/////////////////////////////////////////// // Running ProWRAS on folding_kr-vs-k-three_vs_eleven /////////////////////////////////////////// Load 'data_input/folding_kr-vs-k-three_vs_eleven' 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 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 1, 16 LR f1 score: 0.970 LR cohens kappa score: 0.969 LR average precision score: 0.997 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 567, 4 KNN fn, tp: 0, 17 KNN f1 score: 0.895 KNN cohens kappa score: 0.891 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 1, 16 LR f1 score: 0.970 LR cohens kappa score: 0.969 LR average precision score: 0.997 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 568, 3 KNN fn, tp: 0, 17 KNN f1 score: 0.919 KNN cohens kappa score: 0.916 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 0, 17 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 570, 1 KNN fn, tp: 0, 17 KNN f1 score: 0.971 KNN cohens kappa score: 0.971 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 1, 16 LR f1 score: 0.970 LR cohens kappa score: 0.969 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 568, 3 KNN fn, tp: 0, 17 KNN f1 score: 0.919 KNN cohens kappa score: 0.916 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 566, 4 LR fn, tp: 1, 12 LR f1 score: 0.828 LR cohens kappa score: 0.823 LR average precision score: 0.911 -> test with 'GB' GB tn, fp: 568, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.927 -> test with 'KNN' KNN tn, fp: 569, 1 KNN fn, tp: 0, 13 KNN f1 score: 0.963 KNN cohens kappa score: 0.962 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 0, 17 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 1, 16 KNN f1 score: 0.970 KNN cohens kappa score: 0.969 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 1, 16 LR f1 score: 0.970 LR cohens kappa score: 0.969 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 0, 17 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 570, 1 LR fn, tp: 2, 15 LR f1 score: 0.909 LR cohens kappa score: 0.906 LR average precision score: 0.979 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 566, 5 KNN fn, tp: 0, 17 KNN f1 score: 0.872 KNN cohens kappa score: 0.867 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 0, 17 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 567, 4 KNN fn, tp: 1, 16 KNN f1 score: 0.865 KNN cohens kappa score: 0.861 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 570, 0 LR fn, tp: 0, 13 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 570, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 566, 4 KNN fn, tp: 0, 13 KNN f1 score: 0.867 KNN cohens kappa score: 0.863 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 570, 1 LR fn, tp: 0, 17 LR f1 score: 0.971 LR cohens kappa score: 0.971 LR average precision score: 0.997 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 568, 3 KNN fn, tp: 0, 17 KNN f1 score: 0.919 KNN cohens kappa score: 0.916 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 1, 16 LR f1 score: 0.970 LR cohens kappa score: 0.969 LR average precision score: 0.994 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 570, 1 KNN fn, tp: 0, 17 KNN f1 score: 0.971 KNN cohens kappa score: 0.971 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 0, 17 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 568, 3 KNN fn, tp: 0, 17 KNN f1 score: 0.919 KNN cohens kappa score: 0.916 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 0, 17 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 568, 3 KNN fn, tp: 0, 17 KNN f1 score: 0.919 KNN cohens kappa score: 0.916 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 569, 1 LR fn, tp: 2, 11 LR f1 score: 0.880 LR cohens kappa score: 0.877 LR average precision score: 0.984 -> test with 'GB' GB tn, fp: 570, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 569, 1 KNN fn, tp: 0, 13 KNN f1 score: 0.963 KNN cohens kappa score: 0.962 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 0, 17 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 565, 6 KNN fn, tp: 0, 17 KNN f1 score: 0.850 KNN cohens kappa score: 0.845 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 1, 16 LR f1 score: 0.970 LR cohens kappa score: 0.969 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 1, 16 KNN f1 score: 0.970 KNN cohens kappa score: 0.969 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 0, 17 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 568, 3 KNN fn, tp: 0, 17 KNN f1 score: 0.919 KNN cohens kappa score: 0.916 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 568, 3 LR fn, tp: 0, 17 LR f1 score: 0.919 LR cohens kappa score: 0.916 LR average precision score: 0.935 -> test with 'GB' GB tn, fp: 569, 2 GB fn, tp: 0, 17 GB f1 score: 0.944 GB cohens kappa score: 0.943 -> test with 'KNN' KNN tn, fp: 569, 2 KNN fn, tp: 0, 17 KNN f1 score: 0.944 KNN cohens kappa score: 0.943 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 570, 0 LR fn, tp: 1, 12 LR f1 score: 0.960 LR cohens kappa score: 0.959 LR average precision score: 0.982 -> test with 'GB' GB tn, fp: 570, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 568, 2 KNN fn, tp: 0, 13 KNN f1 score: 0.929 KNN cohens kappa score: 0.927 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 0, 17 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 569, 2 KNN fn, tp: 0, 17 KNN f1 score: 0.944 KNN cohens kappa score: 0.943 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 570, 1 LR fn, tp: 0, 17 LR f1 score: 0.971 LR cohens kappa score: 0.971 LR average precision score: 0.990 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 571, 0 KNN fn, tp: 0, 17 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 571, 0 LR fn, tp: 1, 16 LR f1 score: 0.970 LR cohens kappa score: 0.969 LR average precision score: 0.991 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 567, 4 KNN fn, tp: 0, 17 KNN f1 score: 0.895 KNN cohens kappa score: 0.891 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 570, 1 LR fn, tp: 2, 15 LR f1 score: 0.909 LR cohens kappa score: 0.906 LR average precision score: 0.990 -> test with 'GB' GB tn, fp: 571, 0 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 569, 2 KNN fn, tp: 0, 17 KNN f1 score: 0.944 KNN cohens kappa score: 0.943 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 570, 0 LR fn, tp: 0, 13 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 570, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 569, 1 KNN fn, tp: 0, 13 KNN f1 score: 0.963 KNN cohens kappa score: 0.962 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 571, 4 LR fn, tp: 2, 17 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 average: LR tn, fp: 570.32, 0.48 LR fn, tp: 0.6, 15.6 LR f1 score: 0.965 LR cohens kappa score: 0.964 LR average precision score: 0.990 minimum: LR tn, fp: 566, 0 LR fn, tp: 0, 11 LR f1 score: 0.828 LR cohens kappa score: 0.823 LR average precision score: 0.911 -----[ GB ]----- maximum: GB tn, fp: 571, 2 GB fn, tp: 0, 17 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 570.64, 0.16 GB fn, tp: 0.0, 16.2 GB f1 score: 0.995 GB cohens kappa score: 0.995 minimum: GB tn, fp: 568, 0 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.927 -----[ KNN ]----- maximum: KNN tn, fp: 571, 6 KNN fn, tp: 1, 17 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 average: KNN tn, fp: 568.48, 2.32 KNN fn, tp: 0.12, 16.08 KNN f1 score: 0.932 KNN cohens kappa score: 0.929 minimum: KNN tn, fp: 565, 0 KNN fn, tp: 0, 13 KNN f1 score: 0.850 KNN cohens kappa score: 0.845