/////////////////////////////////////////// // 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 'RF' RF tn, fp: 559, 12 RF fn, tp: 0, 17 RF f1 score: 0.739 RF cohens kappa score: 0.729 -> 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: 553, 18 LR fn, tp: 0, 17 LR f1 score: 0.654 LR cohens kappa score: 0.640 LR average precision score: 0.993 -> test with 'RF' RF tn, fp: 566, 5 RF fn, tp: 0, 17 RF f1 score: 0.872 RF cohens kappa score: 0.867 -> 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: 555, 16 KNN fn, tp: 0, 17 KNN f1 score: 0.680 KNN cohens kappa score: 0.667 ------ 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 'RF' RF tn, fp: 562, 9 RF fn, tp: 0, 17 RF f1 score: 0.791 RF cohens kappa score: 0.783 -> 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: 555, 16 KNN fn, tp: 0, 17 KNN f1 score: 0.680 KNN cohens kappa score: 0.667 ------ 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: 0.994 -> test with 'RF' RF tn, fp: 558, 13 RF fn, tp: 0, 17 RF f1 score: 0.723 RF cohens kappa score: 0.713 -> 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: 547, 24 KNN fn, tp: 0, 17 KNN f1 score: 0.586 KNN cohens kappa score: 0.569 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 551, 19 LR fn, tp: 0, 13 LR f1 score: 0.578 LR cohens kappa score: 0.564 LR average precision score: 0.995 -> test with 'RF' RF tn, fp: 563, 7 RF fn, tp: 0, 13 RF f1 score: 0.788 RF cohens kappa score: 0.782 -> 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: 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 'RF' RF tn, fp: 564, 7 RF fn, tp: 0, 17 RF f1 score: 0.829 RF cohens kappa score: 0.823 -> 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: 545, 26 LR fn, tp: 0, 17 LR f1 score: 0.567 LR cohens kappa score: 0.548 LR average precision score: 0.994 -> test with 'RF' RF tn, fp: 567, 4 RF fn, tp: 0, 17 RF f1 score: 0.895 RF cohens kappa score: 0.891 -> 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: 554, 17 KNN fn, tp: 0, 17 KNN f1 score: 0.667 KNN cohens kappa score: 0.653 ------ Step 2/5: Slice 3/5 ------- -> 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: 0.969 -> test with 'RF' RF tn, fp: 558, 13 RF fn, tp: 0, 17 RF f1 score: 0.723 RF cohens kappa score: 0.713 -> 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: 552, 19 KNN fn, tp: 0, 17 KNN f1 score: 0.642 KNN cohens kappa score: 0.627 ------ 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: 0.991 -> test with 'RF' RF tn, fp: 563, 8 RF fn, tp: 0, 17 RF f1 score: 0.810 RF cohens kappa score: 0.803 -> 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: 557, 14 KNN fn, tp: 0, 17 KNN f1 score: 0.708 KNN cohens kappa score: 0.697 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 536, 34 LR fn, tp: 0, 13 LR f1 score: 0.433 LR cohens kappa score: 0.413 LR average precision score: 0.990 -> test with 'RF' RF tn, fp: 560, 10 RF fn, tp: 0, 13 RF f1 score: 0.722 RF cohens kappa score: 0.714 -> 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: 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 'RF' RF tn, fp: 565, 6 RF fn, tp: 0, 17 RF f1 score: 0.850 RF cohens kappa score: 0.845 -> 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: 555, 16 KNN fn, tp: 0, 17 KNN f1 score: 0.680 KNN cohens kappa score: 0.667 ------ 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: 0.997 -> test with 'RF' RF tn, fp: 564, 7 RF fn, tp: 0, 17 RF f1 score: 0.829 RF cohens kappa score: 0.823 -> 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 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 542, 29 LR fn, tp: 0, 17 LR f1 score: 0.540 LR cohens kappa score: 0.519 LR average precision score: 0.991 -> test with 'RF' RF tn, fp: 561, 10 RF fn, tp: 0, 17 RF f1 score: 0.773 RF cohens kappa score: 0.764 -> 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: 553, 18 KNN fn, tp: 0, 17 KNN f1 score: 0.654 KNN cohens kappa score: 0.640 ------ Step 3/5: Slice 4/5 ------- -> 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: 0.997 -> test with 'RF' RF tn, fp: 566, 5 RF fn, tp: 0, 17 RF f1 score: 0.872 RF cohens kappa score: 0.867 -> 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: 555, 16 KNN fn, tp: 0, 17 KNN f1 score: 0.680 KNN cohens kappa score: 0.667 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 543, 27 LR fn, tp: 0, 13 LR f1 score: 0.491 LR cohens kappa score: 0.473 LR average precision score: 0.990 -> test with 'RF' RF tn, fp: 562, 8 RF fn, tp: 0, 13 RF f1 score: 0.765 RF cohens kappa score: 0.758 -> test with 'GB' GB tn, fp: 560, 10 GB fn, tp: 0, 13 GB f1 score: 0.722 GB cohens kappa score: 0.714 -> test