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: 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 '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: 183, 149
LR fn, tp: 4, 10
LR f1 score: 0.116
LR cohens kappa score: 0.045
LR average precision score: 0.085

-> 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: 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: 0.056

-> 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: 325, 7
KNN fn, tp: 3, 11
KNN f1 score: 0.688
KNN cohens kappa score: 0.673


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 183, 149
LR fn, tp: 5, 9
LR f1 score: 0.105
LR cohens kappa score: 0.033
LR average precision score: 0.076

-> 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: 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: 5, 8
LR f1 score: 0.094
LR cohens kappa score: 0.026
LR average precision score: 0.057

-> 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: 167, 165
LR fn, tp: 4, 10
LR f1 score: 0.106
LR cohens kappa score: 0.033
LR average precision score: 0.067

-> 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: 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: 3, 11
LR f1 score: 0.123
LR cohens kappa score: 0.052
LR average precision score: 0.069

-> 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: 196, 136
LR fn, tp: 5, 9
LR f1 score: 0.113
LR cohens kappa score: 0.043
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: 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: 193, 139
LR fn, tp: 9, 5
LR f1 score: 0.063
LR cohens kappa score: -0.011
LR average precision score: 0.050

-> 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: 193, 138
LR fn, tp: 5, 8
LR f1 score: 0.101
LR cohens kappa score: 0.034
LR average precision score: 0.075

-> 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: 170, 162
LR fn, tp: 4, 10
LR f1 score: 0.108
LR cohens kappa score: 0.035
LR average precision score: 0.076

-> 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: 0.066

-> 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: 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: 180, 152
LR fn, tp: 5, 9
LR f1 score: 0.103
LR cohens kappa score: 0.031
LR average precision score: 0.056

-> 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: 181, 151
LR fn, tp: 4, 10
LR f1 score: 0.114
LR cohens kappa score: 0.043
LR average precision score: 0.084

-> 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: 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: 0.053

-> test with 'GB'
GB tn, fp: 330, 1
GB fn, tp: 2, 11
GB f1 score: 0.880
GB cohens kappa score: 0.875

-> 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: 3, 11
LR f1 score: 0.125
LR cohens kappa score: 0.055
LR average precision score: 0.064

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 0, 14
GB f1 score: 0.966
GB cohens kappa score: 0.964

-> 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: 184, 148
LR fn, tp: 6, 8
LR f1 score: 0.094
LR cohens kappa score: 0.021
LR average precision score: 0.060

-> 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: 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: 175, 157
LR fn, tp: 5, 9
LR f1 score: 0.100
LR cohens kappa score: 0.027
LR average precision score: 0.069

-> 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 '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: 0.081

-> test with 'GB'
GB tn, fp: 327, 4
GB fn, tp: 5, 8
GB f1 score: 0.640
GB cohens kappa score: 0.626

-> 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: 188, 144
LR fn, tp: 8, 6
LR f1 score: 0.073
LR cohens kappa score: -0.001
LR average precision score: 0.052

-> 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: 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: 192, 140
LR fn, tp: 6, 8
LR f1 score: 0.099
LR cohens kappa score: 0.027
LR average precision score: 0.070

-> 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: 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: 167, 165
LR fn, tp: 4, 10
LR f1 score: 0.106
LR cohens kappa score: 0.033
LR average precision score: 0.078

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 1, 13
GB f1 score: 0.867
GB cohens kappa score: 0.861

-> 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: 186, 146
LR fn, tp: 5, 9
LR f1 score: 0.107
LR cohens kappa score: 0.035
LR average precision score: 0.079

-> 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: 182, 149
LR fn, tp: 4, 9
LR f1 score: 0.105
LR cohens kappa score: 0.038
LR average precision score: 0.064

-> test with 'GB'
GB tn, fp: 330, 1
GB fn, tp: 1, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> 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, 165
LR fn, tp: 9, 11
LR f1 score: 0.125
LR cohens kappa score: 0.058
LR average precision score: 0.085


average:
LR tn, fp: 182.44, 149.36
LR fn, tp: 5.16, 8.64
LR f1 score: 0.100
LR cohens kappa score: 0.029
LR average precision score: 0.067


minimum:
LR tn, fp: 167, 136
LR fn, tp: 2, 5
LR f1 score: 0.063
LR cohens kappa score: -0.011
LR average precision score: 0.050


-----[ GB ]-----
maximum:
GB tn, fp: 332, 4
GB fn, tp: 7, 14
GB f1 score: 0.966
GB cohens kappa score: 0.964


average:
GB tn, fp: 330.68, 1.12
GB fn, tp: 2.8, 11.0
GB f1 score: 0.845
GB cohens kappa score: 0.840


minimum:
GB tn, fp: 327, 0
GB fn, tp: 0, 7
GB f1 score: 0.636
GB cohens kappa score: 0.625


-----[ 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: 324.84, 6.96
KNN fn, tp: 2.24, 11.56
KNN f1 score: 0.720
KNN cohens kappa score: 0.706


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