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

///////////////////////////////////////////
// Running convGAN-majority-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 GAN.predict
GAN tn, fp: 208, 102
GAN fn, tp: 4, 7
GAN f1 score: 0.117
GAN cohens kappa score: 0.058

-> 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: 0.130

-> test with 'GB'
GB tn, fp: 287, 23
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.049

-> test with 'KNN'
KNN tn, fp: 210, 100
KNN fn, tp: 6, 5
KNN f1 score: 0.086
KNN cohens kappa score: 0.026


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 252, 58
GAN fn, tp: 6, 5
GAN f1 score: 0.135
GAN cohens kappa score: 0.082

-> test with 'LR'
LR tn, fp: 228, 82
LR fn, tp: 3, 8
LR f1 score: 0.158
LR cohens kappa score: 0.104
LR average precision score: 0.118

-> 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: 243, 67
KNN fn, tp: 9, 2
KNN f1 score: 0.050
KNN cohens kappa score: -0.010


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 252, 58
GAN fn, tp: 6, 5
GAN f1 score: 0.135
GAN cohens kappa score: 0.082

-> test with 'LR'
LR tn, fp: 203, 107
LR fn, tp: 2, 9
LR f1 score: 0.142
LR cohens kappa score: 0.084
LR average precision score: 0.224

-> test with 'GB'
GB tn, fp: 284, 26
GB fn, tp: 7, 4
GB f1 score: 0.195
GB cohens kappa score: 0.153

-> test with 'KNN'
KNN tn, fp: 198, 112
KNN fn, tp: 8, 3
KNN f1 score: 0.048
KNN cohens kappa score: -0.016


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 259, 51
GAN fn, tp: 10, 1
GAN f1 score: 0.032
GAN cohens kappa score: -0.026

-> 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: 0.106

-> 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: 241, 69
KNN fn, tp: 10, 1
KNN f1 score: 0.025
KNN cohens kappa score: -0.037


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with GAN.predict
GAN tn, fp: 242, 64
GAN fn, tp: 4, 5
GAN f1 score: 0.128
GAN cohens kappa score: 0.082

-> test with 'LR'
LR tn, fp: 227, 79
LR fn, tp: 4, 5
LR f1 score: 0.108
LR cohens kappa score: 0.059
LR average precision score: 0.227

-> test with 'GB'
GB tn, fp: 287, 19
GB fn, tp: 7, 2
GB f1 score: 0.133
GB cohens kappa score: 0.097

-> test with 'KNN'
KNN tn, fp: 237, 69
KNN fn, tp: 5, 4
KNN f1 score: 0.098
KNN cohens kappa score: 0.049


====== 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 GAN.predict
GAN tn, fp: 252, 58
GAN fn, tp: 8, 3
GAN f1 score: 0.083
GAN cohens kappa score: 0.027

-> test with 'LR'
LR tn, fp: 206, 104
LR fn, tp: 2, 9
LR f1 score: 0.145
LR cohens kappa score: 0.088
LR average precision score: 0.139

-> test with 'GB'
GB tn, fp: 293, 17
GB fn, tp: 8, 3
GB f1 score: 0.194
GB cohens kappa score: 0.156

-> test with 'KNN'
KNN tn, fp: 225, 85
KNN fn, tp: 8, 3
KNN f1 score: 0.061
KNN cohens kappa score: -0.000


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 247, 63
GAN fn, tp: 7, 4
GAN f1 score: 0.103
GAN cohens kappa score: 0.046

-> test with 'LR'
LR tn, fp: 209, 101
LR fn, tp: 4, 7
LR f1 score: 0.118
LR cohens kappa score: 0.059
LR average precision score: 0.132

-> test with 'GB'
GB tn, fp: 292, 18
GB fn, tp: 10, 1
GB f1 score: 0.067
GB cohens kappa score: 0.024

-> test with 'KNN'
KNN tn, fp: 225, 85
KNN fn, tp: 6, 5
KNN f1 score: 0.099
KNN cohens kappa score: 0.040


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 277, 33
GAN fn, tp: 8, 3
GAN f1 score: 0.128
GAN cohens kappa score: 0.079

-> test with 'LR'
LR tn, fp: 227, 83
LR fn, tp: 3, 8
LR f1 score: 0.157
LR cohens kappa score: 0.102
LR average precision score: 0.184

-> 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: 259, 51
KNN fn, tp: 9, 2
KNN f1 score: 0.062
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 GAN.predict
GAN tn, fp: 258, 52
GAN fn, tp: 10, 1
GAN f1 score: 0.031
GAN cohens kappa score: -0.027

-> test with 'LR'
LR tn, fp: 226, 84
LR fn, tp: 4, 7
LR f1 score: 0.137
LR cohens kappa score: 0.081
LR average precision score: 0.287

-> 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: 231, 79
KNN fn, tp: 10, 1
KNN f1 score: 0.022
KNN cohens kappa score: -0.041


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with GAN.predict
GAN tn, fp: 260, 46
GAN fn, tp: 6, 3
GAN f1 score: 0.103
GAN cohens kappa score: 0.058

