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

///////////////////////////////////////////
// Running convGAN-proximary-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: 192, 118
GAN fn, tp: 7, 4
GAN f1 score: 0.060
GAN cohens kappa score: -0.003

-> test with 'LR'
LR tn, fp: 212, 98
LR fn, tp: 6, 5
LR f1 score: 0.088
LR cohens kappa score: 0.027
LR average precision score: 0.099

-> 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: 210, 100
KNN fn, tp: 7, 4
KNN f1 score: 0.070
KNN cohens kappa score: 0.008


------ 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: 219, 91
GAN fn, tp: 3, 8
GAN f1 score: 0.145
GAN cohens kappa score: 0.089

-> 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: 0.096

-> 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: 238, 72
KNN fn, tp: 6, 5
KNN f1 score: 0.114
KNN cohens kappa score: 0.057


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

-> test with 'LR'
LR tn, fp: 201, 109
LR fn, tp: 4, 7
LR f1 score: 0.110
LR cohens kappa score: 0.051
LR average precision score: 0.192

-> test with 'GB'
GB tn, fp: 295, 15
GB fn, tp: 7, 4
GB f1 score: 0.267
GB cohens kappa score: 0.233

-> test with 'KNN'
KNN tn, fp: 235, 75
KNN fn, tp: 8, 3
KNN f1 score: 0.067
KNN cohens kappa score: 0.008


------ 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: 186, 124
GAN fn, tp: 4, 7
GAN f1 score: 0.099
GAN cohens kappa score: 0.038

-> test with 'LR'
LR tn, fp: 230, 80
LR fn, tp: 5, 6
LR f1 score: 0.124
LR cohens kappa score: 0.067
LR average precision score: 0.130

-> 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: 224, 86
KNN fn, tp: 6, 5
KNN f1 score: 0.098
KNN cohens kappa score: 0.039


------ 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: 219, 87
GAN fn, tp: 3, 6
GAN f1 score: 0.118
GAN cohens kappa score: 0.069

-> 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: 0.223

-> test with 'GB'
GB tn, fp: 287, 19
GB fn, tp: 8, 1
GB f1 score: 0.069
GB cohens kappa score: 0.031

-> test with 'KNN'
KNN tn, fp: 235, 71
KNN fn, tp: 6, 3
KNN f1 score: 0.072
KNN cohens kappa score: 0.022


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

-> 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: 0.132

-> test with 'GB'
GB tn, fp: 290, 20
GB fn, tp: 9, 2
GB f1 score: 0.121
GB cohens kappa score: 0.079

-> test with 'KNN'
KNN tn, fp: 221, 89
KNN fn, tp: 10, 1
KNN f1 score: 0.020
KNN cohens kappa score: -0.044


------ 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: 116, 194
GAN fn, tp: 7, 4
GAN f1 score: 0.038
GAN cohens kappa score: -0.029

-> 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: 0.171

-> test with 'GB'
GB tn, fp: 297, 13
GB fn, tp: 9, 2
GB f1 score: 0.154
GB cohens kappa score: 0.119

-> 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 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1194 synthetic samples
-> test with GAN.predict
GAN tn, fp: 278, 32
GAN fn, tp: 9, 2
GAN f1 score: 0.089
GAN cohens kappa score: 0.039

-> 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: 0.126

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


------ 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: 230, 80
GAN fn, tp: 3, 8
GAN f1 score: 0.162
GAN cohens kappa score: 0.107

-> test with 'LR'
LR tn, fp: 213, 97
LR fn, tp: 6, 5
LR f1 score: 0.088
LR cohens kappa score: 0.028
LR average precision score: 0.288

-> 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: 238, 72
KNN fn, tp: 7, 4
KNN f1 score: 0.092
KNN cohens kappa score: 0.034


------ 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: 231, 75
GAN fn, tp: 5, 4
GAN f1 score: 0.091
GAN cohens kappa score: 0.042

-> test with 'LR'
LR tn, fp: 217, 89
LR fn, tp: 3, 6
LR f1 score: 0.115
LR cohens kappa score: 0.067
LR average precision score: 0.116

