ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_winequality-red-4.log

///////////////////////////////////////////
// Running CTAB-GAN 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

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 296, 14
LR fn, tp: 10, 1
LR f1 score: 0.077
LR cohens kappa score: 0.039
LR average precision score: 0.070

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

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


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 261, 49
LR fn, tp: 7, 4
LR f1 score: 0.125
LR cohens kappa score: 0.072
LR average precision score: 0.108

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

-> test with 'KNN'
KNN tn, fp: 279, 31
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.053


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 296, 14
LR fn, tp: 7, 4
LR f1 score: 0.276
LR cohens kappa score: 0.244
LR average precision score: 0.330

-> test with 'GB'
GB tn, fp: 306, 4
GB fn, tp: 10, 1
GB f1 score: 0.125
GB cohens kappa score: 0.106

-> test with 'KNN'
KNN tn, fp: 284, 26
KNN fn, tp: 10, 1
KNN f1 score: 0.053
KNN cohens kappa score: 0.004


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 219, 91
LR fn, tp: 6, 5
LR f1 score: 0.093
LR cohens kappa score: 0.034
LR average precision score: 0.078

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 10, 1
GB f1 score: 0.118
GB cohens kappa score: 0.096

-> test with 'KNN'
KNN tn, fp: 276, 34
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.055


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 37
LR fn, tp: 6, 3
LR f1 score: 0.122
LR cohens kappa score: 0.080
LR average precision score: 0.131

-> test with 'GB'
GB tn, fp: 304, 2
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.010

-> test with 'KNN'
KNN tn, fp: 267, 39
KNN fn, tp: 7, 2
KNN f1 score: 0.080
KNN cohens kappa score: 0.035


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 296, 14
LR fn, tp: 9, 2
LR f1 score: 0.148
LR cohens kappa score: 0.112
LR average precision score: 0.148

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 10, 1
GB f1 score: 0.143
GB cohens kappa score: 0.130

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


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 305, 5
LR fn, tp: 10, 1
LR f1 score: 0.118
LR cohens kappa score: 0.096
LR average precision score: 0.109

-> test with 'GB'
GB tn, fp: 310, 0
GB fn, tp: 10, 1
GB f1 score: 0.167
GB cohens kappa score: 0.162

-> test with 'KNN'
KNN tn, fp: 289, 21
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.047


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 41
LR fn, tp: 6, 5
LR f1 score: 0.175
LR cohens kappa score: 0.127
LR average precision score: 0.093

-> test with 'GB'
GB tn, fp: 304, 6
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.025

-> test with 'KNN'
KNN tn, fp: 268, 42
KNN fn, tp: 10, 1
KNN f1 score: 0.037
KNN cohens kappa score: -0.019


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 224, 86
LR fn, tp: 5, 6
LR f1 score: 0.117
LR cohens kappa score: 0.059
LR average precision score: 0.208

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011

-> test with 'KNN'
KNN tn, fp: 276, 34
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.055


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 262, 44
LR fn, tp: 6, 3
LR f1 score: 0.107
LR cohens kappa score: 0.062
LR average precision score: 0.100

-> test with 'GB'
GB tn, fp: 302, 4
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.018

-> test with 'KNN'
KNN tn, fp: 278, 28
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.045


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 248, 62
LR fn, tp: 6, 5
LR f1 score: 0.128
LR cohens kappa score: 0.074
LR average precision score: 0.092

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 10, 1
GB f1 score: 0.118
GB cohens kappa score: 0.096

-> test with 'KNN'
KNN tn, fp: 276, 34
KNN fn, tp: 8, 3
KNN f1 score: 0.125
KNN cohens kappa score: 0.076


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 277, 33
LR fn, tp: 7, 4
LR f1 score: 0.167
LR cohens kappa score: 0.120
LR average precision score: 0.253

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 9, 2
GB f1 score: 0.250
GB cohens kappa score: 0.234

-> test with 'KNN'
KNN tn, fp: 286, 24
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.049


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 264, 46
LR fn, tp: 6, 5
LR f1 score: 0.161
LR cohens kappa score: 0.111
LR average precision score: 0.097

-> test with 'GB'
GB tn, fp: 303, 7
GB fn, tp: 10, 1
GB f1 score: 0.105
GB cohens kappa score: 0.079

-> test with 'KNN'
KNN tn, fp: 264, 46
KNN fn, tp: 8, 3
KNN f1 score: 0.100
KNN cohens kappa score: 0.047


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 265, 45
LR fn, tp: 7, 4
LR f1 score: 0.133
LR cohens kappa score: 0.082
LR average precision score: 0.078

-> test with 'GB'
GB tn, fp: 310, 0
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 289, 21
KNN fn, tp: 10, 1
KNN f1 score: 0.061
KNN cohens kappa score: 0.016


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:08<00:00,  1.21it/s]
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 255, 51
LR fn, tp: 6, 3
LR f1 score: 0.095
LR cohens kappa score: 0.049
LR average precision score: 0.050

-> test with 'GB'
GB tn, fp: 302, 4
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.018

-> test with 'KNN'
KNN tn, fp: 266, 40
KNN fn, tp: 7, 2
KNN f1 score: 0.078
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

