/////////////////////////////////////////// // Running convGAN-full 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: 275, 13 LR fn, tp: 0, 9 LR f1 score: 0.581 LR cohens kappa score: 0.562 LR average precision score: 0.878 -> 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: 0, 9 KNN f1 score: 0.692 KNN cohens kappa score: 0.680 ------ 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: 0.709 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 1, 8 GB f1 score: 0.762 GB cohens kappa score: 0.753 -> 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: 278, 10 LR fn, tp: 0, 9 LR f1 score: 0.643 LR cohens kappa score: 0.628 LR average precision score: 0.574 -> test with 'GB' GB tn, fp: 285, 3 GB fn, tp: 3, 6 GB f1 score: 0.667 GB cohens kappa score: 0.656 -> 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 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: 0.767 -> 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: 285, 3 KNN fn, tp: 0, 9 KNN f1 score: 0.857 KNN cohens kappa score: 0.852 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 274, 14 LR fn, tp: 0, 8 LR f1 score: 0.533 LR cohens kappa score: 0.514 LR average precision score: 0.673 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 2, 6 GB f1 score: 0.750 GB cohens kappa score: 0.743 -> 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: 0.728 -> 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: 279, 9 KNN fn, tp: 0, 9 KNN f1 score: 0.667 KNN cohens kappa score: 0.653 ------ 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: 0.346 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.334 -> 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: 281, 7 LR fn, tp: 1, 8 LR f1 score: 0.667 LR cohens kappa score: 0.654 LR average precision score: 0.738 -> 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: 279, 9 KNN fn, tp: 0, 9 KNN f1 score: 0.667 KNN cohens kappa score: 0.653 ------ Step 2/5: Slice 4/5 ------- -> 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: 0.870 -> 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 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: 0, 8 LR f1 score: 0.640 LR cohens kappa score: 0.626 LR average precision score: 0.607 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 4, 4 GB f1 score: 0.571 GB cohens kappa score: 0.561 -> test with 'KNN' KNN tn, fp: 280, 8 KNN fn, tp: 1, 7 KNN f1 score: 0.609 KNN cohens kappa score: 0.594 ====== 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: 272, 16 LR fn, tp: 0, 9 LR f1 score: 0.529 LR cohens kappa score: 0.507 LR average precision score: 0.689 -> 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 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: 0.676 -> test with 'GB' GB tn, fp: 285, 3 GB fn, tp: 3, 6 GB f1 score: 0.667 GB cohens kappa score: 0.656 -> 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: 281, 7 LR fn, tp: 1, 8 LR f1 score: 0.667 LR cohens kappa score: 0.654 LR average precision score: 0.816 -> 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: 284, 4 KNN fn, tp: 0, 9 KNN f1 score: 0.818 KNN cohens kappa score: 0.811 ------ 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: 288, 0 GB fn, tp: 4, 5 GB f1 score: 0.714 GB cohens kappa score: 0.708 -> 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 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: 0.348 -> 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: 276, 12 KNN fn, tp: 0, 8 KNN f1 score: 0.571 KNN cohens kappa score: 0.554 ====== 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: 274, 14 LR fn, tp: 0, 9 LR f1 score: 0.562 LR cohens kappa score: 0.543 LR average precision score: 0.723 -> 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: 276, 12 KNN fn, tp: 0, 9 KNN f1 score: 0.600 KNN cohens kappa score: 0.582 ------ Step 4/5: Slice 2/5 ------- -> 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: 0.628 -> 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: 280, 8 KNN fn, tp: 0, 9 KNN f1 score: 0.692 KNN cohens kappa score: 0.680 ------ 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: 0.596 -> 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: 1, 8 KNN f1 score: 0.615 KNN cohens kappa score: 0.600 ------ Step 4/5: 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: 0.715 -> 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: 282, 6 KNN fn, tp: 1, 8 KNN f1 score: 0.696 KNN cohens kappa score: 0.684 ------ 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: 0.640 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 1, 7 GB f1 score: 0.824 GB cohens kappa score: 0.818 -> test with 'KNN' KNN tn, fp: 273, 15 KNN fn, tp: 0, 8 KNN f1 score: 0.516 KNN cohens kappa score: 0.496 ====== 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: 0.713 -> 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: 273, 15 KNN fn, tp: 0, 9 KNN f1 score: 0.545 KNN cohens kappa score: 0.524 ------ 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: 0.792 -> 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: 284, 4 KNN fn, tp: 0, 9 KNN f1 score: 0.818 KNN cohens kappa score: 0.811 ------ 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: 0.706 -> 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 5/5: Slice 4/5 ------- -> 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: 0.568 -> 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: 273, 15 LR fn, tp: 1, 7 LR f1 score: 0.467 LR cohens kappa score: 0.445 LR average precision score: 0.372 -> test with 'GB' GB tn, fp: 282, 6 GB fn, tp: 3, 5 GB f1 score: 0.526 GB cohens kappa score: 0.511 -> test with 'KNN' KNN tn, fp: 274, 14 KNN fn, tp: 1, 7 KNN f1 score: 0.483 KNN cohens kappa score: 0.462 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 281, 17 LR fn, tp: 2, 9 LR f1 score: 0.692 LR cohens kappa score: 0.680 LR average precision score: 0.878 average: LR tn, fp: 276.12, 11.88 LR fn, tp: 0.28, 8.52 LR f1 score: 0.589 LR cohens kappa score: 0.571 LR average precision score: 0.665 minimum: LR tn, fp: 271, 7 LR fn, tp: 0, 7 LR f1 score: 0.467 LR cohens kappa score: 0.445 LR average precision score: 0.346 -----[ GB ]----- maximum: GB tn, fp: 288, 6 GB fn, tp: 6, 8 GB f1 score: 0.889 GB cohens kappa score: 0.885 average: GB tn, fp: 285.96, 2.04 GB fn, tp: 2.48, 6.32 GB f1 score: 0.735 GB cohens kappa score: 0.728 minimum: GB tn, fp: 282, 0 GB fn, tp: 1, 3 GB f1 score: 0.353 GB cohens kappa score: 0.334 -----[ KNN ]----- maximum: KNN tn, fp: 285, 15 KNN fn, tp: 1, 9 KNN f1 score: 0.857 KNN cohens kappa score: 0.852 average: KNN tn, fp: 278.72, 9.28 KNN fn, tp: 0.24, 8.56 KNN f1 score: 0.653 KNN cohens kappa score: 0.638 minimum: KNN tn, fp: 273, 3 KNN fn, tp: 0, 7 KNN f1 score: 0.483 KNN cohens kappa score: 0.462