-> test with 'GB'
GB tn, fp: 287, 19
GB fn, tp: 6, 3
GB f1 score: 0.194
GB cohens kappa score: 0.159

-> test with 'KNN'
KNN tn, fp: 229, 77
KNN fn, tp: 5, 4
KNN f1 score: 0.089
KNN cohens kappa score: 0.039


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

-> 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: 0.209

-> test with 'GB'
GB tn, fp: 301, 9
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.032

-> 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: 164, 146
GAN fn, tp: 5, 6
GAN f1 score: 0.074
GAN cohens kappa score: 0.010

-> test with 'LR'
LR tn, fp: 215, 95
LR fn, tp: 2, 9
LR f1 score: 0.157
LR cohens kappa score: 0.101
LR average precision score: 0.215

-> 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: 235, 75
KNN fn, tp: 9, 2
KNN f1 score: 0.045
KNN cohens kappa score: -0.015


------ 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: 190, 120
GAN fn, tp: 7, 4
GAN f1 score: 0.059
GAN cohens kappa score: -0.004

-> 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: 0.071

-> test with 'GB'
GB tn, fp: 283, 27
GB fn, tp: 9, 2
GB f1 score: 0.100
GB cohens kappa score: 0.053

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


------ 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: 173, 137
GAN fn, tp: 3, 8
GAN f1 score: 0.103
GAN cohens kappa score: 0.042

-> test with 'LR'
LR tn, fp: 197, 113
LR fn, tp: 2, 9
LR f1 score: 0.135
LR cohens kappa score: 0.077
LR average precision score: 0.169

-> 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: 229, 81
KNN fn, tp: 5, 6
KNN f1 score: 0.122
KNN cohens kappa score: 0.066


------ 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: 223, 83
GAN fn, tp: 5, 4
GAN f1 score: 0.083
GAN cohens kappa score: 0.033

-> test with 'LR'
LR tn, fp: 236, 70
LR fn, tp: 2, 7
LR f1 score: 0.163
LR cohens kappa score: 0.118
LR average precision score: 0.094

-> test with 'GB'
GB tn, fp: 292, 14
GB fn, tp: 6, 3
GB f1 score: 0.231
GB cohens kappa score: 0.201

-> test with 'KNN'
KNN tn, fp: 223, 83
KNN fn, tp: 6, 3
KNN f1 score: 0.063
KNN cohens kappa score: 0.012


====== 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: 251, 59
GAN fn, tp: 7, 4
GAN f1 score: 0.108
GAN cohens kappa score: 0.053

-> 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: 0.392

-> test with 'GB'
GB tn, fp: 300, 10
GB fn, tp: 8, 3
GB f1 score: 0.250
GB cohens kappa score: 0.221

-> test with 'KNN'
KNN tn, fp: 224, 86
KNN fn, tp: 9, 2
KNN f1 score: 0.040
KNN cohens kappa score: -0.022


------ 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: 206, 104
GAN fn, tp: 5, 6
GAN f1 score: 0.099
GAN cohens kappa score: 0.039

-> test with 'LR'
LR tn, fp: 229, 81
LR fn, tp: 3, 8
LR f1 score: 0.160
LR cohens kappa score: 0.105
LR average precision score: 0.194

-> 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: 207, 103
KNN fn, tp: 7, 4
KNN f1 score: 0.068
KNN cohens kappa score: 0.006


------ 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: 281, 29
GAN fn, tp: 9, 2
GAN f1 score: 0.095
GAN cohens kappa score: 0.047

-> 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: 0.107

-> 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: 240, 70
KNN fn, tp: 8, 3
KNN f1 score: 0.071
KNN cohens kappa score: 0.013


------ 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: 203, 107
GAN fn, tp: 4, 7
GAN f1 score: 0.112
GAN cohens kappa score: 0.053

-> 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: 0.147

-> 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: 240, 70
KNN fn, tp: 8, 3
KNN f1 score: 0.071
KNN cohens kappa score: 0.013


------ 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: 203, 103
GAN fn, tp: 5, 4
GAN f1 score: 0.069
GAN cohens kappa score: 0.017

