/////////////////////////////////////////// // Running ctGAN 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: 547, 24 LR fn, tp: 0, 17 LR f1 score: 0.586 LR cohens kappa score: 0.569 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 558, 13 GB fn, tp: 0, 17 GB f1 score: 0.723 GB cohens kappa score: 0.713 -> test with 'KNN' KNN tn, fp: 551, 20 KNN fn, tp: 0, 17 KNN f1 score: 0.630 KNN cohens kappa score: 0.614 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 554, 17 LR fn, tp: 0, 17 LR f1 score: 0.667 LR cohens kappa score: 0.653 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 564, 7 GB fn, tp: 0, 17 GB f1 score: 0.829 GB cohens kappa score: 0.823 -> test with 'KNN' KNN tn, fp: 557, 14 KNN fn, tp: 0, 17 KNN f1 score: 0.708 KNN cohens kappa score: 0.697 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 545, 26 LR fn, tp: 0, 17 LR f1 score: 0.567 LR cohens kappa score: 0.548 LR average precision score: 0.997 -> test with 'GB' GB tn, fp: 561, 10 GB fn, tp: 0, 17 GB f1 score: 0.773 GB cohens kappa score: 0.764 -> test with 'KNN' KNN tn, fp: 556, 15 KNN fn, tp: 0, 17 KNN f1 score: 0.694 KNN cohens kappa score: 0.682 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 532, 39 LR fn, tp: 0, 17 LR f1 score: 0.466 LR cohens kappa score: 0.441 LR average precision score: 0.987 -> test with 'GB' GB tn, fp: 557, 14 GB fn, tp: 0, 17 GB f1 score: 0.708 GB cohens kappa score: 0.697 -> test with 'KNN' KNN tn, fp: 546, 25 KNN fn, tp: 0, 17 KNN f1 score: 0.576 KNN cohens kappa score: 0.558 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 552, 18 LR fn, tp: 0, 13 LR f1 score: 0.591 LR cohens kappa score: 0.578 LR average precision score: 0.982 -> test with 'GB' GB tn, fp: 558, 12 GB fn, tp: 0, 13 GB f1 score: 0.684 GB cohens kappa score: 0.675 -> test with 'KNN' KNN tn, fp: 554, 16 KNN fn, tp: 0, 13 KNN f1 score: 0.619 KNN cohens kappa score: 0.607 ====== 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: 540, 31 LR fn, tp: 0, 17 LR f1 score: 0.523 LR cohens kappa score: 0.502 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 561, 10 GB fn, tp: 0, 17 GB f1 score: 0.773 GB cohens kappa score: 0.764 -> test with 'KNN' KNN tn, fp: 556, 15 KNN fn, tp: 0, 17 KNN f1 score: 0.694 KNN cohens kappa score: 0.682 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 548, 23 LR fn, tp: 0, 17 LR f1 score: 0.596 LR cohens kappa score: 0.579 LR average precision score: 0.991 -> test with 'GB' GB tn, fp: 560, 11 GB fn, tp: 0, 17 GB f1 score: 0.756 GB cohens kappa score: 0.746 -> test with 'KNN' KNN tn, fp: 550, 21 KNN fn, tp: 0, 17 KNN f1 score: 0.618 KNN cohens kappa score: 0.602 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 552, 19 LR fn, tp: 0, 17 LR f1 score: 0.642 LR cohens kappa score: 0.627 LR average precision score: 0.977 -> test with 'GB' GB tn, fp: 557, 14 GB fn, tp: 0, 17 GB f1 score: 0.708 GB cohens kappa score: 0.697 -> test with 'KNN' KNN tn, fp: 550, 21 KNN fn, tp: 0, 17 KNN f1 score: 0.618 KNN cohens kappa score: 0.602 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 552, 19 LR fn, tp: 0, 17 LR f1 score: 0.642 LR cohens kappa score: 0.627 LR average precision score: 0.989 -> test with 'GB' GB tn, fp: 561, 10 GB fn, tp: 0, 17 GB f1 score: 0.773 GB cohens kappa score: 0.764 -> test with 'KNN' KNN tn, fp: 558, 13 KNN fn, tp: 0, 17 KNN f1 score: 0.723 KNN cohens kappa score: 0.713 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 547, 23 LR