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: 209, 101
GAN fn, tp: 5, 6
GAN f1 score: 0.102
GAN cohens kappa score: 0.042

-> test with 'LR'
LR tn, fp: 208, 102
LR fn, tp: 4, 7
LR f1 score: 0.117
LR cohens kappa score: 0.058
LR average precision score: 0.121

-> test with 'GB'
GB tn, fp: 289, 21
GB fn, tp: 9, 2
GB f1 score: 0.118
GB cohens kappa score: 0.075

-> test with 'KNN'
KNN tn, fp: 221, 89
KNN fn, tp: 8, 3
KNN f1 score: 0.058
KNN cohens kappa score: -0.003


------ 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: 7, 4
GAN f1 score: 0.110
GAN cohens kappa score: 0.055

-> 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: 0.118

-> 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: 232, 78
KNN fn, tp: 6, 5
KNN f1 score: 0.106
KNN cohens kappa score: 0.049


------ 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: 188, 122
GAN fn, tp: 7, 4
GAN f1 score: 0.058
GAN cohens kappa score: -0.005

-> test with 'LR'
LR tn, fp: 194, 116
LR fn, tp: 2, 9
LR f1 score: 0.132
LR cohens kappa score: 0.074
LR average precision score: 0.239

-> test with 'GB'
GB tn, fp: 281, 29
GB fn, tp: 6, 5
GB f1 score: 0.222
GB cohens kappa score: 0.180

-> test with 'KNN'
KNN tn, fp: 202, 108
KNN fn, tp: 7, 4
KNN f1 score: 0.065
KNN cohens kappa score: 0.003


------ 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: 9, 2
GAN f1 score: 0.062
GAN cohens kappa score: 0.006

-> 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: 0.148

-> test with 'GB'
GB tn, fp: 296, 14
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.040

-> test with 'KNN'
KNN tn, fp: 230, 80
KNN fn, tp: 9, 2
KNN f1 score: 0.043
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 GAN.predict
GAN tn, fp: 213, 93
GAN fn, tp: 6, 3
GAN f1 score: 0.057
GAN cohens kappa score: 0.005

-> test with 'LR'
LR tn, fp: 225, 81
LR fn, tp: 4, 5
LR f1 score: 0.105
LR cohens kappa score: 0.056
LR average precision score: 0.226

-> test with 'GB'
GB tn, fp: 286, 20
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.041

-> test with 'KNN'
KNN tn, fp: 231, 75
KNN fn, tp: 7, 2
KNN f1 score: 0.047
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 GAN.predict
GAN tn, fp: 214, 96
GAN fn, tp: 4, 7
GAN f1 score: 0.123
GAN cohens kappa score: 0.065

-> test with 'LR'
LR tn, fp: 210, 100
LR fn, tp: 3, 8
LR f1 score: 0.134
LR cohens kappa score: 0.077
LR average precision score: 0.127

-> test with 'GB'
GB tn, fp: 286, 24
GB fn, tp: 7, 4
GB f1 score: 0.205
GB cohens kappa score: 0.164

-> test with 'KNN'
KNN tn, fp: 216, 94
KNN fn, tp: 7, 4
KNN f1 score: 0.073
KNN cohens kappa score: 0.013


------ 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: 229, 81
GAN fn, tp: 7, 4
GAN f1 score: 0.083
GAN cohens kappa score: 0.024

-> test with 'LR'
LR tn, fp: 217, 93
LR fn, tp: 3, 8
LR f1 score: 0.143
LR cohens kappa score: 0.086
LR average precision score: 0.142

-> test with 'GB'
GB tn, fp: 297, 13
GB fn, tp: 10, 1
GB f1 score: 0.080
GB cohens kappa score: 0.043

-> test with 'KNN'
KNN tn, fp: 235, 75
KNN fn, tp: 10, 1
KNN f1 score: 0.023
KNN cohens kappa score: -0.039


------ 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: 255, 55
GAN fn, tp: 9, 2
GAN f1 score: 0.059
GAN cohens kappa score: 0.001

-> test with 'LR'
LR tn, fp: 219, 91
LR fn, tp: 3, 8
LR f1 score: 0.145
LR cohens kappa score: 0.089
LR average precision score: 0.169

-> test with 'GB'
GB tn, fp: 285, 25
GB fn, tp: 9, 2
GB f1 score: 0.105
GB cohens kappa score: 0.059

-> test with 'KNN'
KNN tn, fp: 232, 78
KNN fn, tp: 9, 2
KNN f1 score: 0.044
KNN cohens kappa score: -0.017


------ 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: 243, 67
GAN fn, tp: 7, 4
GAN f1 score: 0.098
GAN cohens kappa score: 0.041

-> test with 'LR'
LR tn, fp: 231, 79
LR fn, tp: 6, 5
LR f1 score: 0.105
LR cohens kappa score: 0.048
LR average precision score: 0.273

-> 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: 216, 94
KNN fn, tp: 8, 3
KNN f1 score: 0.056
KNN cohens kappa score: -0.006


------ 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: 259, 47
GAN fn, tp: 6, 3
GAN f1 score: 0.102
GAN cohens kappa score: 0.056

