/////////////////////////////////////////// // Running ProWRAS on folding_yeast5 /////////////////////////////////////////// Load 'data_input/folding_yeast5' 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 1117 synthetic samples -> test with 'LR' LR tn, fp: 279, 9 LR fn, tp: 1, 8 LR f1 score: 0.615 LR cohens kappa score: 0.600 LR average precision score: 0.870 -> test with 'RF' RF tn, fp: 288, 0 RF fn, tp: 5, 4 RF f1 score: 0.615 RF cohens kappa score: 0.608 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 5, 4 GB f1 score: 0.571 GB cohens kappa score: 0.562 -> test with 'KNN' KNN tn, fp: 283, 5 KNN fn, tp: 1, 8 KNN f1 score: 0.727 KNN cohens kappa score: 0.717 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 273, 15 LR fn, tp: 0, 9 LR f1 score: 0.545 LR cohens kappa score: 0.524 LR average precision score: 0.706 -> test with 'RF' RF tn, fp: 284, 4 RF fn, tp: 0, 9 RF f1 score: 0.818 RF cohens kappa score: 0.811 -> test with 'GB' GB tn, fp: 285, 3 GB fn, tp: 1, 8 GB f1 score: 0.800 GB cohens kappa score: 0.793 -> test with 'KNN' KNN tn, fp: 276, 12 KNN fn, tp: 0, 9 KNN f1 score: 0.600 KNN cohens kappa score: 0.582 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 278, 10 LR fn, tp: 1, 8 LR f1 score: 0.593 LR cohens kappa score: 0.575 LR average precision score: 0.600 -> test with 'RF' RF tn, fp: 284, 4 RF fn, tp: 3, 6 RF f1 score: 0.632 RF cohens kappa score: 0.619 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 2, 7 GB f1 score: 0.700 GB cohens kappa score: 0.690 -> test with 'KNN' KNN tn, fp: 278, 10 KNN fn, tp: 1, 8 KNN f1 score: 0.593 KNN cohens kappa score: 0.575 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 281, 7 LR fn, tp: 2, 7 LR f1 score: 0.609 LR cohens kappa score: 0.594 LR average precision score: 0.746 -> test with 'RF' RF tn, fp: 288, 0 RF fn, tp: 4, 5 RF f1 score: 0.714 RF cohens kappa score: 0.708 -> test with 'GB' GB tn, fp: 288, 0 GB fn, tp: 4, 5 GB f1 score: 0.714 GB cohens kappa score: 0.708 -> test with 'KNN' KNN tn, fp: 287, 1 KNN fn, tp: 1, 8 KNN f1 score: 0.889 KNN cohens kappa score: 0.885 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 275, 13 LR fn, tp: 0, 8 LR f1 score: 0.552 LR cohens kappa score: 0.533 LR average precision score: 0.697 -> test with 'RF' RF tn, fp: 283, 5 RF fn, tp: 1, 7 RF f1 score: 0.700 RF cohens kappa score: 0.690 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 1, 7 GB f1 score: 0.737 GB cohens kappa score: 0.728 -> test with 'KNN' KNN tn, fp: 280, 8 KNN fn, tp: 0, 8 KNN f1 score: 0.667 KNN cohens kappa score: 0.654 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 276, 12 LR fn, tp: 0, 9 LR f1 score: 0.600 LR cohens kappa score: 0.582 LR average precision score: 0.637 -> test with 'RF' RF tn, fp: 287, 1 RF fn, tp: 1, 8 RF f1 score: 0.889 RF cohens kappa score: 0.885 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 1, 8 GB f1 score: 0.842 GB cohens kappa score: 0.837 -> test with 'KNN' KNN tn, fp: 283, 5 KNN fn, tp: 1, 8 KNN f1 score: 0.727 KNN cohens kappa score: 0.717 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 273, 15 LR fn, tp: 1, 8 LR f1 score: 0.500 LR cohens kappa score: 0.477 LR average precision score: 0.349 -> test with 'RF' RF tn, fp: 284, 4 RF fn, tp: 6, 3 RF f1 score: 0.375 RF cohens kappa score: 0.358 -> test with 'GB' GB tn, fp: 280, 8 