ConvGeNBenchmarkResults/data_result/convGAN-majority-5/folding_winequality-red-4.log

///////////////////////////////////////////
// Running convGAN-majority-5 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: 149, 161
GAN fn, tp: 2, 9
GAN f1 score: 0.099
GAN cohens kappa score: 0.037

-> test with 'LR'
LR tn, fp: 205, 105
LR fn, tp: 5, 6
LR f1 score: 0.098
LR cohens kappa score: 0.038
LR average precision score: 0.103

-> test with 'GB'
GB tn, fp: 285, 25
GB fn, tp: 10, 1
GB f1 score: 0.054
GB cohens kappa score: 0.006

-> test with 'KNN'
KNN tn, fp: 209, 101
KNN fn, tp: 8, 3
KNN f1 score: 0.052
KNN cohens kappa score: -0.010


------ 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: 275, 35
GAN fn, tp: 7, 4
GAN f1 score: 0.160
GAN cohens kappa score: 0.113

-> test with 'LR'
LR tn, fp: 228, 82
LR fn, tp: 4, 7
LR f1 score: 0.140
LR cohens kappa score: 0.084
LR average precision score: 0.093

-> test with 'GB'
GB tn, fp: 293, 17
GB fn, tp: 6, 5
GB f1 score: 0.303
GB cohens kappa score: 0.270

-> test with 'KNN'
KNN tn, fp: 241, 69
KNN fn, tp: 6, 5
KNN f1 score: 0.118
KNN cohens kappa score: 0.062


------ 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: 163, 147
GAN fn, tp: 3, 8
GAN f1 score: 0.096
GAN cohens kappa score: 0.035

-> 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: 0.199

-> 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: 220, 90
KNN fn, tp: 8, 3
KNN f1 score: 0.058
KNN cohens kappa score: -0.004


------ 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: 199, 111
GAN fn, tp: 7, 4
GAN f1 score: 0.063
GAN cohens kappa score: 0.001

-> test with 'LR'
LR tn, fp: 240, 70
LR fn, tp: 5, 6
LR f1 score: 0.138
LR cohens kappa score: 0.083
LR average precision score: 0.129

-> test with 'GB'
GB tn, fp: 286, 24
GB fn, tp: 10, 1
GB f1 score: 0.056
GB cohens kappa score: 0.008

-> test with 'KNN'
KNN tn, fp: 226, 84
KNN fn, tp: 6, 5
KNN f1 score: 0.100
KNN cohens kappa score: 0.042


------ 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: 168, 138
GAN fn, tp: 5, 4
GAN f1 score: 0.053
GAN cohens kappa score: -0.001

-> test with 'LR'
LR tn, fp: 222, 84
LR fn, tp: 4, 5
LR f1 score: 0.102
LR cohens kappa score: 0.053
LR average precision score: 0.225

-> test with 'GB'
GB tn, fp: 290, 16
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.038

-> 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: 257, 53
GAN fn, tp: 6, 5
GAN f1 score: 0.145
GAN cohens kappa score: 0.093

-> test with 'LR'
LR tn, fp: 210, 100
LR fn, tp: 1, 10
LR f1 score: 0.165
LR cohens kappa score: 0.110
LR average precision score: 0.140

-> 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: 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: 256, 54
GAN fn, tp: 8, 3
GAN f1 score: 0.088
GAN cohens kappa score: 0.033

-> 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: 0.226

-> test with 'GB'
GB tn, fp: 295, 15
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.041

-> test with 'KNN'
KNN tn, fp: 238, 72
KNN fn, tp: 6, 5
KNN f1 score: 0.114
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 GAN.predict
GAN tn, fp: 226, 84
GAN fn, tp: 8, 3
GAN f1 score: 0.061
GAN cohens kappa score: 0.000

-> 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: 295, 15
GB fn, tp: 9, 2
GB f1 score: 0.143
GB cohens kappa score: 0.106

-> test with 'KNN'
KNN tn, fp: 234, 76
KNN fn, tp: 10, 1
KNN f1 score: 0.023
KNN cohens kappa score: -0.040


------ 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: 202, 108
GAN fn, tp: 6, 5
GAN f1 score: 0.081
GAN cohens kappa score: 0.019

-> test with 'LR'
LR tn, fp: 217, 93
LR fn, tp: 5, 6
LR f1 score: 0.109
LR cohens kappa score: 0.051
LR average precision score: 0.297

