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

///////////////////////////////////////////
// Running convGAN-proximary-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: 177, 133
GAN fn, tp: 1, 10
GAN f1 score: 0.130
GAN cohens kappa score: 0.071

-> test with 'LR'
LR tn, fp: 204, 106
LR fn, tp: 4, 7
LR f1 score: 0.113
LR cohens kappa score: 0.054
LR average precision score: 0.115

-> test with 'GB'
GB tn, fp: 284, 26
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.051

-> test with 'KNN'
KNN tn, fp: 207, 103
KNN fn, tp: 6, 5
KNN f1 score: 0.084
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 GAN.predict
GAN tn, fp: 207, 103
GAN fn, tp: 7, 4
GAN f1 score: 0.068
GAN cohens kappa score: 0.006

-> 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: 0.109

-> 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: 232, 78
KNN fn, tp: 8, 3
KNN f1 score: 0.065
KNN cohens kappa score: 0.005


------ 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: 193, 117
GAN fn, tp: 5, 6
GAN f1 score: 0.090
GAN cohens kappa score: 0.028

-> test with 'LR'
LR tn, fp: 196, 114
LR fn, tp: 2, 9
LR f1 score: 0.134
LR cohens kappa score: 0.076
LR average precision score: 0.180

-> test with 'GB'
GB tn, fp: 282, 28
GB fn, tp: 6, 5
GB f1 score: 0.227
GB cohens kappa score: 0.185

-> test with 'KNN'
KNN tn, fp: 211, 99
KNN fn, tp: 8, 3
KNN f1 score: 0.053
KNN cohens kappa score: -0.009


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

-> 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: 0.097

-> test with 'GB'
GB tn, fp: 294, 16
GB fn, tp: 10, 1
GB f1 score: 0.071
GB cohens kappa score: 0.031

-> 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 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with GAN.predict
GAN tn, fp: 228, 78
GAN fn, tp: 4, 5
GAN f1 score: 0.109
GAN cohens kappa score: 0.060

-> test with 'LR'
LR tn, fp: 226, 80
LR fn, tp: 4, 5
LR f1 score: 0.106
LR cohens kappa score: 0.058
LR average precision score: 0.226

-> test with 'GB'
GB tn, fp: 286, 20
GB fn, tp: 6, 3
GB f1 score: 0.188
GB cohens kappa score: 0.153

-> test with 'KNN'
KNN tn, fp: 235, 71
KNN fn, tp: 5, 4
KNN f1 score: 0.095
KNN cohens kappa score: 0.047


====== 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: 240, 70
GAN fn, tp: 6, 5
GAN f1 score: 0.116
GAN cohens kappa score: 0.060

-> 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: 0.158

-> test with 'GB'
GB tn, fp: 291, 19
GB fn, tp: 7, 4
GB f1 score: 0.235
GB cohens kappa score: 0.198

-> 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 2/5: Slice 2/5 -------
-> 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: 214, 96
LR fn, tp: 4, 7
LR f1 score: 0.123
LR cohens kappa score: 0.065
LR average precision score: 0.142

-> test with 'GB'
GB tn, fp: 294, 16
GB fn, tp: 10, 1
GB f1 score: 0.071
GB cohens kappa score: 0.031

-> test with 'KNN'
KNN tn, fp: 225, 85
KNN fn, tp: 9, 2
KNN f1 score: 0.041
KNN cohens kappa score: -0.021


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

-> test with 'LR'
LR tn, fp: 208, 102
LR fn, tp: 3, 8
LR f1 score: 0.132
LR cohens kappa score: 0.075
LR average precision score: 0.198

-> test with 'GB'
GB tn, fp: 284, 26
GB fn, tp: 9, 2
GB f1 score: 0.103
GB cohens kappa score: 0.056

-> test with 'KNN'
KNN tn, fp: 230, 80
KNN fn, tp: 8, 3
KNN f1 score: 0.064
KNN cohens kappa score: 0.004


------ 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: 214, 96
GAN fn, tp: 7, 4
GAN f1 score: 0.072
GAN cohens kappa score: 0.011

-> 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: 0.310

-> test with 'GB'
GB tn, fp: 294, 16
GB fn, tp: 10, 1
GB f1 score: 0.071
GB cohens kappa score: 0.031

-> 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 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with GAN.predict
GAN tn, fp: 183, 123
GAN fn, tp: 4, 5
GAN f1 score: 0.073
GAN cohens kappa score: 0.021

-> test with 'LR'
LR tn, fp: 232, 74
LR fn, tp: 3, 6
LR f1 score: 0.135
LR cohens kappa score: 0.088
LR average precision score: 0.102