GB fn, tp: 6, 3 GB f1 score: 0.300 GB cohens kappa score: 0.276 -> test with 'KNN' KNN tn, fp: 277, 11 KNN fn, tp: 2, 7 KNN f1 score: 0.519 KNN cohens kappa score: 0.498 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 281, 7 LR fn, tp: 1, 8 LR f1 score: 0.667 LR cohens kappa score: 0.654 LR average precision score: 0.713 -> test with 'RF' RF tn, fp: 287, 1 RF fn, tp: 2, 7 RF f1 score: 0.824 RF cohens kappa score: 0.818 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 1, 8 GB f1 score: 0.842 GB cohens kappa score: 0.837 -> test with 'KNN' KNN tn, fp: 281, 7 KNN fn, tp: 0, 9 KNN f1 score: 0.720 KNN cohens kappa score: 0.709 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 274, 14 LR fn, tp: 0, 9 LR f1 score: 0.562 LR cohens kappa score: 0.543 LR average precision score: 0.878 -> test with 'RF' RF tn, fp: 286, 2 RF fn, tp: 3, 6 RF f1 score: 0.706 RF cohens kappa score: 0.697 -> test with 'GB' GB tn, fp: 285, 3 GB fn, tp: 1, 8 GB f1 score: 0.800 GB cohens kappa score: 0.793 -> test with 'KNN' KNN tn, fp: 283, 5 KNN fn, tp: 0, 9 KNN f1 score: 0.783 KNN cohens kappa score: 0.774 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 279, 9 LR fn, tp: 1, 7 LR f1 score: 0.583 LR cohens kappa score: 0.568 LR average precision score: 0.589 -> test with 'RF' RF tn, fp: 286, 2 RF fn, tp: 4, 4 RF f1 score: 0.571 RF cohens kappa score: 0.561 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 5, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> test with 'KNN' KNN tn, fp: 284, 4 KNN fn, tp: 1, 7 KNN f1 score: 0.737 KNN cohens kappa score: 0.728 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 273, 15 LR fn, tp: 0, 9 LR f1 score: 0.545 LR cohens kappa score: 0.524 LR average precision score: 0.674 -> test with 'RF' RF tn, fp: 287, 1 RF fn, tp: 2, 7 RF f1 score: 0.824 RF cohens kappa score: 0.818 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 3, 6 GB f1 score: 0.750 GB cohens kappa score: 0.743 -> test with 'KNN' KNN tn, fp: 279, 9 KNN fn, tp: 1, 8 KNN f1 score: 0.615 KNN cohens kappa score: 0.600 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 275, 13 LR fn, tp: 0, 9 LR f1 score: 0.581 LR cohens kappa score: 0.562 LR average precision score: 0.685 -> test with 'RF' RF tn, fp: 287, 1 RF fn, tp: 2, 7 RF f1 score: 0.824 RF cohens kappa score: 0.818 -> test with 'GB' GB tn, fp: 285, 3 GB fn, tp: 2, 7 GB f1 score: 0.737 GB cohens kappa score: 0.728 -> test with 'KNN' KNN tn, fp: 279, 9 KNN fn, tp: 1, 8 KNN f1 score: 0.615 KNN cohens kappa score: 0.600 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 282, 6 LR fn, tp: 2, 7 LR f1 score: 0.636 LR cohens kappa score: 0.623 LR average precision score: 0.808 -> test with 'RF' RF tn, fp: 288, 0 RF fn, tp: 4, 5 RF f1 score: 0.714 RF cohens kappa score: 0.708 -> test with 'GB' GB tn, fp: 288, 0 GB fn, tp: 3, 6 GB f1 score: 0.800 GB cohens kappa score: 0.795 -> test with 'KNN' KNN tn, fp: 285, 3 KNN fn, tp: 0, 9 KNN f1 score: 0.857 KNN cohens kappa score: 0.852 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 281, 7 LR fn, tp: 0, 9 LR f1 score: 0.720 LR cohens kappa score: 0.709 LR average precision score: 0.738 -> test with 'RF' RF tn, fp: 286, 2 RF fn, tp: 3, 6 RF f1 score: 0.706 RF cohens kappa score: 0.697 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 4, 5 GB f1 score: 0.667 GB cohens