ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_car_good.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_car_good
///////////////////////////////////////////

Load 'data_input/folding_car_good'
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 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 162, 170
LR fn, tp: 4, 10
LR f1 score: 0.103
LR cohens kappa score: 0.030
LR average precision score: 0.067

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 1, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 294, 38
KNN fn, tp: 0, 14
KNN f1 score: 0.424
KNN cohens kappa score: 0.385


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 197, 135
LR fn, tp: 4, 10
LR f1 score: 0.126
LR cohens kappa score: 0.056
LR average precision score: 0.096

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 6, 8
GB f1 score: 0.727
GB cohens kappa score: 0.719

-> test with 'KNN'
KNN tn, fp: 280, 52
KNN fn, tp: 0, 14
KNN f1 score: 0.350
KNN cohens kappa score: 0.303


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 150
LR fn, tp: 6, 8
LR f1 score: 0.093
LR cohens kappa score: 0.020
LR average precision score: 0.060

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 4, 10
GB f1 score: 0.800
GB cohens kappa score: 0.793

-> test with 'KNN'
KNN tn, fp: 291, 41
KNN fn, tp: 0, 14
KNN f1 score: 0.406
KNN cohens kappa score: 0.365


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 168, 164
LR fn, tp: 4, 10
LR f1 score: 0.106
LR cohens kappa score: 0.034
LR average precision score: 0.086

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 5, 9
GB f1 score: 0.750
GB cohens kappa score: 0.741

-> test with 'KNN'
KNN tn, fp: 292, 40
KNN fn, tp: 0, 14
KNN f1 score: 0.412
KNN cohens kappa score: 0.371


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 146
LR fn, tp: 5, 8
LR f1 score: 0.096
LR cohens kappa score: 0.028
LR average precision score: 0.046

-> test with 'GB'
GB tn, fp: 327, 4
GB fn, tp: 2, 11
GB f1 score: 0.786
GB cohens kappa score: 0.777

-> test with 'KNN'
KNN tn, fp: 292, 39
KNN fn, tp: 1, 12
KNN f1 score: 0.375
KNN cohens kappa score: 0.335


====== 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 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 164, 168
LR fn, tp: 5, 9
LR f1 score: 0.094
LR cohens kappa score: 0.021
LR average precision score: 0.078

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 2, 12
GB f1 score: 0.889
GB cohens kappa score: 0.884

-> test with 'KNN'
KNN tn, fp: 298, 34
KNN fn, tp: 0, 14
KNN f1 score: 0.452
KNN cohens kappa score: 0.415


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 171, 161
LR fn, tp: 3, 11
LR f1 score: 0.118
LR cohens kappa score: 0.047
LR average precision score: 0.093

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 1, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 307, 25
KNN fn, tp: 0, 14
KNN f1 score: 0.528
KNN cohens kappa score: 0.498


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 181, 151
LR fn, tp: 4, 10
LR f1 score: 0.114
LR cohens kappa score: 0.043
LR average precision score: 0.081

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 3, 11
GB f1 score: 0.880
GB cohens kappa score: 0.876

-> test with 'KNN'
KNN tn, fp: 292, 40
KNN fn, tp: 1, 13
KNN f1 score: 0.388
KNN cohens kappa score: 0.346


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 132
LR fn, tp: 7, 7
LR f1 score: 0.092
LR cohens kappa score: 0.019
LR average precision score: 0.058

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 5, 9
GB f1 score: 0.750
GB cohens kappa score: 0.741

-> test with 'KNN'
KNN tn, fp: 261, 71
KNN fn, tp: 1, 13
KNN f1 score: 0.265
KNN cohens kappa score: 0.211


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 145
LR fn, tp: 4, 9
LR f1 score: 0.108
LR cohens kappa score: 0.041
LR average precision score: 0.093

-> test with 'GB'
GB tn, fp: 327, 4
GB fn, tp: 2, 11
GB f1 score: 0.786
GB cohens kappa score: 0.777