-> test with 'GB'
GB tn, fp: 289, 17
GB fn, tp: 6, 3
GB f1 score: 0.207
GB cohens kappa score: 0.174

-> test with 'KNN'
KNN tn, fp: 227, 79
KNN fn, tp: 6, 3
KNN f1 score: 0.066
KNN cohens kappa score: 0.015


====== 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: 247, 63
GAN fn, tp: 7, 4
GAN f1 score: 0.103
GAN cohens kappa score: 0.046

-> test with 'LR'
LR tn, fp: 231, 79
LR fn, tp: 4, 7
LR f1 score: 0.144
LR cohens kappa score: 0.089
LR average precision score: 0.167

-> 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: 241, 69
KNN fn, tp: 9, 2
KNN f1 score: 0.049
KNN cohens kappa score: -0.011


------ 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: 197, 113
GAN fn, tp: 5, 6
GAN f1 score: 0.092
GAN cohens kappa score: 0.032

-> test with 'LR'
LR tn, fp: 212, 98
LR fn, tp: 2, 9
LR f1 score: 0.153
LR cohens kappa score: 0.096
LR average precision score: 0.263

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


------ 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: 222, 88
GAN fn, tp: 6, 5
GAN f1 score: 0.096
GAN cohens kappa score: 0.037

-> test with 'LR'
LR tn, fp: 221, 89
LR fn, tp: 6, 5
LR f1 score: 0.095
LR cohens kappa score: 0.036
LR average precision score: 0.075

-> 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: 239, 71
KNN fn, tp: 9, 2
KNN f1 score: 0.048
KNN cohens kappa score: -0.013


------ 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: 206, 104
GAN fn, tp: 3, 8
GAN f1 score: 0.130
GAN cohens kappa score: 0.072

-> test with 'LR'
LR tn, fp: 209, 101
LR fn, tp: 2, 9
LR f1 score: 0.149
LR cohens kappa score: 0.092
LR average precision score: 0.200

-> 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: 220, 90
KNN fn, tp: 7, 4
KNN f1 score: 0.076
KNN cohens kappa score: 0.016


------ 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: 189, 117
GAN fn, tp: 3, 6
GAN f1 score: 0.091
GAN cohens kappa score: 0.040

-> test with 'LR'
LR tn, fp: 216, 90
LR fn, tp: 2, 7
LR f1 score: 0.132
LR cohens kappa score: 0.084
LR average precision score: 0.199

-> 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: 220, 86
KNN fn, tp: 8, 1
KNN f1 score: 0.021
KNN cohens kappa score: -0.033


====== 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: 214, 96
GAN fn, tp: 4, 7
GAN f1 score: 0.123
GAN cohens kappa score: 0.065

-> test with 'LR'
LR tn, fp: 219, 91
LR fn, tp: 2, 9
LR f1 score: 0.162
LR cohens kappa score: 0.107
LR average precision score: 0.360

-> test with 'GB'
GB tn, fp: 297, 13
GB fn, tp: 8, 3
GB f1 score: 0.222
GB cohens kappa score: 0.189

-> test with 'KNN'
KNN tn, fp: 218, 92
KNN fn, tp: 9, 2
KNN f1 score: 0.038
KNN cohens kappa score: -0.025


------ 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: 210, 100
GAN fn, tp: 5, 6
GAN f1 score: 0.103
GAN cohens kappa score: 0.043

-> 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: 0.194

-> test with 'GB'
GB tn, fp: 288, 22
GB fn, tp: 10, 1
GB f1 score: 0.059
GB cohens kappa score: 0.013

-> 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 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 205, 105
GAN fn, tp: 9, 2
GAN f1 score: 0.034
GAN cohens kappa score: -0.030

-> test with 'LR'
LR tn, fp: 232, 78
LR fn, tp: 6, 5
LR f1 score: 0.106
LR cohens kappa score: 0.049
LR average precision score: 0.093

-> test with 'GB'
GB tn, fp: 294, 16
GB fn, tp: 10, 1
GB f1 score: 0.071
GB cohens kappa score: 0.031

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

-> test with 'LR'
LR tn, fp: 213, 97
LR fn, tp: 1, 10
LR f1 score: 0.169
LR cohens kappa score: 0.114
LR average precision score: 0.157

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


------ 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: 153, 153
GAN fn, tp: 2, 7
GAN f1 score: 0.083
GAN cohens kappa score: 0.030