-> test with 'LR'
LR tn, fp: 228, 78
LR fn, tp: 3, 6
LR f1 score: 0.129
LR cohens kappa score: 0.082
LR average precision score: 0.120

-> test with 'GB'
GB tn, fp: 290, 16
GB fn, tp: 6, 3
GB f1 score: 0.214
GB cohens kappa score: 0.183

-> test with 'KNN'
KNN tn, fp: 213, 93
KNN fn, tp: 7, 2
KNN f1 score: 0.038
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: 241, 69
GAN fn, tp: 7, 4
GAN f1 score: 0.095
GAN cohens kappa score: 0.038

-> test with 'LR'
LR tn, fp: 232, 78
LR fn, tp: 5, 6
LR f1 score: 0.126
LR cohens kappa score: 0.070
LR average precision score: 0.185

-> test with 'GB'
GB tn, fp: 296, 14
GB fn, tp: 9, 2
GB f1 score: 0.148
GB cohens kappa score: 0.112

-> test with 'KNN'
KNN tn, fp: 232, 78
KNN fn, tp: 9, 2
KNN f1 score: 0.044
KNN cohens kappa score: -0.017


------ 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: 225, 85
GAN fn, tp: 6, 5
GAN f1 score: 0.099
GAN cohens kappa score: 0.040

-> test with 'LR'
LR tn, fp: 216, 94
LR fn, tp: 4, 7
LR f1 score: 0.125
LR cohens kappa score: 0.067
LR average precision score: 0.257

-> test with 'GB'
GB tn, fp: 293, 17
GB fn, tp: 9, 2
GB f1 score: 0.133
GB cohens kappa score: 0.094

-> 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: 218, 92
GAN fn, tp: 5, 6
GAN f1 score: 0.110
GAN cohens kappa score: 0.052

-> test with 'LR'
LR tn, fp: 223, 87
LR fn, tp: 5, 6
LR f1 score: 0.115
LR cohens kappa score: 0.058
LR average precision score: 0.067

-> 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: 211, 99
KNN fn, tp: 7, 4
KNN f1 score: 0.070
KNN cohens kappa score: 0.009


------ 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: 240, 70
GAN fn, tp: 7, 4
GAN f1 score: 0.094
GAN cohens kappa score: 0.037

-> test with 'LR'
LR tn, fp: 207, 103
LR fn, tp: 2, 9
LR f1 score: 0.146
LR cohens kappa score: 0.090
LR average precision score: 0.181

-> test with 'GB'
GB tn, fp: 283, 27
GB fn, tp: 10, 1
GB f1 score: 0.051
GB cohens kappa score: 0.002

-> 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 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with GAN.predict
GAN tn, fp: 259, 47
GAN fn, tp: 8, 1
GAN f1 score: 0.035
GAN cohens kappa score: -0.014

-> test with 'LR'
LR tn, fp: 224, 82
LR fn, tp: 2, 7
LR f1 score: 0.143
LR cohens kappa score: 0.096
LR average precision score: 0.106

-> test with 'GB'
GB tn, fp: 294, 12
GB fn, tp: 6, 3
GB f1 score: 0.250
GB cohens kappa score: 0.222

-> test with 'KNN'
KNN tn, fp: 238, 68
KNN fn, tp: 5, 4
KNN f1 score: 0.099
KNN cohens kappa score: 0.051


====== 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: 246, 64
GAN fn, tp: 6, 5
GAN f1 score: 0.125
GAN cohens kappa score: 0.070

-> test with 'LR'
LR tn, fp: 231, 79
LR fn, tp: 3, 8
LR f1 score: 0.163
LR cohens kappa score: 0.109
LR average precision score: 0.407

-> 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: 229, 81
KNN fn, tp: 9, 2
KNN f1 score: 0.043
KNN cohens kappa score: -0.019


------ 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: 6, 5
GAN f1 score: 0.079
GAN cohens kappa score: 0.017

-> test with 'LR'
LR tn, fp: 215, 95
LR fn, tp: 3, 8
LR f1 score: 0.140
LR cohens kappa score: 0.084
LR average precision score: 0.182

-> 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: 205, 105
KNN fn, tp: 8, 3
KNN f1 score: 0.050
KNN cohens kappa score: -0.013


------ 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: 245, 65
GAN fn, tp: 9, 2
GAN f1 score: 0.051
GAN cohens kappa score: -0.008

-> 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: 0.090

-> 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: 237, 73
KNN fn, tp: 9, 2
KNN f1 score: 0.047
KNN cohens kappa score: -0.014


------ 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: 195, 115
GAN fn, tp: 4, 7
GAN f1 score: 0.105
GAN cohens kappa score: 0.045

-> test with 'LR'
LR tn, fp: 212, 98
LR fn, tp: 1, 10
LR f1 score: 0.168
LR cohens kappa score: 0.113
LR average precision score: 0.142

-> 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: 218, 92
KNN fn, tp: 8, 3
KNN f1 score: 0.057
KNN cohens kappa score: -0.005


------ 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: 252, 54
GAN fn, tp: 9, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.052

