/////////////////////////////////////////// // Running ProWRAS 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: 550, 10 LR fn, tp: 3, 18 LR f1 score: 0.735 LR cohens kappa score: 0.723 LR average precision score: 0.860 -> test with 'RF' RF tn, fp: 559, 1 RF fn, tp: 3, 18 RF f1 score: 0.900 RF cohens kappa score: 0.896 -> test with 'GB' GB tn, fp: 559, 1 GB fn, tp: 3, 18 GB f1 score: 0.900 GB cohens kappa score: 0.896 -> test with 'KNN' KNN tn, fp: 555, 5 KNN fn, tp: 3, 18 KNN f1 score: 0.818 KNN cohens kappa score: 0.811 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 550, 10 LR fn, tp: 0, 21 LR f1 score: 0.808 LR cohens kappa score: 0.799 LR average precision score: 0.940 -> test with 'RF' RF tn, fp: 560, 0 RF fn, tp: 1, 20 RF f1 score: 0.976 RF cohens kappa score: 0.975 -> 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: 1, 20 KNN f1 score: 0.952 KNN cohens kappa score: 0.951 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 541, 19 LR fn, tp: 1, 20 LR f1 score: 0.667 LR cohens kappa score: 0.650 LR average precision score: 0.850 -> test with 'RF' RF tn, fp: 560, 0 RF fn, tp: 2, 19 RF f1 score: 0.950 RF cohens kappa 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: 555, 5 KNN fn, tp: 1, 20 KNN f1 score: 0.870 KNN cohens kappa score: 0.864 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> 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: 0.886 -> test with 'RF' RF tn, fp: 555, 5 RF fn, tp: 1, 20 RF f1 score: 0.870 RF cohens kappa score: 0.864 -> 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: 555, 5 KNN fn, tp: 1, 20 KNN f1 score: 0.870 KNN cohens kappa score: 0.864 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 543, 13 LR fn, tp: 0, 21 LR f1 score: 0.764 LR cohens kappa score: 0.752 LR average precision score: 0.988 -> test with 'RF' RF tn, fp: 556, 0 RF fn, tp: 1, 20 RF f1 score: 0.976 RF cohens kappa score: 0.975 -> 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: 553, 3 KNN fn, tp: 1, 20 KNN f1 score: 0.909 KNN cohens kappa score: 0.905 ====== 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: 551, 9 LR fn, tp: 1, 20 LR f1 score: 0.800 LR cohens kappa score: 0.791 LR average precision score: 0.936 -> test with 'RF' RF tn, fp: 558, 2 RF fn, tp: 0, 21 RF f1 score: 0.955 RF cohens kappa score: 0.953 -> 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 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: 0.919 -> test with 'RF' RF tn, fp: 560, 0 RF fn, tp: 2, 19 RF f1 score: 0.950 RF cohens kappa score: 0.948 -> 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: 558, 2 KNN fn, tp: 2, 19 KNN f1 score: 0.905 KNN cohens kappa score: 0.901 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 545, 15 LR fn, tp: 0, 21 LR f1 score: 0.737 LR cohens kappa score: 0.724 LR average precision score: 0.910 -> test with 'RF' RF tn, fp: 560, 0 RF fn, tp: 3, 18 RF f1 score: 0.923 RF cohens kappa 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: 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: 538, 22 LR fn, tp: 0, 21 LR f1 score: 0.656 LR cohens kappa score: 0.639 LR average precision score: 0.883 -> test with 'RF' RF tn, fp: 559, 1 RF fn, tp: 2, 19 RF f1 score: 0.927 RF cohens kappa score: 0.924 -> test with 'GB' GB tn, fp: 558, 2 GB fn, tp: 2, 19 GB f1 score: 0.905 GB cohens kappa score: 0.901 -> 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 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 539, 17 LR fn, tp: 2, 19 LR f1 score: 0.667 LR cohens kappa score: 0.651 LR average precision score: 0.907 -> test with 'RF' RF tn, fp: 556, 0 RF fn, tp: 2, 19 RF f1 score: 0.950 RF cohens kappa score: 0.948 -> 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: 0, 21 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ====== 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: 550, 10 LR fn, tp: 0, 21 LR f1 score: 0.808 LR cohens kappa score: 0.799 LR average precision score: 0.962 -> test with 'RF' RF tn, fp: 559, 1 RF fn, tp: 2, 19 RF f1 score: 0.927 RF cohens kappa 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: 554, 6 KNN fn, tp: 2, 19 KNN f1 score: 0.826 KNN cohens kappa score: 0.819 ------ 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: 2, 19 LR f1 score: 0.844 LR cohens kappa score: 0.838 LR average precision score: 0.908 -> test with 'RF' RF tn, fp: 560, 0 RF fn, tp: 2, 19 RF f1 score: 0.950 RF cohens kappa score: 0.948 -> 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: 557, 3 KNN fn, tp: 2, 19 KNN f1 score: 0.884 KNN cohens kappa score: 0.879 ------ 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: 0.869 -> test with 'RF' RF tn, fp: 560, 0 RF fn, tp: 3, 18 RF f1 score: 0.923 RF cohens kappa 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: 555, 5 KNN fn, tp: 2, 19 KNN f1 score: 0.844 KNN cohens kappa score: 0.838 ------ Step 3/5: Slice 4/5 ------- -> 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: 0.948 -> test with 'RF' RF tn, fp: 560, 0 RF fn, tp: 1, 20 RF f1 