/////////////////////////////////////////// // Running SpheredNoise on folding_shuttle-2_vs_5 /////////////////////////////////////////// Load 'data_input/folding_shuttle-2_vs_5' 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 Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:106.4377752492037 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 652, 2 LR fn, tp: 0, 10 LR f1 score: 0.909 LR cohens kappa score: 0.908 LR average precision score: 0.833 -> test with 'GB' GB tn, fp: 653, 1 GB fn, tp: 0, 10 GB f1 score: 0.952 GB cohens kappa score: 0.952 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 0, 10 KNN f1 score: 0.952 KNN cohens kappa score: 0.952 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:18.083141320025124 max:106.4377752492037 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 2, 8 KNN f1 score: 0.842 KNN cohens kappa score: 0.840 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:106.4377752492037 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 1, 9 KNN f1 score: 0.947 KNN cohens kappa score: 0.947 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:90.82400563727631 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 0, 10 KNN f1 score: 0.952 KNN cohens kappa score: 0.952 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2616/40 points -> new disc -> calc distances -> statistics trained 40 points min:16.186414056238647 max:109.5810202544218 -> create 2576 synthetic samples -> test with 'LR' LR tn, fp: 651, 0 LR fn, tp: 0, 9 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 651, 0 GB fn, tp: 0, 9 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 651, 0 KNN fn, tp: 0, 9 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:106.4377752492037 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 2, 8 KNN f1 score: 0.889 KNN cohens kappa score: 0.887 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:106.4377752492037 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 652, 2 GB fn, tp: 0, 10 GB f1 score: 0.909 GB cohens kappa score: 0.908 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 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 Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:23.173260452512935 max:90.64215354899729 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 653, 1 GB fn, tp: 0, 10 GB f1 score: 0.952 GB cohens kappa score: 0.952 -> test with 'KNN' KNN tn, fp: 652, 2 KNN fn, tp: 2, 8 KNN f1 score: 0.800 KNN cohens kappa score: 0.797 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:106.4377752492037 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2616/40 points -> new disc -> calc distances -> statistics trained 40 points min:15.652475842498529 max:109.5810202544218 -> create 2576 synthetic samples -> test with 'LR' LR tn, fp: 651, 0 LR fn, tp: 0, 9 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 651, 0 GB fn, tp: 0, 9 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 650, 1 KNN fn, tp: 0, 9 KNN f1 score: 0.947 KNN cohens kappa score: 0.947 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:16.186414056238647 max:109.5810202544218 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:106.4377752492037 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 653, 1 GB fn, tp: 0, 10 GB f1 score: 0.952 GB cohens kappa score: 0.952 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 1, 9 KNN f1 score: 0.900 KNN cohens kappa score: 0.898 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:106.4377752492037 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:90.82400563727631 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 2, 8 KNN f1 score: 0.842 KNN cohens kappa score: 0.840 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2616/40 points -> new disc -> calc distances -> statistics trained 40 points min:18.083141320025124 max:106.4377752492037 -> create 2576 synthetic samples -> test with 'LR' LR tn, fp: 650, 1 LR fn, tp: 0, 9 LR f1 score: 0.947 LR cohens kappa score: 0.947 LR average precision score: 0.900 -> test with 'GB' GB tn, fp: 650, 1 GB fn, tp: 0, 9 GB f1 score: 0.947 GB cohens kappa score: 0.947 -> test with 'KNN' KNN tn, fp: 650, 1 KNN fn, tp: 1, 8 KNN f1 score: 0.889 KNN cohens kappa score: 0.887 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:106.4377752492037 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 653, 1 LR fn, tp: 0, 10 LR f1 score: 0.952 LR cohens kappa score: 0.952 LR average precision score: 0.882 -> test with 'GB' GB tn, fp: 653, 1 GB fn, tp: 0, 10 GB f1 score: 0.952 GB cohens kappa score: 0.952 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:109.5810202544218 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 0, 10 KNN f1 score: 0.952 KNN cohens kappa score: 0.952 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:91.0 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 1, 9 KNN f1 score: 0.947 KNN cohens kappa score: 0.947 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:23.366642891095847 max:106.4377752492037 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 653, 1 LR fn, tp: 0, 10 LR f1 score: 0.952 LR cohens kappa score: 0.952 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 2, 8 KNN f1 score: 0.842 KNN cohens kappa score: 0.840 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2616/40 points -> new disc -> calc distances -> statistics trained 40 points min:16.186414056238647 max:106.4377752492037 -> create 2576 synthetic samples -> test with 'LR' LR tn, fp: 649, 2 LR fn, tp: 0, 9 LR f1 score: 0.900 LR cohens kappa score: 0.898 LR average precision score: 0.989 -> test with 'GB' GB tn, fp: 651, 0 GB fn, tp: 0, 9 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 650, 1 KNN fn, tp: 0, 9 KNN f1 score: 0.947 KNN cohens kappa score: 0.947 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:106.4377752492037 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 0, 10 KNN f1 score: 0.952 KNN cohens kappa score: 0.952 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:90.82400563727631 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 1, 9 KNN f1 score: 0.947 KNN cohens kappa score: 0.947 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:23.173260452512935 max:109.5810202544218 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 653, 1 LR fn, tp: 0, 10 LR f1 score: 0.952 LR cohens kappa score: 0.952 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 652, 2 KNN fn, tp: 2, 8 KNN f1 score: 0.800 KNN cohens kappa score: 0.797 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2613/39 points -> new disc -> calc distances -> statistics trained 39 points min:15.652475842498529 max:106.4377752492037 -> create 2574 synthetic samples -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2616/40 points -> new disc -> calc distances -> statistics trained 40 points min:16.186414056238647 max:106.4377752492037 -> create 2576 synthetic samples -> test with 'LR' LR tn, fp: 650, 1 LR fn, tp: 0, 9 LR f1 score: 0.947 LR cohens kappa score: 0.947 LR average precision score: 0.879 -> test with 'GB' GB tn, fp: 650, 1 GB fn, tp: 0, 9 GB f1 score: 0.947 GB cohens kappa score: 0.947 -> test with 'KNN' KNN tn, fp: 650, 1 KNN fn, tp: 1, 8 KNN f1 score: 0.889 KNN cohens kappa score: 0.887 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 654, 2 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 average: LR tn, fp: 653.04, 0.36 LR fn, tp: 0.0, 9.8 LR f1 score: 0.982 LR cohens kappa score: 0.982 LR average precision score: 0.979 minimum: LR tn, fp: 649, 0 LR fn, tp: 0, 9 LR f1 score: 0.900 LR cohens kappa score: 0.898 LR average precision score: 0.833 -----[ GB ]----- maximum: GB tn, fp: 654, 2 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 653.08, 0.32 GB fn, tp: 0.0, 9.8 GB f1 score: 0.985 GB cohens kappa score: 0.984 minimum: GB tn, fp: 650, 0 GB fn, tp: 0, 9 GB f1 score: 0.909 GB cohens kappa score: 0.908 -----[ KNN ]----- maximum: KNN tn, fp: 654, 2 KNN fn, tp: 2, 10 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 average: KNN tn, fp: 652.76, 0.64 KNN fn, tp: 0.72, 9.08 KNN f1 score: 0.930 KNN cohens kappa score: 0.929 minimum: KNN tn, fp: 650, 0 KNN fn, tp: 0, 8 KNN f1 score: 0.800 KNN cohens kappa score: 0.797