ConvGeNBenchmarkResults/data_result/ProWRAS/folding_car_good.log

///////////////////////////////////////////
// Running ProWRAS 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: 0.060

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

-> 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: 329, 3
KNN fn, tp: 1, 13
KNN f1 score: 0.867
KNN cohens kappa score: 0.861


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> 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: 0.082

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

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 0, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 326, 6
KNN fn, tp: 1, 13
KNN f1 score: 0.788
KNN cohens kappa score: 0.778


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 153
LR fn, tp: 8, 6
LR f1 score: 0.069
LR cohens kappa score: -0.005
LR average precision score: 0.058

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

-> 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: 326, 6
KNN fn, tp: 3, 11
KNN f1 score: 0.710
KNN cohens kappa score: 0.696


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 146
LR fn, tp: 6, 8
LR f1 score: 0.095
LR cohens kappa score: 0.023
LR average precision score: 0.079

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 10, 4
RF f1 score: 0.444
RF cohens kappa score: 0.434

-> 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: 326, 6
KNN fn, tp: 4, 10
KNN f1 score: 0.667
KNN cohens kappa score: 0.652


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 181, 150
LR fn, tp: 4, 9
LR f1 score: 0.105
LR cohens kappa score: 0.037
LR average precision score: 0.057

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 11, 2
RF f1 score: 0.267
RF cohens kappa score: 0.259

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 2, 11
GB f1 score: 0.815
GB cohens kappa score: 0.807

-> test with 'KNN'
KNN tn, fp: 326, 5
KNN fn, tp: 1, 12
KNN f1 score: 0.800
KNN cohens kappa score: 0.791


====== 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: 170, 162
LR fn, tp: 4, 10
LR f1 score: 0.108
LR cohens kappa score: 0.035
LR average precision score: 0.067

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

-> 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: 328, 4
KNN fn, tp: 2, 12
KNN f1 score: 0.800
KNN cohens kappa score: 0.791


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 178, 154
LR fn, tp: 4, 10
LR f1 score: 0.112
LR cohens kappa score: 0.041
LR average precision score: 0.070

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

-> 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: 326, 6
KNN fn, tp: 3, 11
KNN f1 score: 0.710
KNN cohens kappa score: 0.696


------ 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 'RF'
RF tn, fp: 332, 0
RF fn, tp: 7, 7
RF f1 score: 0.667
RF cohens kappa score: 0.657

-> 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: 327, 5
KNN fn, tp: 3, 11
KNN f1 score: 0.733
KNN cohens kappa score: 0.721


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 192, 140
LR fn, tp: 9, 5
LR f1 score: 0.063
LR cohens kappa score: -0.012
LR average precision score: 0.050

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 10, 4
RF f1 score: 0.444
RF cohens kappa score: 0.434

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 3, 11
GB f1 score: 0.815
GB cohens kappa score: 0.807

-> test with 'KNN'
KNN tn, fp: 322, 10
KNN fn, tp: 6, 8
KNN f1 score: 0.500
KNN cohens kappa score: 0.476


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 194, 137
LR fn, tp: 5, 8
LR f1 score: 0.101
LR cohens kappa score: 0.034
LR average precision score: 0.075

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 8, 5
RF f1 score: 0.556
RF cohens kappa score: 0.546

-> 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: 319, 12
KNN fn, tp: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.668


====== 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: 173, 159
LR fn, tp: 5, 9
LR f1 score: 0.099
LR cohens kappa score: 0.026
LR average precision score: 0.075

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

-> 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: 326, 6
KNN fn, tp: 3, 11
KNN f1 score: 0.710
KNN cohens kappa score: 0.696


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 196, 136
LR fn, tp: 5, 9
LR f1 score: 0.113
LR cohens kappa score: 0.043
LR average precision score: 0.067

-> test with 'RF'
RF tn, fp: 331, 1
RF fn, tp: 10, 4
RF f1 score: 0.421
RF cohens kappa score: 0.408

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 1, 13
GB f1 score: 0.897
GB cohens kappa score: 0.892

-> test with 'KNN'
KNN tn, fp: 324, 8
KNN fn, tp: 3, 11
KNN f1 score: 0.667
KNN cohens kappa score: 0.650


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 187, 145
LR fn, tp: 5, 9
LR f1 score: 0.107
LR cohens kappa score: 0.036
LR average precision score: 0.056

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 9, 5
RF f1 score: 0.526
RF cohens kappa score: 0.516

-> 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: 328, 4
KNN fn, tp: 3, 11
KNN f1 score: 0.759
KNN cohens kappa score: 0.748


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 174, 158
LR fn, tp: 4, 10
LR f1 score: 0.110
LR cohens kappa score: 0.038
LR average precision score: 0.082

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

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 2, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 324, 8
KNN fn, tp: 1, 13
KNN f1 score: 0.743
KNN cohens kappa score: 0.730


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 174, 157
LR fn, tp: 6, 7
LR f1 score: 0.079
LR cohens kappa score: 0.010
LR average precision score: 0.054

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 4, 9
RF f1 score: 0.818
RF cohens kappa score: 0.812

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 1, 12
GB f1 score: 0.889
GB cohens kappa score: 0.884

-> 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 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: 181, 151
LR fn, tp: 4, 10
LR f1 score: 0.114
LR cohens kappa score: 0.043
LR average precision score: 0.067

