/////////////////////////////////////////// // Running SimpleGAN 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> 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: 0.793 -> 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: 559, 1 KNN fn, tp: 7, 14 KNN f1 score: 0.778 KNN cohens kappa score: 0.771 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 557, 3 LR fn, tp: 5, 16 LR f1 score: 0.800 LR cohens kappa score: 0.793 LR average precision score: 0.916 -> 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: 4, 17 KNN f1 score: 0.872 KNN cohens kappa score: 0.867 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 545, 15 LR fn, tp: 7, 14 LR f1 score: 0.560 LR cohens kappa score: 0.541 LR average precision score: 0.678 -> 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: 3, 18 KNN f1 score: 0.878 KNN cohens kappa score: 0.874 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 555, 5 LR fn, tp: 3, 18 LR f1 score: 0.818 LR cohens kappa score: 0.811 LR average precision score: 0.888 -> 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: 560, 0 KNN fn, tp: 5, 16 KNN f1 score: 0.865 KNN cohens kappa score: 0.861 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 553, 3 LR fn, tp: 1, 20 LR f1 score: 0.909 LR cohens kappa score: 0.905 LR average precision score: 0.988 -> 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: 4, 17 KNN f1 score: 0.895 KNN cohens kappa score: 0.891 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 547, 13 LR fn, tp: 4, 17 LR f1 score: 0.667 LR cohens kappa score: 0.652 LR average precision score: 0.809 -> 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: 560, 0 KNN fn, tp: 3, 18 KNN f1 score: 0.923 KNN cohens kappa score: 0.920 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 559, 1 LR fn, tp: 4, 17 LR f1 score: 0.872 LR cohens kappa score: 0.867 LR average precision score: 0.920 -> 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: 560, 0 KNN fn, tp: 2, 19 KNN f1 score: 0.950 KNN cohens kappa score: 0.948 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 548, 12 LR fn, tp: 5, 16 LR f1 score: 0.653 LR cohens kappa score: 0.638 LR average precision score: 0.808 -> 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: 5, 16 KNN f1 score: 0.842 KNN cohens kappa score: 0.837 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 539, 21 LR fn, tp: 2, 19 LR f1 score: 0.623 LR cohens kappa score: 0.604 LR average precision score: 0.851 -> 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: 556, 4 KNN fn, tp: 6, 15 KNN f1 score: 0.750 KNN cohens kappa score: 0.741 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 551, 5 LR fn, tp: 2, 19 LR f1 score: 0.844 LR cohens kappa score: 0.838 LR average precision score: 0.927 -> 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: 5, 16 KNN f1 score: 0.865 KNN cohens kappa score: 0.860 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> 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: 0.935 -> 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: 2, 19 KNN f1 score: 0.905 KNN cohens kappa score: 0.901 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 557, 3 LR fn, tp: 5, 16 LR f1 score: 0.800 LR cohens kappa score: 0.793 LR average precision score: 0.898 -> 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: 6, 15 KNN f1 score: 0.789 KNN cohens kappa score: 0.782 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 554, 6 LR fn, tp: 6, 15 LR f1 score: 0.714 LR cohens kappa score: 0.704 LR average precision score: 0.854 -> 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: 560, 0 KNN fn, tp: 6, 15 KNN f1 score: 0.833 KNN cohens kappa score: 0.828 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 552, 8 LR fn, tp: 2, 19 LR f1 score: 0.792 LR cohens kappa score: 0.783 LR average precision score: 0.911 -> 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: 560, 0 KNN fn, tp: 3, 18 KNN f1 score: 0.923 KNN cohens kappa score: 0.920 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 544, 12 LR fn, tp: 5, 16 LR f1 score: 0.653 LR cohens kappa score: 0.638 LR average precision score: 0.786 -> 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: 3, 18 KNN f1 score: 0.923 KNN cohens kappa score: 0.920 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> 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: 0.864 -> 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: 2, 19 KNN f1 score: 0.905 KNN cohens kappa score: 0.901 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 554, 6 LR fn, tp: 1, 20 LR f1 score: 0.851 LR cohens kappa score: 0.845 LR average precision score: 0.975 -> 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: 560, 0 KNN fn, tp: 3, 18 KNN f1 score: 0.923 KNN cohens kappa score: 0.920 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 554, 6 LR fn, tp: 7, 14 LR f1 score: 0.683 LR cohens kappa score: 0.671 LR average precision score: 0.806 -> 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: 8, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.714 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 552, 8 LR fn, tp: 2, 19 LR f1 score: 0.792 LR cohens kappa score: 0.783 LR average precision score: 0.887 -> 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: 3, 18 KNN f1 score: 0.900 KNN cohens kappa score: 0.896 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 543, 13 LR fn, tp: 3, 18 LR f1 score: 0.692 LR cohens kappa score: 0.678 LR average precision score: 0.823 -> 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: 5, 16 KNN f1 score: 0.865 KNN cohens kappa score: 0.860 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 551, 9 LR fn, tp: 4, 17 LR f1 score: 0.723 LR cohens kappa score: 0.712 LR average precision score: 0.887 -> 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: 560, 0 KNN fn, tp: 4, 17 KNN f1 score: 0.895 KNN cohens kappa score: 0.891 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> 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: 0.880 -> 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: 559, 1 KNN fn, tp: 5, 16 KNN f1 score: 0.842 KNN cohens kappa score: 0.837 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 545, 15 LR fn, tp: 5, 16 LR f1 score: 0.615 LR cohens kappa score: 0.598 LR average precision score: 0.702 -> 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: 4, 17 KNN f1 score: 0.850 KNN cohens kappa score: 0.845 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 554, 6 LR fn, tp: 4, 17 LR f1 score: 0.773 LR cohens kappa score: 0.764 LR average precision score: 0.854 -> 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: 5, 16 KNN f1 score: 0.842 KNN cohens kappa score: 0.837 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> 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: 0.936 -> 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: 555, 1 KNN fn, tp: 2, 19 KNN f1 score: 0.927 KNN cohens kappa score: 0.924 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 559, 21 LR fn, tp: 7, 20 LR f1 score: 0.909 LR cohens kappa score: 0.905 LR average precision score: 0.988 average: LR tn, fp: 551.32, 7.88 LR fn, tp: 3.6, 17.4 LR f1 score: 0.758 LR cohens kappa score: 0.748 LR average precision score: 0.863 minimum: LR tn, fp: 539, 1 LR fn, tp: 1, 14 LR f1 score: 0.560 LR cohens kappa score: 0.541 LR average precision score: 0.678 -----[ GB ]----- maximum: GB tn, fp: 560, 1 GB fn, tp: 4, 21 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 559.08, 0.12 GB fn, tp: 1.0, 20.0 GB f1 score: 0.972 GB cohens kappa score: 0.971 minimum: GB tn, fp: 556, 0 GB fn, tp: 0, 17 GB f1 score: 0.895 GB cohens kappa score: 0.891 -----[ KNN ]----- maximum: KNN tn, fp: 560, 4 KNN fn, tp: 8, 19 KNN f1 score: 0.950 KNN cohens kappa score: 0.948 average: KNN tn, fp: 558.28, 0.92 KNN fn, tp: 4.2, 16.8 KNN f1 score: 0.866 KNN cohens kappa score: 0.862 minimum: KNN tn, fp: 555, 0 KNN fn, tp: 2, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.714