-> 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: 240, 70
KNN fn, tp: 6, 5
KNN f1 score: 0.116
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 GAN.predict
GAN tn, fp: 276, 30
GAN fn, tp: 7, 2
GAN f1 score: 0.098
GAN cohens kappa score: 0.055

-> test with 'LR'
LR tn, fp: 215, 91
LR fn, tp: 3, 6
LR f1 score: 0.113
LR cohens kappa score: 0.064
LR average precision score: 0.132

-> test with 'GB'
GB tn, fp: 297, 9
GB fn, tp: 7, 2
GB f1 score: 0.200
GB cohens kappa score: 0.174

-> test with 'KNN'
KNN tn, fp: 232, 74
KNN fn, tp: 5, 4
KNN f1 score: 0.092
KNN cohens kappa score: 0.043


====== 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: 212, 98
GAN fn, tp: 7, 4
GAN f1 score: 0.071
GAN cohens kappa score: 0.010

-> test with 'LR'
LR tn, fp: 227, 83
LR fn, tp: 4, 7
LR f1 score: 0.139
LR cohens kappa score: 0.083
LR average precision score: 0.152

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


------ 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: 256, 54
GAN fn, tp: 8, 3
GAN f1 score: 0.088
GAN cohens kappa score: 0.033

-> test with 'LR'
LR tn, fp: 200, 110
LR fn, tp: 2, 9
LR f1 score: 0.138
LR cohens kappa score: 0.081
LR average precision score: 0.197

-> 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: 234, 76
KNN fn, tp: 9, 2
KNN f1 score: 0.045
KNN cohens kappa score: -0.016


------ 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: 202, 108
GAN fn, tp: 5, 6
GAN f1 score: 0.096
GAN cohens kappa score: 0.036

-> 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: 286, 24
GB fn, tp: 9, 2
GB f1 score: 0.108
GB cohens kappa score: 0.063

-> test with 'KNN'
KNN tn, fp: 226, 84
KNN fn, tp: 7, 4
KNN f1 score: 0.081
KNN cohens kappa score: 0.021


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

-> 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: 0.173

-> 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: 228, 82
KNN fn, tp: 4, 7
KNN f1 score: 0.140
KNN cohens kappa score: 0.084


------ 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: 263, 43
GAN fn, tp: 5, 4
GAN f1 score: 0.143
GAN cohens kappa score: 0.100

-> 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: 0.089

-> 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: 239, 67
KNN fn, tp: 6, 3
KNN f1 score: 0.076
KNN cohens kappa score: 0.027


====== 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: 208, 102
GAN fn, tp: 6, 5
GAN f1 score: 0.085
GAN cohens kappa score: 0.024

-> 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: 0.352

-> test with 'GB'
GB tn, fp: 301, 9
GB fn, tp: 8, 3
GB f1 score: 0.261
GB cohens kappa score: 0.233

-> test with 'KNN'
KNN tn, fp: 240, 70
KNN fn, tp: 8, 3
KNN f1 score: 0.071
KNN cohens kappa score: 0.013


------ 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: 221, 89
GAN fn, tp: 5, 6
GAN f1 score: 0.113
GAN cohens kappa score: 0.055

-> test with 'LR'
LR tn, fp: 220, 90
LR fn, tp: 3, 8
LR f1 score: 0.147
LR cohens kappa score: 0.091
LR average precision score: 0.186

-> 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: 209, 101
KNN fn, tp: 5, 6
KNN f1 score: 0.102
KNN cohens kappa score: 0.042


------ 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: 203, 107
GAN fn, tp: 5, 6
GAN f1 score: 0.097
GAN cohens kappa score: 0.037

-> test with 'LR'
LR tn, fp: 225, 85
LR fn, tp: 5, 6
LR f1 score: 0.118
LR cohens kappa score: 0.060
LR average precision score: 0.103

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


------ 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: 201, 109
GAN fn, tp: 6, 5
GAN f1 score: 0.080
GAN cohens kappa score: 0.019

-> test with 'LR'
LR tn, fp: 210, 100
LR fn, tp: 1, 10
LR f1 score: 0.165
LR cohens kappa score: 0.110
LR average precision score: 0.155

-> test with 'GB'
GB tn, fp: 294, 16
GB fn, tp: 8, 3
GB f1 score: 0.200
GB cohens kappa score: 0.164

