/////////////////////////////////////////// // Running ctGAN 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: 278, 10 LR fn, tp: 0, 9 LR f1 score: 0.643 LR cohens kappa score: 0.628 LR average precision score: 0.840 -> test with 'RF' RF tn, fp: 281, 7 RF fn, tp: 4, 5 RF f1 score: 0.476 RF cohens kappa score: 0.457 -> test with 'GB' GB tn, fp: 282, 6 GB fn, tp: 4, 5 GB f1 score: 0.500 GB cohens kappa score: 0.483 -> 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 'LR' LR tn, fp: 262, 26 LR fn, tp: 0, 9 LR f1 score: 0.409 LR cohens kappa score: 0.379 LR average precision score: 0.681 -> test with 'RF' RF tn, fp: 279, 9 RF fn, tp: 0, 9 RF f1 score: 0.667 RF cohens kappa score: 0.653 -> test with 'GB' GB tn, fp: 279, 9 GB fn, tp: 2, 7 GB f1 score: 0.560 GB cohens kappa score: 0.542 -> test with 'KNN' KNN tn, fp: 271, 17 KNN fn, tp: 0, 9 KNN f1 score: 0.514 KNN cohens kappa score: 0.491 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 268, 20 LR fn, tp: 1, 8 LR f1 score: 0.432 LR cohens kappa score: 0.405 LR average precision score: 0.454 -> test with 'RF' RF tn, fp: 284, 4 RF fn, tp: 1, 8 RF f1 score: 0.762 RF cohens kappa score: 0.753 -> 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: 273, 15 KNN fn, tp: 1, 8 KNN f1 score: 0.500 KNN cohens kappa score: 0.477 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 281, 7 LR fn, tp: 2, 7 LR f1 score: 0.609 LR cohens kappa score: 0.594 LR average precision score: 0.783 -> test with 'RF' RF tn, fp: 287, 1 RF fn, tp: 0, 9 RF f1 score: 0.947 RF cohens kappa score: 0.946 -> 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: 287, 1 KNN fn, tp: 1, 8 KNN f1 score: 0.889 KNN cohens kappa score: 0.885 ------ Step 1/5: 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: 1, 7 LR f1 score: 0.538 LR cohens kappa score: 0.521 LR average precision score: 0.627 -> test with 'RF' RF tn, fp: 285, 3 RF fn, tp: 2, 6 RF f1 score: 0.706 RF cohens kappa score: 0.697 -> test with 'GB' GB tn, fp: 281, 7 GB fn, tp: 1, 7 GB f1 score: 0.636 GB cohens kappa score: 0.623 -> 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 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: 281, 7 LR fn, tp: 0, 9 LR f1 score: 0.720 LR cohens kappa score: 0.709 LR average precision score: 0.748 -> test with 'RF' RF tn, fp: 283, 5 RF fn, tp: 0, 9 RF f1 score: 0.783 RF cohens kappa score: 0.774 -> test with 'GB' GB tn, fp: 281, 7 GB fn, tp: 1, 8 GB f1 score: 0.667 GB cohens kappa score: 0.654 -> 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 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: 4, 5 LR f1 score: 0.345 LR cohens kappa score: 0.316 LR average precision score: 0.334 -> test with 'RF' RF tn, fp: 280, 8 RF fn, tp: 5, 4 RF f1 score: 0.381 RF cohens kappa score: 0.359 -> test with 'GB' GB tn, fp: 278, 10 GB fn, tp: 4, 5 GB f1 score: 0.417 GB cohens kappa score: 0.394 -> test with 'KNN' KNN tn, fp: 274, 14 KNN fn, tp: 2, 7 KNN f1 score: 0.467 KNN cohens kappa score: 0.443 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> 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: 0.561 -> test with 'RF' RF tn, fp: 284, 4 RF fn, tp: 1, 8 RF f1 score: 0.762 RF cohens kappa score: 0.753 -> 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: 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 'LR' LR tn, fp: 261, 27 LR fn, tp: 0, 9 LR f1 score: 0.400 LR cohens kappa score: 0.369 LR average precision score: 0.622 -> test with 'RF' RF tn, fp: 278, 10 RF fn, tp: 1, 8 RF f1 score: 0.593 RF cohens kappa score: 0.575 -> test with 'GB' GB tn, fp: 280, 8 GB fn, tp: 1, 8 GB f1 score: 0.640 GB cohens kappa score: 0.625 -> test with 'KNN' KNN tn, fp: 270, 18 KNN fn, tp: 0, 9 KNN f1 score: 0.500 KNN cohens kappa score: 0.476 