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100%|██████████| 10/10 [00:07<00:00,  1.27it/s]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 36
LR fn, tp: 7, 4
LR f1 score: 0.157
LR cohens kappa score: 0.109
LR average precision score: 0.094

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 10, 1
GB f1 score: 0.133
GB cohens kappa score: 0.117

-> test with 'KNN'
KNN tn, fp: 276, 34
KNN fn, tp: 9, 2
KNN f1 score: 0.085
KNN cohens kappa score: 0.034


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 257, 53
LR fn, tp: 6, 5
LR f1 score: 0.145
LR cohens kappa score: 0.093
LR average precision score: 0.111

-> test with 'GB'
GB tn, fp: 306, 4
GB fn, tp: 10, 1
GB f1 score: 0.125
GB cohens kappa score: 0.106

-> test with 'KNN'
KNN tn, fp: 283, 27
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.051


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 250, 60
LR fn, tp: 7, 4
LR f1 score: 0.107
LR cohens kappa score: 0.051
LR average precision score: 0.082

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 10, 1
GB f1 score: 0.143
GB cohens kappa score: 0.130

-> test with 'KNN'
KNN tn, fp: 278, 32
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.054


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 30
LR fn, tp: 9, 2
LR f1 score: 0.093
LR cohens kappa score: 0.044
LR average precision score: 0.098

-> test with 'GB'
GB tn, fp: 303, 7
GB fn, tp: 10, 1
GB f1 score: 0.105
GB cohens kappa score: 0.079

-> test with 'KNN'
KNN tn, fp: 280, 30
KNN fn, tp: 10, 1
KNN f1 score: 0.048
KNN cohens kappa score: -0.003


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 254, 52
LR fn, tp: 8, 1
LR f1 score: 0.032
LR cohens kappa score: -0.017
LR average precision score: 0.031

-> test with 'GB'
GB tn, fp: 305, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.173

-> test with 'KNN'
KNN tn, fp: 267, 39
KNN fn, tp: 7, 2
KNN f1 score: 0.080
KNN cohens kappa score: 0.035


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 216, 94
LR fn, tp: 5, 6
LR f1 score: 0.108
LR cohens kappa score: 0.049
LR average precision score: 0.061

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

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


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 260, 50
LR fn, tp: 7, 4
LR f1 score: 0.123
LR cohens kappa score: 0.070
LR average precision score: 0.067

-> test with 'GB'
GB tn, fp: 309, 1
GB fn, tp: 9, 2
GB f1 score: 0.286
GB cohens kappa score: 0.275

-> test with 'KNN'
KNN tn, fp: 281, 29
KNN fn, tp: 10, 1
KNN f1 score: 0.049
KNN cohens kappa score: -0.001


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 40
LR fn, tp: 6, 5
LR f1 score: 0.179
LR cohens kappa score: 0.131
LR average precision score: 0.195

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.022

-> test with 'KNN'
KNN tn, fp: 276, 34
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.055


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 43
LR fn, tp: 8, 3
LR f1 score: 0.105
LR cohens kappa score: 0.053
LR average precision score: 0.182

-> test with 'GB'
GB tn, fp: 309, 1
GB fn, tp: 9, 2
GB f1 score: 0.286
GB cohens kappa score: 0.275

-> test with 'KNN'
KNN tn, fp: 286, 24
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.049


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:08<00:00,  1.25it/s]
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 295, 11
LR fn, tp: 8, 1
LR f1 score: 0.095
LR cohens kappa score: 0.065
LR average precision score: 0.083

-> test with 'GB'
GB tn, fp: 302, 4
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.018

-> test with 'KNN'
KNN tn, fp: 276, 30
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.046

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 305, 94
LR fn, tp: 10, 6
LR f1 score: 0.276
LR cohens kappa score: 0.244
LR average precision score: 0.330


average:
LR tn, fp: 265.16, 44.04
LR fn, tp: 7.0, 3.6
LR f1 score: 0.127
LR cohens kappa score: 0.080
LR average precision score: 0.118


minimum:
LR tn, fp: 216, 5
LR fn, tp: 5, 1
LR f1 score: 0.032
LR cohens kappa score: -0.017
LR average precision score: 0.031


-----[ GB ]-----
maximum:
GB tn, fp: 310, 7
GB fn, tp: 11, 2
GB f1 score: 0.286
GB cohens kappa score: 0.275


average:
GB tn, fp: 305.96, 3.24
GB fn, tp: 9.92, 0.68
GB f1 score: 0.091
GB cohens kappa score: 0.076


minimum:
GB tn, fp: 302, 0
GB fn, tp: 8, 0
GB f1 score: 0.000
GB cohens kappa score: -0.025


-----[ KNN ]-----
maximum:
KNN tn, fp: 289, 46
KNN fn, tp: 11, 3
KNN f1 score: 0.140
KNN cohens kappa score: 0.093


average:
KNN tn, fp: 278.08, 31.12
KNN fn, tp: 9.68, 0.92
KNN f1 score: 0.040
KNN cohens kappa score: -0.010


minimum:
KNN tn, fp: 264, 21
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.055