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

///////////////////////////////////////////
// Running ProWRAS 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: 225, 85
LR fn, tp: 6, 5
LR f1 score: 0.099
LR cohens kappa score: 0.040
LR average precision score: 0.126

-> 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: 294, 16
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.042

-> 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 1/5: 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: 4, 7
LR f1 score: 0.149
LR cohens kappa score: 0.094
LR average precision score: 0.120

-> 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: 303, 7
GB fn, tp: 10, 1
GB f1 score: 0.105
GB cohens kappa score: 0.079

-> test with 'KNN'
KNN tn, fp: 272, 38
KNN fn, tp: 7, 4
KNN f1 score: 0.151
KNN cohens kappa score: 0.102


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 209, 101
LR fn, tp: 1, 10
LR f1 score: 0.164
LR cohens kappa score: 0.108
LR average precision score: 0.266

-> 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: 302, 8
GB fn, tp: 9, 2
GB f1 score: 0.190
GB cohens kappa score: 0.163

-> 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 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 260, 50
LR fn, tp: 6, 5
LR f1 score: 0.152
LR cohens kappa score: 0.100
LR average precision score: 0.153

-> test with 'RF'
RF tn, fp: 307, 3
RF fn, tp: 10, 1
RF f1 score: 0.133
RF cohens kappa score: 0.117

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


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 232, 74
LR fn, tp: 4, 5
LR f1 score: 0.114
LR cohens kappa score: 0.066
LR average precision score: 0.215

-> 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: 303, 3
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.014

-> test with 'KNN'
KNN tn, fp: 268, 38
KNN fn, tp: 8, 1
KNN f1 score: 0.042
KNN cohens kappa score: -0.005


====== 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: 224, 86
LR fn, tp: 3, 8
LR f1 score: 0.152
LR cohens kappa score: 0.097
LR average precision score: 0.136

-> 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: 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: 235, 75
LR fn, tp: 3, 8
LR f1 score: 0.170
LR cohens kappa score: 0.117
LR average precision score: 0.155

-> 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: 307, 3
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

-> test with 'KNN'
KNN tn, fp: 270, 40
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: 242, 68
LR fn, tp: 3, 8
LR f1 score: 0.184
LR cohens kappa score: 0.132
LR average precision score: 0.159

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

-> 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: 285, 25
KNN fn, tp: 10, 1
KNN f1 score: 0.054
KNN cohens kappa score: 0.006


------ 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: 6, 5
LR f1 score: 0.108
LR cohens kappa score: 0.050
LR average precision score: 0.259

-> 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: 307, 3
GB fn, tp: 10, 1
GB f1 score: 0.133
GB cohens kappa score: 0.117

-> 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: 239, 67
LR fn, tp: 3, 6
LR f1 score: 0.146
LR cohens kappa score: 0.101
LR average precision score: 0.172

-> 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: 301, 5
GB fn, tp: 8, 1
GB f1 score: 0.133
GB cohens kappa score: 0.113

-> test with 'KNN'
KNN tn, fp: 271, 35
KNN fn, tp: 5, 4
KNN f1 score: 0.167
KNN cohens kappa score: 0.126


====== 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: 241, 69
LR fn, tp: 5, 6
LR f1 score: 0.140
LR cohens kappa score: 0.085
LR average precision score: 0.178

-> 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: 308, 2
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011

-> test with 'KNN'
KNN tn, fp: 276, 34
KNN fn, tp: 9, 2
KNN f1 score: 0.085
KNN cohens kappa score: 0.034


------ Step 3/5: 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: 2, 9
LR f1 score: 0.180
LR cohens kappa score: 0.127
LR average precision score: 0.182

-> 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: 275, 35
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.055


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 245, 65
LR fn, tp: 5, 6
LR f1 score: 0.146
LR cohens kappa score: 0.092
LR average precision score: 0.063

-> test with 'RF'
RF tn, fp: 306, 4
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.019

-> 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: 272, 38
KNN fn, tp: 8, 3
KNN f1 score: 0.115
KNN cohens kappa score: 0.065


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 218, 92
LR fn, tp: 2, 9
LR f1 score: 0.161
LR cohens kappa score: 0.105
LR average precision score: 0.198

