/////////////////////////////////////////// // Running SpheredNoise on folding_kr-vs-k-zero-one_vs_draw /////////////////////////////////////////// Load 'data_input/folding_kr-vs-k-zero-one_vs_draw' 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 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.082762530298219 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 556, 4 LR fn, tp: 3, 18 LR f1 score: 0.837 LR cohens kappa score: 0.831 LR average precision score: 0.833 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 2, 19 GB f1 score: 0.950 GB cohens kappa score: 0.948 -> test with 'KNN' KNN tn, fp: 559, 1 KNN fn, tp: 7, 14 KNN f1 score: 0.778 KNN cohens kappa score: 0.771 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 554, 6 LR fn, tp: 5, 16 LR f1 score: 0.744 LR cohens kappa score: 0.734 LR average precision score: 0.892 -> test with 'GB' GB tn, fp: 559, 1 GB fn, tp: 1, 20 GB f1 score: 0.952 GB cohens kappa score: 0.951 -> test with 'KNN' KNN tn, fp: 559, 1 KNN fn, tp: 4, 17 KNN f1 score: 0.872 KNN cohens kappa score: 0.867 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 548, 12 LR fn, tp: 5, 16 LR f1 score: 0.653 LR cohens kappa score: 0.638 LR average precision score: 0.771 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 1, 20 GB f1 score: 0.976 GB cohens kappa score: 0.975 -> test with 'KNN' KNN tn, fp: 558, 2 KNN fn, tp: 3, 18 KNN f1 score: 0.878 KNN cohens kappa score: 0.874 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:5.5677643628300215 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 549, 11 LR fn, tp: 3, 18 LR f1 score: 0.720 LR cohens kappa score: 0.708 LR average precision score: 0.838 -> test with 'GB' GB tn, fp: 557, 3 GB fn, tp: 1, 20 GB f1 score: 0.909 GB cohens kappa score: 0.906 -> test with 'KNN' KNN tn, fp: 560, 0 KNN fn, tp: 4, 17 KNN f1 score: 0.895 KNN cohens kappa score: 0.891 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2240/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 550, 6 LR fn, tp: 3, 18 LR f1 score: 0.800 LR cohens kappa score: 0.792 LR average precision score: 0.921 -> test with 'GB' GB tn, fp: 556, 0 GB fn, tp: 1, 20 GB f1 score: 0.976 GB cohens kappa score: 0.975 -> test with 'KNN' KNN tn, fp: 556, 0 KNN fn, tp: 3, 18 KNN f1 score: 0.923 KNN cohens kappa score: 0.920 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 553, 7 LR fn, tp: 2, 19 LR f1 score: 0.809 LR cohens kappa score: 0.801 LR average precision score: 0.893 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 1, 20 GB f1 score: 0.976 GB cohens kappa score: 0.975 -> test with 'KNN' KNN tn, fp: 560, 0 KNN fn, tp: 3, 18 KNN f1 score: 0.923 KNN cohens kappa score: 0.920 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.082762530298219 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 557, 3 LR fn, tp: 4, 17 LR f1 score: 0.829 LR cohens kappa score: 0.823 LR average precision score: 0.917 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 2, 19 GB f1 score: 0.950 GB cohens kappa score: 0.948 -> test with 'KNN' KNN tn, fp: 560, 0 KNN fn, tp: 4, 17 KNN f1 score: 0.895 KNN cohens kappa score: 0.891 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 555, 5 LR fn, tp: 3, 18 LR f1 score: 0.818 LR cohens kappa score: 0.811 LR average precision score: 0.894 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 0, 21 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 559, 1 KNN fn, tp: 6, 15 KNN f1 score: 0.811 KNN cohens kappa score: 0.805 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:5.5677643628300215 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 547, 13 LR fn, tp: 3, 18 LR f1 score: 0.692 LR cohens kappa score: 0.678 LR average precision score: 0.826 -> test with 'GB' GB tn, fp: 559, 1 GB fn, tp: 1, 20 GB f1 score: 0.952 GB cohens kappa score: 0.951 -> test with 'KNN' KNN tn, fp: 556, 