/////////////////////////////////////////// // Running convGAN-majority-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 GAN.predict GAN tn, fp: 281, 7 GAN fn, tp: 1, 8 GAN f1 score: 0.667 GAN cohens kappa score: 0.654 -> test with 'LR' LR tn, fp: 276, 12 LR fn, tp: 1, 8 LR f1 score: 0.552 LR cohens kappa score: 0.532 LR average precision score: 0.897 -> 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: 280, 8 KNN fn, tp: 1, 8 KNN f1 score: 0.640 KNN cohens kappa score: 0.625 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with GAN.predict GAN tn, fp: 273, 15 GAN fn, tp: 1, 8 GAN f1 score: 0.500 GAN cohens kappa score: 0.477 -> 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: 0.695 -> 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 GAN.predict GAN tn, fp: 277, 11 GAN fn, tp: 1, 8 GAN f1 score: 0.571 GAN cohens kappa score: 0.553 -> 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: 0.616 -> 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: 276, 12 KNN fn, tp: 1, 8 KNN f1 score: 0.552 KNN cohens kappa score: 0.532 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with GAN.predict GAN tn, fp: 284, 4 GAN fn, tp: 4, 5 GAN f1 score: 0.556 GAN cohens kappa score: 0.542 -> 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: 0.777 -> 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: 286, 2 KNN fn, tp: 1, 8 KNN f1 score: 0.842 KNN cohens kappa score: 0.837 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with GAN.predict GAN tn, fp: 273, 15 GAN fn, tp: 0, 8 GAN f1 score: 0.516 GAN cohens kappa score: 0.496 -> 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: 0.678 -> 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: 277, 11 KNN fn, tp: 0, 8 KNN f1 score: 0.593 KNN cohens kappa score: 0.576 ====== 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 GAN.predict GAN tn, fp: 278, 10 GAN fn, tp: 1, 8 GAN f1 score: 0.593 GAN cohens kappa score: 0.575 -> 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: 0.704 -> test with 'GB' GB tn, fp: 288, 0 GB fn, tp: 1, 8 GB f1 score: 0.941 GB cohens kappa score: 0.939 -> 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 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with GAN.predict GAN tn, fp: 279, 9 GAN fn, tp: 5, 4 GAN f1 score: 0.364 GAN cohens kappa score: 0.340 -> 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: 0.337 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 5, 4 GB f1 score: 0.444 GB cohens kappa score: 0.427 -> test with 'KNN' KNN tn, fp: 275, 13 KNN fn, tp: 1, 8 KNN f1 score: 0.533 KNN cohens kappa score: 0.513 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with GAN.predict GAN tn, fp: 282, 6 GAN fn, tp: 1, 8 GAN f1 score: 0.696 GAN cohens kappa score: 0.684 -> test with 'LR' LR tn, fp: 282, 6 LR fn, tp: 1, 8 LR f1 score: 0.696 LR cohens kappa score: 0.684 LR average precision score: 0.735 -> test with 'GB' GB tn, fp: 288, 0 GB fn, tp: 1, 8 GB f1 score: 0.941 GB cohens kappa score: 0.939 -> 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 GAN.predict GAN tn, fp: 279, 9 GAN fn, tp: 0, 9 GAN f1 score: 0.667 GAN cohens kappa score: 0.653 -> 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: 0.885 -> 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: 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 GAN.predict GAN tn, fp: 279, 9 GAN fn, tp: 1, 7 GAN f1 score: 0.583 GAN cohens kappa score: 0.568 -> 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: 0.603 -> 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: 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 GAN.predict GAN tn, fp: 279, 9 GAN fn, tp: 1, 8 GAN f1 score: 0.615 GAN cohens kappa score: 0.600 -> 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: 0.685 -> 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: 1, 8 KNN f1 score: 0.571 KNN cohens kappa score: 0.553 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with GAN.predict GAN tn, fp: 277, 11 GAN fn, tp: 1, 8 GAN f1 score: 0.571 GAN cohens kappa score: 0.553 -> 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: 0.708 -> 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: 275, 13 KNN fn, tp: 1, 8 KNN f1 score: 0.533 KNN cohens kappa score: 0.513 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with GAN.predict GAN tn, fp: 283, 5 GAN fn, tp: 2, 7 GAN f1 score: 0.667 GAN cohens kappa score: 0.655 -> test with 'LR' LR tn, fp: 283, 5 LR fn, tp: 2, 7 LR f1 score: 0.667 LR cohens kappa score: 0.655 LR average precision score: 0.783 -> 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: 1, 8 KNN f1 score: 0.800 KNN cohens kappa score: 0.793 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with GAN.predict GAN tn, fp: 280, 8 GAN fn, tp: 0, 9 GAN f1 score: 0.692 GAN cohens kappa score: 0.680 -> 