/////////////////////////////////////////// // Running convGAN-majority-full 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 -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 560, 0 GAN fn, tp: 4, 17 GAN f1 score: 0.895 GAN cohens kappa score: 0.891 -> 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: 0.873 -> 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: 553, 7 KNN fn, tp: 2, 19 KNN f1 score: 0.809 KNN cohens kappa score: 0.801 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 560, 0 GAN fn, tp: 1, 20 GAN f1 score: 0.976 GAN cohens kappa score: 0.975 -> test with 'LR' LR tn, fp: 554, 6 LR fn, tp: 1, 20 LR f1 score: 0.851 LR cohens kappa score: 0.845 LR average precision score: 0.955 -> 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: 0, 21 KNN f1 score: 0.955 KNN cohens kappa score: 0.953 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 560, 0 GAN fn, tp: 0, 21 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> test with 'LR' LR tn, fp: 547, 13 LR fn, tp: 0, 21 LR f1 score: 0.764 LR cohens kappa score: 0.753 LR average precision score: 0.915 -> 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: 552, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 558, 2 GAN fn, tp: 2, 19 GAN f1 score: 0.905 GAN cohens kappa score: 0.901 -> test with 'LR' LR tn, fp: 550, 10 LR fn, tp: 2, 19 LR f1 score: 0.760 LR cohens kappa score: 0.749 LR average precision score: 0.898 -> test with 'GB' GB tn, fp: 555, 5 GB fn, tp: 1, 20 GB f1 score: 0.870 GB cohens kappa score: 0.864 -> test with 'KNN' KNN tn, fp: 554, 6 KNN fn, tp: 0, 21 KNN f1 score: 0.875 KNN cohens kappa score: 0.870 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with GAN.predict GAN tn, fp: 556, 0 GAN fn, tp: 1, 20 GAN f1 score: 0.976 GAN cohens kappa score: 0.975 -> test with 'LR' LR tn, fp: 547, 9 LR fn, tp: 0, 21 LR f1 score: 0.824 LR cohens kappa score: 0.816 LR average precision score: 0.987 -> 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: 551, 5 KNN fn, tp: 0, 21 KNN f1 score: 0.894 KNN cohens kappa score: 0.889 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 560, 0 GAN fn, tp: 0, 21 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> test with 'LR' LR tn, fp: 551, 9 LR fn, tp: 1, 20 LR f1 score: 0.800 LR cohens kappa score: 0.791 LR average precision score: 0.941 -> 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: 551, 9 KNN fn, tp: 0, 21 KNN f1 score: 0.824 KNN cohens kappa score: 0.816 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 559, 1 GAN fn, tp: 1, 20 GAN f1 score: 0.952 GAN cohens kappa score: 0.951 -> test with 'LR' LR tn, fp: 554, 6 LR fn, tp: 2, 19 LR f1 score: 0.826 LR cohens kappa score: 0.819 LR average precision score: 0.922 -> 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: 555, 5 KNN fn, tp: 2, 19 KNN f1 score: 0.844 KNN cohens kappa score: 0.838 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 559, 1 GAN fn, tp: 0, 21 GAN f1 score: 0.977 GAN cohens kappa score: 0.976 -> test with 'LR' LR tn, fp: 549, 11 LR fn, tp: 0, 21 LR f1 score: 0.792 LR cohens kappa score: 0.783 LR average precision score: 0.936 -> 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: 1, 20 KNN f1 score: 0.930 KNN cohens kappa score: 0.928 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 560, 0 GAN fn, tp: 1, 20 GAN f1 score: 0.976 GAN cohens kappa score: 0.975 -> test with 'LR' LR tn, fp: 544, 16 LR fn, tp: 1, 20 LR f1 score: 0.702 LR cohens kappa score: 0.687 LR average precision score: 0.900 -> 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: 549, 11 KNN fn, tp: 0, 21 KNN f1 score: 0.792 KNN cohens kappa score: 0.783 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with GAN.predict GAN tn, fp: 556, 0 GAN fn, tp: 2, 19 GAN f1 score: 0.950 GAN cohens kappa score: 0.948 -> test with 'LR' LR tn, fp: 548, 8 LR fn, tp: 2, 19 LR f1 score: 0.792 LR cohens kappa score: 0.783 LR average precision score: 0.941 -> 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: 554, 2 KNN fn, tp: 1, 20 KNN f1 score: 0.930 KNN cohens kappa score: 0.928 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 560, 0 GAN fn, tp: 2, 19 GAN f1 score: 0.950 GAN cohens kappa score: 0.948 -> test with 'LR' LR tn, fp: 558, 2 LR fn, tp: 2, 19 LR f1 score: 0.905 LR cohens kappa score: 0.901 LR average precision score: 0.955 -> 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: 555, 5 KNN fn, tp: 0, 21 KNN f1 score: 0.894 KNN cohens kappa score: 0.889 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 560, 0 GAN fn, tp: 1, 20 GAN f1 score: 0.976 GAN cohens kappa score: 0.975 -> test with 'LR' LR tn, fp: 557, 3 LR fn, tp: 1, 20 LR f1 score: 0.909 LR cohens kappa score: 0.906 LR average precision score: 0.899 -> 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: 556, 4 KNN fn, tp: 0, 21 KNN f1 score: 0.913 KNN cohens kappa score: 0.909 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 560, 0 GAN fn, tp: 0, 21 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> test with 'LR' LR tn, fp: 547, 13 LR fn, tp: 2, 19 LR f1 score: 0.717 LR cohens kappa score: 0.704 LR average precision score: 0.887 -> 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: 552, 8 KNN fn, tp: 2, 19 KNN f1 score: 0.792 KNN cohens kappa score: 0.783 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 560, 0 GAN fn, tp: 1, 20 GAN f1 score: 0.976 GAN cohens kappa score: 0.975 -> test with 'LR' LR tn, fp: 548, 12 LR fn, tp: 1, 20 LR f1 score: 0.755 LR cohens kappa score: 0.744 LR average precision score: 0.948 -> 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: 557, 3 KNN fn, tp: 0, 21 KNN f1 score: 0.933 KNN cohens kappa score: 0.931 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with GAN.predict GAN tn, fp: 552, 4 GAN fn, tp: 0, 21 GAN f1 score: 0.913 GAN cohens kappa score: 0.909 -> test with 'LR' LR tn, fp: 544, 12 LR fn, tp: 0, 21 LR f1 score: 0.778 LR cohens kappa score: 0.767 LR average precision score: 0.968 -> 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: 548, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 560, 0 GAN fn, tp: 0, 21 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> test with 'LR' LR tn, fp: 552, 8 LR fn, tp: 1, 20 LR f1 score: 0.816 LR cohens kappa score: 0.808 LR average precision score: 0.935 -> test with 'GB' GB tn, fp: 558, 2 GB fn, tp: 0, 21 GB f1 score: 0.955 GB cohens kappa score: 0.953 -> test with 'KNN' KNN tn, fp: 552, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 558, 2 GAN fn, tp: 0, 21 GAN f1 score: 0.955 GAN cohens kappa score: 0.953 -> test with 'LR' LR tn, fp: 544, 16 LR fn, tp: 0, 21 LR f1 score: 0.724 LR cohens kappa score: 0.711 LR average precision score: 0.973 -> 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: 554, 6 KNN fn, tp: 0, 21 KNN f1 score: 0.875 KNN cohens kappa score: 0.870 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 558, 2 GAN fn, tp: 5, 16 GAN f1 score: 0.821 GAN cohens kappa score: 0.814 -> test with 'LR' LR tn, fp: 549, 11 LR fn, tp: 5, 16 LR f1 score: 0.667 LR cohens kappa score: 0.653 LR average precision score: 0.822 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 5, 16 GB f1 score: 0.865 GB cohens kappa score: 0.861 -> test with 'KNN' KNN tn, fp: 553, 7 KNN fn, tp: 1, 20 KNN f1 score: 0.833 KNN cohens kappa score: 0.826 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 559, 1 GAN fn, tp: 0, 21 GAN f1 score: 0.977 GAN cohens kappa score: 0.976 -> test with 'LR' LR tn, fp: 551, 9 LR fn, tp: 0, 21 LR f1 score: 0.824 LR cohens kappa score: 0.816 LR average precision score: 0.963 -> 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: 