/////////////////////////////////////////// // Running convGAN-majority-full 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 GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 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: 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 GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> test with 'LR' LR tn, fp: 570, 1 LR fn, tp: 1, 16 LR f1 score: 0.941 LR cohens kappa score: 0.939 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: 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 GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 565, 6 KNN fn, tp: 0, 17 KNN f1 score: 0.850 KNN cohens kappa score: 0.845 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with GAN.predict GAN tn, fp: 569, 1 GAN fn, tp: 0, 13 GAN f1 score: 0.963 GAN cohens kappa score: 0.962 -> 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 GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 0, 17 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 570, 1 KNN fn, tp: 0, 17 KNN f1 score: 0.971 KNN cohens kappa score: 0.971 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 567, 4 KNN fn, tp: 0, 17 KNN f1 score: 0.895 KNN cohens kappa score: 0.891 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 1, 16 KNN f1 score: 0.889 KNN cohens kappa score: 0.885 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with GAN.predict GAN tn, fp: 570, 0 GAN fn, tp: 0, 13 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> test with 'LR' LR tn, fp: 569, 1 LR fn, tp: 0, 13 LR f1 score: 0.963 LR cohens kappa score: 0.962 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: 564, 6 KNN fn, tp: 0, 13 KNN f1 score: 0.813 KNN cohens kappa score: 0.807 ====== 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 GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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 GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 564, 7 KNN fn, tp: 0, 17 KNN f1 score: 0.829 KNN cohens kappa score: 0.823 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 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: 0, 17 KNN f1 score: 0.895 KNN cohens kappa score: 0.891 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 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: 0, 17 KNN f1 score: 0.895 KNN cohens kappa score: 0.891 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with GAN.predict GAN tn, fp: 570, 0 GAN fn, tp: 1, 12 GAN f1 score: 0.960 GAN cohens kappa score: 0.959 -> 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: 0.973 -> 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 GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 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 GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 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 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 0, 17 KNN f1 score: 0.895 KNN cohens kappa score: 0.891 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with GAN.predict GAN tn, fp: 569, 2 GAN fn, tp: 0, 17 GAN f1 score: 0.944 GAN cohens kappa score: 0.943 -> 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 GAN.predict GAN tn, fp: 570, 0 GAN fn, tp: 0, 13 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> test with 'LR' LR tn, fp: 569, 1 LR fn, tp: 1, 12 LR f1 score: 0.923 LR cohens kappa score: 0.921 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 GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 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: 0, 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 -> create 2219 synthetic samples -> test with GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 570, 1 KNN fn, tp: 0, 17 KNN f1 score: 0.971 KNN cohens kappa score: 0.971 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 566, 5 KNN fn, tp: 0, 17 KNN f1 score: 0.872 KNN cohens kappa score: 0.867 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with GAN.predict GAN tn, fp: 571, 0 GAN fn, tp: 0, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> test with 'LR' LR tn, fp: 570, 1 LR fn, tp: 1, 16 LR f1 score: 0.941 LR cohens kappa score: 0.939 LR average precision score: 0.993 -> 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 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with GAN.predict GAN tn, fp: 570, 0 GAN fn, tp: 0, 13 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 -> 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: 568, 2 KNN fn, tp: 0, 13 KNN f1 score: 0.929 KNN cohens kappa score: 0.927 ### 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: 569.84, 0.96 LR fn, tp: 0.44, 15.76 LR f1 score: 0.956 LR cohens kappa score: 0.955 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, 7 KNN fn, tp: 1, 17 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 average: KNN tn, fp: 567.52, 3.28 KNN fn, tp: 0.04, 16.16 KNN f1 score: 0.909 KNN cohens kappa score: 0.907 minimum: KNN tn, fp: 564, 0 KNN fn, tp: 0, 13 KNN f1 score: 0.813 KNN cohens kappa score: 0.807 -----[ GAN ]----- maximum: GAN tn, fp: 571, 2 GAN fn, tp: 1, 17 GAN f1 score: 1.000 GAN cohens kappa score: 1.000 average: GAN tn, fp: 570.68, 0.12 GAN fn, tp: 0.04, 16.16 GAN f1 score: 0.995 GAN cohens kappa score: 0.995 minimum: GAN tn, fp: 569, 0 GAN fn, tp: 0, 12 GAN f1 score: 0.944 GAN cohens kappa score: 0.943