kappa score: 0.658 -> test with 'KNN' KNN tn, fp: 282, 6 KNN fn, tp: 1, 8 KNN f1 score: 0.696 KNN cohens kappa score: 0.684 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 275, 13 LR fn, tp: 0, 8 LR f1 score: 0.552 LR cohens kappa score: 0.533 LR average precision score: 0.327 -> test with 'RF' RF tn, fp: 284, 4 RF fn, tp: 2, 6 RF f1 score: 0.667 RF cohens kappa score: 0.656 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 1, 7 GB f1 score: 0.700 GB cohens kappa score: 0.690 -> test with 'KNN' KNN tn, fp: 282, 6 KNN fn, tp: 0, 8 KNN f1 score: 0.727 KNN cohens kappa score: 0.718 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 277, 11 LR fn, tp: 1, 8 LR f1 score: 0.571 LR cohens kappa score: 0.553 LR average precision score: 0.718 -> test with 'RF' RF tn, fp: 286, 2 RF fn, tp: 2, 7 RF f1 score: 0.778 RF cohens kappa score: 0.771 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 3, 6 GB f1 score: 0.706 GB cohens kappa score: 0.697 -> test with 'KNN' KNN tn, fp: 282, 6 KNN fn, tp: 0, 9 KNN f1 score: 0.750 KNN cohens kappa score: 0.740 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 272, 16 LR fn, tp: 1, 8 LR f1 score: 0.485 LR cohens kappa score: 0.461 LR average precision score: 0.655 -> test with 'RF' RF tn, fp: 287, 1 RF fn, tp: 2, 7 RF f1 score: 0.824 RF cohens kappa score: 0.818 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 2, 7 GB f1 score: 0.824 GB cohens kappa score: 0.818 -> test with 'KNN' KNN tn, fp: 282, 6 KNN fn, tp: 0, 9 KNN f1 score: 0.750 KNN cohens kappa score: 0.740 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 280, 8 LR fn, tp: 2, 7 LR f1 score: 0.583 LR cohens kappa score: 0.567 LR average precision score: 0.647 -> test with 'RF' RF tn, fp: 283, 5 RF fn, tp: 4, 5 RF f1 score: 0.526 RF cohens kappa score: 0.511 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 4, 5 GB f1 score: 0.526 GB cohens kappa score: 0.511 -> test with 'KNN' KNN tn, fp: 280, 8 KNN fn, tp: 3, 6 KNN f1 score: 0.522 KNN cohens kappa score: 0.503 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 281, 7 LR fn, tp: 0, 9 LR f1 score: 0.720 LR cohens kappa score: 0.709 LR average precision score: 0.696 -> test with 'RF' RF tn, fp: 288, 0 RF fn, tp: 3, 6 RF f1 score: 0.800 RF cohens kappa score: 0.795 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 3, 6 GB f1 score: 0.750 GB cohens kappa score: 0.743 -> test with 'KNN' KNN tn, fp: 284, 4 KNN fn, tp: 1, 8 KNN f1 score: 0.762 KNN cohens kappa score: 0.753 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 276, 12 LR fn, tp: 0, 8 LR f1 score: 0.571 LR cohens kappa score: 0.554 LR average precision score: 0.745 -> test with 'RF' RF tn, fp: 285, 3 RF fn, tp: 1, 7 RF f1 score: 0.778 RF cohens kappa score: 0.771 -> test with 'GB' GB tn, fp: 285, 3 GB fn, tp: 1, 7 GB f1 score: 0.778 GB cohens kappa score: 0.771 -> test with 'KNN' KNN tn, fp: 277, 11 KNN fn, tp: 0, 8 KNN f1 score: 0.593 KNN cohens kappa score: 0.576 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 273, 15 LR fn, tp: 0, 9 LR f1 score: 0.545 LR cohens kappa score: 0.524 LR average precision score: 0.724 -> test with 'RF' RF tn, fp: 283, 5 RF fn, tp: 1, 8 RF f1 score: 0.727 RF cohens kappa score: 0.717 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 1, 8 GB f1 score: 0.727 GB cohens kappa score: 0.717 -> test