/////////////////////////////////////////// // Running ProWRAS on folding_hypothyroid /////////////////////////////////////////// Load 'folding_hypothyroid' from pickle file non empty cut in folding_hypothyroid! (1 points) 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 2289 synthetic samples -> test with 'LR' LR tn, fp: 550, 53 LR fn, tp: 6, 25 LR f1 score: 0.459 LR cohens kappa score: 0.418 LR average precision score: 0.510 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 12, 19 RF f1 score: 0.731 RF cohens kappa score: 0.720 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 8, 23 GB f1 score: 0.780 GB cohens kappa score: 0.769 -> test with 'KNN' KNN tn, fp: 587, 16 KNN fn, tp: 6, 25 KNN f1 score: 0.694 KNN cohens kappa score: 0.676 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 533, 70 LR fn, tp: 5, 26 LR f1 score: 0.409 LR cohens kappa score: 0.362 LR average precision score: 0.457 -> test with 'RF' RF tn, fp: 595, 8 RF fn, tp: 12, 19 RF f1 score: 0.655 RF cohens kappa score: 0.639 -> test with 'GB' GB tn, fp: 594, 9 GB fn, tp: 4, 27 GB f1 score: 0.806 GB cohens kappa score: 0.795 -> test with 'KNN' KNN tn, fp: 581, 22 KNN fn, tp: 6, 25 KNN f1 score: 0.641 KNN cohens kappa score: 0.619 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 531, 72 LR fn, tp: 8, 23 LR f1 score: 0.365 LR cohens kappa score: 0.315 LR average precision score: 0.367 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 10, 21 RF f1 score: 0.778 RF cohens kappa score: 0.768 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 5, 26 GB f1 score: 0.852 GB cohens kappa score: 0.845 -> test with 'KNN' KNN tn, fp: 586, 17 KNN fn, tp: 9, 22 KNN f1 score: 0.629 KNN cohens kappa score: 0.607 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 523, 80 LR fn, tp: 4, 27 LR f1 score: 0.391 LR cohens kappa score: 0.341 LR average precision score: 0.408 -> test with 'RF' RF tn, fp: 603, 0 RF fn, tp: 15, 16 RF f1 score: 0.681 RF cohens kappa score: 0.670 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 12, 19 GB f1 score: 0.717 GB cohens kappa score: 0.705 -> test with 'KNN' KNN tn, fp: 580, 23 KNN fn, tp: 12, 19 KNN f1 score: 0.521 KNN cohens kappa score: 0.492 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 544, 56 LR fn, tp: 3, 24 LR f1 score: 0.449 LR cohens kappa score: 0.411 LR average precision score: 0.560 -> test with 'RF' RF tn, fp: 599, 1 RF fn, tp: 8, 19 RF f1 score: 0.809 RF cohens kappa score: 0.801 -> test with 'GB' GB tn, fp: 597, 3 GB fn, tp: 5, 22 GB f1 score: 0.846 GB cohens kappa score: 0.840 -> test with 'KNN' KNN tn, fp: 583, 17 KNN fn, tp: 6, 21 KNN f1 score: 0.646 KNN cohens kappa score: 0.627 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 542, 61 LR fn, tp: 8, 23 LR f1 score: 0.400 LR cohens kappa score: 0.354 LR average precision score: 0.484 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 11, 20 RF f1 score: 0.741 RF cohens kappa score: 0.729 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 9, 22 GB f1 score: 0.746 GB cohens kappa score: 0.733 -> test with 'KNN' KNN tn, fp: 586, 17 KNN fn, tp: 9, 22 KNN f1 score: 0.629 KNN cohens kappa score: 0.607 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 546, 57 LR fn, tp: 5, 26 LR f1 score: 0.456 LR cohens kappa score: 0.414 LR average precision score: 0.501 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 16, 15 RF f1 score: 0.625 RF cohens kappa score: 0.612 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 3, 28 GB f1 score: 0.862 GB cohens kappa score: 0.854 -> test with 'KNN' KNN tn, fp: 580, 23 KNN fn, tp: 7, 24 KNN f1 score: 0.615 KNN