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

///////////////////////////////////////////
// Running convGAN-proxymary-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: 183, 127
GAN fn, tp: 5, 6
GAN f1 score: 0.083
GAN cohens kappa score: 0.021

-> 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: 0.136

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


------ 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: 184, 126
GAN fn, tp: 4, 7
GAN f1 score: 0.097
GAN cohens kappa score: 0.036

-> test with 'LR'
LR tn, fp: 225, 85
LR fn, tp: 4, 7
LR f1 score: 0.136
LR cohens kappa score: 0.080
LR average precision score: 0.110

-> 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: 233, 77
KNN fn, tp: 6, 5
KNN f1 score: 0.108
KNN cohens kappa score: 0.050


------ 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: 152, 158
GAN fn, tp: 6, 5
GAN f1 score: 0.057
GAN cohens kappa score: -0.007

-> test with 'LR'
LR tn, fp: 198, 112
LR fn, tp: 1, 10
LR f1 score: 0.150
LR cohens kappa score: 0.093
LR average precision score: 0.239

-> 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: 195, 115
KNN fn, tp: 7, 4
KNN f1 score: 0.062
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 GAN.predict
GAN tn, fp: 230, 80
GAN fn, tp: 7, 4
GAN f1 score: 0.084
GAN cohens kappa score: 0.025

-> test with 'LR'
LR tn, fp: 237, 73
LR fn, tp: 6, 5
LR f1 score: 0.112
LR cohens kappa score: 0.056
LR average precision score: 0.149

-> test with 'GB'
GB tn, fp: 291, 19
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.045

-> test with 'KNN'
KNN tn, fp: 231, 79
KNN fn, tp: 8, 3
KNN f1 score: 0.065
KNN cohens kappa score: 0.004


------ 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: 191, 115
GAN fn, tp: 4, 5
GAN f1 score: 0.078
GAN cohens kappa score: 0.026

-> test with 'LR'
LR tn, fp: 221, 85
LR fn, tp: 4, 5
LR f1 score: 0.101
LR cohens kappa score: 0.052
LR average precision score: 0.231

-> 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


====== 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: 207, 103
GAN fn, tp: 5, 6
GAN f1 score: 0.100
GAN cohens kappa score: 0.040

-> 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: 0.131

-> 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: 214, 96
KNN fn, tp: 6, 5
KNN f1 score: 0.089
KNN cohens kappa score: 0.029


------ 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: 257, 53
GAN fn, tp: 9, 2
GAN f1 score: 0.061
GAN cohens kappa score: 0.004

-> test with 'LR'
LR tn, fp: 216, 94
LR fn, tp: 3, 8
LR f1 score: 0.142
LR cohens kappa score: 0.085
LR average precision score: 0.132

-> test with 'GB'
GB tn, fp: 296, 14
GB fn, tp: 10, 1
GB f1 score: 0.077
GB cohens kappa score: 0.039

-> 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 GAN.predict
GAN tn, fp: 256, 54
GAN fn, tp: 9, 2
GAN f1 score: 0.060
GAN cohens kappa score: 0.003

-> 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: 0.174

-> test with 'GB'
GB tn, fp: 286, 24
GB fn, tp: 9, 2
GB f1 score: 0.108
GB cohens kappa score: 0.063

-> test with 'KNN'
KNN tn, fp: 227, 83
KNN fn, tp: 10, 1
KNN f1 score: 0.021
KNN cohens kappa score: -0.042


------ 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: 196, 114
GAN fn, tp: 6, 5
GAN f1 score: 0.077
GAN cohens kappa score: 0.015

-> 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: 0.269

-> test with 'GB'
GB tn, fp: 288, 22
GB fn, tp: 8, 3
GB f1 score: 0.167
GB cohens kappa score: 0.125

-> test with 'KNN'
KNN tn, fp: 221, 89
KNN fn, tp: 7, 4
KNN f1 score: 0.077
KNN cohens kappa score: 0.017


------ 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: 197, 109
GAN fn, tp: 5, 4
GAN f1 score: 0.066
GAN cohens kappa score: 0.013

-> test with 'LR'
LR tn, fp: 227, 79
LR fn, tp: 3, 6
LR f1 score: 0.128
LR cohens kappa score: 0.080
LR average precision score: 0.147

-> 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: 216, 90
KNN fn, tp: 7, 2
KNN f1 score: 0.040
KNN cohens kappa score: -0.013


====== 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: 180, 130
GAN fn, tp: 3, 8
GAN f1 score: 0.107
GAN cohens kappa score: 0.047

-> test with 'LR'
LR tn, fp: 225, 85
LR fn, tp: 4, 7
LR f1 score: 0.136
LR cohens kappa score: 0.080
LR average precision score: 0.149