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: 0.738 -> 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 GAN.predict GAN tn, fp: 275, 13 GAN fn, tp: 1, 7 GAN f1 score: 0.500 GAN cohens kappa score: 0.480 -> 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: 0.368 -> 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: 278, 10 KNN fn, tp: 0, 8 KNN f1 score: 0.615 KNN cohens kappa score: 0.600 ====== 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 GAN.predict GAN tn, fp: 278, 10 GAN fn, tp: 2, 7 GAN f1 score: 0.538 GAN cohens kappa score: 0.519 -> test with 'LR' LR tn, fp: 274, 14 LR fn, tp: 1, 8 LR f1 score: 0.516 LR cohens kappa score: 0.494 LR average precision score: 0.721 -> 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 GAN.predict GAN tn, fp: 278, 10 GAN fn, tp: 2, 7 GAN f1 score: 0.538 GAN cohens kappa score: 0.519 -> 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: 0.650 -> 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 GAN.predict GAN tn, fp: 282, 6 GAN fn, tp: 2, 7 GAN f1 score: 0.636 GAN cohens kappa score: 0.623 -> 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: 0.578 -> test with 'GB' GB tn, fp: 282, 6 GB fn, tp: 3, 6 GB f1 score: 0.571 GB cohens kappa score: 0.556 -> 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 GAN.predict GAN tn, fp: 282, 6 GAN fn, tp: 1, 8 GAN f1 score: 0.696 GAN cohens kappa score: 0.684 -> 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 '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: 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 GAN.predict GAN tn, fp: 278, 10 GAN fn, tp: 1, 7 GAN f1 score: 0.560 GAN cohens kappa score: 0.543 -> 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: 0.731 -> 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: 272, 16 KNN fn, tp: 0, 8 KNN f1 score: 0.500 KNN cohens kappa score: 0.479 ====== 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 GAN.predict GAN tn, fp: 273, 15 GAN fn, tp: 0, 9 GAN f1 score: 0.545 GAN cohens kappa score: 0.524 -> 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: 0.724 -> 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: 272, 16 KNN fn, tp: 0, 9 KNN f1 score: 0.529 KNN cohens kappa score: 0.507 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with GAN.predict GAN tn, fp: 282, 6 GAN fn, tp: 1, 8 GAN f1 score: 0.696 GAN cohens kappa score: 0.684 -> 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: 0.785 -> 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: 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 GAN.predict GAN tn, fp: 278, 10 GAN fn, tp: 0, 9 GAN f1 score: 0.643 GAN cohens kappa score: 0.628 -> 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: 0.710 -> 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 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with GAN.predict GAN tn, fp: 282, 6 GAN fn, tp: 2, 7 GAN f1 score: 0.636 GAN cohens kappa score: 0.623 -> 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: 0.590 -> 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 GAN.predict GAN tn, fp: 272, 16 GAN fn, tp: 2, 6 GAN f1 score: 0.400 GAN cohens kappa score: 0.375 -> test with 'LR' LR tn, fp: 275, 13 LR fn, tp: 1, 7 LR f1 score: 0.500 LR cohens kappa score: 0.480 LR average precision score: 0.419 -> 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: 275, 13 KNN fn, tp: 2, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.422 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 283, 16 LR fn, tp: 2, 9 LR f1 score: 0.720 LR cohens kappa score: 0.709 LR average precision score: 0.897 average: LR tn, fp: 276.88, 11.12 LR fn, tp: 0.56, 8.24 LR f1 score: 0.592 LR cohens kappa score: 0.575 LR average precision score: 0.673 minimum: LR tn, fp: 272, 5 LR fn, tp: 0, 7 LR f1 score: 0.485 LR cohens kappa score: 0.461 LR average precision score: 0.337 -----[ GB ]----- maximum: GB tn, fp: 288, 6 GB fn, tp: 5, 8 GB f1 score: 0.941 GB cohens kappa score: 0.939 average: GB tn, fp: 285.92, 2.08 GB fn, tp: 2.56, 6.24 GB f1 score: 0.728 GB cohens kappa score: 0.720 minimum: GB tn, fp: 282, 0 GB fn, tp: 1, 3 GB f1 score: 0.444 GB cohens kappa score: 0.427 -----[ KNN ]----- maximum: KNN tn, fp: 286, 16 KNN fn, tp: 3, 9 KNN f1 score: 0.842 KNN cohens kappa score: 0.837 average: KNN tn, fp: 278.96, 9.04 KNN fn, tp: 0.64, 8.16 KNN f1 score: 0.641 KNN cohens kappa score: 0.626 minimum: KNN tn, fp: 272, 2 KNN fn, tp: 0, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.422 -----[ GAN ]----- maximum: GAN tn, fp: 284, 16 GAN fn, tp: 5, 9 GAN f1 score: 0.696 GAN cohens kappa score: 0.684 average: GAN tn, fp: 278.56, 9.44 GAN fn, tp: 1.32, 7.48 GAN f1 score: 0.586 GAN cohens kappa score: 0.569 minimum: GAN tn, fp: 272, 4 GAN fn, tp: 0, 4 GAN f1 score: 0.364 GAN cohens kappa score: 0.340