1, 20 KNN f1 score: 0.889 KNN cohens kappa score: 0.884 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with GAN.predict GAN tn, fp: 554, 2 GAN fn, tp: 1, 20 GAN f1 score: 0.930 GAN cohens kappa score: 0.928 -> test with 'LR' LR tn, fp: 553, 3 LR fn, tp: 1, 20 LR f1 score: 0.909 LR cohens kappa score: 0.905 LR average precision score: 0.949 -> 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: 552, 4 KNN fn, tp: 0, 21 KNN f1 score: 0.913 KNN cohens kappa score: 0.909 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 560, 0 GAN fn, tp: 1, 20 GAN f1 score: 0.976 GAN cohens kappa score: 0.975 -> test with 'LR' LR tn, fp: 552, 8 LR fn, tp: 0, 21 LR f1 score: 0.840 LR cohens kappa score: 0.833 LR average precision score: 0.980 -> 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: 555, 5 KNN fn, tp: 0, 21 KNN f1 score: 0.894 KNN cohens kappa score: 0.889 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 559, 1 GAN fn, tp: 4, 17 GAN f1 score: 0.872 GAN cohens kappa score: 0.867 -> test with 'LR' LR tn, fp: 554, 6 LR fn, tp: 3, 18 LR f1 score: 0.800 LR cohens kappa score: 0.792 LR average precision score: 0.930 -> 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: 556, 4 KNN fn, tp: 2, 19 KNN f1 score: 0.864 KNN cohens kappa score: 0.858 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 558, 2 GAN fn, tp: 1, 20 GAN f1 score: 0.930 GAN cohens kappa score: 0.928 -> 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: 0.896 -> 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: 553, 7 KNN fn, tp: 0, 21 KNN f1 score: 0.857 KNN cohens kappa score: 0.851 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 560, 0 GAN fn, tp: 0, 21 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> test with 'LR' LR tn, fp: 548, 12 LR fn, tp: 1, 20 LR f1 score: 0.755 LR cohens kappa score: 0.744 LR average precision score: 0.928 -> 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: 553, 7 KNN fn, tp: 0, 21 KNN f1 score: 0.857 KNN cohens kappa score: 0.851 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with GAN.predict GAN tn, fp: 556, 0 GAN fn, tp: 0, 21 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> test with 'LR' LR tn, fp: 543, 13 LR fn, tp: 1, 20 LR f1 score: 0.741 LR cohens kappa score: 0.729 LR average precision score: 0.926 -> 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: 551, 5 KNN fn, tp: 0, 21 KNN f1 score: 0.894 KNN cohens kappa score: 0.889 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 558, 16 LR fn, tp: 5, 21 LR f1 score: 0.909 LR cohens kappa score: 0.906 LR average precision score: 0.987 average: LR tn, fp: 550.0, 9.2 LR fn, tp: 1.24, 19.76 LR f1 score: 0.795 LR cohens kappa score: 0.786 LR average precision score: 0.929 minimum: LR tn, fp: 543, 2 LR fn, tp: 0, 16 LR f1 score: 0.667 LR cohens kappa score: 0.653 LR average precision score: 0.822 -----[ GB ]----- maximum: GB tn, fp: 560, 5 GB fn, tp: 5, 21 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 558.72, 0.48 GB fn, tp: 1.2, 19.8 GB f1 score: 0.959 GB cohens kappa score: 0.958 minimum: GB tn, fp: 555, 0 GB fn, tp: 0, 16 GB f1 score: 0.865 GB cohens kappa score: 0.861 -----[ KNN ]----- maximum: KNN tn, fp: 558, 11 KNN fn, tp: 2, 21 KNN f1 score: 0.955 KNN cohens kappa score: 0.953 average: KNN tn, fp: 553.52, 5.68 KNN fn, tp: 0.48, 20.52 KNN f1 score: 0.871 KNN cohens kappa score: 0.866 minimum: KNN tn, fp: 548, 2 KNN fn, tp: 0, 19 KNN f1 score: 0.792 KNN cohens kappa score: 0.783 -----[ GAN ]----- maximum: GAN tn, fp: 560, 4 GAN fn, tp: 5, 21 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 average: GAN tn, fp: 558.48, 0.72 GAN fn, tp: 1.12, 19.88 GAN f1 score: 0.955 GAN cohens kappa score: 0.954 minimum: GAN tn, fp: 552, 0 GAN fn, tp: 0, 16 GAN f1 score: 0.821 GAN cohens kappa score: 0.814