with 'KNN' KNN tn, fp: 274, 14 KNN fn, tp: 0, 9 KNN f1 score: 0.562 KNN cohens kappa score: 0.543 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 280, 8 LR fn, tp: 1, 8 LR f1 score: 0.640 LR cohens kappa score: 0.625 LR average precision score: 0.781 -> test with 'RF' RF tn, fp: 287, 1 RF fn, tp: 4, 5 RF f1 score: 0.667 RF cohens kappa score: 0.658 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 3, 6 GB f1 score: 0.750 GB cohens kappa score: 0.743 -> test with 'KNN' KNN tn, fp: 284, 4 KNN fn, tp: 1, 8 KNN f1 score: 0.762 KNN cohens kappa score: 0.753 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 278, 10 LR fn, tp: 0, 9 LR f1 score: 0.643 LR cohens kappa score: 0.628 LR average precision score: 0.747 -> test with 'RF' RF tn, fp: 286, 2 RF fn, tp: 0, 9 RF f1 score: 0.900 RF cohens kappa score: 0.897 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 1, 8 GB f1 score: 0.889 GB cohens kappa score: 0.885 -> test with 'KNN' KNN tn, fp: 284, 4 KNN fn, tp: 0, 9 KNN f1 score: 0.818 KNN cohens kappa score: 0.811 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 281, 7 LR fn, tp: 1, 8 LR f1 score: 0.667 LR cohens kappa score: 0.654 LR average precision score: 0.587 -> test with 'RF' RF tn, fp: 287, 1 RF fn, tp: 6, 3 RF f1 score: 0.462 RF cohens kappa score: 0.451 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 4, 5 GB f1 score: 0.667 GB cohens kappa score: 0.658 -> test with 'KNN' KNN tn, fp: 284, 4 KNN fn, tp: 0, 9 KNN f1 score: 0.818 KNN cohens kappa score: 0.811 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 276, 12 LR fn, tp: 1, 7 LR f1 score: 0.519 LR cohens kappa score: 0.499 LR average precision score: 0.396 -> test with 'RF' RF tn, fp: 284, 4 RF fn, tp: 3, 5 RF f1 score: 0.588 RF cohens kappa score: 0.576 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 2, 6 GB f1 score: 0.632 GB cohens kappa score: 0.620 -> test with 'KNN' KNN tn, fp: 276, 12 KNN fn, tp: 2, 6 KNN f1 score: 0.462 KNN cohens kappa score: 0.441 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 282, 16 LR fn, tp: 2, 9 LR f1 score: 0.720 LR cohens kappa score: 0.709 LR average precision score: 0.878 average: LR tn, fp: 277.16, 10.84 LR fn, tp: 0.64, 8.16 LR f1 score: 0.592 LR cohens kappa score: 0.575 LR average precision score: 0.668 minimum: LR tn, fp: 272, 6 LR fn, tp: 0, 7 LR f1 score: 0.485 LR cohens kappa score: 0.461 LR average precision score: 0.327 -----[ RF ]----- maximum: RF tn, fp: 288, 5 RF fn, tp: 6, 9 RF f1 score: 0.900 RF cohens kappa score: 0.897 average: RF tn, fp: 285.8, 2.2 RF fn, tp: 2.72, 6.08 RF f1 score: 0.705 RF cohens kappa score: 0.697 minimum: RF tn, fp: 283, 0 RF fn, tp: 0, 3 RF f1 score: 0.375 RF cohens kappa score: 0.358 -----[ GB ]----- maximum: GB tn, fp: 288, 8 GB fn, tp: 6, 8 GB f1 score: 0.889 GB cohens kappa score: 0.885 average: GB tn, fp: 285.44, 2.56 GB fn, tp: 2.56, 6.24 GB f1 score: 0.707 GB cohens kappa score: 0.698 minimum: GB tn, fp: 280, 0 GB fn, tp: 1, 3 GB f1 score: 0.300 GB cohens kappa score: 0.276 -----[ KNN ]----- maximum: KNN tn, fp: 287, 14 KNN fn, tp: 3, 9 KNN f1 score: 0.889 KNN cohens kappa score: 0.885 average: KNN tn, fp: 281.04, 6.96 KNN fn, tp: 0.68, 8.12 KNN f1 score: 0.691 KNN cohens kappa score: 0.679 minimum: KNN tn, fp: 274, 1 KNN fn, tp: 0, 6 KNN f1 score: 0.462 KNN cohens kappa score: 0.441