fn, tp: 0, 13 LR f1 score: 0.531 LR cohens kappa score: 0.515 LR average precision score: 0.990 -> test with 'GB' GB tn, fp: 559, 11 GB fn, tp: 0, 13 GB f1 score: 0.703 GB cohens kappa score: 0.694 -> test with 'KNN' KNN tn, fp: 548, 22 KNN fn, tp: 0, 13 KNN f1 score: 0.542 KNN cohens kappa score: 0.526 ====== 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: 551, 20 LR fn, tp: 0, 17 LR f1 score: 0.630 LR cohens kappa score: 0.614 LR average precision score: 0.994 -> test with 'GB' GB tn, fp: 561, 10 GB fn, tp: 0, 17 GB f1 score: 0.773 GB cohens kappa score: 0.764 -> test with 'KNN' KNN tn, fp: 553, 18 KNN fn, tp: 0, 17 KNN f1 score: 0.654 KNN cohens kappa score: 0.640 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 548, 23 LR fn, tp: 0, 17 LR f1 score: 0.596 LR cohens kappa score: 0.579 LR average precision score: 0.994 -> test with 'GB' GB tn, fp: 562, 9 GB fn, tp: 0, 17 GB f1 score: 0.791 GB cohens kappa score: 0.783 -> test with 'KNN' KNN tn, fp: 552, 19 KNN fn, tp: 0, 17 KNN f1 score: 0.642 KNN cohens kappa score: 0.627 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 543, 28 LR fn, tp: 0, 17 LR f1 score: 0.548 LR cohens kappa score: 0.529 LR average precision score: 0.997 -> test with 'GB' GB tn, fp: 559, 12 GB fn, tp: 0, 17 GB f1 score: 0.739 GB cohens kappa score: 0.729 -> test with 'KNN' KNN tn, fp: 554, 17 KNN fn, tp: 0, 17 KNN f1 score: 0.667 KNN cohens kappa score: 0.653 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 550, 21 LR fn, tp: 0, 17 LR f1 score: 0.618 LR cohens kappa score: 0.602 LR average precision score: 0.997 -> test with 'GB' GB tn, fp: 560, 11 GB fn, tp: 0, 17 GB f1 score: 0.756 GB cohens kappa score: 0.746 -> test with 'KNN' KNN tn, fp: 551, 20 KNN fn, tp: 0, 17 KNN f1 score: 0.630 KNN cohens kappa score: 0.614 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 540, 30 LR fn, tp: 0, 13 LR f1 score: 0.464 LR cohens kappa score: 0.445 LR average precision score: 0.990 -> test with 'GB' GB tn, fp: 558, 12 GB fn, tp: 0, 13 GB f1 score: 0.684 GB cohens kappa score: 0.675 -> test with 'KNN' KNN tn, fp: 547, 23 KNN fn, tp: 0, 13 KNN f1 score: 0.531 KNN cohens kappa score: 0.515 ====== 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: 536, 35 LR fn, tp: 0, 17 LR f1 score: 0.493 LR cohens kappa score: 0.470 LR average precision score: 0.997 -> test with 'GB' GB tn, fp: 558, 13 GB fn, tp: 0, 17 GB f1 score: 0.723 GB cohens kappa score: 0.713 -> test with 'KNN' KNN tn, fp: 549, 22 KNN fn, tp: 0, 17 KNN f1 score: 0.607 KNN cohens kappa score: 0.591 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 538, 33 LR fn, tp: 0, 17 LR f1 score: 0.507 LR cohens kappa score: 0.485 LR average precision score: 0.997 -> test with 'GB' GB tn, fp: 558, 13 GB fn, tp: 0, 17 GB f1 score: 0.723 GB cohens kappa score: 0.713 -> test with 'KNN' KNN tn, fp: 550, 21 KNN fn, tp: 0, 17 KNN f1 score: 0.618 KNN cohens kappa score: 0.602 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 547, 24 LR fn, tp: 0, 17 LR f1 score: 0.586 LR cohens kappa score: 0.569 LR average precision score: 0.989 -> test with 'GB' GB tn, fp: 558, 13 GB fn, tp: 0, 17 GB f1 score: 0.723 GB cohens kappa score: 0.713 -> test with 'KNN' KNN tn, fp: 552, 19 KNN fn, tp: 0, 17 KNN f1 score: 0.642 KNN cohens kappa score: 0.627 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 553, 18 LR fn, tp: 0, 17 LR f1 score: 0.654 LR cohens kappa score: 0.640 LR average precision score: 0.991 -> test with 'GB' GB tn, fp: 562, 9 GB fn, tp: 0, 17 GB f1 score: 0.791 GB cohens kappa score: 0.783 -> test with 'KNN' KNN tn, fp: 555, 16 KNN fn, tp: 0, 17 KNN f1 score: 0.680 KNN cohens kappa score: 0.667 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 555, 15 LR fn, tp: 0, 13 LR f1 score: 0.634 LR cohens kappa score: 0.623 LR average precision score: 0.995 -> test with 'GB' GB tn, fp: 565, 5 GB fn, tp: 0, 13 GB f1 score: 0.839 GB cohens kappa score: 0.834 -> test with 'KNN' KNN tn, fp: 559, 11 KNN fn, tp: 0, 13 KNN f1 score: 0.703 KNN cohens kappa score: 0.694 ====== 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: 544, 27 LR fn, tp: 0, 17 LR f1 score: 0.557 LR cohens kappa score: 0.538 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 562, 9 GB fn, tp: 0, 17 GB f1 score: 0.791 GB cohens kappa score: 0.783 -> test with 'KNN' KNN tn, fp: 551, 20 KNN fn, tp: 0, 17 KNN f1 score: 0.630 KNN cohens kappa score: 0.614 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 540, 31 LR fn, tp: 0, 17 LR f1 score: 0.523 LR cohens kappa score: 0.502 LR average precision score: 0.973 -> test with 'GB' GB tn, fp: 557, 14 GB fn, tp: 0, 17 GB f1 score: 0.708 GB cohens kappa score: 0.697 -> test with 'KNN' KNN tn, fp: 545, 26 KNN fn, tp: 0, 17 KNN f1 score: 0.567 KNN cohens kappa score: 0.548 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 551, 20 LR fn, tp: 0, 17 LR f1 score: 0.630 LR cohens kappa score: 0.614 LR average precision score: 0.991 -> test with 'GB' GB tn, fp: 559, 12 GB fn, tp: 0, 17 GB f1 score: 0.739 GB cohens kappa score: 0.729 -> test with 'KNN' KNN tn, fp: 555, 16 KNN fn, tp: 0, 17 KNN f1 score: 0.680 KNN cohens kappa score: 0.667 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 540, 31 LR fn, tp: 0, 17 LR f1 score: 0.523 LR cohens kappa score: 0.502 LR average precision score: 0.987 -> test with 'GB' GB tn, fp: 559, 12 GB fn, tp: 0, 17 GB f1 score: 0.739 GB cohens kappa score: 0.729 -> test with 'KNN' KNN tn, fp: 548, 23 KNN fn, tp: 0, 17 KNN f1 score: 0.596 KNN cohens kappa score: 0.579 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 553, 17 LR fn, tp: 0, 13 LR f1 score: 0.605 LR cohens kappa score: 0.592 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 561, 9 GB fn, tp: 0, 13 GB f1 score: 0.743 GB cohens kappa score: 0.735 -> test with 'KNN' KNN tn, fp: 558, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.675 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 555, 39 LR fn, tp: 0, 17 LR f1 score: 0.667 LR cohens kappa score: 0.653 LR average precision score: 1.000 average: LR tn, fp: 546.32, 24.48 LR fn, tp: 0.0, 16.2 LR f1 score: 0.575 LR cohens kappa score: 0.558 LR average precision score: 0.992 minimum: LR tn, fp: 532, 15 LR fn, tp: 0, 13 LR f1 score: 0.464 LR cohens kappa score: 0.441 LR average precision score: 0.973 -----[ GB ]----- maximum: GB tn, fp: 565, 14 GB fn, tp: 0, 17 GB f1 score: 0.839 GB cohens kappa score: 0.834 average: GB tn, fp: 559.8, 11.0 GB fn, tp: 0.0, 16.2 GB f1 score: 0.748 GB cohens kappa score: 0.739 minimum: GB tn, fp: 557, 5 GB fn, tp: 0, 13 GB f1 score: 0.684 GB cohens kappa score: 0.675 -----[ KNN ]----- maximum: KNN tn, fp: 559, 26 KNN fn, tp: 0, 17 KNN f1 score: 0.723 KNN cohens kappa score: 0.713 average: KNN tn, fp: 552.2, 18.6 KNN fn, tp: 0.0, 16.2 KNN f1 score: 0.638 KNN cohens kappa score: 0.624 minimum: KNN tn, fp: 545, 11 KNN fn, tp: 0, 13 KNN f1 score: 0.531 KNN cohens kappa score: 0.515