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 280, 8 LR fn, tp: 2, 6 LR f1 score: 0.545 LR cohens kappa score: 0.529 LR average precision score: 0.607 -> test with 'RF' RF tn, fp: 284, 4 RF fn, tp: 2, 6 RF f1 score: 0.667 RF cohens kappa score: 0.656 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 3, 5 GB f1 score: 0.588 GB cohens kappa score: 0.576 -> 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 '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: 0.664 -> test with 'RF' RF tn, fp: 282, 6 RF fn, tp: 3, 6 RF f1 score: 0.571 RF cohens kappa score: 0.556 -> 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: 280, 8 KNN fn, tp: 3, 6 KNN f1 score: 0.522 KNN cohens kappa score: 0.503 ------ Step 3/5: 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: 0.573 -> test with 'RF' RF tn, fp: 282, 6 RF fn, tp: 2, 7 RF f1 score: 0.636 RF cohens kappa score: 0.623 -> test with 'GB' GB tn, fp: 280, 8 GB fn, tp: 2, 7 GB f1 score: 0.583 GB cohens kappa score: 0.567 -> 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 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 284, 4 LR fn, tp: 2, 7 LR f1 score: 0.700 LR cohens kappa score: 0.690 LR average precision score: 0.841 -> test with 'RF' RF tn, fp: 286, 2 RF fn, tp: 0, 9 RF f1 score: 0.900 RF cohens kappa score: 0.897 -> 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: 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 'LR' LR tn, fp: 267, 21 LR fn, tp: 1, 8 LR f1 score: 0.421 LR cohens kappa score: 0.393 LR average precision score: 0.366 -> test with 'RF' RF tn, fp: 279, 9 RF fn, tp: 6, 3 RF f1 score: 0.286 RF cohens kappa score: 0.260 -> test with 'GB' GB tn, fp: 279, 9 GB fn, tp: 7, 2 GB f1 score: 0.200 GB cohens kappa score: 0.172 -> test with 'KNN' KNN tn, fp: 268, 20 KNN fn, tp: 3, 6 KNN f1 score: 0.343 KNN cohens kappa score: 0.312 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 257, 31 LR fn, tp: 0, 8 LR f1 score: 0.340 LR cohens kappa score: 0.309 LR average precision score: 0.279 -> test with 'RF' RF tn, fp: 280, 8 RF fn, tp: 1, 7 RF f1 score: 0.609 RF cohens kappa score: 0.594 -> test with 'GB' GB tn, fp: 277, 11 GB fn, tp: 1, 7 GB f1 score: 0.538 GB cohens kappa score: 0.521 -> test with 'KNN' KNN tn, fp: 263, 25 KNN fn, tp: 0, 8 KNN f1 score: 0.390 KNN cohens kappa score: 0.363 ====== 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: 269, 19 LR fn, tp: 0, 9 LR f1 score: 0.486 LR cohens kappa score: 0.462 LR average precision score: 0.551 -> test with 'RF' RF tn, fp: 283, 5 RF fn, tp: 1, 8 RF f1 score: 0.727 RF cohens kappa score: 0.717 -> test with 'GB' GB tn, fp: 282, 6 GB fn, tp: 2, 7 GB f1 score: 0.636 GB cohens kappa score: 0.623 -> 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 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 274, 14 LR fn, tp: 3, 6 LR f1 score: 0.414 LR cohens kappa score: 0.388 LR average precision score: 0.525 -> test with 'RF' RF tn, fp: 281, 7 RF fn, tp: 2, 7 RF f1 score: 0.609 RF cohens kappa score: 0.594 -> test with 'GB' GB tn, fp: 281, 7 GB fn, tp: 2, 7 GB f1 score: 0.609 GB cohens kappa score: 0.594 -> test with 'KNN' KNN tn, fp: 278, 10 KNN fn, tp: 2, 7 KNN f1 score: 0.538 KNN cohens kappa score: 0.519 ------ Step 4/5: 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: 0.635 -> test with 'RF' RF tn, fp: 281, 7 RF fn, tp: 1, 8 RF f1 score: 0.667 RF cohens kappa score: 0.654 -> test with 'GB' GB tn, fp: 281, 7 GB fn, tp: 2, 7 GB f1 score: 0.609 GB cohens kappa score: 0.594 -> 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 4/5 ------- -> 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: 0.473 -> test with 'RF' RF tn, fp: 282, 6 RF fn, tp: 3, 6 RF f1 score: 0.571 RF cohens kappa score: 0.556 -> 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: 