/////////////////////////////////////////// // Running convGAN-proximary-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: 552, 8 GAN fn, tp: 2, 19 GAN f1 score: 0.792 GAN cohens kappa score: 0.783 -> 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: 0.865 -> test with 'GB' GB tn, fp: 559, 1 GB fn, tp: 2, 19 GB f1 score: 0.927 GB cohens kappa score: 0.924 -> test with 'KNN' KNN tn, fp: 554, 6 KNN fn, tp: 1, 20 KNN f1 score: 0.851 KNN cohens kappa score: 0.845 ------ 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: 553, 7 GAN fn, tp: 0, 21 GAN f1 score: 0.857 GAN cohens kappa score: 0.851 -> 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: 0.939 -> 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 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 545, 15 GAN fn, tp: 0, 21 GAN f1 score: 0.737 GAN cohens kappa score: 0.724 -> test with 'LR' LR tn, fp: 542, 18 LR fn, tp: 0, 21 LR f1 score: 0.700 LR cohens kappa score: 0.685 LR average precision score: 0.880 -> 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: 550, 10 KNN fn, tp: 0, 21 KNN f1 score: 0.808 KNN cohens kappa score: 0.799 ------ 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: 554, 6 GAN fn, tp: 0, 21 GAN f1 score: 0.875 GAN cohens kappa score: 0.870 -> test with 'LR' LR tn, fp: 542, 18 LR fn, tp: 2, 19 LR f1 score: 0.655 LR cohens kappa score: 0.639 LR average precision score: 0.887 -> test with 'GB' GB tn, fp: 555, 5 GB fn, tp: 0, 21 GB f1 score: 0.894 GB cohens kappa score: 0.889 -> 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 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with GAN.predict GAN tn, fp: 551, 5 GAN fn, tp: 0, 21 GAN f1 score: 0.894 GAN cohens kappa score: 0.889 -> 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: 0.989 -> 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: 548, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ====== 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: 556, 4 GAN fn, tp: 0, 21 GAN f1 score: 0.913 GAN cohens kappa score: 0.909 -> test with 'LR' LR tn, fp: 550, 10 LR fn, tp: 1, 20 LR f1 score: 0.784 LR cohens kappa score: 0.775 LR average precision score: 0.939 -> 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: 550, 10 KNN fn, tp: 0, 21 KNN f1 score: 0.808 KNN cohens kappa score: 0.799 ------ 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: 551, 9 GAN fn, tp: 0, 21 GAN f1 score: 0.824 GAN cohens kappa score: 0.816 -> 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: 0.924 -> 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 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 552, 8 GAN fn, tp: 0, 21 GAN f1 score: 0.840 GAN cohens kappa score: 0.833 -> 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: 0.930 -> 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: 1, 20 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 -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 547, 13 GAN fn, tp: 0, 21 GAN f1 score: 0.764 GAN cohens kappa score: 0.753 -> test with 'LR' LR tn, fp: 543, 17 LR fn, tp: 0, 21 LR f1 score: 0.712 LR cohens kappa score: 0.698 LR average precision score: 0.893 -> 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: 548, 12 KNN fn, tp: 0, 21 KNN f1 score: 0.778 KNN cohens kappa score: 0.768 ------ 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: 547, 9 GAN fn, tp: 1, 20 GAN f1 score: 0.800 GAN cohens kappa score: 0.791 -> test with 'LR' LR tn, fp: 542, 14 LR fn, tp: 1, 20 LR f1 