-> 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 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with GAN.predict
GAN tn, fp: 269, 37
GAN fn, tp: 9, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.048

-> test with 'LR'
LR tn, fp: 210, 96
LR fn, tp: 6, 3
LR f1 score: 0.056
LR cohens kappa score: 0.003
LR average precision score: 0.059

-> 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: 230, 76
KNN fn, tp: 7, 2
KNN f1 score: 0.046
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 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: 234, 76
LR fn, tp: 5, 6
LR f1 score: 0.129
LR cohens kappa score: 0.073
LR average precision score: 0.087

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


------ 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: 224, 86
GAN fn, tp: 5, 6
GAN f1 score: 0.117
GAN cohens kappa score: 0.059

-> test with 'LR'
LR tn, fp: 217, 93
LR fn, tp: 6, 5
LR f1 score: 0.092
LR cohens kappa score: 0.032
LR average precision score: 0.086

-> 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: 215, 95
KNN fn, tp: 7, 4
KNN f1 score: 0.073
KNN cohens kappa score: 0.012


------ 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: 273, 37
GAN fn, tp: 7, 4
GAN f1 score: 0.154
GAN cohens kappa score: 0.106

-> test with 'LR'
LR tn, fp: 201, 109
LR fn, tp: 1, 10
LR f1 score: 0.154
LR cohens kappa score: 0.097
LR average precision score: 0.236

-> 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: 253, 57
KNN fn, tp: 6, 5
KNN f1 score: 0.137
KNN cohens kappa score: 0.084


------ 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: 238, 72
GAN fn, tp: 5, 6
GAN f1 score: 0.135
GAN cohens kappa score: 0.080

-> test with 'LR'
LR tn, fp: 206, 104
LR fn, tp: 3, 8
LR f1 score: 0.130
LR cohens kappa score: 0.072
LR average precision score: 0.178

-> 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: 222, 88
KNN fn, tp: 8, 3
KNN f1 score: 0.059
KNN cohens kappa score: -0.002


------ 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: 6, 3
GAN f1 score: 0.092
GAN cohens kappa score: 0.045

-> test with 'LR'
LR tn, fp: 237, 69
LR fn, tp: 3, 6
LR f1 score: 0.143
LR cohens kappa score: 0.097
LR average precision score: 0.139

-> 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: 224, 82
KNN fn, tp: 7, 2
KNN f1 score: 0.043
KNN cohens kappa score: -0.009

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 240, 110
LR fn, tp: 6, 10
LR f1 score: 0.165
LR cohens kappa score: 0.110
LR average precision score: 0.352


average:
LR tn, fp: 218.28, 90.92
LR fn, tp: 3.56, 7.04
LR f1 score: 0.129
LR cohens kappa score: 0.074
LR average precision score: 0.160


minimum:
LR tn, fp: 200, 69
LR fn, tp: 1, 3
LR f1 score: 0.056
LR cohens kappa score: 0.003
LR average precision score: 0.059


-----[ GB ]-----
maximum:
GB tn, fp: 301, 32
GB fn, tp: 11, 5
GB f1 score: 0.303
GB cohens kappa score: 0.270


average:
GB tn, fp: 291.0, 18.2
GB fn, tp: 8.56, 2.04
GB f1 score: 0.136
GB cohens kappa score: 0.097


minimum:
GB tn, fp: 274, 9
GB fn, tp: 6, 0
GB f1 score: 0.000
GB cohens kappa score: -0.041


-----[ KNN ]-----
maximum:
KNN tn, fp: 253, 101
KNN fn, tp: 10, 7
KNN f1 score: 0.140
KNN cohens kappa score: 0.084


average:
KNN tn, fp: 228.04, 81.16
KNN fn, tp: 6.96, 3.64
KNN f1 score: 0.077
KNN cohens kappa score: 0.019


minimum:
KNN tn, fp: 209, 57
KNN fn, tp: 4, 1
KNN f1 score: 0.023
KNN cohens kappa score: -0.040


-----[ GAN ]-----
maximum:
GAN tn, fp: 276, 161
GAN fn, tp: 9, 9
GAN f1 score: 0.160
GAN cohens kappa score: 0.113


average:
GAN tn, fp: 228.64, 80.56
GAN fn, tp: 6.2, 4.4
GAN f1 score: 0.095
GAN cohens kappa score: 0.040


minimum:
GAN tn, fp: 149, 30
GAN fn, tp: 2, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.048