ConvGeNBenchmarkResults/data_result/convGAN-old-full/folding_car_good.log

///////////////////////////////////////////
// Running convGAN-full 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
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 153
LR fn, tp: 6, 8
LR f1 score: 0.091
LR cohens kappa score: 0.018
LR average precision score: 0.057

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 4, 10
GB f1 score: 0.769
GB cohens kappa score: 0.760

-> 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 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 153
LR fn, tp: 4, 10
LR f1 score: 0.113
LR cohens kappa score: 0.042
LR average precision score: 0.064

-> 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: 321, 11
KNN fn, tp: 0, 14
KNN f1 score: 0.718
KNN cohens kappa score: 0.703


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 178, 154
LR fn, tp: 6, 8
LR f1 score: 0.091
LR cohens kappa score: 0.018
LR average precision score: 0.056

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 3, 11
GB f1 score: 0.846
GB cohens kappa score: 0.840

-> test with 'KNN'
KNN tn, fp: 320, 12
KNN fn, tp: 0, 14
KNN f1 score: 0.700
KNN cohens kappa score: 0.683


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 150
LR fn, tp: 3, 11
LR f1 score: 0.126
LR cohens kappa score: 0.055
LR average precision score: 0.078

-> 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: 308, 24
KNN fn, tp: 1, 13
KNN f1 score: 0.510
KNN cohens kappa score: 0.479


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 147
LR fn, tp: 2, 11
LR f1 score: 0.129
LR cohens kappa score: 0.063
LR average precision score: 0.058

-> test with 'GB'
GB tn, fp: 330, 1
GB fn, tp: 4, 9
GB f1 score: 0.783
GB cohens kappa score: 0.775

-> test with 'KNN'
KNN tn, fp: 318, 13
KNN fn, tp: 0, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.649


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.066

-> 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: 323, 9
KNN fn, tp: 1, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.708


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 174, 158
LR fn, tp: 3, 11
LR f1 score: 0.120
LR cohens kappa score: 0.049
LR average precision score: 0.070

-> 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: 323, 9
KNN fn, tp: 1, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.708


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 194, 138
LR fn, tp: 4, 10
LR f1 score: 0.123
LR cohens kappa score: 0.053
LR average precision score: 0.071

-> 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: 327, 5
KNN fn, tp: 1, 13
KNN f1 score: 0.813
KNN cohens kappa score: 0.804


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 190, 142
LR fn, tp: 9, 5
LR f1 score: 0.062
LR cohens kappa score: -0.013
LR average precision score: 0.051

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 3, 11
GB f1 score: 0.846
GB cohens kappa score: 0.840

-> test with 'KNN'
KNN tn, fp: 322, 10
KNN fn, tp: 3, 11
KNN f1 score: 0.629
KNN cohens kappa score: 0.610


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 187, 144
LR fn, tp: 5, 8
LR f1 score: 0.097
LR cohens kappa score: 0.029
LR average precision score: 0.073

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 1, 12
GB f1 score: 0.857
GB cohens kappa score: 0.851

-> test with 'KNN'
KNN tn, fp: 314, 17
KNN fn, tp: 0, 13
KNN f1 score: 0.605
KNN cohens kappa score: 0.583


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 177, 155
LR fn, tp: 3, 11
LR f1 score: 0.122
LR cohens kappa score: 0.051
LR average precision score: 0.068

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 5, 9
GB f1 score: 0.720
GB cohens kappa score: 0.710

-> test with 'KNN'
KNN tn, fp: 310, 22
KNN fn, tp: 1, 13
KNN f1 score: 0.531
KNN cohens kappa score: 0.502


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 193, 139
LR fn, tp: 5, 9
LR f1 score: 0.111
LR cohens kappa score: 0.040
LR average precision score: 0.067

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 0, 14
GB f1 score: 0.933
GB cohens kappa score: 0.930

-> test with 'KNN'
KNN tn, fp: 311, 21
KNN fn, tp: 0, 14
KNN f1 score: 0.571
KNN cohens kappa score: 0.545


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 181, 151
LR fn, tp: 6, 8
LR f1 score: 0.092
LR cohens kappa score: 0.020
LR average precision score: 0.056

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 8, 6
GB f1 score: 0.545
GB cohens kappa score: 0.532

-> test with 'KNN'
KNN tn, fp: 319, 13
KNN fn, tp: 2, 12
KNN f1 score: 0.615
KNN cohens kappa score: 0.594


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 153
LR fn, tp: 3, 11
LR f1 score: 0.124
LR cohens kappa score: 0.053
LR average precision score: 0.083

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 8, 6
GB f1 score: 0.600
GB cohens kappa score: 0.590

-> test with 'KNN'
KNN tn, fp: 314, 18
KNN fn, tp: 0, 14
KNN f1 score: 0.609
KNN cohens kappa score: 0.585


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 173, 158
LR fn, tp: 5, 8
LR f1 score: 0.089
LR cohens kappa score: 0.021
LR average precision score: 0.052