-> test with 'LR'
LR tn, fp: 215, 91
LR fn, tp: 5, 4
LR f1 score: 0.077
LR cohens kappa score: 0.026
LR average precision score: 0.065

-> test with 'GB'
GB tn, fp: 278, 28
GB fn, tp: 8, 1
GB f1 score: 0.053
GB cohens kappa score: 0.009

-> test with 'KNN'
KNN tn, fp: 227, 79
KNN fn, tp: 8, 1
KNN f1 score: 0.022
KNN cohens kappa score: -0.030


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

-> test with 'LR'
LR tn, fp: 236, 74
LR fn, tp: 5, 6
LR f1 score: 0.132
LR cohens kappa score: 0.076
LR average precision score: 0.075

-> 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: 227, 83
KNN fn, tp: 9, 2
KNN f1 score: 0.042
KNN cohens kappa score: -0.020


------ 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: 6, 5
GAN f1 score: 0.110
GAN cohens kappa score: 0.053

-> test with 'LR'
LR tn, fp: 223, 87
LR fn, tp: 6, 5
LR f1 score: 0.097
LR cohens kappa score: 0.038
LR average precision score: 0.088

-> test with 'GB'
GB tn, fp: 287, 23
GB fn, tp: 9, 2
GB f1 score: 0.111
GB cohens kappa score: 0.067

-> test with 'KNN'
KNN tn, fp: 212, 98
KNN fn, tp: 7, 4
KNN f1 score: 0.071
KNN cohens kappa score: 0.010


------ 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: 235, 75
GAN fn, tp: 6, 5
GAN f1 score: 0.110
GAN cohens kappa score: 0.053

-> test with 'LR'
LR tn, fp: 202, 108
LR fn, tp: 0, 11
LR f1 score: 0.169
LR cohens kappa score: 0.114
LR average precision score: 0.294

-> test with 'GB'
GB tn, fp: 286, 24
GB fn, tp: 7, 4
GB f1 score: 0.205
GB cohens kappa score: 0.164

-> 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 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 255, 55
GAN fn, tp: 5, 6
GAN f1 score: 0.167
GAN cohens kappa score: 0.115

-> test with 'LR'
LR tn, fp: 223, 87
LR fn, tp: 4, 7
LR f1 score: 0.133
LR cohens kappa score: 0.077
LR average precision score: 0.190

-> test with 'GB'
GB tn, fp: 296, 14
GB fn, tp: 8, 3
GB f1 score: 0.214
GB cohens kappa score: 0.180

-> test with 'KNN'
KNN tn, fp: 230, 80
KNN fn, tp: 7, 4
KNN f1 score: 0.084
KNN cohens kappa score: 0.025


------ 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: 253, 53
GAN fn, tp: 5, 4
GAN f1 score: 0.121
GAN cohens kappa score: 0.076

-> 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: 0.147

-> test with 'GB'
GB tn, fp: 288, 18
GB fn, tp: 8, 1
GB f1 score: 0.071
GB cohens kappa score: 0.034

-> test with 'KNN'
KNN tn, fp: 214, 92
KNN fn, tp: 8, 1
KNN f1 score: 0.020
KNN cohens kappa score: -0.034

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 242, 116
LR fn, tp: 6, 11
LR f1 score: 0.169
LR cohens kappa score: 0.114
LR average precision score: 0.407


average:
LR tn, fp: 221.12, 88.08
LR fn, tp: 3.6, 7.0
LR f1 score: 0.132
LR cohens kappa score: 0.077
LR average precision score: 0.166


minimum:
LR tn, fp: 194, 68
LR fn, tp: 0, 4
LR f1 score: 0.077
LR cohens kappa score: 0.026
LR average precision score: 0.065


-----[ GB ]-----
maximum:
GB tn, fp: 301, 29
GB fn, tp: 11, 5
GB f1 score: 0.250
GB cohens kappa score: 0.222


average:
GB tn, fp: 290.16, 19.04
GB fn, tp: 8.68, 1.92
GB f1 score: 0.119
GB cohens kappa score: 0.079


minimum:
GB tn, fp: 278, 9
GB fn, tp: 6, 0
GB f1 score: 0.000
GB cohens kappa score: -0.041


-----[ KNN ]-----
maximum:
KNN tn, fp: 238, 108
KNN fn, tp: 10, 5
KNN f1 score: 0.106
KNN cohens kappa score: 0.051


average:
KNN tn, fp: 223.16, 86.04
KNN fn, tp: 7.72, 2.88
KNN f1 score: 0.058
KNN cohens kappa score: -0.002


minimum:
KNN tn, fp: 202, 68
KNN fn, tp: 5, 1
KNN f1 score: 0.020
KNN cohens kappa score: -0.039


-----[ GAN ]-----
maximum:
GAN tn, fp: 259, 122
GAN fn, tp: 9, 7
GAN f1 score: 0.167
GAN cohens kappa score: 0.115


average:
GAN tn, fp: 235.04, 74.16
GAN fn, tp: 6.6, 4.0
GAN f1 score: 0.089
GAN cohens kappa score: 0.033


minimum:
GAN tn, fp: 188, 47
GAN fn, tp: 4, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.052