ConvGeNBenchmarkResults/data_result/SpheredNoise/folding_car_good.log

///////////////////////////////////////////
// Running SpheredNoise 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
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 313, 19
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.049
LR average precision score: 0.038

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

-> test with 'KNN'
KNN tn, fp: 330, 2
KNN fn, tp: 12, 2
KNN f1 score: 0.222
KNN cohens kappa score: 0.208


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 314, 18
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.048
LR average precision score: 0.033

-> 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: 331, 1
KNN fn, tp: 11, 3
KNN f1 score: 0.333
KNN cohens kappa score: 0.321


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 313, 19
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.049
LR average precision score: 0.043

-> 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: 329, 3
KNN fn, tp: 12, 2
KNN f1 score: 0.211
KNN cohens kappa score: 0.193


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 311, 21
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.051
LR average precision score: 0.038

-> 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: 329, 3
KNN fn, tp: 14, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.014


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1328/56 points
-> new disc
-> calc distances
-> statistics
trained 56 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 311, 20
LR fn, tp: 13, 0
LR f1 score: 0.000
LR cohens kappa score: -0.048
LR average precision score: 0.048

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 4, 9
GB f1 score: 0.720
GB cohens kappa score: 0.709

-> test with 'KNN'
KNN tn, fp: 330, 1
KNN fn, tp: 11, 2
KNN f1 score: 0.250
KNN cohens kappa score: 0.239


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 312, 20
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.050
LR average precision score: 0.040

-> 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: 329, 3
KNN fn, tp: 10, 4
KNN f1 score: 0.381
KNN cohens kappa score: 0.364


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 15
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.044
LR average precision score: 0.035

-> 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: 328, 4
KNN fn, tp: 8, 6
KNN f1 score: 0.500
KNN cohens kappa score: 0.483


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 15
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.044
LR average precision score: 0.043

-> 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: 331, 1
KNN fn, tp: 13, 1
KNN f1 score: 0.125
KNN cohens kappa score: 0.116


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 314, 18
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.048
LR average precision score: 0.040

-> 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: 331, 1
KNN fn, tp: 12, 2
KNN f1 score: 0.235
KNN cohens kappa score: 0.224


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1328/56 points
-> new disc
-> calc distances
-> statistics
trained 56 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 307, 24
LR fn, tp: 13, 0
LR f1 score: 0.000
LR cohens kappa score: -0.052
LR average precision score: 0.040

-> test with 'GB'
GB tn, fp: 330, 1
GB fn, tp: 3, 10
GB f1 score: 0.833
GB cohens kappa score: 0.827

-> test with 'KNN'
KNN tn, fp: 327, 4
KNN fn, tp: 12, 1
KNN f1 score: 0.111
KNN cohens kappa score: 0.092


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.4142135623730951
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 312, 20
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.050
LR average precision score: 0.039

-> 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: 330, 2
KNN fn, tp: 11, 3
KNN f1 score: 0.316
KNN cohens kappa score: 0.301


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 315, 17
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.046
LR average precision score: 0.045

-> 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: 330, 2
KNN fn, tp: 12, 2
KNN f1 score: 0.222
KNN cohens kappa score: 0.208


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 15
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.044
LR average precision score: 0.039

-> 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: 328, 4
KNN fn, tp: 12, 2
KNN f1 score: 0.200
KNN cohens kappa score: 0.180


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 14
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.042
LR average precision score: 0.037

-> 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: 330, 2
KNN fn, tp: 13, 1
KNN f1 score: 0.118
KNN cohens kappa score: 0.105


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1328/56 points
-> new disc
-> calc distances
-> statistics
trained 56 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 304, 27
LR fn, tp: 13, 0
LR f1 score: 0.000
LR cohens kappa score: -0.054
LR average precision score: 0.037

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 5, 8
GB f1 score: 0.667
GB cohens kappa score: 0.655

-> test with 'KNN'
KNN tn, fp: 329, 2
KNN fn, tp: 9, 4
KNN f1 score: 0.421
KNN cohens kappa score: 0.407