4 KNN fn, tp: 6, 15 KNN f1 score: 0.750 KNN cohens kappa score: 0.741 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2240/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 546, 10 LR fn, tp: 6, 15 LR f1 score: 0.652 LR cohens kappa score: 0.638 LR average precision score: 0.813 -> test with 'GB' GB tn, fp: 555, 1 GB fn, tp: 0, 21 GB f1 score: 0.977 GB cohens kappa score: 0.976 -> test with 'KNN' KNN tn, fp: 556, 0 KNN fn, tp: 5, 16 KNN f1 score: 0.865 KNN cohens kappa score: 0.860 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 552, 8 LR fn, tp: 3, 18 LR f1 score: 0.766 LR cohens kappa score: 0.756 LR average precision score: 0.902 -> test with 'GB' GB tn, fp: 559, 1 GB fn, tp: 1, 20 GB f1 score: 0.952 GB cohens kappa score: 0.951 -> test with 'KNN' KNN tn, fp: 558, 2 KNN fn, tp: 3, 18 KNN f1 score: 0.878 KNN cohens kappa score: 0.874 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 558, 2 LR fn, tp: 4, 17 LR f1 score: 0.850 LR cohens kappa score: 0.845 LR average precision score: 0.868 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 1, 20 GB f1 score: 0.976 GB cohens kappa score: 0.975 -> test with 'KNN' KNN tn, fp: 558, 2 KNN fn, tp: 7, 14 KNN f1 score: 0.757 KNN cohens kappa score: 0.749 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 546, 14 LR fn, tp: 5, 16 LR f1 score: 0.627 LR cohens kappa score: 0.611 LR average precision score: 0.763 -> test with 'GB' GB tn, fp: 559, 1 GB fn, tp: 1, 20 GB f1 score: 0.952 GB cohens kappa score: 0.951 -> test with 'KNN' KNN tn, fp: 560, 0 KNN fn, tp: 6, 15 KNN f1 score: 0.833 KNN cohens kappa score: 0.828 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:5.5677643628300215 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 549, 11 LR fn, tp: 3, 18 LR f1 score: 0.720 LR cohens kappa score: 0.708 LR average precision score: 0.891 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 1, 20 GB f1 score: 0.976 GB cohens kappa score: 0.975 -> test with 'KNN' KNN tn, fp: 560, 0 KNN fn, tp: 3, 18 KNN f1 score: 0.923 KNN cohens kappa score: 0.920 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2240/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 554, 2 LR fn, tp: 3, 18 LR f1 score: 0.878 LR cohens kappa score: 0.874 LR average precision score: 0.906 -> test with 'GB' GB tn, fp: 556, 0 GB fn, tp: 0, 21 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 556, 0 KNN fn, tp: 4, 17 KNN f1 score: 0.895 KNN cohens kappa score: 0.891 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 552, 8 LR fn, tp: 3, 18 LR f1 score: 0.766 LR cohens kappa score: 0.756 LR average precision score: 0.877 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 0, 21 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 558, 2 KNN fn, tp: 3, 18 KNN f1 score: 0.878 KNN cohens kappa score: 0.874 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 552, 8 LR fn, tp: 2, 19 LR f1 score: 0.792 LR cohens kappa score: 0.783 LR average precision score: 0.937 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 0, 21 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 560, 0 KNN fn, tp: 3, 18 KNN f1 score: 0.923 KNN cohens kappa score: 0.920 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 555, 5 LR fn, tp: 7, 14 LR f1 score: 0.700 LR cohens kappa score: 0.689 LR average precision score: 0.777 -> test with 'GB' GB tn, fp: 559, 1 GB fn, tp: 1, 20 GB f1 score: 0.952 GB cohens kappa score: 0.951 -> test with 'KNN' KNN tn, fp: 558, 2 KNN fn, tp: 8, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.714 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:5.5677643628300215 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 551, 9 LR fn, tp: 3, 18 LR f1 score: 0.750 LR cohens kappa score: 0.739 LR average precision score: 0.855 -> test with 'GB' GB tn, fp: 559, 1 GB fn, tp: 0, 21 GB f1 score: 0.977 GB cohens