score: 0.727 LR cohens kappa score: 0.714 LR average precision score: 0.937 -> 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: 550, 6 KNN fn, tp: 1, 20 KNN f1 score: 0.851 KNN cohens kappa score: 0.845 ====== 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: 556, 4 GAN fn, tp: 0, 21 GAN f1 score: 0.913 GAN cohens kappa score: 0.909 -> 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: 0.950 -> 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: 550, 10 KNN fn, tp: 0, 21 KNN f1 score: 0.808 KNN cohens kappa score: 0.799 ------ 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: 557, 3 GAN fn, tp: 1, 20 GAN f1 score: 0.909 GAN cohens kappa score: 0.906 -> test with 'LR' LR tn, fp: 556, 4 LR fn, tp: 1, 20 LR f1 score: 0.889 LR cohens kappa score: 0.884 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: 554, 6 KNN fn, tp: 0, 21 KNN f1 score: 0.875 KNN cohens kappa score: 0.870 ------ 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: 553, 7 GAN fn, tp: 0, 21 GAN f1 score: 0.857 GAN cohens kappa score: 0.851 -> test with 'LR' LR tn, fp: 543, 17 LR fn, tp: 1, 20 LR f1 score: 0.690 LR cohens kappa score: 0.675 LR average precision score: 0.885 -> 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: 551, 9 KNN fn, tp: 0, 21 KNN f1 score: 0.824 KNN cohens kappa score: 0.816 ------ 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: 556, 4 GAN fn, tp: 1, 20 GAN f1 score: 0.889 GAN cohens kappa score: 0.884 -> 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: 0.944 -> 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: 0, 21 KNN f1 score: 0.913 KNN cohens kappa score: 0.909 ------ 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: 539, 17 GAN fn, tp: 0, 21 GAN f1 score: 0.712 GAN cohens kappa score: 0.698 -> test with 'LR' LR tn, fp: 538, 18 LR fn, tp: 0, 21 LR f1 score: 0.700 LR cohens kappa score: 0.685 LR average precision score: 0.959 -> 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: 547, 9 KNN fn, tp: 0, 21 KNN f1 score: 0.824 KNN cohens kappa score: 0.816 ====== 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: 555, 5 GAN fn, tp: 0, 21 GAN f1 score: 0.894 GAN cohens kappa score: 0.889 -> 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: 0.934 -> test with 'GB' GB tn, fp: 558, 2 GB fn, tp: 1, 20 GB f1 score: 0.930 GB cohens kappa score: 0.928 -> 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 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with GAN.predict GAN tn, fp: 551, 9 GAN fn, tp: 0, 21 GAN f1 score: 0.824 GAN cohens kappa score: 0.816 -> test with 'LR' LR tn, fp: 540, 20 LR fn, tp: 0, 21 LR f1 score: 0.677 LR cohens kappa score: 0.661 LR average precision score: 0.971 -> 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: 551, 9 KNN fn, tp: 0, 21 KNN f1 score: 0.824 KNN cohens kappa score: 0.816 ------ 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: 553, 7 GAN fn, tp: 2, 19 GAN f1 score: 0.809 GAN cohens kappa score: 0.801 -> test with 'LR' LR tn, fp: 544, 16 LR fn, tp: 3, 18 LR f1 score: 0.655 LR cohens kappa score: 0.638 LR average precision score: 0.828 -> test with 'GB' GB tn, fp: 560, 0 GB fn, tp: 4, 17 GB f1 score: 0.895 GB cohens kappa score: 0.891 -> test with 'KNN' KNN tn, fp: 551, 9 KNN fn, tp: 1, 20 KNN f1 score: 0.800 KNN cohens kappa score: 0.791 ------ 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: 557, 3 GAN fn, tp: 0, 21 GAN f1 score: 0.933 GAN cohens kappa score: 0.931 -> test with 'LR' LR tn, fp: 542, 18 