.077
LR average precision score: 0.312

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> 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: 272, 38
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.056


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 183, 127
LR fn, tp: 3, 8
LR f1 score: 0.110
LR cohens kappa score: 0.049
LR average precision score: 0.203

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> 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: 271, 39
KNN fn, tp: 8, 3
KNN f1 score: 0.113
KNN cohens kappa score: 0.062


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 212, 98
LR fn, tp: 4, 7
LR f1 score: 0.121
LR cohens kappa score: 0.063
LR average precision score: 0.081

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> 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: 276, 34
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.055


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 110
LR fn, tp: 1, 10
LR f1 score: 0.153
LR cohens kappa score: 0.096
LR average precision score: 0.120

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> 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: 266, 44
KNN fn, tp: 9, 2
KNN f1 score: 0.070
KNN cohens kappa score: 0.016


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 124
LR fn, tp: 5, 4
LR f1 score: 0.058
LR cohens kappa score: 0.005
LR average precision score: 0.056

-> test with 'RF'
RF tn, fp: 306, 0
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 278, 28
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.045

-> test with 'KNN'
KNN tn, fp: 248, 58
KNN fn, tp: 8, 1
KNN f1 score: 0.029
KNN cohens kappa score: -0.021


====== 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: 194, 116
LR fn, tp: 5, 6
LR f1 score: 0.090
LR cohens kappa score: 0.029
LR average precision score: 0.071

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> 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: 270, 40
KNN fn, tp: 9, 2
KNN f1 score: 0.075
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: 201, 109
LR fn, tp: 5, 6
LR f1 score: 0.095
LR cohens kappa score: 0.035
LR average precision score: 0.086

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

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

-> test with 'KNN'
KNN tn, fp: 256, 54
KNN fn, tp: 8, 3
KNN f1 score: 0.088
KNN cohens kappa score: 0.033


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 170, 140
LR fn, tp: 0, 11
LR f1 score: 0.136
LR cohens kappa score: 0.077
LR average precision score: 0.235

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 293, 17
GB fn, tp: 7, 4
GB f1 score: 0.250
GB cohens kappa score: 0.215

-> test with 'KNN'
KNN tn, fp: 272, 38
KNN fn, tp: 8, 3
KNN f1 score: 0.115
KNN cohens kappa score: 0.065


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 194, 116
LR fn, tp: 3, 8
LR f1 score: 0.119
LR cohens kappa score: 0.059
LR average precision score: 0.174

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 10, 1
RF f1 score: 0.154
RF cohens kappa score: 0.145

-> test with 'GB'
GB tn, fp: 297, 13
GB fn, tp: 8, 3
GB f1 score: 0.222
GB cohens kappa score: 0.189

-> test with 'KNN'
KNN tn, fp: 269, 41
KNN fn, tp: 9, 2
KNN f1 score: 0.074
KNN cohens kappa score: 0.021


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 221, 85
LR fn, tp: 3, 6
LR f1 score: 0.120
LR cohens kappa score: 0.072
LR average precision score: 0.126

-> test with 'RF'
RF tn, fp: 306, 0
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

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

-> test with 'KNN'
KNN tn, fp: 267, 39
KNN fn, tp: 8, 1
KNN f1 score: 0.041
KNN cohens kappa score: -0.006

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 221, 142
LR fn, tp: 6, 11
LR f1 score: 0.153
LR cohens kappa score: 0.096
LR average precision score: 0.312


average:
LR tn, fp: 196.72, 112.48
LR fn, tp: 3.16, 7.44
LR f1 score: 0.113
LR cohens kappa score: 0.056
LR average precision score: 0.146


minimum:
LR tn, fp: 168, 85
LR fn, tp: 0, 4
LR f1 score: 0.058
LR cohens kappa score: 0.005
LR average precision score: 0.056


-----[ RF ]-----
maximum:
RF tn, fp: 310, 3
RF fn, tp: 11, 1
RF f1 score: 0.200
RF cohens kappa score: 0.195


average:
RF tn, fp: 308.36, 0.84
RF fn, tp: 10.52, 0.08
RF f1 score: 0.014
RF cohens kappa score: 0.009


minimum:
RF tn, fp: 305, 0
RF fn, tp: 8, 0
RF f1 score: 0.000
RF cohens kappa score: -0.015


-----[ GB ]-----
maximum:
GB tn, fp: 305, 28
GB fn, tp: 11, 4
GB f1 score: 0.296
GB cohens kappa score: 0.267


average:
GB tn, fp: 296.48, 12.72
GB fn, tp: 9.0, 1.6
GB f1 score: 0.127
GB cohens kappa score: 0.094


minimum:
GB tn, fp: 278, 5
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.045


-----[ KNN ]-----
maximum:
KNN tn, fp: 276, 58
KNN fn, tp: 11, 4
KNN f1 score: 0.148
KNN cohens kappa score: 0.099


average:
KNN tn, fp: 266.4, 42.8
KNN fn, tp: 8.68, 1.92
KNN f1 score: 0.069
KNN cohens kappa score: 0.016


minimum:
KNN tn, fp: 248, 34
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.056

  wall time: 00:00:30s, process time: 00:01:14s