-> test with 'LR'
LR tn, fp: 230, 76
LR fn, tp: 3, 6
LR f1 score: 0.132
LR cohens kappa score: 0.085
LR average precision score: 0.118

-> test with 'GB'
GB tn, fp: 287, 19
GB fn, tp: 5, 4
GB f1 score: 0.250
GB cohens kappa score: 0.218

-> test with 'KNN'
KNN tn, fp: 226, 80
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 GAN.predict
GAN tn, fp: 248, 62
GAN fn, tp: 5, 6
GAN f1 score: 0.152
GAN cohens kappa score: 0.099

-> test with 'LR'
LR tn, fp: 233, 77
LR fn, tp: 4, 7
LR f1 score: 0.147
LR cohens kappa score: 0.092
LR average precision score: 0.166

-> 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: 249, 61
KNN fn, tp: 9, 2
KNN f1 score: 0.054
KNN cohens kappa score: -0.005


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 242, 68
GAN fn, tp: 5, 6
GAN f1 score: 0.141
GAN cohens kappa score: 0.087

-> test with 'LR'
LR tn, fp: 213, 97
LR fn, tp: 3, 8
LR f1 score: 0.138
LR cohens kappa score: 0.081
LR average precision score: 0.277

-> test with 'GB'
GB tn, fp: 290, 20
GB fn, tp: 8, 3
GB f1 score: 0.176
GB cohens kappa score: 0.136

-> test with 'KNN'
KNN tn, fp: 228, 82
KNN fn, tp: 6, 5
KNN f1 score: 0.102
KNN cohens kappa score: 0.044


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 235, 75
GAN fn, tp: 7, 4
GAN f1 score: 0.089
GAN cohens kappa score: 0.031

-> test with 'LR'
LR tn, fp: 220, 90
LR fn, tp: 5, 6
LR f1 score: 0.112
LR cohens kappa score: 0.054
LR average precision score: 0.084

-> 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: 227, 83
KNN fn, tp: 7, 4
KNN f1 score: 0.082
KNN cohens kappa score: 0.022


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 257, 53
GAN fn, tp: 6, 5
GAN f1 score: 0.145
GAN cohens kappa score: 0.093

-> test with 'LR'
LR tn, fp: 220, 90
LR fn, tp: 2, 9
LR f1 score: 0.164
LR cohens kappa score: 0.109
LR average precision score: 0.159

-> 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: 233, 77
KNN fn, tp: 8, 3
KNN f1 score: 0.066
KNN cohens kappa score: 0.006


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with GAN.predict
GAN tn, fp: 251, 55
GAN fn, tp: 6, 3
GAN f1 score: 0.090
GAN cohens kappa score: 0.042

-> test with 'LR'
LR tn, fp: 219, 87
LR fn, tp: 2, 7
LR f1 score: 0.136
LR cohens kappa score: 0.088
LR average precision score: 0.201

-> test with 'GB'
GB tn, fp: 284, 22
GB fn, tp: 7, 2
GB f1 score: 0.121
GB cohens kappa score: 0.083

-> test with 'KNN'
KNN tn, fp: 214, 92
KNN fn, tp: 6, 3
KNN f1 score: 0.058
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 GAN.predict
GAN tn, fp: 272, 38
GAN fn, tp: 6, 5
GAN f1 score: 0.185
GAN cohens kappa score: 0.138

-> 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: 0.340

-> 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: 225, 85
KNN fn, tp: 5, 6
KNN f1 score: 0.118
KNN cohens kappa score: 0.060


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 247, 63
GAN fn, tp: 8, 3
GAN f1 score: 0.078
GAN cohens kappa score: 0.020

-> test with 'LR'
LR tn, fp: 214, 96
LR fn, tp: 3, 8
LR f1 score: 0.139
LR cohens kappa score: 0.082
LR average precision score: 0.185

-> test with 'GB'
GB tn, fp: 285, 25
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.050

-> 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 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 235, 75
GAN fn, tp: 9, 2
GAN f1 score: 0.045
GAN cohens kappa score: -0.015

-> 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: 0.090

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


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 256, 54
GAN fn, tp: 7, 4
GAN f1 score: 0.116
GAN cohens kappa score: 0.062

-> test with 'LR'
LR tn, fp: 218, 92
LR fn, tp: 1, 10
LR f1 score: 0.177
LR cohens kappa score: 0.123
LR average precision score: 0.156

-> test with 'GB'
GB tn, fp: 295, 15
GB fn, tp: 8, 3
GB f1 score: 0.207
GB cohens kappa score: 0.172

-> test with 'KNN'
KNN tn, fp: 251, 59
KNN fn, tp: 9, 2
KNN f1 score: 0.056
KNN cohens kappa score: -0.003


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with GAN.predict
GAN tn, fp: 239, 67
GAN fn, tp: 8, 1
GAN f1 score: 0.026
GAN cohens kappa score: -0.026

-> test with 'LR'
LR tn, fp: 211, 95
LR fn, tp: 5, 4
LR f1 score: 0.074
LR cohens kappa score: 0.023
LR average precision score: 0.058

