/////////////////////////////////////////// // Running Repeater 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: 274, 14 LR fn, tp: 0, 9 LR f1 score: 0.562 LR cohens kappa score: 0.543 LR average precision score: 0.902 -> test with 'GB' GB tn, fp: 285, 3 GB fn, tp: 5, 4 GB f1 score: 0.500 GB cohens kappa score: 0.486 -> 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 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: 0.698 -> test with 'GB' GB tn, fp: 285, 3 GB fn, tp: 0, 9 GB f1 score: 0.857 GB cohens kappa score: 0.852 -> 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 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 277, 11 LR fn, tp: 0, 9 LR f1 score: 0.621 LR cohens kappa score: 0.604 LR average precision score: 0.602 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 3, 6 GB f1 score: 0.632 GB cohens kappa score: 0.619 -> test with 'KNN' KNN tn, fp: 280, 8 KNN fn, tp: 1, 8 KNN f1 score: 0.640 KNN cohens kappa score: 0.625 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 280, 8 LR fn, tp: 0, 9 LR f1 score: 0.692 LR cohens kappa score: 0.680 LR average precision score: 0.776 -> test with 'GB' GB tn, fp: 288, 0 GB fn, tp: 2, 7 GB f1 score: 0.875 GB cohens kappa score: 0.872 -> test with 'KNN' KNN tn, fp: 286, 2 KNN fn, tp: 0, 9 KNN f1 score: 0.900 KNN cohens kappa score: 0.897 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 271, 17 LR fn, tp: 0, 8 LR f1 score: 0.485 LR cohens kappa score: 0.463 LR average precision score: 0.688 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 2, 6 GB f1 score: 0.667 GB cohens kappa score: 0.656 -> test with 'KNN' KNN tn, fp: 276, 12 KNN fn, tp: 0, 8 KNN f1 score: 0.571 KNN cohens kappa score: 0.554 ====== 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: 275, 13 LR fn, tp: 0, 9 LR f1 score: 0.581 LR cohens kappa score: 0.562 LR average precision score: 0.710 -> 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: 280, 8 KNN fn, tp: 0, 9 KNN f1 score: 0.692 KNN cohens kappa score: 0.680 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 269, 19 LR fn, tp: 0, 9 LR f1 score: 0.486 LR cohens kappa score: 0.462 LR average precision score: 0.416 -> test with 'GB' GB tn, fp: 280, 8 GB fn, tp: 4, 5 GB f1 score: 0.455 GB cohens kappa score: 0.434 -> test with 'KNN' KNN tn, fp: 275, 13 KNN fn, tp: 0, 9 KNN f1 score: 0.581 KNN cohens kappa score: 0.562 ------ Step 2/5: 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: 0.773 -> 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 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 271, 17 LR fn, tp: 0, 9 LR f1 score: 0.514 LR cohens kappa score: 0.491 LR average precision score: 0.894 -> 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: 278, 10 KNN fn, tp: 0, 9 KNN f1 score: 0.643 KNN cohens kappa score: 0.628 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 277, 11 LR fn, tp: 0, 8 LR f1 score: 0.593 LR cohens kappa score: 0.576 LR average precision score: 0.639 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 6, 2 GB f1 score: 0.333 GB cohens kappa score: 0.321 -> 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 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: 271, 17 LR fn, tp: 0, 9 LR f1 score: 0.514 LR cohens kappa score: 0.491 LR average precision score: 0.671 -> 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: 275, 13 KNN fn, tp: 0, 9 KNN f1 score: 0.581 KNN cohens kappa score: 0.562 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 270, 18 LR fn, tp: 0, 9 LR f1 score: 0.500 LR cohens kappa score: 0.476 LR average precision score: 0.707 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 2, 7 GB f1 score: 0.778 GB cohens kappa score: 0.771 -> test with 'KNN' KNN tn, fp: 277, 11 KNN fn, tp: 0, 9 KNN f1 score: 0.621 KNN cohens kappa score: 0.604 ------ Step 3/5: 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: 0, 9 LR f1 score: 0.692 LR cohens kappa score: 0.680 LR average precision score: 0.838 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 2, 7 GB f1 score: 0.778 GB cohens kappa score: 0.771 -> 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 3/5: Slice 4/5 ------- -> 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: 