with 'KNN' KNN tn, fp: 549, 21 KNN fn, tp: 0, 13 KNN f1 score: 0.553 KNN cohens kappa score: 0.538 ====== 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: 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 'RF' RF tn, fp: 558, 13 RF fn, tp: 0, 17 RF f1 score: 0.723 RF cohens kappa score: 0.713 -> 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: 548, 23 KNN fn, tp: 0, 17 KNN f1 score: 0.596 KNN cohens kappa score: 0.579 ------ Step 4/5: 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: 1.000 -> test with 'RF' RF tn, fp: 559, 12 RF fn, tp: 0, 17 RF f1 score: 0.739 RF cohens kappa score: 0.729 -> 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 3/5 ------- -> 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: 0.985 -> test with 'RF' RF tn, fp: 559, 12 RF fn, tp: 0, 17 RF f1 score: 0.739 RF cohens kappa score: 0.729 -> 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 4/5 ------- -> 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: 0.988 -> test with 'RF' RF tn, fp: 566, 5 RF fn, tp: 0, 17 RF f1 score: 0.872 RF cohens kappa score: 0.867 -> 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: 553, 18 KNN fn, tp: 0, 17 KNN f1 score: 0.654 KNN cohens kappa score: 0.640 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 554, 16 LR fn, tp: 0, 13 LR f1 score: 0.619 LR cohens kappa score: 0.607 LR average precision score: 0.990 -> test with 'RF' RF tn, fp: 562, 8 RF fn, tp: 0, 13 RF f1 score: 0.765 RF cohens kappa score: 0.758 -> test with 'GB' GB tn, fp: 564, 6 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.807 -> test with 'KNN' KNN tn, fp: 558, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.675 ====== 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 'RF' RF tn, fp: 563, 8 RF fn, tp: 0, 17 RF f1 score: 0.810 RF cohens kappa score: 0.803 -> 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 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2219 synthetic samples -> test with 'LR' LR tn, fp: 549, 22 LR fn, tp: 0, 17 LR f1 score: 0.607 LR cohens kappa score: 0.591 LR average precision score: 0.973 -> test with 'RF' RF tn, fp: 563, 8 RF fn, tp: 0, 17 RF f1 score: 0.810 RF cohens kappa score: 0.803 -> 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 5/5: Slice 3/5 ------- -> 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: 1.000 -> test with 'RF' RF tn, fp: 561, 10 RF fn, tp: 0, 17 RF f1 score: 0.773 RF cohens kappa score: 0.764 -> 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: 553, 18 KNN fn, tp: 0, 17 KNN f1 score: 0.654 KNN cohens kappa score: 0.640 ------ 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: 0.993 -> test with 'RF' RF tn, fp: 561, 10 RF fn, tp: 0, 17 RF f1 score: 0.773 RF cohens kappa score: 0.764 -> 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: 550, 21 KNN fn, tp: 0, 17 KNN f1 score: 0.618 KNN cohens kappa score: 0.602 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2216 synthetic samples -> test with 'LR' LR tn, fp: 556, 14 LR fn, tp: 0, 13 LR f1 score: 0.650 LR cohens kappa score: 0.639 LR average precision score: 1.000 -> test with 'RF' RF tn, fp: 563, 7 RF fn, tp: 0, 13 RF f1 score: 0.788 RF cohens kappa score: 0.782 -> 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: 559, 11 KNN fn, tp: 0, 13 KNN f1 score: 0.703 KNN cohens kappa score: 0.694 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 556, 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: 546.4, 24.4 LR fn, tp: 0.0, 16.2 LR f1 score: 0.576 LR cohens kappa score: 0.559 LR average precision score: 0.992 minimum: LR tn, fp: 532, 14 LR fn, tp: 0, 13 LR f1 score: 0.433 LR cohens kappa score: 0.413 LR average precision score: 0.969 -----[ RF ]----- maximum: RF tn, fp: 567, 13 RF fn, tp: 0, 17 RF f1 score: 0.895 RF cohens kappa score: 0.891 average: RF tn, fp: 562.12, 8.68 RF fn, tp: 0.0, 16.2 RF f1 score: 0.791 RF cohens kappa score: 0.784 minimum: RF tn, fp: 558, 4 RF fn, tp: 0, 13 RF f1 score: 0.722 RF cohens kappa score: 0.713 -----[ GB ]----- maximum: GB tn, fp: 564, 14 GB fn, tp: 0, 17 GB f1 score: 0.829 GB cohens kappa score: 0.823 average: GB tn, fp: 560.0, 10.8 GB fn, tp: 0.0, 16.2 GB f1 score: 0.751 GB cohens kappa score: 0.742 minimum: GB tn, fp: 557, 6 GB fn, tp: 0, 13 GB f1 score: 0.703 GB cohens kappa score: 0.694 -----[ KNN ]----- maximum: KNN tn, fp: 559, 24 KNN fn, tp: 0, 17 KNN f1 score: 0.708 KNN cohens kappa score: 0.697 average: KNN tn, fp: 553.04, 17.76 KNN fn, tp: 0.0, 16.2 KNN f1 score: 0.648 KNN cohens kappa score: 0.634 minimum: KNN tn, fp: 547, 11 KNN fn, tp: 0, 13 KNN f1 score: 0.542 KNN cohens kappa score: 0.526