kappa score: 0.976 -> test with 'KNN' KNN tn, fp: 559, 1 KNN fn, tp: 3, 18 KNN f1 score: 0.900 KNN cohens kappa score: 0.896 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2240/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.082762530298219 -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 550, 6 LR fn, tp: 3, 18 LR f1 score: 0.800 LR cohens kappa score: 0.792 LR average precision score: 0.868 -> test with 'GB' GB tn, fp: 556, 0 GB fn, tp: 2, 19 GB f1 score: 0.950 GB cohens kappa score: 0.948 -> test with 'KNN' KNN tn, fp: 556, 0 KNN fn, tp: 6, 15 KNN f1 score: 0.833 KNN cohens kappa score: 0.828 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:5.5677643628300215 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 556, 4 LR fn, tp: 3, 18 LR f1 score: 0.837 LR cohens kappa score: 0.831 LR average precision score: 0.952 -> test with 'GB' GB tn, fp: 559, 1 GB fn, tp: 1, 20 GB f1 score: 0.952 GB cohens kappa score: 0.951 -> test with 'KNN' KNN tn, fp: 560, 0 KNN fn, tp: 4, 17 KNN f1 score: 0.895 KNN cohens kappa score: 0.891 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.082762530298219 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 553, 7 LR fn, tp: 7, 14 LR f1 score: 0.667 LR cohens kappa score: 0.654 LR average precision score: 0.836 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 3, 18 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> test with 'KNN' KNN tn, fp: 560, 0 KNN fn, tp: 5, 16 KNN f1 score: 0.865 KNN cohens kappa score: 0.861 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 552, 8 LR fn, tp: 4, 17 LR f1 score: 0.739 LR cohens kappa score: 0.728 LR average precision score: 0.785 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 1, 20 GB f1 score: 0.976 GB cohens kappa score: 0.975 -> test with 'KNN' KNN tn, fp: 558, 2 KNN fn, tp: 5, 16 KNN f1 score: 0.821 KNN cohens kappa score: 0.814 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2236/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 550, 10 LR fn, tp: 5, 16 LR f1 score: 0.681 LR cohens kappa score: 0.668 LR average precision score: 0.816 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 1, 20 GB f1 score: 0.976 GB cohens kappa score: 0.975 -> test with 'KNN' KNN tn, fp: 559, 1 KNN fn, tp: 5, 16 KNN f1 score: 0.842 KNN cohens kappa score: 0.837 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2240/84 points -> new disc -> calc distances -> statistics trained 84 points min:1.0 max:6.0 -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 548, 8 LR fn, tp: 1, 20 LR f1 score: 0.816 LR cohens kappa score: 0.808 LR average precision score: 0.900 -> test with 'GB' GB tn, fp: 556, 0 GB fn, tp: 0, 21 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 555, 1 KNN fn, tp: 2, 19 KNN f1 score: 0.927 KNN cohens kappa score: 0.924 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 558, 14 LR fn, tp: 7, 20 LR f1 score: 0.878 LR cohens kappa score: 0.874 LR average precision score: 0.952 average: LR tn, fp: 551.72, 7.48 LR fn, tp: 3.72, 17.28 LR f1 score: 0.758 LR cohens kappa score: 0.748 LR average precision score: 0.861 minimum: LR tn, fp: 546, 2 LR fn, tp: 1, 14 LR f1 score: 0.627 LR cohens kappa score: 0.611 LR average precision score: 0.763 -----[ GB ]----- maximum: GB tn, fp: 560, 3 GB fn, tp: 3, 21 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 558.76, 0.44 GB fn, tp: 0.92, 20.08 GB f1 score: 0.967 GB cohens kappa score: 0.966 minimum: GB tn, fp: 555, 0 GB fn, tp: 0, 18 GB f1 score: 0.909 GB cohens kappa score: 0.906 -----[ KNN ]----- maximum: KNN tn, fp: 560, 4 KNN fn, tp: 8, 19 KNN f1 score: 0.927 KNN cohens kappa score: 0.924 average: KNN tn, fp: 558.32, 0.88 KNN fn, tp: 4.48, 16.52 KNN f1 score: 0.859 KNN cohens kappa score: 0.855 minimum: KNN tn, fp: 555, 0 KNN fn, tp: 2, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.714