/////////////////////////////////////////// // Running convGAN-proximary-5 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: 163, 125 GAN fn, tp: 5, 4 GAN f1 score: 0.058 GAN cohens kappa score: 0.001 -> 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: 0.872 -> 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: 281, 7 KNN fn, tp: 1, 8 KNN f1 score: 0.667 KNN cohens kappa score: 0.654 ------ 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: 254, 34 GAN fn, tp: 2, 7 GAN f1 score: 0.280 GAN cohens kappa score: 0.242 -> 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: 0.641 -> 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: 275, 13 KNN fn, tp: 0, 9 KNN f1 score: 0.581 KNN cohens kappa score: 0.562 ------ 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: 273, 15 GAN fn, tp: 1, 8 GAN f1 score: 0.500 GAN cohens kappa score: 0.477 -> 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: 0.609 -> 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: 276, 12 KNN fn, tp: 0, 9 KNN f1 score: 0.600 KNN cohens kappa score: 0.582 ------ 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: 181, 107 GAN fn, tp: 4, 5 GAN f1 score: 0.083 GAN cohens kappa score: 0.028 -> 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: 0.764 -> 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 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with GAN.predict GAN tn, fp: 265, 23 GAN fn, tp: 1, 7 GAN f1 score: 0.368 GAN cohens kappa score: 0.340 -> 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: 0.695 -> 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: 275, 13 KNN fn, tp: 0, 8 KNN f1 score: 0.552 KNN cohens kappa score: 0.533 ====== 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: 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: 0.703 -> 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 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with GAN.predict GAN tn, fp: 247, 41 GAN fn, tp: 5, 4 GAN f1 score: 0.148 GAN cohens kappa score: 0.103 -> 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: 0.351 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 6, 3 GB f1 score: 0.375 GB cohens kappa score: 0.358 -> 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 2/5: Slice 3/5 ------- -> 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: 0, 9 LR f1 score: 0.720 LR cohens kappa score: 0.709 LR average precision score: 0.762 -> 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: 278, 10 KNN fn, tp: 0, 9 KNN f1 score: 0.643 KNN cohens kappa score: 0.628 ------ 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: 2, 7 GAN f1 score: 0.636 GAN cohens kappa score: 0.623 -> 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: 0.878 -> 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: 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: 277, 11 GAN fn, tp: 4, 4 GAN f1 score: 0.348 GAN cohens kappa score: 0.324 -> 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: 0.607 -> 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: 281, 7 KNN fn, tp: 0, 8 KNN f1 score: 0.696 KNN cohens kappa score: 0.685 ====== 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: 273, 15 GAN fn, tp: 2, 7 GAN f1 score: 0.452 GAN cohens kappa score: 0.427 -> 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: 0.673 -> 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: 250, 38 GAN fn, tp: 2, 7 GAN f1 score: 0.259 GAN cohens kappa score: 0.220 -> 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: 0.697 -> 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: 0, 9 KNN f1 score: 0.600 KNN cohens kappa score: 0.582 ------ 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: 277, 11 GAN fn, tp: 3, 6 GAN f1 score: 0.462 GAN cohens kappa score: 0.439 -> 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: 0.813 -> 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: 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 GAN.predict GAN tn, fp: 264, 24 GAN fn, tp: 6, 3 GAN f1 score: 0.167 GAN cohens kappa score: 0.127 -> 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: 0.768 -> 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: 281, 7 KNN fn, tp: 1, 8 KNN f1 score: 0.667 KNN cohens kappa score: 0.654 ------ 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: 279, 9 