0.758 -> 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: 0.387 -> 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: 277, 11 KNN fn, tp: 0, 8 KNN f1 score: 0.593 KNN cohens kappa score: 0.576 ====== 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: 273, 15 LR fn, tp: 0, 9 LR f1 score: 0.545 LR cohens kappa score: 0.524 LR average precision score: 0.744 -> 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: 278, 10 KNN fn, tp: 0, 9 KNN f1 score: 0.643 KNN cohens kappa score: 0.628 ------ 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: 0, 9 LR f1 score: 0.529 LR cohens kappa score: 0.507 LR average precision score: 0.598 -> 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: 281, 7 KNN fn, tp: 0, 9 KNN f1 score: 0.720 KNN cohens kappa score: 0.709 ------ Step 4/5: Slice 3/5 ------- -> 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: 0.688 -> 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: 279, 9 KNN fn, tp: 0, 9 KNN f1 score: 0.667 KNN cohens kappa score: 0.653 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 279, 9 LR fn, tp: 0, 9 LR f1 score: 0.667 LR cohens kappa score: 0.653 LR average precision score: 0.681 -> 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: 280, 8 KNN fn, tp: 1, 8 KNN f1 score: 0.640 KNN cohens kappa score: 0.625 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 273, 15 LR fn, tp: 0, 8 LR f1 score: 0.516 LR cohens kappa score: 0.496 LR average precision score: 0.640 -> 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: 275, 13 KNN fn, tp: 0, 8 KNN f1 score: 0.552 KNN cohens kappa score: 0.533 ====== 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: 271, 17 LR fn, tp: 0, 9 LR f1 score: 0.514 LR cohens kappa score: 0.491 LR average precision score: 0.720 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 0, 9 GB f1 score: 0.818 GB cohens kappa score: 0.811 -> 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 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 279, 9 LR fn, tp: 0, 9 LR f1 score: 0.667 LR cohens kappa score: 0.653 LR average precision score: 0.799 -> 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: 283, 5 KNN fn, tp: 0, 9 KNN f1 score: 0.783 KNN cohens kappa score: 0.774 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 277, 11 LR fn, tp: 0, 9 LR f1 score: 0.621 LR cohens kappa score: 0.604 LR average precision score: 0.775 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 2, 7 GB f1 score: 0.778 GB cohens kappa score: 0.771 -> 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 5/5: Slice 4/5 ------- -> 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: 0.582 -> 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: 283, 5 KNN fn, tp: 0, 9 KNN f1 score: 0.783 KNN cohens kappa score: 0.774 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 271, 17 LR fn, tp: 0, 8 LR f1 score: 0.485 LR cohens kappa score: 0.463 LR average precision score: 0.469 -> test with 'GB' GB tn, fp: 281, 7 GB fn, tp: 2, 6 GB f1 score: 0.571 GB cohens kappa score: 0.557 -> test with 'KNN' KNN tn, fp: 275, 13 KNN fn, tp: 1, 7 KNN f1 score: 0.500 KNN cohens kappa score: 0.480 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 280, 19 LR fn, tp: 1, 9 LR f1 score: 0.692 LR cohens kappa score: 0.680 LR average precision score: 0.902 average: LR tn, fp: 274.68, 13.32 LR fn, tp: 0.04, 8.76 LR f1 score: 0.574 LR cohens kappa score: 0.555 LR average precision score: 0.686 minimum: LR tn, fp: 269, 8 LR fn, tp: 0, 8 LR f1 score: 0.485 LR cohens kappa score: 0.462 LR average precision score: 0.387 -----[ GB ]----- maximum: GB tn, fp: 288, 8 GB fn, tp: 6, 9 GB f1 score: 0.889 GB cohens kappa score: 0.885 average: GB tn, fp: 285.08, 2.92 GB fn, tp: 2.24, 6.56 GB f1 score: 0.714 GB cohens kappa score: 0.705 minimum: GB tn, fp: 280, 0 GB fn, tp: 0, 2 GB f1 score: 0.333 GB cohens kappa score: 0.321 -----[ KNN ]----- maximum: KNN tn, fp: 286, 14 KNN fn, tp: 1, 9 KNN f1 score: 0.900 KNN cohens kappa score: 0.897 average: KNN tn, fp: 279.28, 8.72 KNN fn, tp: 0.16, 8.64 KNN f1 score: 0.670 KNN cohens kappa score: 0.657 minimum: KNN tn, fp: 274, 2 KNN fn, tp: 0, 7 KNN f1 score: 0.500 KNN cohens kappa score: 0.480