score: 0.976 RF cohens kappa score: 0.975 -> 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 3/5: 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: 0.926 -> test with 'RF' RF tn, fp: 556, 0 RF fn, tp: 0, 21 RF f1 score: 1.000 RF cohens kappa score: 1.000 -> 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: 550, 6 KNN fn, tp: 0, 21 KNN f1 score: 0.875 KNN cohens kappa score: 0.870 ====== 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: 551, 9 LR fn, tp: 1, 20 LR f1 score: 0.800 LR cohens kappa score: 0.791 LR average precision score: 0.937 -> test with 'RF' RF tn, fp: 558, 2 RF fn, tp: 0, 21 RF f1 score: 0.955 RF cohens kappa score: 0.953 -> 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: 555, 5 KNN fn, tp: 0, 21 KNN f1 score: 0.894 KNN cohens kappa score: 0.889 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> 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: 0.955 -> test with 'RF' RF tn, fp: 560, 0 RF fn, tp: 1, 20 RF f1 score: 0.976 RF cohens kappa score: 0.975 -> 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: 1, 20 KNN f1 score: 0.889 KNN cohens kappa score: 0.884 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 552, 8 LR fn, tp: 6, 15 LR f1 score: 0.682 LR cohens kappa score: 0.669 LR average precision score: 0.752 -> test with 'RF' RF tn, fp: 560, 0 RF fn, tp: 6, 15 RF f1 score: 0.833 RF cohens kappa score: 0.828 -> 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: 4, 17 KNN f1 score: 0.791 KNN cohens kappa score: 0.783 ------ Step 4/5: 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: 0, 21 LR f1 score: 0.712 LR cohens kappa score: 0.698 LR average precision score: 0.927 -> test with 'RF' RF tn, fp: 559, 1 RF fn, tp: 1, 20 RF f1 score: 0.952 RF cohens kappa score: 0.951 -> 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: 557, 3 KNN fn, tp: 1, 20 KNN f1 score: 0.909 KNN cohens kappa score: 0.906 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 549, 7 LR fn, tp: 1, 20 LR f1 score: 0.833 LR cohens kappa score: 0.826 LR average precision score: 0.937 -> test with 'RF' RF tn, fp: 556, 0 RF fn, tp: 3, 18 RF f1 score: 0.923 RF cohens kappa score: 0.920 -> 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: 553, 3 KNN fn, tp: 1, 20 KNN f1 score: 0.909 KNN cohens kappa score: 0.905 ====== 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: 546, 14 LR fn, tp: 0, 21 LR f1 score: 0.750 LR cohens kappa score: 0.738 LR average precision score: 0.964 -> test with 'RF' RF tn, fp: 559, 1 RF fn, tp: 2, 19 RF f1 score: 0.927 RF cohens kappa 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: 557, 3 KNN fn, tp: 3, 18 KNN f1 score: 0.857 KNN cohens kappa score: 0.852 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 549, 11 LR fn, tp: 1, 20 LR f1 score: 0.769 LR cohens kappa score: 0.759 LR average precision score: 0.895 -> test with 'RF' RF tn, fp: 560, 0 RF fn, tp: 4, 17 RF f1 score: 0.895 RF cohens kappa score: 0.891 -> 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: 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 '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: 0.894 -> test with 'RF' RF tn, fp: 560, 0 RF fn, tp: 2, 19 RF f1 score: 0.950 RF cohens kappa 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: 555, 5 KNN fn, tp: 0, 21 KNN f1 score: 0.894 KNN cohens kappa score: 0.889 ------ Step 5/5: Slice 4/5 ------- -> 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: 0.920 -> test with 'RF' RF tn, fp: 559, 1 RF fn, tp: 1, 20 RF f1 score: 0.952 RF cohens kappa score: 0.951 -> 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: 1, 20 KNN f1 score: 0.851 KNN cohens kappa score: 0.845 ------ Step 5/5: 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: 0.922 -> test with 'RF' RF tn, fp: 556, 0 RF fn, tp: 2, 19 RF f1 score: 0.950 RF cohens kappa score: 0.948 -> 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: 555, 22 LR fn, tp: 6, 21 LR f1 score: 0.844 LR cohens kappa score: 0.838 LR average precision score: 0.988 average: LR tn, fp: 546.28, 12.92 LR fn, tp: 1.08, 19.92 LR f1 score: 0.744 LR cohens kappa score: 0.732 LR average precision score: 0.912 minimum: LR tn, fp: 536, 5 LR fn, tp: 0, 15 LR f1 score: 0.656 LR cohens kappa score: 0.639 LR average precision score: 0.752 -----[ RF ]----- maximum: RF tn, fp: 560, 5 RF fn, tp: 6, 21 RF f1 score: 1.000 RF cohens kappa score: 1.000 average: RF tn, fp: 558.6, 0.6 RF fn, tp: 1.88, 19.12 RF f1 score: 0.939 RF cohens kappa score: 0.936 minimum: RF tn, fp: 555, 0 RF fn, tp: 0, 15 RF f1 score: 0.833 RF cohens kappa 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: 558.64, 0.56 GB fn, tp: 1.12, 19.88 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: 559, 7 KNN fn, tp: 4, 21 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 average: KNN tn, fp: 555.28, 3.92 KNN fn, tp: 1.24, 19.76 KNN f1 score: 0.885 KNN cohens kappa score: 0.881 minimum: KNN tn, fp: 550, 0 KNN fn, tp: 0, 17 KNN f1 score: 0.791 KNN cohens kappa score: 0.783