/////////////////////////////////////////// // Running convGAN-proxymary-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: 559, 1 GAN fn, tp: 4, 17 GAN f1 score: 0.872 GAN cohens kappa score: 0.867 -> test with 'LR' LR tn, fp: 550, 10 LR fn, tp: 3, 18 LR f1 score: 0.735 LR cohens kappa score: 0.723 LR average precision score: 0.841 -> 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: 3, 18 KNN f1 score: 0.766 KNN cohens kappa score: 0.756 ------ 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: 559, 1 GAN fn, tp: 1, 20 GAN f1 score: 0.952 GAN cohens kappa score: 0.951 -> test with 'LR' LR tn, fp: 548, 12 LR fn, tp: 0, 21 LR f1 score: 0.778 LR cohens kappa score: 0.768 LR average precision score: 0.927 -> 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: 559, 1 GAN fn, tp: 2, 19 GAN f1 score: 0.927 GAN cohens kappa score: 0.924 -> test with 'LR' LR tn, fp: 541, 19 LR fn, tp: 0, 21 LR f1 score: 0.689 LR cohens kappa score: 0.673 LR average precision score: 0.870 -> 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: 559, 1 GAN fn, tp: 1, 20 GAN f1 score: 0.952 GAN cohens kappa score: 0.951 -> test with 'LR' LR tn, fp: 544, 16 LR fn, tp: 2, 19 LR f1 score: 0.679 LR cohens kappa score: 0.663 LR average precision score: 0.888 -> 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: 552, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ------ 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: 0, 21 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 0.988 -> 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: 549, 7 KNN fn, tp: 0, 21 KNN f1 score: 0.857 KNN cohens kappa score: 0.851 ====== 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: 1, 20 GAN f1 score: 0.976 GAN cohens kappa score: 0.975 -> test with 'LR' LR tn, fp: 547, 13 LR fn, tp: 1, 20 LR f1 score: 0.741 LR cohens kappa score: 0.729 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: 552, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ------ 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: 560, 0 GAN fn, tp: 2, 19 GAN f1 score: 0.950 GAN cohens kappa score: 0.948 -> 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: 0.925 -> 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: 558, 2 GAN fn, tp: 1, 20 GAN f1 score: 0.930 GAN cohens kappa score: 0.928 -> test with 'LR' LR tn, fp: 548, 12 LR fn, tp: 0, 21 LR f1 score: 0.778 LR cohens kappa score: 0.768 LR average precision score: 0.924 -> 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: 536, 24 LR fn, tp: 0, 21 LR f1 score: 0.636 LR cohens kappa score: 0.618 LR average precision score: 0.864 -> 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: 550, 10 KNN fn, tp: 0, 21 KNN f1 score: 0.808 KNN cohens kappa score: 0.799 ------ 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: 1, 20 GAN f1 score: 0.976 GAN cohens kappa score: 0.975 -> test with 'LR' LR tn, fp: 537, 19 LR fn, tp: 2, 19 LR f1 score: 0.644 LR cohens kappa score: 0.627 LR average precision score: 0.886 -> 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: 0, 21 KNN f1 score: 0.955 KNN cohens kappa score: 0.953 ====== 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: 1, 20 GAN f1 score: 0.976 GAN cohens kappa score: 0.975 -> test with 'LR' LR tn, fp: 548, 12 LR fn, tp: 0, 21 LR f1 score: 0.778 LR cohens kappa score: 0.768 LR average precision score: 0.943 -> 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: 552, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ------ 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: 2, 19 GAN f1 score: 0.950 GAN cohens kappa score: 0.948 -> 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: 0.876 -> 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: 559, 1 GAN fn, tp: 2, 19 GAN f1 score: 0.927 GAN cohens kappa score: 0.924 -> test with 'LR' LR tn, fp: 542, 18 LR fn, tp: 1, 20 LR f1 score: 0.678 LR cohens kappa score: 0.662 LR average precision score: 0.888 -> 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: 560, 0 GAN fn, tp: 0, 21 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> test with 'LR' LR tn, fp: 542, 18 LR fn, tp: 1, 20 LR f1 score: 0.678 LR cohens kappa score: 0.662 LR average precision score: 0.937 -> 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: 551, 5 GAN fn, tp: 0, 21 GAN f1 score: 0.894 GAN cohens kappa score: 0.889 -> test with 'LR' LR tn, fp: 537, 19 LR fn, tp: 0, 21 LR f1 score: 0.689 LR cohens kappa score: 0.673 LR average precision score: 0.944 -> 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: 543, 13 KNN fn, tp: 0, 21 KNN f1 