ConvGeNBenchmarkResults/data_result/SpheredNoise/folding_kddcup-guess_passwd_vs_satan.log

///////////////////////////////////////////
// Running SpheredNoise on folding_kddcup-guess_passwd_vs_satan
///////////////////////////////////////////

Load 'data_input/folding_kddcup-guess_passwd_vs_satan'
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 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:148.4225447834661 max:1929.7078500384455
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:199.5711026175884 max:284.9796045333771
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 317, 1
GB fn, tp: 0, 11
GB f1 score: 0.957
GB cohens kappa score: 0.955

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1272/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:147.82845395931056 max:1877.517291717975
-> create 1228 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 317, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 0
KNN fn, tp: 0, 9
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:148.4225447834661 max:234.9446483748885
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:1929.7078500384455
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 1
LR fn, tp: 0, 11
LR f1 score: 0.957
LR cohens kappa score: 0.955
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 317, 1
GB fn, tp: 0, 11
GB f1 score: 0.957
GB cohens kappa score: 0.955

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1272/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:199.5731382225574 max:1877.517291717975
-> create 1228 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 316, 1
GB fn, tp: 0, 9
GB f1 score: 0.947
GB cohens kappa score: 0.946

-> test with 'KNN'
KNN tn, fp: 316, 1
KNN fn, tp: 0, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.946


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 316, 2
LR fn, tp: 0, 11
LR f1 score: 0.917
LR cohens kappa score: 0.914
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 315, 3
GB fn, tp: 0, 11
GB f1 score: 0.880
GB cohens kappa score: 0.875

-> test with 'KNN'
KNN tn, fp: 316, 2
KNN fn, tp: 0, 11
KNN f1 score: 0.917
KNN cohens kappa score: 0.914


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:234.9446483748885
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1272/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:199.56770655594556 max:1983.7451250349677
-> create 1228 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 317, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 0
KNN fn, tp: 0, 9
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:148.4225447834661 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 1
LR fn, tp: 0, 11
LR f1 score: 0.957
LR cohens kappa score: 0.955
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:199.56770655594556 max:284.9796045333771
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 317, 1
GB fn, tp: 0, 11
GB f1 score: 0.957
GB cohens kappa score: 0.955

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1272/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:147.82845395931056 max:1877.517291717975
-> create 1228 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 317, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 316, 1
KNN fn, tp: 0, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.946


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:149.02768803145273 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:234.9446483748885
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1271/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:147.82845395931056 max:1877.517291717975
-> create 1229 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 317, 1
GB fn, tp: 0, 11
GB f1 score: 0.957
GB cohens kappa score: 0.955

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1272/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:213.2104687861269 max:1929.7078500384455
-> create 1228 synthetic samples
-> test with 'LR'
LR tn, fp: 315, 2
LR fn, tp: 0, 9
LR f1 score: 0.900
LR cohens kappa score: 0.897
LR average precision score: 0.989

-> test with 'GB'
GB tn, fp: 315, 2
GB fn, tp: 0, 9
GB f1 score: 0.900
GB cohens kappa score: 0.897

-> test with 'KNN'
KNN tn, fp: 315, 2
KNN fn, tp: 0, 9
KNN f1 score: 0.900
KNN cohens kappa score: 0.897

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 318, 2
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000


average:
LR tn, fp: 317.56, 0.24
LR fn, tp: 0.0, 10.6
LR f1 score: 0.989
LR cohens kappa score: 0.989
LR average precision score: 1.000


minimum:
LR tn, fp: 315, 0
LR fn, tp: 0, 9
LR f1 score: 0.900
LR cohens kappa score: 0.897
LR average precision score: 0.989


-----[ GB ]-----
maximum:
GB tn, fp: 318, 3
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 317.4, 0.4
GB fn, tp: 0.0, 10.6
GB f1 score: 0.982
GB cohens kappa score: 0.982


minimum:
GB tn, fp: 315, 0
GB fn, tp: 0, 9
GB f1 score: 0.880
GB cohens kappa score: 0.875


-----[ KNN ]-----
maximum:
KNN tn, fp: 318, 2
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


average:
KNN tn, fp: 317.2, 0.6
KNN fn, tp: 0.0, 10.6
KNN f1 score: 0.973
KNN cohens kappa score: 0.972


minimum:
KNN tn, fp: 315, 0
KNN fn, tp: 0, 9
KNN f1 score: 0.900
KNN cohens kappa score: 0.897