-> 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: 223, 87
KNN fn, tp: 9, 2
KNN f1 score: 0.040
KNN cohens kappa score: -0.022


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

-> test with 'LR'
LR tn, fp: 210, 100
LR fn, tp: 4, 7
LR f1 score: 0.119
LR cohens kappa score: 0.060
LR average precision score: 0.265

-> 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: 228, 82
KNN fn, tp: 8, 3
KNN f1 score: 0.062
KNN cohens kappa score: 0.002


------ 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: 230, 80
GAN fn, tp: 5, 6
GAN f1 score: 0.124
GAN cohens kappa score: 0.067

-> 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: 0.068

-> 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: 203, 107
KNN fn, tp: 7, 4
KNN f1 score: 0.066
KNN cohens kappa score: 0.003


------ 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: 222, 88
GAN fn, tp: 4, 7
GAN f1 score: 0.132
GAN cohens kappa score: 0.075

-> 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: 0.194

-> 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: 219, 91
KNN fn, tp: 7, 4
KNN f1 score: 0.075
KNN cohens kappa score: 0.015


------ 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: 201, 105
GAN fn, tp: 6, 3
GAN f1 score: 0.051
GAN cohens kappa score: -0.002

-> test with 'LR'
LR tn, fp: 225, 81
LR fn, tp: 2, 7
LR f1 score: 0.144
LR cohens kappa score: 0.098
LR average precision score: 0.095

-> 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: 225, 81
KNN fn, tp: 7, 2
KNN f1 score: 0.043
KNN cohens kappa score: -0.009


====== 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: 171, 139
GAN fn, tp: 4, 7
GAN f1 score: 0.089
GAN cohens kappa score: 0.027

-> test with 'LR'
LR tn, fp: 225, 85
LR fn, tp: 3, 8
LR f1 score: 0.154
LR cohens kappa score: 0.099
LR average precision score: 0.403

-> test with 'GB'
GB tn, fp: 298, 12
GB fn, tp: 8, 3
GB f1 score: 0.231
GB cohens kappa score: 0.199

-> test with 'KNN'
KNN tn, fp: 232, 78
KNN fn, tp: 10, 1
KNN f1 score: 0.022
KNN cohens kappa score: -0.040


------ 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: 199, 111
GAN fn, tp: 9, 2
GAN f1 score: 0.032
GAN cohens kappa score: -0.032

-> test with 'LR'
LR tn, fp: 221, 89
LR fn, tp: 3, 8
LR f1 score: 0.148
LR cohens kappa score: 0.092
LR average precision score: 0.179

-> test with 'GB'
GB tn, fp: 288, 22
GB fn, tp: 9, 2
GB f1 score: 0.114
GB cohens kappa score: 0.071

-> test with 'KNN'
KNN tn, fp: 219, 91
KNN fn, tp: 10, 1
KNN f1 score: 0.019
KNN cohens kappa score: -0.045


------ 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: 188, 122
GAN fn, tp: 9, 2
GAN f1 score: 0.030
GAN cohens kappa score: -0.036

-> test with 'LR'
LR tn, fp: 229, 81
LR fn, tp: 5, 6
LR f1 score: 0.122
LR cohens kappa score: 0.066
LR average precision score: 0.109

-> 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: 234, 76
KNN fn, tp: 8, 3
KNN f1 score: 0.067
KNN cohens kappa score: 0.007


------ 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: 215, 95
GAN fn, tp: 6, 5
GAN f1 score: 0.090
GAN cohens kappa score: 0.030

-> 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: 0.135

-> test with 'GB'
GB tn, fp: 289, 21
GB fn, tp: 8, 3
GB f1 score: 0.171
GB cohens kappa score: 0.131

-> test with 'KNN'
KNN tn, fp: 224, 86
KNN fn, tp: 6, 5
KNN f1 score: 0.098
KNN cohens kappa score: 0.039


------ 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: 216, 90
GAN fn, tp: 6, 3
GAN f1 score: 0.059
GAN cohens kappa score: 0.007

-> test with 'LR'
LR tn, fp: 222, 84
LR fn, tp: 7, 2
LR f1 score: 0.042
LR cohens kappa score: -0.010
LR average precision score: 0.037

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

-> test with 'KNN'
KNN tn, fp: 224, 82
KNN fn, tp: 6, 3
KNN f1 score: 0.064
KNN cohens kappa score: 0.013


====== 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: 187, 123
GAN fn, tp: 7, 4
GAN f1 score: 0.058
GAN cohens kappa score: -0.005

