/////////////////////////////////////////// // 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: 282, 6 GAN fn, tp: 2, 7 GAN f1 score: 0.636 GAN cohens kappa score: 0.623 -> 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: 0.895 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 4, 5 GB f1 score: 0.625 GB cohens kappa score: 0.615 -> 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 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: 0, 9 GAN f1 score: 0.545 GAN cohens kappa score: 0.524 -> 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: 0.701 -> 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: 273, 15 KNN fn, tp: 0, 9 KNN f1 score: 0.545 KNN cohens kappa score: 0.524 ------ 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: 280, 8 GAN fn, tp: 1, 8 GAN f1 score: 0.640 GAN cohens kappa score: 0.625 -> 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: 0.603 -> 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: 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 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: 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: 3, 6 GB f1 score: 0.800 GB cohens kappa score: 0.795 -> 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 GAN.predict GAN tn, fp: 276, 12 GAN fn, tp: 0, 8 GAN f1 score: 0.571 GAN cohens kappa score: 0.554 -> 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: 0.701 -> 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: 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 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: 276, 12 LR fn, tp: 0, 9 LR f1 score: 0.600 LR cohens kappa score: 0.582 LR average precision score: 0.680 -> 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: 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 GAN.predict GAN tn, fp: 272, 16 GAN fn, tp: 1, 8 GAN f1 score: 0.485 GAN cohens kappa score: 0.461 -> 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: 0.414 -> 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: 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 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: 0.773 -> 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: 1, 8 KNN f1 score: 0.640 KNN cohens kappa score: 0.625 ------ 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: 282, 6 GAN fn, tp: 0, 9 GAN f1 score: 0.750 GAN cohens kappa score: 0.740 -> 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: 0.900 -> 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: 277, 11 KNN fn, tp: 0, 9 KNN f1 score: 0.621 KNN cohens kappa score: 0.604 ------ 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: 281, 7 GAN fn, tp: 1, 7 GAN f1 score: 0.636 GAN cohens kappa score: 0.623 -> test with 'LR' LR tn, fp: 280, 8 LR fn, tp: 0, 8 LR f1 score: 0.667 LR cohens kappa score: 0.654 LR average precision score: 0.635 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 3, 5 GB f1 score: 0.667 GB cohens kappa score: 0.658 -> 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: 276, 12 GAN fn, tp: 1, 8 GAN f1 score: 0.552 GAN cohens kappa score: 0.532 -> 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: 0.670 -> 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: 274, 14 KNN fn, tp: 0, 9 KNN f1 score: 0.562 KNN cohens kappa score: 0.543 ------ 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: 280, 8 GAN fn, tp: 1, 8 GAN f1 score: 0.640 GAN cohens kappa score: 0.625 -> 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: 0.714 -> 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: 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 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: 0.835 -> 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: 1, 8 KNN f1 score: 0.727 KNN cohens kappa score: 0.717 ------ 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: 281, 7 GAN fn, tp: 0, 9 GAN f1 score: 0.720 GAN cohens kappa score: 0.709 -> 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: 0.738 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 5, 4 GB f1 score: 0.533 GB cohens kappa score: 0.522 -> 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 GAN.predict GAN tn, fp: 275, 13 GAN fn, tp: 0, 8 GAN f1 score: 0.552 GAN cohens kappa score: 0.533 -> 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: 0.397 -> 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 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: 275, 13 LR fn, tp: 0, 9 LR f1 score: 0.581 LR cohens kappa score: 0.562 LR average precision score: 0.744 -> 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: 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: 276, 12 GAN fn, tp: 0, 9 GAN f1 score: 0.600 GAN cohens kappa score: 0.582 -> 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: 0.604 -> 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: 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 GAN.predict GAN tn, fp: 280, 8 GAN fn, tp: 3, 6 GAN f1 score: 0.522 GAN cohens kappa score: 0.503 -> 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: 0.678 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 3, 6 GB f1 score: 0.600 GB cohens kappa score: 0.586 -> test with 'KNN' KNN tn, fp: 279, 9 KNN fn, tp: 2, 7 KNN f1 score: 0.560 KNN cohens kappa score: 0.542 ------ 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: 280, 8 LR fn, tp: 0, 9 LR f1 score: 0.692 LR cohens kappa score: 0.680 LR average precision score: 0.668 -> 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: 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 GAN.predict GAN tn, fp: 276, 12 GAN fn, tp: 0, 8 GAN f1 score: 0.571 GAN cohens kappa score: 0.554 -> 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: 0.754 -> 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: 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: 274, 14 GAN fn, tp: 0, 9 GAN f1 score: 0.562 GAN cohens kappa score: 0.543 -> 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: 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 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: 0, 9 LR f1 score: 0.692 LR cohens kappa score: 0.680 LR average precision score: 0.762 -> 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: 281, 7 KNN fn, tp: 1, 8 KNN f1 score: 0.667 KNN cohens kappa score: 0.654 ------ 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: 281, 7 GAN fn, tp: 0, 9 GAN f1 score: 0.720 GAN cohens kappa score: 0.709 -> 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: 0.792 -> 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 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: 1, 8 GAN f1 score: 0.696 GAN cohens kappa score: 0.684 -> 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: 0.584 -> 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: 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 GAN.predict GAN tn, fp: 273, 15 GAN fn, tp: 1, 7 GAN f1 score: 0.467 GAN cohens kappa score: 0.445 -> test with 'LR' LR tn, fp: 274, 14 LR fn, tp: 1, 7 LR f1 score: 0.483 LR cohens kappa score: 0.462 LR average precision score: 0.438 -> 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, 16 LR fn, tp: 2, 9 LR f1 score: 0.720 LR cohens kappa score: 0.709 LR average precision score: 0.900 average: LR tn, fp: 276.76, 11.24 LR fn, tp: 0.2, 8.6 LR f1 score: 0.606 LR cohens kappa score: 0.589 LR average precision score: 0.687 minimum: LR tn, fp: 272, 7 LR fn, tp: 0, 7 LR f1 score: 0.483 LR cohens kappa score: 0.461 LR average precision score: 0.397 -----[ GB ]----- maximum: GB tn, fp: 288, 8 GB fn, tp: 6, 8 GB f1 score: 0.842 GB cohens kappa score: 0.837 average: GB tn, fp: 285.2, 2.8 GB fn, tp: 2.4, 6.4 GB f1 score: 0.712 GB cohens kappa score: 0.703 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: 286, 16 KNN fn, tp: 2, 9 KNN f1 score: 0.900 KNN cohens kappa score: 0.897 average: KNN tn, fp: 278.44, 9.56 KNN fn, tp: 0.36, 8.44 KNN f1 score: 0.640 KNN cohens kappa score: 0.625 minimum: KNN tn, fp: 272, 2 KNN fn, tp: 0, 7 KNN f1 score: 0.483 KNN cohens kappa score: 0.462 -----[ GAN ]----- maximum: GAN tn, fp: 284, 16 GAN fn, tp: 4, 9 GAN f1 score: 0.750 GAN cohens kappa score: 0.740 average: GAN tn, fp: 278.68, 9.32 GAN fn, tp: 0.88, 7.92 GAN f1 score: 0.614 GAN cohens kappa score: 0.599 minimum: GAN tn, fp: 272, 4 GAN fn, tp: 0, 5 GAN f1 score: 0.467 GAN cohens kappa score: 0.445