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 3, 10
GB f1 score: 0.769
GB cohens kappa score: 0.760

-> test with 'KNN'
KNN tn, fp: 305, 26
KNN fn, tp: 1, 12
KNN f1 score: 0.471
KNN cohens kappa score: 0.439


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 166, 166
LR fn, tp: 4, 10
LR f1 score: 0.105
LR cohens kappa score: 0.033
LR average precision score: 0.061

-> 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: 321, 11
KNN fn, tp: 0, 14
KNN f1 score: 0.718
KNN cohens kappa score: 0.703


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 148
LR fn, tp: 6, 8
LR f1 score: 0.094
LR cohens kappa score: 0.021
LR average precision score: 0.055

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 7, 7
GB f1 score: 0.609
GB cohens kappa score: 0.596

-> 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 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 173, 159
LR fn, tp: 4, 10
LR f1 score: 0.109
LR cohens kappa score: 0.037
LR average precision score: 0.069

-> 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: 313, 19
KNN fn, tp: 1, 13
KNN f1 score: 0.565
KNN cohens kappa score: 0.539


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 147
LR fn, tp: 6, 8
LR f1 score: 0.095
LR cohens kappa score: 0.022
LR average precision score: 0.053

-> test with 'GB'
GB tn, fp: 327, 5
GB fn, tp: 6, 8
GB f1 score: 0.593
GB cohens kappa score: 0.576

-> test with 'KNN'
KNN tn, fp: 321, 11
KNN fn, tp: 0, 14
KNN f1 score: 0.718
KNN cohens kappa score: 0.703


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 172, 159
LR fn, tp: 2, 11
LR f1 score: 0.120
LR cohens kappa score: 0.054
LR average precision score: 0.082

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 8, 5
GB f1 score: 0.476
GB cohens kappa score: 0.461

-> test with 'KNN'
KNN tn, fp: 313, 18
KNN fn, tp: 1, 12
KNN f1 score: 0.558
KNN cohens kappa score: 0.534


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 187, 145
LR fn, tp: 8, 6
LR f1 score: 0.073
LR cohens kappa score: -0.001
LR average precision score: 0.051

-> 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: 313, 19
KNN fn, tp: 3, 11
KNN f1 score: 0.500
KNN cohens kappa score: 0.471


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.069

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 5, 9
GB f1 score: 0.720
GB cohens kappa score: 0.710

-> test with 'KNN'
KNN tn, fp: 313, 19
KNN fn, tp: 0, 14
KNN f1 score: 0.596
KNN cohens kappa score: 0.571


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 166, 166
LR fn, tp: 4, 10
LR f1 score: 0.105
LR cohens kappa score: 0.033
LR average precision score: 0.075

-> 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: 319, 13
KNN fn, tp: 0, 14
KNN f1 score: 0.683
KNN cohens kappa score: 0.665


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 172, 160
LR fn, tp: 4, 10
LR f1 score: 0.109
LR cohens kappa score: 0.037
LR average precision score: 0.072

-> 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: 322, 10
KNN fn, tp: 0, 14
KNN f1 score: 0.737
KNN cohens kappa score: 0.723


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 176, 155
LR fn, tp: 3, 10
LR f1 score: 0.112
LR cohens kappa score: 0.045
LR average precision score: 0.057

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 2, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914

-> test with 'KNN'
KNN tn, fp: 315, 16
KNN fn, tp: 0, 13
KNN f1 score: 0.619
KNN cohens kappa score: 0.598

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 194, 168
LR fn, tp: 9, 11
LR f1 score: 0.129
LR cohens kappa score: 0.063
LR average precision score: 0.083


average:
LR tn, fp: 179.12, 152.68
LR fn, tp: 4.56, 9.24
LR f1 score: 0.105
LR cohens kappa score: 0.034
LR average precision score: 0.064


minimum:
LR tn, fp: 164, 138
LR fn, tp: 2, 5
LR f1 score: 0.062
LR cohens kappa score: -0.013
LR average precision score: 0.051


-----[ GB ]-----
maximum:
GB tn, fp: 332, 5
GB fn, tp: 8, 14
GB f1 score: 0.963
GB cohens kappa score: 0.961


average:
GB tn, fp: 330.0, 1.8
GB fn, tp: 4.68, 9.12
GB f1 score: 0.730
GB cohens kappa score: 0.721


minimum:
GB tn, fp: 327, 0
GB fn, tp: 0, 5
GB f1 score: 0.476
GB cohens kappa score: 0.461


-----[ KNN ]-----
maximum:
KNN tn, fp: 327, 32
KNN fn, tp: 3, 14
KNN f1 score: 0.813
KNN cohens kappa score: 0.804


average:
KNN tn, fp: 315.72, 16.08
KNN fn, tp: 0.64, 13.16
KNN f1 score: 0.623
KNN cohens kappa score: 0.602


minimum:
KNN tn, fp: 300, 5
KNN fn, tp: 0, 11
KNN f1 score: 0.467
KNN cohens kappa score: 0.431