-> test with 'KNN'
KNN tn, fp: 288, 43
KNN fn, tp: 0, 13
KNN f1 score: 0.377
KNN cohens kappa score: 0.336


====== 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 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 146
LR fn, tp: 3, 11
LR f1 score: 0.129
LR cohens kappa score: 0.059
LR average precision score: 0.071

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 3, 11
GB f1 score: 0.880
GB cohens kappa score: 0.876

-> test with 'KNN'
KNN tn, fp: 283, 49
KNN fn, tp: 0, 14
KNN f1 score: 0.364
KNN cohens kappa score: 0.319


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 183, 149
LR fn, tp: 4, 10
LR f1 score: 0.116
LR cohens kappa score: 0.045
LR average precision score: 0.060

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 6, 8
GB f1 score: 0.667
GB cohens kappa score: 0.655

-> test with 'KNN'
KNN tn, fp: 282, 50
KNN fn, tp: 0, 14
KNN f1 score: 0.359
KNN cohens kappa score: 0.313


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 167, 165
LR fn, tp: 3, 11
LR f1 score: 0.116
LR cohens kappa score: 0.044
LR average precision score: 0.056

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 10, 4
GB f1 score: 0.400
GB cohens kappa score: 0.385

-> test with 'KNN'
KNN tn, fp: 297, 35
KNN fn, tp: 0, 14
KNN f1 score: 0.444
KNN cohens kappa score: 0.407


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 170, 162
LR fn, tp: 3, 11
LR f1 score: 0.118
LR cohens kappa score: 0.046
LR average precision score: 0.074

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 4, 10
GB f1 score: 0.833
GB cohens kappa score: 0.828

-> test with 'KNN'
KNN tn, fp: 290, 42
KNN fn, tp: 0, 14
KNN f1 score: 0.400
KNN cohens kappa score: 0.358


------ 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 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 188, 143
LR fn, tp: 6, 7
LR f1 score: 0.086
LR cohens kappa score: 0.018
LR average precision score: 0.079

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 3, 10
GB f1 score: 0.800
GB cohens kappa score: 0.792

-> test with 'KNN'
KNN tn, fp: 270, 61
KNN fn, tp: 0, 13
KNN f1 score: 0.299
KNN cohens kappa score: 0.251


====== 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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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 177, 155
LR fn, tp: 1, 13
LR f1 score: 0.143
LR cohens kappa score: 0.074
LR average precision score: 0.078

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 7, 7
GB f1 score: 0.636
GB cohens kappa score: 0.625

-> test with 'KNN'
KNN tn, fp: 308, 24
KNN fn, tp: 0, 14
KNN f1 score: 0.538
KNN cohens kappa score: 0.509


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

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100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 169, 163
LR fn, tp: 5, 9
LR f1 score: 0.097
LR cohens kappa score: 0.024
LR average precision score: 0.057

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 6, 8
GB f1 score: 0.667
GB cohens kappa score: 0.655

-> test with 'KNN'
KNN tn, fp: 267, 65
KNN fn, tp: 0, 14
KNN f1 score: 0.301
KNN cohens kappa score: 0.249


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 167, 165
LR fn, tp: 5, 9
LR f1 score: 0.096
LR cohens kappa score: 0.023
LR average precision score: 0.053

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 2, 12
GB f1 score: 0.857
GB cohens kappa score: 0.851

-> test with 'KNN'
KNN tn, fp: 276, 56
KNN fn, tp: 0, 14
KNN f1 score: 0.333
KNN cohens kappa score: 0.285


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 175, 157
LR fn, tp: 3, 11
LR f1 score: 0.121
LR cohens kappa score: 0.050
LR average precision score: 0.051

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 6, 8
GB f1 score: 0.696
GB cohens kappa score: 0.686

-> test with 'KNN'
KNN tn, fp: 300, 32
KNN fn, tp: 0, 14
KNN f1 score: 0.467
KNN cohens kappa score: 0.431


------ 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,  1.24it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 166, 165
LR fn, tp: 1, 12
LR f1 score: 0.126
LR cohens kappa score: 0.060
LR average precision score: 0.075

