/////////////////////////////////////////// // Running SpheredNoise on folding_kr-vs-k-three_vs_eleven /////////////////////////////////////////// Load 'data_input/folding_kr-vs-k-three_vs_eleven' 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 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 570, 1 LR fn, tp: 0, 17 LR f1 score: 0.971 LR cohens kappa score: 0.971 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 1, 16 KNN f1 score: 0.970 KNN cohens kappa score: 0.969 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 2, 15 LR f1 score: 0.938 LR cohens kappa score: 0.936 LR average precision score: 0.990 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 1, 16 KNN f1 score: 0.970 KNN cohens kappa score: 0.969 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 568, 3 LR fn, tp: 1, 16 LR f1 score: 0.889 LR cohens kappa score: 0.885 LR average precision score: 0.991 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 1, 16 KNN f1 score: 0.970 KNN cohens kappa score: 0.969 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 570, 1 LR fn, tp: 1, 16 LR f1 score: 0.941 LR cohens kappa score: 0.939 LR average precision score: 0.994 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 2, 15 KNN f1 score: 0.938 KNN cohens kappa score: 0.936 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2284/68 points -> new disc -> calc distances -> statistics trained 68 points min:1.0 max:3.3166247903554 -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 565, 5 LR fn, tp: 1, 12 LR f1 score: 0.800 LR cohens kappa score: 0.795 LR average precision score: 0.782 -> test with 'GB' GB tn, fp: 570, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 568, 2 KNN fn, tp: 1, 12 KNN f1 score: 0.889 KNN cohens kappa score: 0.886 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.1622776601683795 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 570, 1 LR fn, tp: 0, 17 LR f1 score: 0.971 LR cohens kappa score: 0.971 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 2, 15 KNN f1 score: 0.938 KNN cohens kappa score: 0.936 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 1, 16 LR f1 score: 0.970 LR cohens kappa score: 0.969 LR average precision score: 0.991 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 1, 16 KNN f1 score: 0.970 KNN cohens kappa score: 0.969 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 568, 3 LR fn, tp: 2, 15 LR f1 score: 0.857 LR cohens kappa score: 0.853 LR average precision score: 0.978 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 570, 1 KNN fn, tp: 2, 15 KNN f1 score: 0.909 KNN cohens kappa score: 0.906 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 569, 2 LR fn, tp: 1, 16 LR f1 score: 0.914 LR cohens kappa score: 0.912 LR average precision score: 0.994 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 3, 14 KNN f1 score: 0.903 KNN cohens kappa score: 0.901 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2284/68 points -> new disc -> calc distances -> statistics trained 68 points min:1.0 max:3.3166247903554 -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 569, 1 LR fn, tp: 1, 12 LR f1 score: 0.923 LR cohens kappa score: 0.921 LR average precision score: 0.990 -> test with 'GB' GB tn, fp: 570, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 569, 1 KNN fn, tp: 0, 13 KNN f1 score: 0.963 KNN cohens kappa score: 0.962 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 569, 2 LR fn, tp: 0, 17 LR f1 score: 0.944 LR cohens kappa score: 0.943 LR average precision score: 0.990 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 0, 17 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 570, 1 LR fn, tp: 1, 16 LR f1 score: 0.941 LR cohens kappa score: 0.939 LR average precision score: 0.994 -> test with 'GB' GB tn, fp: 570, 1 GB fn, tp: 0, 17 GB f1 score: 0.971 GB cohens kappa score: 0.971 -> test with 'KNN' KNN tn, fp: 570, 1 KNN fn, tp: 3, 14 KNN f1 score: 0.875 KNN cohens kappa score: 0.872 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 569, 2 LR fn, tp: 1, 16 LR f1 score: 0.914 LR cohens kappa score: 0.912 LR average precision score: 0.994 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 570, 1 KNN fn, tp: 0, 17 KNN f1 score: 0.971 KNN cohens kappa score: 0.971 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 568, 3 LR fn, tp: 1, 16 LR f1 score: 0.889 LR cohens kappa score: 0.885 LR average precision score: 0.987 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 1, 16 KNN f1 score: 0.970 KNN cohens kappa score: 0.969 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2284/68 points -> new disc -> calc distances -> statistics trained 68 points min:1.0 max:3.3166247903554 -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 569, 1 LR fn, tp: 1, 12 LR f1 score: 0.923 LR cohens kappa score: 0.921 LR average precision score: 0.990 -> test with 'GB' GB tn, fp: 570, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 570, 0 KNN fn, tp: 3, 10 KNN f1 score: 0.870 KNN cohens kappa score: 0.867 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 569, 2 LR fn, tp: 1, 16 LR f1 score: 0.914 LR cohens kappa score: 0.912 LR average precision score: 0.989 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 1, 16 KNN f1 score: 0.970 KNN cohens kappa score: 0.969 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 1, 16 LR f1 score: 0.970 LR cohens kappa score: 0.969 LR average precision score: 0.989 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 3, 14 KNN f1 score: 0.903 KNN cohens kappa score: 0.901 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 1, 16 LR f1 score: 0.970 LR cohens kappa score: 0.969 LR average precision score: 0.997 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 570, 1 KNN fn, tp: 0, 17 KNN f1 score: 0.971 KNN cohens kappa score: 0.971 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 568, 3 LR fn, tp: 0, 17 LR f1 score: 0.919 LR cohens kappa score: 0.916 LR average precision score: 0.936 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 570, 1 KNN fn, tp: 0, 17 KNN f1 score: 0.971 KNN cohens kappa score: 0.971 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2284/68 points -> new disc -> calc distances -> statistics trained 68 points min:1.0 max:3.3166247903554 -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 569, 1 LR fn, tp: 1, 12 LR f1 score: 0.923 LR cohens kappa score: 0.921 LR average precision score: 0.990 -> test with 'GB' GB tn, fp: 570, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 570, 0 KNN fn, tp: 2, 11 KNN f1 score: 0.917 KNN cohens kappa score: 0.915 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 567, 4 LR fn, tp: 0, 17 LR f1 score: 0.895 LR cohens kappa score: 0.891 LR average precision score: 0.991 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 570, 1 KNN fn, tp: 2, 15 KNN f1 score: 0.909 KNN cohens kappa score: 0.906 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 568, 3 LR fn, tp: 1, 16 LR f1 score: 0.889 LR cohens kappa score: 0.885 LR average precision score: 0.970 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 2, 15 KNN f1 score: 0.938 KNN cohens kappa score: 0.936 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 1, 16 LR f1 score: 0.970 LR cohens kappa score: 0.969 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 1, 16 KNN f1 score: 0.970 KNN cohens kappa score: 0.969 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2283/64 points -> new disc -> calc distances -> statistics trained 64 points min:1.0 max:3.3166247903554 -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 2, 15 LR f1 score: 0.938 LR cohens kappa score: 0.936 LR average precision score: 0.977 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 570, 1 KNN fn, tp: 0, 17 KNN f1 score: 0.971 KNN cohens kappa score: 0.971 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2284/68 points -> new disc -> calc distances -> statistics trained 68 points min:1.0 max:3.3166247903554 -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 570, 0 LR fn, tp: 0, 13 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 570, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 570, 0 KNN fn, tp: 1, 12 KNN f1 score: 0.960 KNN cohens kappa score: 0.959 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 571, 5 LR fn, tp: 2, 17 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 average: LR tn, fp: 569.24, 1.56 LR fn, tp: 0.88, 15.32 LR f1 score: 0.927 LR cohens kappa score: 0.925 LR average precision score: 0.980 minimum: LR tn, fp: 565, 0 LR fn, tp: 0, 12 LR f1 score: 0.800 LR cohens kappa score: 0.795 LR average precision score: 0.782 -----[ GB ]----- maximum: GB tn, fp: 571, 1 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 570.76, 0.04 GB fn, tp: 0.0, 16.2 GB f1 score: 0.999 GB cohens kappa score: 0.999 minimum: GB tn, fp: 570, 0 GB fn, tp: 0, 13 GB f1 score: 0.971 GB cohens kappa score: 0.971 -----[ KNN ]----- maximum: KNN tn, fp: 571, 2 KNN fn, tp: 3, 17 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 average: KNN tn, fp: 570.4, 0.4 KNN fn, tp: 1.32, 14.88 KNN f1 score: 0.943 KNN cohens kappa score: 0.942 minimum: KNN tn, fp: 568, 0 KNN fn, tp: 0, 10 KNN f1 score: 0.870 KNN cohens kappa score: 0.867