/////////////////////////////////////////// // Running convGAN-majority-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: 554, 6 GAN fn, tp: 2, 19 GAN f1 score: 0.826 GAN cohens kappa score: 0.819 -> 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: 0.850 -> 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: 3, 18 KNN f1 score: 0.750 KNN cohens kappa score: 0.739 ------ 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: 548, 12 LR fn, tp: 0, 21 LR f1 score: 0.778 LR cohens kappa score: 0.768 LR average precision score: 0.927 -> 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 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: 537, 23 LR fn, tp: 1, 20 LR f1 score: 0.625 LR cohens kappa score: 0.606 LR average precision score: 0.834 -> 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: 551, 9 KNN fn, tp: 0, 21 KNN f1 score: 0.824 KNN cohens kappa score: 0.816 ------ 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: 556, 4 GAN fn, tp: 0, 21 GAN f1 score: 0.913 GAN cohens kappa score: 0.909 -> 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: 0.856 -> 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: 553, 7 KNN fn, tp: 0, 21 KNN f1 score: 0.857 KNN cohens kappa score: 0.851 ------ 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: 542, 14 LR fn, tp: 0, 21 LR f1 score: 0.750 LR cohens kappa score: 0.738 LR average precision score: 0.989 -> 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 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: 545, 15 LR fn, tp: 1, 20 LR f1 score: 0.714 LR cohens kappa score: 0.701 LR average precision score: 0.927 -> 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: 0, 21 KNN f1 score: 0.824 KNN cohens kappa score: 0.816 ------ 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: 560, 0 GAN fn, tp: 2, 19 GAN f1 score: 0.950 GAN cohens kappa score: 0.948 -> test with 'LR' LR tn, fp: 554, 6 LR fn, tp: 2, 19 LR f1 score: 0.826 LR cohens kappa score: 0.819 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: 556, 4 KNN fn, tp: 2, 19 KNN f1 score: 0.864 KNN cohens kappa score: 0.858 ------ 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: 558, 2 GAN fn, tp: 1, 20 GAN f1 score: 0.930 GAN cohens kappa score: 0.928 -> 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: 0.896 -> 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: 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: 553, 7 GAN fn, tp: 0, 21 GAN f1 score: 0.857 GAN cohens kappa score: 0.851 -> 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: 0.871 -> test with 'GB' GB tn, fp: 559, 1 GB fn, tp: 2, 19 GB f1 score: 0.927 GB cohens kappa score: 0.924 -> 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 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: 1, 20 GAN f1 score: 0.952 GAN cohens kappa score: 0.951 -> test with 'LR' LR tn, fp: 538, 18 LR fn, tp: 1, 20 LR f1 score: 0.678 LR cohens kappa score: 0.662 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: 552, 4 KNN fn, tp: 0, 21 KNN f1 score: 0.913 KNN cohens kappa score: 0.909 ====== 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: 558, 2 GAN fn, tp: 0, 21 GAN f1 score: 0.955 GAN cohens kappa score: 0.953 -> 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: 0.943 -> 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 3/5: Slice 2/5 ------- -> 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: 552, 8 LR fn, tp: 1, 20 LR f1 score: 0.816 LR cohens kappa score: 0.808 LR average precision score: 0.896 -> 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 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: 536, 24 LR fn, tp: 1, 20 LR f1 score: 0.615 LR cohens kappa score: 0.596 LR average precision score: 0.820 -> test with 'GB' GB tn, fp: 557, 3 GB fn, tp: 3, 18 GB f1 score: 0.857 GB cohens kappa score: 0.852 -> 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 4/5 ------- -> 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: 544, 16 LR fn, tp: 1, 20 LR f1 score: 0.702 LR cohens kappa score: 0.687 LR average precision score: 0.938 -> 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 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with GAN.predict GAN