/////////////////////////////////////////// // Running convGAN 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 'LR' LR tn, fp: 548, 12 LR fn, tp: 2, 19 LR f1 score: 0.731 LR cohens kappa score: 0.719 LR average precision score: 0.872 -> 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: 551, 9 KNN fn, tp: 1, 20 KNN f1 score: 0.800 KNN cohens kappa score: 0.791 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> 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: 0.934 -> 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 'LR' LR tn, fp: 537, 23 LR fn, tp: 0, 21 LR f1 score: 0.646 LR cohens kappa score: 0.628 LR average precision score: 0.818 -> 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 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> 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: 0.882 -> 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 'LR' LR tn, fp: 536, 20 LR fn, tp: 0, 21 LR f1 score: 0.677 LR cohens kappa score: 0.661 LR average precision score: 0.964 -> 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: 550, 6 KNN fn, tp: 0, 21 KNN f1 score: 0.875 KNN cohens kappa score: 0.870 ====== 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 '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: 0.926 -> 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 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> 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: 0.924 -> 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 '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: 0.902 -> 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 'LR' LR tn, fp: 537, 23 LR fn, tp: 0, 21 LR f1 score: 0.646 LR cohens kappa score: 0.628 LR average precision score: 0.868 -> 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: 550, 10 KNN fn, tp: 1, 20 KNN f1 score: 0.784 KNN cohens kappa score: 0.775 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 544, 12 LR fn, tp: 1, 20 LR f1 score: 0.755 LR cohens kappa score: 0.743 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: 551, 5 KNN fn, tp: 0, 21 KNN f1 score: 0.894 KNN cohens kappa score: 0.889 ====== 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 'LR' LR tn, fp: 546, 14 LR fn, tp: 0, 21 LR f1 score: 0.750 LR cohens kappa score: 0.738 LR average precision score: 0.935 -> 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 'LR' LR tn, fp: 555, 5 LR fn, tp: 1, 20 LR f1 score: 0.870 LR cohens kappa score: 0.864 LR average precision score: 0.889 -> 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 '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: 0.886 -> 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: 1, 20 KNN f1 score: 0.800 KNN cohens kappa score: 0.791 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> 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: 0.920 -> 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: 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 'LR' LR tn, fp: 533, 23 LR fn, tp: 0, 21 LR f1 score: 0.646 LR cohens kappa score: 0.628 LR average precision score: 0.905 -> 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 '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: 0.915 -> 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 'LR' LR tn, fp: 537, 23 LR fn, tp: 0, 21 LR f1 score: 0.646 LR cohens kappa score: 0.628 LR average precision score: 0.940 -> 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 'LR' LR tn, fp: 547, 13 LR fn, tp: 4, 17 LR f1 score: 0.667 LR cohens kappa score: 0.652 LR average precision score: 0.797 -> 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: 553, 7 KNN fn, tp: 0, 21 KNN f1 score: 0.857 KNN cohens kappa score: 0.851 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> 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: 0.894 -> 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 'LR' LR tn, fp: 546, 10 LR fn, tp: 1, 20 LR f1 score: 0.784 LR cohens kappa score: 0.775 LR average precision score: 0.924 -> 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 '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: 0.954 -> 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 '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: 0.909 -> 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: 554, 6 KNN fn, tp: 1, 20 KNN f1 score: 0.851 KNN cohens kappa score: 0.845 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> 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: 0.887 -> 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: 552, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 539, 21 LR fn, tp: 1, 20 LR f1 score: 0.645 LR cohens kappa score: 0.627 LR average precision score: 0.875 -> 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: 551, 9 KNN fn, tp: 0, 21 KNN f1 score: 0.824 KNN cohens kappa score: 0.816 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 541, 15 LR fn, tp: 1, 20 LR f1 score: 0.714 LR cohens kappa score: 0.701 LR average precision score: 0.908 -> 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: 547, 9 KNN fn, tp: 0, 21 KNN f1 score: 0.824 KNN cohens kappa score: 0.816 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 555, 23 LR fn, tp: 4, 21 LR f1 score: 0.870 LR cohens kappa score: 0.864 LR average precision score: 0.964 average: LR tn, fp: 543.24, 15.96 LR fn, tp: 0.76, 20.24 LR f1 score: 0.713 LR cohens kappa score: 0.699 LR average precision score: 0.903 minimum: LR tn, fp: 533, 5 LR fn, tp: 0, 17 LR f1 score: 0.645 LR cohens kappa score: 0.627 LR average precision score: 0.797 -----[ 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: 558.52, 0.68 GB fn, tp: 1.04, 19.96 GB f1 score: 0.958 GB cohens kappa score: 0.957 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, 10 KNN fn, tp: 1, 21 KNN f1 score: 0.955 KNN cohens kappa score: 0.953 average: KNN tn, fp: 552.52, 6.68 KNN fn, tp: 0.32, 20.68 KNN f1 score: 0.857 KNN cohens kappa score: 0.851 minimum: KNN tn, fp: 547, 2 KNN fn, tp: 0, 20 KNN f1 score: 0.784 KNN cohens kappa score: 0.775