270, 18 KNN fn, tp: 0, 9 KNN f1 score: 0.500 KNN cohens kappa score: 0.476 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 270, 18 LR fn, tp: 0, 8 LR f1 score: 0.471 LR cohens kappa score: 0.448 LR average precision score: 0.408 -> test with 'RF' RF tn, fp: 277, 11 RF fn, tp: 1, 7 RF f1 score: 0.538 RF cohens kappa score: 0.521 -> test with 'GB' GB tn, fp: 275, 13 GB fn, tp: 1, 7 GB f1 score: 0.500 GB cohens kappa score: 0.480 -> 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: 267, 21 LR fn, tp: 0, 9 LR f1 score: 0.462 LR cohens kappa score: 0.435 LR average precision score: 0.586 -> test with 'RF' RF tn, fp: 276, 12 RF fn, tp: 0, 9 RF f1 score: 0.600 RF cohens kappa score: 0.582 -> test with 'GB' GB tn, fp: 278, 10 GB fn, tp: 1, 8 GB f1 score: 0.593 GB cohens kappa score: 0.575 -> test with 'KNN' KNN tn, fp: 268, 20 KNN fn, tp: 0, 9 KNN f1 score: 0.474 KNN cohens kappa score: 0.448 ------ Step 5/5: 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: 0.775 -> test with 'RF' RF tn, fp: 284, 4 RF fn, tp: 2, 7 RF f1 score: 0.700 RF cohens kappa score: 0.690 -> 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: 282, 6 KNN fn, tp: 0, 9 KNN f1 score: 0.750 KNN cohens kappa score: 0.740 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> 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: 0.740 -> test with 'RF' RF tn, fp: 285, 3 RF fn, tp: 1, 8 RF f1 score: 0.800 RF cohens kappa score: 0.793 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 1, 8 GB f1 score: 0.727 GB cohens kappa score: 0.717 -> test with 'KNN' KNN tn, fp: 282, 6 KNN fn, tp: 2, 7 KNN f1 score: 0.636 KNN cohens kappa score: 0.623 ------ Step 5/5: 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: 4, 5 LR f1 score: 0.455 LR cohens kappa score: 0.434 LR average precision score: 0.503 -> test with 'RF' RF tn, fp: 285, 3 RF fn, tp: 3, 6 RF f1 score: 0.667 RF cohens kappa score: 0.656 -> 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: 4, 5 KNN f1 score: 0.455 KNN cohens kappa score: 0.434 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 272, 16 LR fn, tp: 1, 7 LR f1 score: 0.452 LR cohens kappa score: 0.429 LR average precision score: 0.439 -> test with 'RF' RF tn, fp: 279, 9 RF fn, tp: 0, 8 RF f1 score: 0.640 RF cohens kappa score: 0.626 -> test with 'GB' GB tn, fp: 279, 9 GB fn, tp: 2, 6 GB f1 score: 0.522 GB cohens kappa score: 0.504 -> 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: 284, 31 LR fn, tp: 4, 9 LR f1 score: 0.720 LR cohens kappa score: 0.709 LR average precision score: 0.841 average: LR tn, fp: 273.24, 14.76 LR fn, tp: 1.08, 7.72 LR f1 score: 0.510 LR cohens kappa score: 0.489 LR average precision score: 0.585 minimum: LR tn, fp: 257, 4 LR fn, tp: 0, 5 LR f1 score: 0.340 LR cohens kappa score: 0.309 LR average precision score: 0.279 -----[ RF ]----- maximum: RF tn, fp: 287, 12 RF fn, tp: 6, 9 RF f1 score: 0.947 RF cohens kappa score: 0.946 average: RF tn, fp: 281.88, 6.12 RF fn, tp: 1.68, 7.12 RF f1 score: 0.651 RF cohens kappa score: 0.638 minimum: RF tn, fp: 276, 1 RF fn, tp: 0, 3 RF f1 score: 0.286 RF cohens kappa score: 0.260 -----[ GB ]----- maximum: GB tn, fp: 287, 13 GB fn, tp: 7, 8 GB f1 score: 0.824 GB cohens kappa score: 0.818 average: GB tn, fp: 281.6, 6.4 GB fn, tp: 2.12, 6.68 GB f1 score: 0.618 GB cohens kappa score: 0.604 minimum: GB tn, fp: 275, 1 GB fn, tp: 1, 2 GB f1 score: 0.200 GB cohens kappa score: 0.172 -----[ KNN ]----- maximum: KNN tn, fp: 287, 25 KNN fn, tp: 4, 9 KNN f1 score: 0.889 KNN cohens kappa score: 0.885 average: KNN tn, fp: 276.6, 11.4 KNN fn, tp: 0.92, 7.88 KNN f1 score: 0.583 KNN cohens kappa score: 0.565 minimum: KNN tn, fp: 263, 1 KNN fn, tp: 0, 5 KNN f1 score: 0.343 KNN cohens kappa score: 0.312