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 5, 8
GB f1 score: 0.696
GB cohens kappa score: 0.685

-> test with 'KNN'
KNN tn, fp: 315, 16
KNN fn, tp: 6, 7
KNN f1 score: 0.389
KNN cohens kappa score: 0.358


====== 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,  1.12it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 159, 173
LR fn, tp: 6, 8
LR f1 score: 0.082
LR cohens kappa score: 0.007
LR average precision score: 0.053

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 5, 9
GB f1 score: 0.750
GB cohens kappa score: 0.741

-> test with 'KNN'
KNN tn, fp: 278, 54
KNN fn, tp: 0, 14
KNN f1 score: 0.341
KNN cohens kappa score: 0.294


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

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100%|██████████| 10/10 [00:09<00:00,  1.07it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 147
LR fn, tp: 5, 9
LR f1 score: 0.106
LR cohens kappa score: 0.034
LR average precision score: 0.079

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 5, 9
GB f1 score: 0.750
GB cohens kappa score: 0.741

-> test with 'KNN'
KNN tn, fp: 291, 41
KNN fn, tp: 0, 14
KNN f1 score: 0.406
KNN cohens kappa score: 0.365


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 168, 164
LR fn, tp: 4, 10
LR f1 score: 0.106
LR cohens kappa score: 0.034
LR average precision score: 0.142

-> test with 'GB'
GB tn, fp: 327, 5
GB fn, tp: 2, 12
GB f1 score: 0.774
GB cohens kappa score: 0.764

-> test with 'KNN'
KNN tn, fp: 305, 27
KNN fn, tp: 4, 10
KNN f1 score: 0.392
KNN cohens kappa score: 0.354


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 193, 139
LR fn, tp: 7, 7
LR f1 score: 0.087
LR cohens kappa score: 0.015
LR average precision score: 0.071

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 5, 9
GB f1 score: 0.783
GB cohens kappa score: 0.775

-> test with 'KNN'
KNN tn, fp: 289, 43
KNN fn, tp: 3, 11
KNN f1 score: 0.324
KNN cohens kappa score: 0.277


------ 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,  1.11it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 176, 155
LR fn, tp: 6, 7
LR f1 score: 0.080
LR cohens kappa score: 0.011
LR average precision score: 0.072

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 3, 10
GB f1 score: 0.870
GB cohens kappa score: 0.865

-> test with 'KNN'
KNN tn, fp: 294, 37
KNN fn, tp: 0, 13
KNN f1 score: 0.413
KNN cohens kappa score: 0.375

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 200, 173
LR fn, tp: 7, 13
LR f1 score: 0.143
LR cohens kappa score: 0.074
LR average precision score: 0.142


average:
LR tn, fp: 177.0, 154.8
LR fn, tp: 4.32, 9.48
LR f1 score: 0.106
LR cohens kappa score: 0.035
LR average precision score: 0.073


minimum:
LR tn, fp: 159, 132
LR fn, tp: 1, 7
LR f1 score: 0.080
LR cohens kappa score: 0.007
LR average precision score: 0.046


-----[ GB ]-----
maximum:
GB tn, fp: 332, 5
GB fn, tp: 10, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961


average:
GB tn, fp: 330.44, 1.36
GB fn, tp: 4.12, 9.68
GB f1 score: 0.773
GB cohens kappa score: 0.765


minimum:
GB tn, fp: 327, 0
GB fn, tp: 1, 4
GB f1 score: 0.400
GB cohens kappa score: 0.385


-----[ KNN ]-----
maximum:
KNN tn, fp: 315, 71
KNN fn, tp: 6, 14
KNN f1 score: 0.538
KNN cohens kappa score: 0.509


average:
KNN tn, fp: 289.6, 42.2
KNN fn, tp: 0.64, 13.16
KNN f1 score: 0.390
KNN cohens kappa score: 0.349


minimum:
KNN tn, fp: 261, 16
KNN fn, tp: 0, 7
KNN f1 score: 0.265
KNN cohens kappa score: 0.211