-> test with 'LR'
LR tn, fp: 228, 82
LR fn, tp: 5, 6
LR f1 score: 0.121
LR cohens kappa score: 0.064
LR average precision score: 0.073

-> 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: 226, 84
KNN fn, tp: 9, 2
KNN f1 score: 0.041
KNN cohens kappa score: -0.021


------ 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: 232, 78
GAN fn, tp: 7, 4
GAN f1 score: 0.086
GAN cohens kappa score: 0.027

-> test with 'LR'
LR tn, fp: 224, 86
LR fn, tp: 6, 5
LR f1 score: 0.098
LR cohens kappa score: 0.039
LR average precision score: 0.098

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


------ 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: 212, 98
GAN fn, tp: 5, 6
GAN f1 score: 0.104
GAN cohens kappa score: 0.045

-> test with 'LR'
LR tn, fp: 199, 111
LR fn, tp: 0, 11
LR f1 score: 0.165
LR cohens kappa score: 0.109
LR average precision score: 0.291

-> test with 'GB'
GB tn, fp: 278, 32
GB fn, tp: 9, 2
GB f1 score: 0.089
GB cohens kappa score: 0.039

-> test with 'KNN'
KNN tn, fp: 222, 88
KNN fn, tp: 7, 4
KNN f1 score: 0.078
KNN cohens kappa score: 0.018


------ 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: 199, 111
GAN fn, tp: 5, 6
GAN f1 score: 0.094
GAN cohens kappa score: 0.033

-> test with 'LR'
LR tn, fp: 218, 92
LR fn, tp: 3, 8
LR f1 score: 0.144
LR cohens kappa score: 0.088
LR average precision score: 0.185

-> test with 'GB'
GB tn, fp: 290, 20
GB fn, tp: 7, 4
GB f1 score: 0.229
GB cohens kappa score: 0.191

-> test with 'KNN'
KNN tn, fp: 231, 79
KNN fn, tp: 6, 5
KNN f1 score: 0.105
KNN cohens kappa score: 0.048


------ 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: 218, 88
GAN fn, tp: 5, 4
GAN f1 score: 0.079
GAN cohens kappa score: 0.029

-> test with 'LR'
LR tn, fp: 245, 61
LR fn, tp: 4, 5
LR f1 score: 0.133
LR cohens kappa score: 0.087
LR average precision score: 0.146

-> test with 'GB'
GB tn, fp: 297, 9
GB fn, tp: 8, 1
GB f1 score: 0.105
GB cohens kappa score: 0.078

-> test with 'KNN'
KNN tn, fp: 220, 86
KNN fn, tp: 6, 3
KNN f1 score: 0.061
KNN cohens kappa score: 0.010

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 245, 112
LR fn, tp: 7, 11
LR f1 score: 0.174
LR cohens kappa score: 0.119
LR average precision score: 0.403


average:
LR tn, fp: 220.64, 88.56
LR fn, tp: 3.64, 6.96
LR f1 score: 0.130
LR cohens kappa score: 0.075
LR average precision score: 0.166


minimum:
LR tn, fp: 198, 61
LR fn, tp: 0, 2
LR f1 score: 0.042
LR cohens kappa score: -0.010
LR average precision score: 0.037


-----[ GB ]-----
maximum:
GB tn, fp: 298, 36
GB fn, tp: 11, 4
GB f1 score: 0.250
GB cohens kappa score: 0.218


average:
GB tn, fp: 289.28, 19.92
GB fn, tp: 8.4, 2.2
GB f1 score: 0.135
GB cohens kappa score: 0.095


minimum:
GB tn, fp: 270, 9
GB fn, tp: 5, 0
GB f1 score: 0.000
GB cohens kappa score: -0.045


-----[ KNN ]-----
maximum:
KNN tn, fp: 234, 115
KNN fn, tp: 10, 5
KNN f1 score: 0.108
KNN cohens kappa score: 0.050


average:
KNN tn, fp: 222.56, 86.64
KNN fn, tp: 7.48, 3.12
KNN f1 score: 0.061
KNN cohens kappa score: 0.002


minimum:
KNN tn, fp: 195, 72
KNN fn, tp: 6, 1
KNN f1 score: 0.019
KNN cohens kappa score: -0.045


-----[ GAN ]-----
maximum:
GAN tn, fp: 257, 158
GAN fn, tp: 9, 8
GAN f1 score: 0.132
GAN cohens kappa score: 0.075


average:
GAN tn, fp: 206.08, 103.12
GAN fn, tp: 5.88, 4.72
GAN f1 score: 0.080
GAN cohens kappa score: 0.021


minimum:
GAN tn, fp: 152, 53
GAN fn, tp: 3, 2
GAN f1 score: 0.030
GAN cohens kappa score: -0.036