-> test with 'GB'
GB tn, fp: 274, 32
GB fn, tp: 8, 1
GB f1 score: 0.048
GB cohens kappa score: 0.003

-> test with 'KNN'
KNN tn, fp: 219, 87
KNN fn, tp: 8, 1
KNN f1 score: 0.021
KNN cohens kappa score: -0.033


====== 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 GAN.predict
GAN tn, fp: 258, 52
GAN fn, tp: 8, 3
GAN f1 score: 0.091
GAN cohens kappa score: 0.036

-> test with 'LR'
LR tn, fp: 221, 89
LR fn, tp: 5, 6
LR f1 score: 0.113
LR cohens kappa score: 0.055
LR average precision score: 0.077

-> test with 'GB'
GB tn, fp: 292, 18
GB fn, tp: 10, 1
GB f1 score: 0.067
GB cohens kappa score: 0.024

-> test with 'KNN'
KNN tn, fp: 220, 90
KNN fn, tp: 8, 3
KNN f1 score: 0.058
KNN cohens kappa score: -0.004


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 235, 75
GAN fn, tp: 9, 2
GAN f1 score: 0.045
GAN cohens kappa score: -0.015

-> test with 'LR'
LR tn, fp: 220, 90
LR fn, tp: 5, 6
LR f1 score: 0.112
LR cohens kappa score: 0.054
LR average precision score: 0.101

-> test with 'GB'
GB tn, fp: 293, 17
GB fn, tp: 10, 1
GB f1 score: 0.069
GB cohens kappa score: 0.028

-> 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 GAN.predict
GAN tn, fp: 245, 65
GAN fn, tp: 5, 6
GAN f1 score: 0.146
GAN cohens kappa score: 0.092

-> test with 'LR'
LR tn, fp: 196, 114
LR fn, tp: 0, 11
LR f1 score: 0.162
LR cohens kappa score: 0.105
LR average precision score: 0.288

-> 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: 218, 92
KNN fn, tp: 7, 4
KNN f1 score: 0.075
KNN cohens kappa score: 0.014


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 246, 64
GAN fn, tp: 6, 5
GAN f1 score: 0.125
GAN cohens kappa score: 0.070

-> 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: 0.188

-> 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: 219, 91
KNN fn, tp: 8, 3
KNN f1 score: 0.057
KNN cohens kappa score: -0.004


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with GAN.predict
GAN tn, fp: 255, 51
GAN fn, tp: 6, 3
GAN f1 score: 0.095
GAN cohens kappa score: 0.049

-> test with 'LR'
LR tn, fp: 238, 68
LR fn, tp: 3, 6
LR f1 score: 0.145
LR cohens kappa score: 0.099
LR average precision score: 0.176

-> test with 'GB'
GB tn, fp: 292, 14
GB fn, tp: 7, 2
GB f1 score: 0.160
GB cohens kappa score: 0.128

-> test with 'KNN'
KNN tn, fp: 239, 67
KNN fn, tp: 7, 2
KNN f1 score: 0.051
KNN cohens kappa score: 0.001

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 241, 114
LR fn, tp: 6, 11
LR f1 score: 0.177
LR cohens kappa score: 0.123
LR average precision score: 0.340


average:
LR tn, fp: 219.64, 89.56
LR fn, tp: 3.36, 7.24
LR f1 score: 0.134
LR cohens kappa score: 0.079
LR average precision score: 0.168


minimum:
LR tn, fp: 196, 68
LR fn, tp: 0, 4
LR f1 score: 0.074
LR cohens kappa score: 0.023
LR average precision score: 0.058


-----[ GB ]-----
maximum:
GB tn, fp: 301, 32
GB fn, tp: 11, 4
GB f1 score: 0.250
GB cohens kappa score: 0.218


average:
GB tn, fp: 290.16, 19.04
GB fn, tp: 8.84, 1.76
GB f1 score: 0.111
GB cohens kappa score: 0.071


minimum:
GB tn, fp: 274, 9
GB fn, tp: 5, 0
GB f1 score: 0.000
GB cohens kappa score: -0.050


-----[ KNN ]-----
maximum:
KNN tn, fp: 259, 112
KNN fn, tp: 10, 6
KNN f1 score: 0.118
KNN cohens kappa score: 0.060


average:
KNN tn, fp: 228.24, 80.96
KNN fn, tp: 7.56, 3.04
KNN f1 score: 0.063
KNN cohens kappa score: 0.005


minimum:
KNN tn, fp: 198, 51
KNN fn, tp: 5, 1
KNN f1 score: 0.021
KNN cohens kappa score: -0.041


-----[ GAN ]-----
maximum:
GAN tn, fp: 277, 102
GAN fn, tp: 10, 7
GAN f1 score: 0.185
GAN cohens kappa score: 0.138


average:
GAN tn, fp: 249.12, 60.08
GAN fn, tp: 6.8, 3.8
GAN f1 score: 0.103
GAN cohens kappa score: 0.049


minimum:
GAN tn, fp: 208, 33
GAN fn, tp: 4, 1
GAN f1 score: 0.026
GAN cohens kappa score: -0.027