LR fn, tp: 0, 21 LR f1 score: 0.700 LR cohens kappa score: 0.685 LR average precision score: 0.942 -> 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: 554, 6 KNN fn, tp: 1, 20 KNN f1 score: 0.851 KNN cohens kappa score: 0.845 ------ 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: 553, 3 GAN fn, tp: 1, 20 GAN f1 score: 0.909 GAN cohens kappa score: 0.905 -> test with 'LR' LR tn, fp: 550, 6 LR fn, tp: 1, 20 LR f1 score: 0.851 LR cohens kappa score: 0.845 LR average precision score: 0.942 -> 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: 544, 16 LR fn, tp: 0, 21 LR f1 score: 0.724 LR cohens kappa score: 0.711 LR average precision score: 0.979 -> 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: 552, 8 KNN fn, tp: 1, 20 KNN f1 score: 0.816 KNN cohens kappa score: 0.808 ------ 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: 554, 6 GAN fn, tp: 1, 20 GAN f1 score: 0.851 GAN cohens kappa score: 0.845 -> test with 'LR' LR tn, fp: 550, 10 LR fn, tp: 1, 20 LR f1 score: 0.784 LR cohens kappa score: 0.775 LR average precision score: 0.940 -> 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: 555, 5 KNN fn, tp: 1, 20 KNN f1 score: 0.870 KNN cohens kappa score: 0.864 ------ 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: 549, 11 GAN fn, tp: 1, 20 GAN f1 score: 0.769 GAN cohens kappa score: 0.759 -> test with 'LR' LR tn, fp: 550, 10 LR fn, tp: 1, 20 LR f1 score: 0.784 LR cohens kappa score: 0.775 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: 550, 10 KNN fn, tp: 0, 21 KNN f1 score: 0.808 KNN cohens kappa score: 0.799 ------ 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: 551, 9 GAN fn, tp: 0, 21 GAN f1 score: 0.824 GAN cohens kappa score: 0.816 -> test with 'LR' LR tn, fp: 546, 14 LR fn, tp: 1, 20 LR f1 score: 0.727 LR cohens kappa score: 0.715 LR average precision score: 0.920 -> 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: 547, 9 GAN fn, tp: 0, 21 GAN f1 score: 0.824 GAN cohens kappa score: 0.816 -> test with 'LR' LR tn, fp: 542, 14 LR fn, tp: 1, 20 LR f1 score: 0.727 LR cohens kappa score: 0.714 LR average precision score: 0.920 -> 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: 556, 20 LR fn, tp: 3, 21 LR f1 score: 0.889 LR cohens kappa score: 0.884 LR average precision score: 0.989 average: LR tn, fp: 546.16, 13.04 LR fn, tp: 0.84, 20.16 LR f1 score: 0.748 LR cohens kappa score: 0.737 LR average precision score: 0.924 minimum: LR tn, fp: 538, 4 LR fn, tp: 0, 18 LR f1 score: 0.655 LR cohens kappa score: 0.638 LR average precision score: 0.828 -----[ GB ]----- maximum: GB tn, fp: 560, 5 GB fn, tp: 4, 21 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 558.6, 0.6 GB fn, tp: 1.08, 19.92 GB f1 score: 0.960 GB cohens kappa score: 0.958 minimum: GB tn, fp: 555, 0 GB fn, tp: 0, 17 GB f1 score: 0.894 GB cohens kappa score: 0.889 -----[ KNN ]----- maximum: KNN tn, fp: 557, 12 KNN fn, tp: 1, 21 KNN f1 score: 0.933 KNN cohens kappa score: 0.931 average: KNN tn, fp: 551.92, 7.28 KNN fn, tp: 0.28, 20.72 KNN f1 score: 0.848 KNN cohens kappa score: 0.841 minimum: KNN tn, fp: 547, 3 KNN fn, tp: 0, 20 KNN f1 score: 0.778 KNN cohens kappa score: 0.768 -----[ GAN ]----- maximum: GAN tn, fp: 560, 17 GAN fn, tp: 2, 21 GAN f1 score: 0.976 GAN cohens kappa score: 0.975 average: GAN tn, fp: 551.96, 7.24 GAN fn, tp: 0.44, 20.56 GAN f1 score: 0.847 GAN cohens kappa score: 0.841 minimum: GAN tn, fp: 539, 0 GAN fn, tp: 0, 19 GAN f1 score: 0.712 GAN cohens kappa score: 0.698