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 309, 23
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.053
LR average precision score: 0.040

-> 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: 329, 3
KNN fn, tp: 9, 5
KNN f1 score: 0.455
KNN cohens kappa score: 0.438


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 319, 13
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.041
LR average precision score: 0.037

-> 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: 330, 2
KNN fn, tp: 12, 2
KNN f1 score: 0.222
KNN cohens kappa score: 0.208


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 15
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.044
LR average precision score: 0.037

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

-> test with 'KNN'
KNN tn, fp: 329, 3
KNN fn, tp: 13, 1
KNN f1 score: 0.111
KNN cohens kappa score: 0.095


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 307, 25
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.055
LR average precision score: 0.037

-> 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: 327, 5
KNN fn, tp: 11, 3
KNN f1 score: 0.273
KNN cohens kappa score: 0.251


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1328/56 points
-> new disc
-> calc distances
-> statistics
trained 56 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 313, 18
LR fn, tp: 13, 0
LR f1 score: 0.000
LR cohens kappa score: -0.046
LR average precision score: 0.046

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 4, 9
GB f1 score: 0.750
GB cohens kappa score: 0.741

-> test with 'KNN'
KNN tn, fp: 328, 3
KNN fn, tp: 10, 3
KNN f1 score: 0.316
KNN cohens kappa score: 0.299


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 309, 23
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.053
LR average precision score: 0.034

-> 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: 329, 3
KNN fn, tp: 12, 2
KNN f1 score: 0.211
KNN cohens kappa score: 0.193


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 316, 16
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.045
LR average precision score: 0.041

-> 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: 331, 1
KNN fn, tp: 13, 1
KNN f1 score: 0.125
KNN cohens kappa score: 0.116


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 316, 16
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.045
LR average precision score: 0.043

-> 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: 329, 3
KNN fn, tp: 11, 3
KNN f1 score: 0.300
KNN cohens kappa score: 0.283


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1327/55 points
-> new disc
-> calc distances
-> statistics
trained 55 points min:1.0 max:1.0
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 311, 21
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.051
LR average precision score: 0.046

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

-> test with 'KNN'
KNN tn, fp: 329, 3
KNN fn, tp: 13, 1
KNN f1 score: 0.111
KNN cohens kappa score: 0.095


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1328/56 points
-> new disc
-> calc distances
-> statistics
trained 56 points min:1.0 max:1.4142135623730951
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 311, 20
LR fn, tp: 13, 0
LR f1 score: 0.000
LR cohens kappa score: -0.048
LR average precision score: 0.034

-> test with 'GB'
GB tn, fp: 330, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 328, 3
KNN fn, tp: 11, 2
KNN f1 score: 0.222
KNN cohens kappa score: 0.206

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 319, 27
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.041
LR average precision score: 0.048


average:
LR tn, fp: 312.92, 18.88
LR fn, tp: 13.8, 0.0
LR f1 score: 0.000
LR cohens kappa score: -0.048
LR average precision score: 0.040


minimum:
LR tn, fp: 304, 13
LR fn, tp: 13, 0
LR f1 score: 0.000
LR cohens kappa score: -0.055
LR average precision score: 0.033


-----[ GB ]-----
maximum:
GB tn, fp: 332, 3
GB fn, tp: 8, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 330.92, 0.88
GB fn, tp: 4.2, 9.6
GB f1 score: 0.782
GB cohens kappa score: 0.775


minimum:
GB tn, fp: 328, 0
GB fn, tp: 0, 6
GB f1 score: 0.571
GB cohens kappa score: 0.560


-----[ KNN ]-----
maximum:
KNN tn, fp: 331, 5
KNN fn, tp: 14, 6
KNN f1 score: 0.500
KNN cohens kappa score: 0.483


average:
KNN tn, fp: 329.24, 2.56
KNN fn, tp: 11.48, 2.32
KNN f1 score: 0.240
KNN cohens kappa score: 0.224


minimum:
KNN tn, fp: 327, 1
KNN fn, tp: 8, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.014