tn, fp: 549, 7 GAN fn, tp: 0, 21 GAN f1 score: 0.857 GAN cohens kappa score: 0.851 -> 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: 0.923 -> 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: 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: 558, 2 GAN fn, tp: 0, 21 GAN f1 score: 0.955 GAN cohens kappa score: 0.953 -> 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: 0.933 -> 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 GAN.predict GAN tn, fp: 557, 3 GAN fn, tp: 0, 21 GAN f1 score: 0.933 GAN cohens kappa score: 0.931 -> 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: 0.945 -> 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 GAN.predict GAN tn, fp: 558, 2 GAN fn, tp: 2, 19 GAN f1 score: 0.905 GAN cohens kappa score: 0.901 -> test with 'LR' LR tn, fp: 540, 20 LR fn, tp: 2, 19 LR f1 score: 0.633 LR cohens kappa score: 0.615 LR average precision score: 0.797 -> 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: 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 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: 540, 20 LR fn, tp: 0, 21 LR f1 score: 0.677 LR cohens kappa score: 0.661 LR average precision score: 0.905 -> 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: 1, 20 KNN f1 score: 0.870 KNN cohens kappa score: 0.864 ------ 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: 554, 2 GAN fn, tp: 2, 19 GAN f1 score: 0.905 GAN cohens kappa score: 0.901 -> 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: 0.926 -> 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 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: 543, 17 LR fn, tp: 0, 21 LR f1 score: 0.712 LR cohens kappa score: 0.698 LR average precision score: 0.958 -> 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: 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: 3, 18 GAN f1 score: 0.900 GAN cohens kappa score: 0.896 -> test with 'LR' LR tn, fp: 545, 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: 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: 558, 2 GAN fn, tp: 1, 20 GAN f1 score: 0.930 GAN cohens kappa score: 0.928 -> 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: 0.878 -> 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 5/5: 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: 1, 20 GAN f1 score: 0.889 GAN cohens kappa score: 0.884 -> 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: 0.893 -> 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: 548, 12 KNN fn, tp: 1, 20 KNN f1 score: 0.755 KNN cohens kappa score: 0.744 ------ 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: 555, 1 GAN fn, tp: 1, 20 GAN f1 score: 0.952 GAN cohens kappa score: 0.951 -> test with 'LR' LR tn, fp: 542, 14 LR fn, tp: 1, 20 LR f1 score: 0.727 LR cohens kappa score: 0.714 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: 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: 554, 24 LR fn, tp: 2, 21 LR f1 score: 0.826 LR cohens kappa score: 0.819 LR average precision score: 0.989 average: LR tn, fp: 542.88, 16.32 LR fn, tp: 0.76, 20.24 LR f1 score: 0.708 LR cohens kappa score: 0.694 LR average precision score: 0.902 minimum: LR tn, fp: 535, 6 LR fn, tp: 0, 19 LR f1 score: 0.615 LR cohens kappa score: 0.596 LR average precision score: 0.797 -----[ GB ]----- maximum: GB tn, fp: 560, 3 GB fn, tp: 5, 21 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 558.6, 0.6 GB fn, tp: 1.16, 19.84 GB f1 score: 0.957 GB cohens kappa score: 0.956 minimum: GB tn, fp: 555, 0 GB fn, tp: 0, 16 GB f1 score: 0.857 GB cohens kappa score: 0.852 -----[ KNN ]----- maximum: KNN tn, fp: 558, 12 KNN fn, tp: 3, 21 KNN f1 score: 0.955 KNN cohens kappa score: 0.953 average: KNN tn, fp: 552.52, 6.68 KNN fn, tp: 0.44, 20.56 KNN f1 score: 0.855 KNN cohens kappa score: 0.848 minimum: KNN tn, fp: 545, 2 KNN fn, tp: 0, 18 KNN f1 score: 0.750 KNN cohens kappa score: 0.739 -----[ GAN ]----- maximum: GAN tn, fp: 560, 7 GAN fn, tp: 3, 21 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 average: GAN tn, fp: 556.84, 2.36 GAN fn, tp: 0.84, 20.16 GAN f1 score: 0.928 GAN cohens kappa score: 0.925 minimum: GAN tn, fp: 549, 0 GAN fn, tp: 0, 18 GAN f1 score: 0.826 GAN cohens kappa score: 0.819