GAN fn, tp: 3, 5 GAN f1 score: 0.455 GAN cohens kappa score: 0.435 -> 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: 0.324 -> 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 GAN.predict GAN tn, fp: 261, 27 GAN fn, tp: 4, 5 GAN f1 score: 0.244 GAN cohens kappa score: 0.206 -> 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: 0.742 -> 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: 277, 11 KNN fn, tp: 0, 9 KNN f1 score: 0.621 KNN cohens kappa score: 0.604 ------ 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: 232, 56 GAN fn, tp: 3, 6 GAN f1 score: 0.169 GAN cohens kappa score: 0.123 -> test with 'LR' LR tn, fp: 271, 17 LR fn, tp: 1, 8 LR f1 score: 0.471 LR cohens kappa score: 0.446 LR average precision score: 0.622 -> 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: 278, 10 KNN fn, tp: 0, 9 KNN f1 score: 0.643 KNN cohens kappa score: 0.628 ------ 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: 279, 9 GAN fn, tp: 3, 6 GAN f1 score: 0.500 GAN cohens kappa score: 0.480 -> test with 'LR' LR tn, fp: 277, 11 LR fn, tp: 2, 7 LR f1 score: 0.519 LR cohens kappa score: 0.498 LR average precision score: 0.660 -> 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 GAN.predict GAN tn, fp: 265, 23 GAN fn, tp: 5, 4 GAN f1 score: 0.222 GAN cohens kappa score: 0.185 -> 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: 0.690 -> 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: 1, 8 KNN f1 score: 0.727 KNN cohens kappa score: 0.717 ------ 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: 284, 4 GAN fn, tp: 2, 6 GAN f1 score: 0.667 GAN cohens kappa score: 0.656 -> 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: 0.709 -> 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: 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 GAN.predict GAN tn, fp: 277, 11 GAN fn, tp: 3, 6 GAN f1 score: 0.462 GAN cohens kappa score: 0.439 -> 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: 283, 5 GB fn, tp: 0, 9 GB f1 score: 0.783 GB cohens kappa score: 0.774 -> 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: 201, 87 GAN fn, tp: 5, 4 GAN f1 score: 0.080 GAN cohens kappa score: 0.026 -> 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: 0.797 -> 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: 281, 7 KNN fn, tp: 0, 9 KNN f1 score: 0.720 KNN cohens kappa score: 0.709 ------ 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: 249, 39 GAN fn, tp: 1, 8 GAN f1 score: 0.286 GAN cohens kappa score: 0.247 -> 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: 0.738 -> 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 GAN.predict GAN tn, fp: 283, 5 GAN fn, tp: 4, 5 GAN f1 score: 0.526 GAN cohens kappa score: 0.511 -> 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: 0.568 -> 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 GAN.predict GAN tn, fp: 248, 40 GAN fn, tp: 1, 7 GAN f1 score: 0.255 GAN cohens kappa score: 0.218 -> 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: 0.395 -> 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: 282, 17 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: 275.92, 12.08 LR fn, tp: 0.32, 8.48 LR f1 score: 0.584 LR cohens kappa score: 0.566 LR average precision score: 0.672 minimum: LR tn, fp: 271, 6 LR fn, tp: 0, 7 LR f1 score: 0.471 LR cohens kappa score: 0.446 LR average precision score: 0.324 -----[ GB ]----- maximum: GB tn, fp: 288, 6 GB fn, tp: 6, 9 GB f1 score: 0.889 GB cohens kappa score: 0.885 average: GB tn, fp: 285.68, 2.32 GB fn, tp: 2.48, 6.32 GB f1 score: 0.721 GB cohens kappa score: 0.713 minimum: GB tn, fp: 282, 0 GB fn, tp: 0, 3 GB f1 score: 0.375 GB cohens kappa score: 0.358 -----[ KNN ]----- maximum: KNN tn, fp: 285, 15 KNN fn, tp: 2, 9 KNN f1 score: 0.857 KNN cohens kappa score: 0.852 average: KNN tn, fp: 278.36, 9.64 KNN fn, tp: 0.2, 8.6 KNN f1 score: 0.647 KNN cohens kappa score: 0.632 minimum: KNN tn, fp: 273, 3 KNN fn, tp: 0, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.422 -----[ GAN ]----- maximum: GAN tn, fp: 284, 125 GAN fn, tp: 6, 8 GAN f1 score: 0.667 GAN cohens kappa score: 0.656 average: GAN tn, fp: 257.12, 30.88 GAN fn, tp: 3.04, 5.76 GAN f1 score: 0.353 GAN cohens kappa score: 0.322 minimum: GAN tn, fp: 163, 4 GAN fn, tp: 1, 3 GAN f1 score: 0.058 GAN cohens kappa score: 0.001