-> test with 'LR'
LR tn, fp: 201, 105
LR fn, tp: 5, 4
LR f1 score: 0.068
LR cohens kappa score: 0.016
LR average precision score: 0.054

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

-> test with 'KNN'
KNN tn, fp: 208, 98
KNN fn, tp: 8, 1
KNN f1 score: 0.019
KNN cohens kappa score: -0.036


====== 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: 219, 91
GAN fn, tp: 9, 2
GAN f1 score: 0.038
GAN cohens kappa score: -0.024

-> test with 'LR'
LR tn, fp: 231, 79
LR fn, tp: 5, 6
LR f1 score: 0.125
LR cohens kappa score: 0.068
LR average precision score: 0.072

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


------ 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: 251, 59
GAN fn, tp: 9, 2
GAN f1 score: 0.056
GAN cohens kappa score: -0.003

-> test with 'LR'
LR tn, fp: 233, 77
LR fn, tp: 5, 6
LR f1 score: 0.128
LR cohens kappa score: 0.071
LR average precision score: 0.097

-> test with 'GB'
GB tn, fp: 295, 15
GB fn, tp: 10, 1
GB f1 score: 0.074
GB cohens kappa score: 0.035

-> test with 'KNN'
KNN tn, fp: 229, 81
KNN fn, tp: 8, 3
KNN f1 score: 0.063
KNN cohens kappa score: 0.003


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

-> test with 'LR'
LR tn, fp: 204, 106
LR fn, tp: 0, 11
LR f1 score: 0.172
LR cohens kappa score: 0.117
LR average precision score: 0.289

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


------ 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: 221, 89
GAN fn, tp: 8, 3
GAN f1 score: 0.058
GAN cohens kappa score: -0.003

-> 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: 0.193

-> test with 'GB'
GB tn, fp: 292, 18
GB fn, tp: 7, 4
GB f1 score: 0.242
GB cohens kappa score: 0.206

-> test with 'KNN'
KNN tn, fp: 240, 70
KNN fn, tp: 9, 2
KNN f1 score: 0.048
KNN cohens kappa score: -0.012


------ 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: 254, 52
GAN fn, tp: 5, 4
GAN f1 score: 0.123
GAN cohens kappa score: 0.078

-> test with 'LR'
LR tn, fp: 243, 63
LR fn, tp: 3, 6
LR f1 score: 0.154
LR cohens kappa score: 0.109
LR average precision score: 0.193

-> 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: 251, 55
KNN fn, tp: 7, 2
KNN f1 score: 0.061
KNN cohens kappa score: 0.012

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 243, 114
LR fn, tp: 6, 11
LR f1 score: 0.172
LR cohens kappa score: 0.117
LR average precision score: 0.360


average:
LR tn, fp: 218.0, 91.2
LR fn, tp: 3.44, 7.16
LR f1 score: 0.131
LR cohens kappa score: 0.076
LR average precision score: 0.170


minimum:
LR tn, fp: 196, 63
LR fn, tp: 0, 4
LR f1 score: 0.068
LR cohens kappa score: 0.016
LR average precision score: 0.054


-----[ GB ]-----
maximum:
GB tn, fp: 297, 38
GB fn, tp: 11, 5
GB f1 score: 0.242
GB cohens kappa score: 0.206


average:
GB tn, fp: 290.2, 19.0
GB fn, tp: 8.72, 1.88
GB f1 score: 0.119
GB cohens kappa score: 0.079


minimum:
GB tn, fp: 268, 9
GB fn, tp: 6, 0
GB f1 score: 0.000
GB cohens kappa score: -0.051


-----[ KNN ]-----
maximum:
KNN tn, fp: 251, 103
KNN fn, tp: 10, 5
KNN f1 score: 0.095
KNN cohens kappa score: 0.047


average:
KNN tn, fp: 228.24, 80.96
KNN fn, tp: 7.84, 2.76
KNN f1 score: 0.058
KNN cohens kappa score: -0.000


minimum:
KNN tn, fp: 207, 55
KNN fn, tp: 5, 1
KNN f1 score: 0.019
KNN cohens kappa score: -0.037


-----[ GAN ]-----
maximum:
GAN tn, fp: 254, 153
GAN fn, tp: 9, 10
GAN f1 score: 0.130
GAN cohens kappa score: 0.078


average:
GAN tn, fp: 214.28, 94.92
GAN fn, tp: 5.64, 4.96
GAN f1 score: 0.090
GAN cohens kappa score: 0.032


minimum:
GAN tn, fp: 153, 52
GAN fn, tp: 1, 2
GAN f1 score: 0.034
GAN cohens kappa score: -0.030