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 7, 7
RF f1 score: 0.667
RF cohens kappa score: 0.657

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 2, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

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


------ Step 4/5: Slice 2/5 -------
-> 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: 0.060

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 12, 2
RF f1 score: 0.250
RF cohens kappa score: 0.242

-> 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: 319, 13
KNN fn, tp: 7, 7
KNN f1 score: 0.412
KNN cohens kappa score: 0.382


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 176, 156
LR fn, tp: 5, 9
LR f1 score: 0.101
LR cohens kappa score: 0.028
LR average precision score: 0.068

-> test with 'RF'
RF tn, fp: 331, 1
RF fn, tp: 8, 6
RF f1 score: 0.571
RF cohens kappa score: 0.560

-> 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: 318, 14
KNN fn, tp: 1, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 193, 139
LR fn, tp: 8, 6
LR f1 score: 0.075
LR cohens kappa score: 0.002
LR average precision score: 0.058

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

-> 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: 330, 2
KNN fn, tp: 1, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.892


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 178, 153
LR fn, tp: 2, 11
LR f1 score: 0.124
LR cohens kappa score: 0.058
LR average precision score: 0.079

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 9, 4
RF f1 score: 0.471
RF cohens kappa score: 0.461

-> test with 'GB'
GB tn, fp: 327, 4
GB fn, tp: 4, 9
GB f1 score: 0.692
GB cohens kappa score: 0.680

-> test with 'KNN'
KNN tn, fp: 322, 9
KNN fn, tp: 2, 11
KNN f1 score: 0.667
KNN cohens kappa score: 0.651


====== 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: 190, 142
LR fn, tp: 8, 6
LR f1 score: 0.074
LR cohens kappa score: 0.000
LR average precision score: 0.053

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 10, 4
RF f1 score: 0.444
RF cohens kappa score: 0.434

-> 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: 322, 10
KNN fn, tp: 7, 7
KNN f1 score: 0.452
KNN cohens kappa score: 0.426


------ Step 5/5: 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: 6, 8
LR f1 score: 0.099
LR cohens kappa score: 0.028
LR average precision score: 0.072

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

-> 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: 329, 3
KNN fn, tp: 0, 14
KNN f1 score: 0.903
KNN cohens kappa score: 0.899


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 163, 169
LR fn, tp: 3, 11
LR f1 score: 0.113
LR cohens kappa score: 0.041
LR average precision score: 0.078

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

-> test with 'GB'
GB tn, fp: 328, 4
GB fn, tp: 1, 13
GB f1 score: 0.839
GB cohens kappa score: 0.831

-> test with 'KNN'
KNN tn, fp: 325, 7
KNN fn, tp: 1, 13
KNN f1 score: 0.765
KNN cohens kappa score: 0.753


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 181, 151
LR fn, tp: 5, 9
LR f1 score: 0.103
LR cohens kappa score: 0.031
LR average precision score: 0.075

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 10, 4
RF f1 score: 0.444
RF cohens kappa score: 0.434

-> 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: 328, 4
KNN fn, tp: 3, 11
KNN f1 score: 0.759
KNN cohens kappa score: 0.748


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 146
LR fn, tp: 4, 9
LR f1 score: 0.107
LR cohens kappa score: 0.040
LR average precision score: 0.065

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 10, 3
RF f1 score: 0.375
RF cohens kappa score: 0.366

-> 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: 324, 7
KNN fn, tp: 0, 13
KNN f1 score: 0.788
KNN cohens kappa score: 0.778

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 196, 169
LR fn, tp: 9, 11
LR f1 score: 0.124
LR cohens kappa score: 0.058
LR average precision score: 0.082


average:
LR tn, fp: 182.36, 149.44
LR fn, tp: 5.2, 8.6
LR f1 score: 0.100
LR cohens kappa score: 0.028
LR average precision score: 0.067


minimum:
LR tn, fp: 163, 136
LR fn, tp: 2, 5
LR f1 score: 0.063
LR cohens kappa score: -0.012
LR average precision score: 0.050


-----[ RF ]-----
maximum:
RF tn, fp: 332, 1
RF fn, tp: 12, 9
RF f1 score: 0.818
RF cohens kappa score: 0.812


average:
RF tn, fp: 331.72, 0.08
RF fn, tp: 8.24, 5.56
RF f1 score: 0.557
RF cohens kappa score: 0.548


minimum:
RF tn, fp: 331, 0
RF fn, tp: 4, 2
RF f1 score: 0.250
RF cohens kappa score: 0.242


-----[ GB ]-----
maximum:
GB tn, fp: 332, 4
GB fn, tp: 6, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 330.6, 1.2
GB fn, tp: 2.6, 11.2
GB f1 score: 0.853
GB cohens kappa score: 0.847


minimum:
GB tn, fp: 327, 0
GB fn, tp: 0, 8
GB f1 score: 0.692
GB cohens kappa score: 0.680


-----[ KNN ]-----
maximum:
KNN tn, fp: 330, 14
KNN fn, tp: 7, 14
KNN f1 score: 0.933
KNN cohens kappa score: 0.930


average:
KNN tn, fp: 324.88, 6.92
KNN fn, tp: 2.24, 11.56
KNN f1 score: 0.720
KNN cohens kappa score: 0.707


minimum:
KNN tn, fp: 318, 2
KNN fn, tp: 0, 7
KNN f1 score: 0.412
KNN cohens kappa score: 0.382