/////////////////////////////////////////// // Running convGAN-proximary-5 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: 5, 16 GAN f1 score: 0.842 GAN cohens kappa score: 0.837 -> test with 'LR' LR tn, fp: 549, 11 LR fn, tp: 2, 19 LR f1 score: 0.745 LR cohens kappa score: 0.734 LR average precision score: 0.856 -> 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: 551, 9 KNN fn, tp: 0, 21 KNN f1 score: 0.824 KNN cohens kappa score: 0.816 ------ 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: 549, 11 LR fn, tp: 0, 21 LR f1 score: 0.792 LR cohens kappa score: 0.783 LR average precision score: 0.922 -> 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: 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: 558, 2 GAN fn, tp: 0, 21 GAN f1 score: 0.955 GAN cohens kappa score: 0.953 -> test with 'LR' LR tn, fp: 533, 27 LR fn, tp: 0, 21 LR f1 score: 0.609 LR cohens kappa score: 0.588 LR average precision score: 0.798 -> 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 1/5: 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: 2, 19 GAN f1 score: 0.950 GAN cohens kappa score: 0.948 -> 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: 0.895 -> 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: 553, 7 KNN fn, tp: 1, 20 KNN f1 score: 0.833 KNN cohens kappa score: 0.826 ------ 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: 537, 19 LR fn, tp: 0, 21 LR f1 score: 0.689 LR cohens kappa score: 0.673 LR average precision score: 0.982 -> 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: 546, 14 LR fn, tp: 1, 20 LR f1 score: 0.727 LR cohens kappa score: 0.715 LR average precision score: 0.931 -> 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 2/5: 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: 550, 10 LR fn, tp: 2, 19 LR f1 score: 0.760 LR cohens kappa score: 0.749 LR average precision score: 0.929 -> 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: 555, 5 KNN fn, tp: 1, 20 KNN f1 score: 0.870 KNN cohens kappa score: 0.864 ------ 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: 559, 1 GAN fn, tp: 1, 20 GAN f1 score: 0.952 GAN cohens kappa score: 0.951 -> 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: 0.907 -> 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: 556, 4 GAN fn, tp: 0, 21 GAN f1 score: 0.913 GAN cohens kappa score: 0.909 -> 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: 0.879 -> 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 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: 538, 18 LR fn, tp: 0, 21 LR f1 score: 0.700 LR cohens kappa score: 0.685 LR average precision score: 0.921 -> 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: 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 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: 549, 11 LR fn, tp: 0, 21 LR f1 score: 0.792 LR cohens kappa score: 0.783 LR average precision score: 0.945 -> 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: 3, 18 GAN f1 score: 0.923 GAN cohens kappa score: 0.920 -> test with 'LR' LR tn, fp: 556, 4 LR fn, tp: 1, 20 LR f1 score: 0.889 LR cohens kappa score: 0.884 LR average precision score: 0.904 -> 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: 0, 21 KNN f1 score: 0.933 KNN cohens kappa score: 0.931 ------ 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: 560, 0 GAN fn, tp: 3, 18 GAN f1 score: 0.923 GAN cohens kappa score: 0.920 -> test with 'LR' LR tn, fp: 538, 22 LR fn, tp: 1, 20 LR f1 score: 0.635 LR cohens kappa score: 0.616 LR average precision score: 0.859 -> test with 'GB' GB tn, fp: 555, 5 GB fn, tp: 2, 19 GB f1 score: 0.844 GB cohens kappa score: 0.838 -> 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: 540, 20 LR fn, tp: 0, 21 LR f1 score: 0.677 LR cohens kappa score: 0.661 LR average precision score: 0.922 -> 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: 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: 547, 9 GAN fn, tp: 0, 21 GAN f1 score: 0.824 GAN cohens kappa score: 0.816 -> test with 'LR' LR tn, fp: 531, 25 LR fn, tp: 0, 21 LR f1 score: 0.627 LR cohens kappa score: 0.607 LR average precision score: 0.895 -> 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: 545, 11 KNN fn, tp: 0, 21 KNN f1 score: 0.792 KNN cohens kappa score: 0.783 ====== 