/////////////////////////////////////////// // Running Repeater 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: 544, 16 LR fn, tp: 2, 19 LR f1 score: 0.679 LR cohens kappa score: 0.663 LR average precision score: 0.877 -> 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: 549, 11 KNN fn, tp: 0, 21 KNN f1 score: 0.792 KNN cohens kappa score: 0.783 ------ Step 1/5: Slice 2/5 ------- -> 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: 0.918 -> 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 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 530, 30 LR fn, tp: 0, 21 LR f1 score: 0.583 LR cohens kappa score: 0.561 LR average precision score: 0.810 -> test with 'GB' GB tn, fp: 557, 3 GB fn, tp: 0, 21 GB f1 score: 0.933 GB cohens kappa score: 0.931 -> 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 'LR' LR tn, fp: 532, 28 LR fn, tp: 1, 20 LR f1 score: 0.580 LR cohens kappa score: 0.557 LR average precision score: 0.826 -> test with 'GB' GB tn, fp: 553, 7 GB fn, tp: 0, 21 GB f1 score: 0.857 GB cohens kappa score: 0.851 -> 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 '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: 0.968 -> 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 '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: 0.925 -> test with 'GB' GB tn, fp: 557, 3 GB fn, tp: 0, 21 GB f1 score: 0.933 GB cohens kappa score: 0.931 -> 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: 541, 19 LR fn, tp: 2, 19 LR f1 score: 0.644 LR cohens kappa score: 0.627 LR average precision score: 0.917 -> 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: 553, 7 KNN fn, tp: 0, 21 KNN f1 score: 0.857 KNN cohens kappa score: 0.851 ------ Step 2/5: Slice 3/5 ------- -> 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: 0.895 -> 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: 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 'LR' LR tn, fp: 535, 25 LR fn, tp: 0, 21 LR f1 score: 0.627 LR cohens kappa score: 0.607 LR average precision score: 0.860 -> 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 'LR' LR tn, fp: 535, 21 LR fn, tp: 0, 21 LR f1 score: 0.667 LR cohens kappa score: 0.650 LR average precision score: 0.872 -> test with 'GB' GB tn, fp: 554, 2 GB fn, tp: 0, 21 GB f1 score: 0.955 GB cohens kappa score: 0.953 -> 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: 544, 16 LR fn, tp: 0, 21 LR f1 score: 0.724 LR cohens kappa score: 0.711 LR average precision score: 0.940 -> 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 3/5: Slice 2/5 ------- -> 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: 0.887 -> 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: 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: 534, 26 LR fn, tp: 1, 20 LR f1 score: 0.597 LR cohens kappa score: 0.576 LR average precision score: 0.794 -> 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: 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 'LR' LR tn, fp: 535, 25 LR fn, tp: 0, 21 LR f1 score: 0.627 LR cohens kappa score: 0.607 LR average precision score: 0.909 -> 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: 554, 6 KNN fn, tp: 0, 21 KNN f1 score: 0.875 KNN cohens kappa score: 0.870 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 524, 32 LR fn, tp: 0, 21 LR f1 score: 0.568 LR cohens kappa score: 0.544 LR average precision score: 0.882 -> test with 'GB' GB tn, fp: 551, 5 GB fn, tp: 0, 21 GB f1 score: 0.894 GB cohens kappa score: 0.889 -> test with 'KNN' KNN tn, fp: 546, 10 KNN fn, tp: 0, 21 KNN f1 score: 0.808 KNN cohens kappa score: 0.799 ====== 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: 540, 20 LR fn, tp: 1, 20 LR f1 score: 0.656 LR cohens kappa score: 0.639 LR average precision score: 0.926 -> 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: 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 'LR' LR tn, fp: 532, 28 LR fn, tp: 0, 21 LR f1 score: 0.600 LR cohens kappa score: 0.579 LR average precision score: 0.936 -> test with 'GB' GB tn, fp: 557, 3 GB fn, tp: 0, 21 GB f1 score: 0.933 GB cohens kappa score: 0.931 -> 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 'LR' LR tn, fp: 532, 28 LR fn, tp: 0, 21 LR f1 score: 0.600 LR cohens kappa score: 0.579 LR average precision score: 0.762 -> 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: 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 '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: 0.889 -> 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: 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: 544, 12 LR fn, tp: 1, 20 LR f1 score: 0.755 LR cohens kappa score: 0.743 LR average precision score: 0.906 -> test with 'GB' GB tn, fp: 555, 1 GB fn, tp: 2, 19 GB f1 score: 0.927 GB cohens kappa score: 0.924 -> 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 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: 539, 21 LR fn, tp: 0, 21 LR f1 score: 0.667 LR cohens kappa score: 0.650 LR average precision score: 0.948 -> 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: 557, 3 KNN fn, tp: 1, 20 KNN f1 score: 0.909 KNN cohens kappa score: 0.906 ------ Step 5/5: 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: 0.901 -> 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: 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: 536, 24 LR fn, tp: 0, 21 LR f1 score: 0.636 LR cohens kappa score: 0.618 LR average precision score: 0.833 -> 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 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 536, 24 LR fn, tp: 1, 20 LR f1 score: 0.615 LR cohens kappa score: 0.596 LR average precision score: 0.851 -> 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: 532, 24 LR fn, tp: 1, 20 LR f1 score: 0.615 LR cohens kappa score: 0.595 LR average precision score: 0.872 -> test with 'GB' GB tn, fp: 554, 2 GB fn, tp: 0, 21 GB f1 score: 0.955 GB cohens kappa score: 0.953 -> test with 'KNN' KNN tn, fp: 548, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 546, 32 LR fn, tp: 2, 21 LR f1 score: 0.755 LR cohens kappa score: 0.743 LR average precision score: 0.968 average: LR tn, fp: 537.2, 22.0 LR fn, tp: 0.48, 20.52 LR f1 score: 0.650 LR cohens kappa score: 0.632 LR average precision score: 0.884 minimum: LR tn, fp: 524, 12 LR fn, tp: 0, 19 LR f1 score: 0.568 LR cohens kappa score: 0.544 LR average precision score: 0.762 -----[ GB ]----- maximum: GB tn, fp: 560, 7 GB fn, tp: 2, 21 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 557.12, 2.08 GB fn, tp: 0.4, 20.6 GB f1 score: 0.944 GB cohens kappa score: 0.942 minimum: GB tn, fp: 551, 0 GB fn, tp: 0, 19 GB f1 score: 0.857 GB cohens kappa score: 0.851 -----[ 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: 551.36, 7.84 KNN fn, tp: 0.2, 20.8 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 minimum: KNN tn, fp: 546, 3 KNN fn, tp: 0, 20 KNN f1 score: 0.778 KNN cohens kappa score: 0.768