/////////////////////////////////////////// // Running ProWRAS 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> 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 'RF' RF tn, fp: 317, 0 RF fn, tp: 0, 9 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 317, 0 RF fn, tp: 0, 9 RF f1 score: 1.000 RF cohens kappa 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 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 317, 0 RF fn, tp: 0, 9 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 -> create 1228 synthetic samples -> test with 'LR' LR tn, fp: 316, 1 LR fn, tp: 0, 9 LR f1 score: 0.947 LR cohens kappa score: 0.946 LR average precision score: 1.000 -> test with 'RF' RF tn, fp: 317, 0 RF fn, tp: 0, 9 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 -> 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 'RF' RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa 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 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> 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 'RF' RF tn, fp: 317, 0 RF fn, tp: 0, 9 RF f1 score: 1.000 RF cohens kappa 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 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 318, 1 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: 317.72, 0.08 LR fn, tp: 0.0, 10.6 LR f1 score: 0.996 LR cohens kappa score: 0.996 LR average precision score: 1.000 minimum: LR tn, fp: 316, 0 LR fn, tp: 0, 9 LR f1 score: 0.947 LR cohens kappa score: 0.946 LR average precision score: 1.000 -----[ RF ]----- maximum: RF tn, fp: 318, 0 RF fn, tp: 0, 11 RF f1 score: 1.000 RF cohens kappa score: 1.000 average: RF tn, fp: 317.8, 0.0 RF fn, tp: 0.0, 10.6 RF f1 score: 1.000 RF cohens kappa score: 1.000 minimum: RF tn, fp: 317, 0 RF fn, tp: 0, 9 RF f1 score: 1.000 RF cohens kappa score: 1.000 -----[ GB ]----- maximum: GB tn, fp: 318, 0 GB fn, tp: 0, 11 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 317.8, 0.0 GB fn, tp: 0.0, 10.6 GB f1 score: 1.000 GB cohens kappa score: 1.000 minimum: GB tn, fp: 317, 0 GB fn, tp: 0, 9 GB f1 score: 1.000 GB cohens kappa score: 1.000 -----[ KNN ]----- maximum: KNN tn, fp: 318, 1 KNN fn, tp: 0, 11 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 average: KNN tn, fp: 317.6, 0.2 KNN fn, tp: 0.0, 10.6 KNN f1 score: 0.991 KNN cohens kappa score: 0.991 minimum: KNN tn, fp: 316, 0 KNN fn, tp: 0, 9 KNN f1 score: 0.947 KNN cohens kappa score: 0.946