-> test with 'LR'
LR tn, fp: 213, 93
LR fn, tp: 6, 3
LR f1 score: 0.057
LR cohens kappa score: 0.005
LR average precision score: 0.034

-> test with 'GB'
GB tn, fp: 279, 27
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.045

-> 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 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: 271, 39
GAN fn, tp: 11, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.056

-> test with 'LR'
LR tn, fp: 245, 65
LR fn, tp: 5, 6
LR f1 score: 0.146
LR cohens kappa score: 0.092
LR average precision score: 0.096

-> 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: 236, 74
KNN fn, tp: 9, 2
KNN f1 score: 0.046
KNN cohens kappa score: -0.015


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

-> 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: 0.085

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

-> test with 'LR'
LR tn, fp: 201, 109
LR fn, tp: 0, 11
LR f1 score: 0.168
LR cohens kappa score: 0.112
LR average precision score: 0.295

-> test with 'GB'
GB tn, fp: 281, 29
GB fn, tp: 8, 3
GB f1 score: 0.140
GB cohens kappa score: 0.093

-> test with 'KNN'
KNN tn, fp: 230, 80
KNN fn, tp: 6, 5
KNN f1 score: 0.104
KNN cohens kappa score: 0.046


------ 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: 200, 110
GAN fn, tp: 5, 6
GAN f1 score: 0.094
GAN cohens kappa score: 0.034

-> 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: 0.187

-> test with 'GB'
GB tn, fp: 300, 10
GB fn, tp: 9, 2
GB f1 score: 0.174
GB cohens kappa score: 0.143

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1196 synthetic samples
-> test with GAN.predict
GAN tn, fp: 187, 119
GAN fn, tp: 4, 5
GAN f1 score: 0.075
GAN cohens kappa score: 0.023

-> test with 'LR'
LR tn, fp: 244, 62
LR fn, tp: 4, 5
LR f1 score: 0.132
LR cohens kappa score: 0.086
LR average precision score: 0.148

-> 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: 226, 80
KNN fn, tp: 7, 2
KNN f1 score: 0.044
KNN cohens kappa score: -0.008

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 245, 113
LR fn, tp: 6, 11
LR f1 score: 0.168
LR cohens kappa score: 0.118
LR average precision score: 0.392


average:
LR tn, fp: 219.16, 90.04
LR fn, tp: 3.68, 6.92
LR f1 score: 0.129
LR cohens kappa score: 0.073
LR average precision score: 0.161


minimum:
LR tn, fp: 197, 62
LR fn, tp: 0, 3
LR f1 score: 0.057
LR cohens kappa score: 0.005
LR average precision score: 0.034


-----[ GB ]-----
maximum:
GB tn, fp: 301, 29
GB fn, tp: 11, 4
GB f1 score: 0.267
GB cohens kappa score: 0.233


average:
GB tn, fp: 291.08, 18.12
GB fn, tp: 8.68, 1.92
GB f1 score: 0.126
GB cohens kappa score: 0.087


minimum:
GB tn, fp: 279, 9
GB fn, tp: 6, 0
GB f1 score: 0.000
GB cohens kappa score: -0.045


-----[ KNN ]-----
maximum:
KNN tn, fp: 241, 103
KNN fn, tp: 10, 6
KNN f1 score: 0.122
KNN cohens kappa score: 0.066


average:
KNN tn, fp: 228.44, 80.76
KNN fn, tp: 7.28, 3.32
KNN f1 score: 0.070
KNN cohens kappa score: 0.012


minimum:
KNN tn, fp: 207, 69
KNN fn, tp: 5, 1
KNN f1 score: 0.020
KNN cohens kappa score: -0.044


-----[ GAN ]-----
maximum:
GAN tn, fp: 281, 194
GAN fn, tp: 11, 8
GAN f1 score: 0.162
GAN cohens kappa score: 0.107


average:
GAN tn, fp: 212.6, 96.6
GAN fn, tp: 5.68, 4.92
GAN f1 score: 0.089
GAN cohens kappa score: 0.031


minimum:
GAN tn, fp: 116, 29
GAN fn, tp: 3, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.056