/////////////////////////////////////////// // 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