/////////////////////////////////////////// // Running CTAB-GAN 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 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 553, 7 LR fn, tp: 3, 18 LR f1 score: 0.783 LR cohens kappa score: 0.774 LR average precision score: 0.863 -> 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: 552, 8 KNN fn, tp: 2, 19 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 0%| | 0/10 [00:00 create 2152 synthetic samples -> 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: 0.899 -> 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: 552, 8 KNN fn, tp: 3, 18 KNN f1 score: 0.766 KNN cohens kappa score: 0.756 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 551, 9 LR fn, tp: 3, 18 LR f1 score: 0.750 LR cohens kappa score: 0.739 LR average precision score: 0.889 -> 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: 553, 7 KNN fn, tp: 3, 18 KNN f1 score: 0.783 KNN cohens kappa score: 0.774 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> 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: 0.857 -> 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: 546, 14 KNN fn, tp: 2, 19 KNN f1 score: 0.704 KNN cohens kappa score: 0.690 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2156 synthetic samples -> test with 'LR' LR tn, fp: 556, 0 LR fn, tp: 1, 20 LR f1 score: 0.976 LR cohens kappa score: 0.975 LR average precision score: 0.962 -> 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: 556, 0 KNN fn, tp: 1, 20 KNN f1 score: 0.976 KNN cohens kappa score: 0.975 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 557, 3 LR fn, tp: 2, 19 LR f1 score: 0.884 LR cohens kappa score: 0.879 LR average precision score: 0.943 -> 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: 1, 20 KNN f1 score: 0.851 KNN cohens kappa score: 0.845 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 533, 27 LR fn, tp: 2, 19 LR f1 score: 0.567 LR cohens kappa score: 0.545 LR average precision score: 0.885 -> 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 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 556, 4 LR fn, tp: 3, 18 LR f1 score: 0.837 LR cohens kappa score: 0.831 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: 4, 17 KNN f1 score: 0.829 KNN cohens kappa score: 0.823 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 553, 7 LR fn, tp: 4, 17 LR f1 score: 0.756 LR cohens kappa score: 0.746 LR average precision score: 0.850 -> 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: 5, 16 KNN f1 score: 0.762 KNN cohens kappa score: 0.753 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2156 synthetic samples -> test with 'LR' LR tn, fp: 554, 2 LR fn, tp: 2, 19 LR f1 score: 0.905 LR cohens kappa score: 0.901 LR average precision score: 0.910 -> 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: 2, 19 KNN f1 score: 0.864 KNN cohens kappa score: 0.858 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 534, 26 LR fn, tp: 0, 21 LR f1 score: 0.618 LR cohens kappa score: 0.598 LR average precision score: 0.927 -> 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: 1, 20 KNN f1 score: 0.755 KNN cohens kappa score: 0.744 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 557, 3 LR fn, tp: 4, 17 LR f1 score: 0.829 LR cohens kappa score: 0.823 LR average precision score: 0.887 -> 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: 4, 17 KNN f1 score: 0.791 KNN cohens kappa score: 0.783 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 555, 5 LR fn, tp: 4, 17 LR f1 score: 0.791 LR cohens kappa score: 0.783 LR average precision score: 0.851 -> 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: 558, 2 KNN fn, tp: 4, 17 KNN f1 score: 0.850 KNN cohens kappa score: 0.845 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 525, 35 LR fn, tp: 0, 21 LR f1 score: 0.545 LR cohens kappa score: 0.520 LR average precision score: 0.906 -> 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: 547, 13 KNN fn, tp: 0, 21 KNN f1 score: 0.764 KNN cohens kappa score: 0.753 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2156 synthetic samples -> test with 'LR' LR tn, fp: 554, 2 LR fn, tp: 2, 19 LR f1 score: 0.905 LR cohens kappa score: 0.901 LR average precision score: 0.959 -> 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: 554, 2 KNN fn, tp: 1, 20 KNN f1 score: 0.930 KNN cohens kappa score: 0.928 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 550, 10 LR fn, tp: 2, 19 LR f1 score: 0.760 LR cohens kappa score: 0.749 LR average precision score: 0.910 -> 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: 553, 7 KNN fn, tp: 1, 20 KNN f1 score: 0.833 KNN cohens kappa score: 0.826 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 522, 38 LR fn, tp: 0, 21 LR f1 score: 0.525 LR cohens kappa score: 0.498 LR average precision score: 0.899 -> 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: 540, 20 KNN fn, tp: 1, 20 KNN f1 score: 0.656 KNN cohens kappa score: 0.639 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 558, 2 LR fn, tp: 7, 14 LR f1 score: 0.757 LR cohens kappa score: 0.749 LR average precision score: 0.715 -> 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: 555, 5 KNN fn, tp: 6, 15 KNN f1 score: 0.732 KNN cohens kappa score: 0.722 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> 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: 0.953 -> 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: 550, 10 KNN fn, tp: 1, 20 KNN f1 score: 0.784 KNN cohens kappa score: 0.775 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2156 synthetic samples -> test with 'LR' LR tn, fp: 555, 1 LR fn, tp: 4, 17 LR f1 score: 0.872 LR cohens kappa score: 0.867 LR average precision score: 0.909 -> 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: 2, 19 KNN f1 score: 0.864 KNN cohens kappa score: 0.858 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 522, 38 LR fn, tp: 0, 21 LR f1 score: 0.525 LR cohens kappa score: 0.498 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: 541, 19 KNN fn, tp: 1, 20 KNN f1 score: 0.667 KNN cohens kappa score: 0.650 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 540, 20 LR fn, tp: 4, 17 LR f1 score: 0.586 LR cohens kappa score: 0.566 LR average precision score: 0.836 -> 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: 550, 10 KNN fn, tp: 5, 16 KNN f1 score: 0.681 KNN cohens kappa score: 0.668 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 556, 4 LR fn, tp: 4, 17 LR f1 score: 0.810 LR cohens kappa score: 0.802 LR average precision score: 0.881 -> 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: 4, 17 KNN f1 score: 0.829 KNN cohens kappa score: 0.823 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2152 synthetic samples -> test with 'LR' LR tn, fp: 543, 17 LR fn, tp: 3, 18 LR f1 score: 0.643 LR cohens kappa score: 0.626 LR average precision score: 0.850 -> 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: 2, 19 KNN f1 score: 0.776 KNN cohens kappa score: 0.766 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2156 synthetic samples -> 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: 0.948 -> 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: 1, 20 KNN f1 score: 0.851 KNN cohens kappa score: 0.845 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 558, 38 LR fn, tp: 7, 21 LR f1 score: 0.976 LR cohens kappa score: 0.975 LR average precision score: 0.962 average: LR tn, fp: 546.36, 12.84 LR fn, tp: 2.36, 18.64 LR f1 score: 0.739 LR cohens kappa score: 0.727 LR average precision score: 0.895 minimum: LR tn, fp: 522, 0 LR fn, tp: 0, 14 LR f1 score: 0.525 LR cohens kappa score: 0.498 LR average precision score: 0.715 -----[ 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: 558.72, 0.48 GB fn, tp: 1.56, 19.44 GB f1 score: 0.949 GB cohens kappa score: 0.948 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, 20 KNN fn, tp: 6, 21 KNN f1 score: 0.976 KNN cohens kappa score: 0.975 average: KNN tn, fp: 551.56, 7.64 KNN fn, tp: 2.32, 18.68 KNN f1 score: 0.795 KNN cohens kappa score: 0.787 minimum: KNN tn, fp: 540, 0 KNN fn, tp: 0, 15 KNN f1 score: 0.656 KNN cohens kappa score: 0.639