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: 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: 1, 20 LR f1 score: 0.755 LR cohens kappa score: 0.744 LR average precision score: 0.923 -> 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: 554, 6 KNN fn, tp: 0, 21 KNN f1 score: 0.875 KNN cohens kappa score: 0.870 ------ 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: 559, 1 GAN fn, tp: 0, 21 GAN f1 score: 0.977 GAN cohens kappa score: 0.976 -> 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: 0.953 -> 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: 0, 21 KNN f1 score: 0.875 KNN cohens kappa score: 0.870 ------ 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: 5, 16 GAN f1 score: 0.865 GAN cohens kappa score: 0.861 -> test with 'LR' LR tn, fp: 543, 17 LR fn, tp: 5, 16 LR f1 score: 0.593 LR cohens kappa score: 0.574 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: 554, 6 KNN fn, tp: 1, 20 KNN f1 score: 0.851 KNN cohens kappa score: 0.845 ------ 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: 560, 0 GAN fn, tp: 1, 20 GAN f1 score: 0.976 GAN cohens kappa score: 0.975 -> 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: 0.899 -> 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 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: 547, 9 LR fn, tp: 1, 20 LR f1 score: 0.800 LR cohens kappa score: 0.791 LR average precision score: 0.929 -> 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: 0, 21 KNN f1 score: 0.913 KNN cohens kappa score: 0.909 ====== 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: 540, 20 LR fn, tp: 0, 21 LR f1 score: 0.677 LR cohens kappa score: 0.661 LR average precision score: 0.960 -> 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: 555, 5 KNN fn, tp: 1, 20 KNN f1 score: 0.870 KNN cohens kappa score: 0.864 ------ 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: 542, 18 LR fn, tp: 0, 21 LR f1 score: 0.700 LR cohens kappa score: 0.685 LR average precision score: 0.887 -> 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: 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 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: 549, 11 LR fn, tp: 1, 20 LR f1 score: 0.769 LR cohens kappa score: 0.759 LR average precision score: 0.892 -> 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: 551, 9 KNN fn, tp: 0, 21 KNN f1 score: 0.824 KNN cohens kappa score: 0.816 ------ 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: 542, 18 LR fn, tp: 1, 20 LR f1 score: 0.678 LR cohens kappa score: 0.662 LR average precision score: 0.892 -> 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: 549, 11 KNN fn, tp: 0, 21 KNN f1 score: 0.792 KNN cohens kappa score: 0.783 ------ 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: 554, 2 GAN fn, tp: 1, 20 GAN f1 score: 0.930 GAN cohens kappa score: 0.928 -> 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: 0.896 -> 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: 549, 7 KNN fn, tp: 0, 21 KNN f1 score: 0.857 KNN cohens kappa score: 0.851 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 556, 27 LR fn, tp: 5, 21 LR f1 score: 0.889 LR cohens kappa score: 0.884 LR average precision score: 0.982 average: LR tn, fp: 543.04, 16.16 LR fn, tp: 0.76, 20.24 LR f1 score: 0.711 LR cohens kappa score: 0.697 LR average precision score: 0.903 minimum: LR tn, fp: 531, 4 LR fn, tp: 0, 16 LR f1 score: 0.593 LR cohens kappa score: 0.574 LR average precision score: 0.798 -----[ 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: 558.4, 0.8 GB fn, tp: 1.0, 20.0 GB f1 score: 0.957 GB cohens kappa score: 0.955 minimum: GB tn, fp: 555, 0 GB fn, tp: 0, 17 GB f1 score: 0.844 GB cohens kappa score: 0.838 -----[ KNN ]----- maximum: KNN tn, fp: 558, 13 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: 0.28, 20.72 KNN f1 score: 0.859 KNN cohens kappa score: 0.853 minimum: KNN tn, fp: 545, 2 KNN fn, tp: 0, 20 KNN f1 score: 0.764 KNN cohens kappa score: 0.753 -----[ GAN ]----- maximum: GAN tn, fp: 560, 9 GAN fn, tp: 5, 21 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 average: GAN tn, fp: 558.16, 1.04 GAN fn, tp: 1.48, 19.52 GAN f1 score: 0.940 GAN cohens kappa score: 0.938 minimum: GAN tn, fp: 547, 0 GAN fn, tp: 0, 16 GAN f1 score: 0.824 GAN cohens kappa score: 0.816