-> 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: 302, 8
GB fn, tp: 10, 1
GB f1 score: 0.100
GB cohens kappa score: 0.071

-> 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 3/5: 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: 0.116

-> test with 'RF'
RF tn, fp: 304, 2
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.010

-> 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: 258, 48
KNN fn, tp: 8, 1
KNN f1 score: 0.034
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 '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: 307, 3
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

-> 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 4/5: 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: 0.168

-> 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: 307, 3
GB fn, tp: 8, 3
GB f1 score: 0.353
GB cohens kappa score: 0.337

-> test with 'KNN'
KNN tn, fp: 260, 50
KNN fn, tp: 10, 1
KNN f1 score: 0.032
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: 251, 59
LR fn, tp: 8, 3
LR f1 score: 0.082
LR cohens kappa score: 0.025
LR average precision score: 0.083

-> 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: 300, 10
GB fn, tp: 10, 1
GB f1 score: 0.091
GB cohens kappa score: 0.059

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

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

-> test with 'GB'
GB tn, fp: 290, 16
GB fn, tp: 8, 1
GB f1 score: 0.077
GB cohens kappa score: 0.041

-> 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 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: 0.089

-> 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: 300, 10
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.034

-> 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: 242, 68
LR fn, tp: 6, 5
LR f1 score: 0.119
LR cohens kappa score: 0.063
LR average precision score: 0.094

-> 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: 299, 11
GB fn, tp: 10, 1
GB f1 score: 0.087
GB cohens kappa score: 0.053

-> 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 5/5: 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: 0, 11
LR f1 score: 0.183
LR cohens kappa score: 0.129
LR average precision score: 0.300

-> 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: 301, 9
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.032

-> 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: 0.191

-> 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: 305, 5
GB fn, tp: 8, 3
GB f1 score: 0.316
GB cohens kappa score: 0.295

-> test with 'KNN'
KNN tn, fp: 266, 44
KNN fn, tp: 8, 3
KNN f1 score: 0.103
KNN cohens kappa score: 0.051


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 252, 54
LR fn, tp: 4, 5
LR f1 score: 0.147
LR cohens kappa score: 0.103
LR average precision score: 0.131

-> 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: 297, 9
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.029

-> test with 'KNN'
KNN tn, fp: 270, 36
KNN fn, tp: 7, 2
KNN f1 score: 0.085
KNN cohens kappa score: 0.041

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 260, 101
LR fn, tp: 8, 11
LR f1 score: 0.184
LR cohens kappa score: 0.132
LR average precision score: 0.394


average:
LR tn, fp: 234.08, 75.12
LR fn, tp: 3.92, 6.68
LR f1 score: 0.143
LR cohens kappa score: 0.090
LR average precision score: 0.166


minimum:
LR tn, fp: 209, 50
LR fn, tp: 0, 3
LR f1 score: 0.065
LR cohens kappa score: 0.014
LR average precision score: 0.063


-----[ RF ]-----
maximum:
RF tn, fp: 310, 5
RF fn, tp: 11, 1
RF f1 score: 0.133
RF cohens kappa score: 0.117


average:
RF tn, fp: 307.64, 1.56
RF fn, tp: 10.56, 0.04
RF f1 score: 0.005
RF cohens kappa score: -0.002


minimum:
RF tn, fp: 301, 0
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.022


-----[ GB ]-----
maximum:
GB tn, fp: 308, 16
GB fn, tp: 11, 3
GB f1 score: 0.353
GB cohens kappa score: 0.337


average:
GB tn, fp: 301.4, 7.8
GB fn, tp: 9.64, 0.96
GB f1 score: 0.099
GB cohens kappa score: 0.073


minimum:
GB tn, fp: 290, 2
GB fn, tp: 6, 0
GB f1 score: 0.000
GB cohens kappa score: -0.042


-----[ KNN ]-----
maximum:
KNN tn, fp: 285, 56
KNN fn, tp: 11, 4
KNN f1 score: 0.167
KNN cohens kappa score: 0.126


average:
KNN tn, fp: 267.0, 42.2
KNN fn, tp: 8.64, 1.96
KNN f1 score: 0.072
KNN cohens kappa score: 0.020


minimum:
KNN tn, fp: 254, 25
KNN fn, tp: 5, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.057

  wall time: 00:14:42s, process time: 03:24:05s