score: 0.764 KNN cohens kappa score: 0.752 ====== 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: 558, 2 GAN fn, tp: 1, 20 GAN f1 score: 0.930 GAN cohens kappa score: 0.928 -> 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: 0.927 -> 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: 554, 6 GAN fn, tp: 0, 21 GAN f1 score: 0.875 GAN cohens kappa score: 0.870 -> 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: 0.960 -> 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: 553, 7 KNN fn, tp: 0, 21 KNN f1 score: 0.857 KNN cohens kappa score: 0.851 ------ 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: 560, 0 GAN fn, tp: 4, 17 GAN f1 score: 0.895 GAN cohens kappa score: 0.891 -> test with 'LR' LR tn, fp: 544, 16 LR fn, tp: 4, 17 LR f1 score: 0.630 LR cohens kappa score: 0.613 LR average precision score: 0.799 -> 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: 552, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ------ 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: 1, 20 GAN f1 score: 0.909 GAN cohens kappa score: 0.906 -> test with 'LR' LR tn, fp: 539, 21 LR fn, tp: 0, 21 LR f1 score: 0.667 LR cohens kappa score: 0.650 LR average precision score: 0.885 -> 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: 555, 5 KNN fn, tp: 1, 20 KNN f1 score: 0.870 KNN cohens kappa score: 0.864 ------ 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: 555, 1 GAN fn, tp: 2, 19 GAN f1 score: 0.927 GAN cohens kappa score: 0.924 -> test with 'LR' LR tn, fp: 551, 5 LR fn, tp: 1, 20 LR f1 score: 0.870 LR cohens kappa score: 0.864 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: 551, 5 KNN fn, tp: 1, 20 KNN f1 score: 0.870 KNN cohens kappa score: 0.864 ====== 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: 2, 19 GAN f1 score: 0.950 GAN cohens kappa score: 0.948 -> 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: 0.960 -> 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 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: 2, 19 GAN f1 score: 0.927 GAN cohens kappa score: 0.924 -> test with 'LR' LR tn, fp: 547, 13 LR fn, tp: 1, 20 LR f1 score: 0.741 LR cohens kappa score: 0.729 LR average precision score: 0.913 -> 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: 1, 20 KNN f1 score: 0.889 KNN cohens kappa score: 0.884 ------ 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: 560, 0 GAN fn, tp: 2, 19 GAN f1 score: 0.950 GAN cohens kappa score: 0.948 -> test with 'LR' LR tn, fp: 547, 13 LR fn, tp: 1, 20 LR f1 score: 0.741 LR cohens kappa score: 0.729 LR average precision score: 0.879 -> 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: 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: 558, 2 GAN fn, tp: 1, 20 GAN f1 score: 0.930 GAN cohens kappa score: 0.928 -> 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: 0.914 -> 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: 1, 20 KNN f1 score: 0.816 KNN cohens kappa score: 0.808 ------ 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: 550, 6 GAN fn, tp: 1, 20 GAN f1 score: 0.851 GAN cohens kappa score: 0.845 -> test with 'LR' LR tn, fp: 540, 16 LR fn, tp: 1, 20 LR f1 score: 0.702 LR cohens kappa score: 0.687 LR average precision score: 0.894 -> 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: 550, 6 KNN fn, tp: 0, 21 KNN f1 score: 0.875 KNN cohens kappa score: 0.870 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 554, 24 LR fn, tp: 4, 21 LR f1 score: 0.870 LR cohens kappa score: 0.864 LR average precision score: 0.988 average: LR tn, fp: 544.48, 14.72 LR fn, tp: 0.96, 20.04 LR f1 score: 0.723 LR cohens kappa score: 0.710 LR average precision score: 0.908 minimum: LR tn, fp: 536, 5 LR fn, tp: 0, 17 LR f1 score: 0.630 LR cohens kappa score: 0.613 LR average precision score: 0.799 -----[ 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: 1.12, 19.88 GB f1 score: 0.959 GB cohens kappa score: 0.957 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: 558, 13 KNN fn, tp: 3, 21 KNN f1 score: 0.955 KNN cohens kappa score: 0.953 average: KNN tn, fp: 552.72, 6.48 KNN fn, tp: 0.4, 20.6 KNN f1 score: 0.859 KNN cohens kappa score: 0.853 minimum: KNN tn, fp: 543, 2 KNN fn, tp: 0, 18 KNN f1 score: 0.764 KNN cohens kappa score: 0.752 -----[ GAN ]----- maximum: GAN tn, fp: 560, 6 GAN fn, tp: 4, 21 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 average: GAN tn, fp: 557.88, 1.32 GAN fn, tp: 1.4, 19.6 GAN f1 score: 0.936 GAN cohens kappa score: 0.934 minimum: GAN tn, fp: 550, 0 GAN fn, tp: 0, 17 GAN f1 score: 0.851 GAN cohens kappa score: 0.845