cohens kappa score: 0.591 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 529, 74 LR fn, tp: 5, 26 LR f1 score: 0.397 LR cohens kappa score: 0.348 LR average precision score: 0.585 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 16, 15 RF f1 score: 0.612 RF cohens kappa score: 0.598 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 11, 20 GB f1 score: 0.727 GB cohens kappa score: 0.715 -> test with 'KNN' KNN tn, fp: 581, 22 KNN fn, tp: 8, 23 KNN f1 score: 0.605 KNN cohens kappa score: 0.581 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 525, 78 LR fn, tp: 7, 24 LR f1 score: 0.361 LR cohens kappa score: 0.309 LR average precision score: 0.313 -> test with 'RF' RF tn, fp: 598, 5 RF fn, tp: 9, 22 RF f1 score: 0.759 RF cohens kappa score: 0.747 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 6, 25 GB f1 score: 0.833 GB cohens kappa score: 0.825 -> test with 'KNN' KNN tn, fp: 587, 16 KNN fn, tp: 8, 23 KNN f1 score: 0.657 KNN cohens kappa score: 0.637 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 514, 86 LR fn, tp: 1, 26 LR f1 score: 0.374 LR cohens kappa score: 0.327 LR average precision score: 0.513 -> test with 'RF' RF tn, fp: 596, 4 RF fn, tp: 5, 22 RF f1 score: 0.830 RF cohens kappa score: 0.823 -> test with 'GB' GB tn, fp: 594, 6 GB fn, tp: 3, 24 GB f1 score: 0.842 GB cohens kappa score: 0.835 -> test with 'KNN' KNN tn, fp: 585, 15 KNN fn, tp: 5, 22 KNN f1 score: 0.688 KNN cohens kappa score: 0.671 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 530, 73 LR fn, tp: 7, 24 LR f1 score: 0.375 LR cohens kappa score: 0.325 LR average precision score: 0.496 -> test with 'RF' RF tn, fp: 602, 1 RF fn, tp: 17, 14 RF f1 score: 0.609 RF cohens kappa score: 0.596 -> test with 'GB' GB tn, fp: 601, 2 GB fn, tp: 8, 23 GB f1 score: 0.821 GB cohens kappa score: 0.813 -> test with 'KNN' KNN tn, fp: 591, 12 KNN fn, tp: 12, 19 KNN f1 score: 0.613 KNN cohens kappa score: 0.593 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 545, 58 LR fn, tp: 12, 19 LR f1 score: 0.352 LR cohens kappa score: 0.303 LR average precision score: 0.315 -> test with 'RF' RF tn, fp: 595, 8 RF fn, tp: 9, 22 RF f1 score: 0.721 RF cohens kappa score: 0.707 -> test with 'GB' GB tn, fp: 593, 10 GB fn, tp: 4, 27 GB f1 score: 0.794 GB cohens kappa score: 0.783 -> test with 'KNN' KNN tn, fp: 570, 33 KNN fn, tp: 6, 25 KNN f1 score: 0.562 KNN cohens kappa score: 0.532 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 531, 72 LR fn, tp: 1, 30 LR f1 score: 0.451 LR cohens kappa score: 0.407 LR average precision score: 0.595 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 9, 22 RF f1 score: 0.786 RF cohens kappa score: 0.776 -> test with 'GB' GB tn, fp: 594, 9 GB fn, tp: 5, 26 GB f1 score: 0.788 GB cohens kappa score: 0.776 -> test with 'KNN' KNN tn, fp: 580, 23 KNN fn, tp: 8, 23 KNN f1 score: 0.597 KNN cohens kappa score: 0.572 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 515, 88 LR fn, tp: 2, 29 LR f1 score: 0.392 LR cohens kappa score: 0.341 LR average precision score: 0.489 -> test with 'RF' RF tn, fp: 598, 5 RF fn, tp: 12, 19 RF f1 score: 0.691 RF cohens kappa score: 0.677 -> test with 'GB' GB tn, fp: 592, 11 GB fn, tp: 8, 23 GB f1 score: 0.708 GB cohens kappa score: 0.692 -> test with 'KNN' KNN tn, fp: 580, 23 KNN fn, tp: 11, 20 KNN f1 score: 0.541 KNN cohens kappa score: 0.513 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 531, 69 LR fn, tp: 6, 21 LR f1 score: 0.359 LR cohens kappa score: 0.313 LR average precision score: 0.349 -> test with 'RF' RF tn, fp: 600, 0 RF fn, tp: 6, 21 RF f1 score: 0.875 RF cohens kappa score: 0.870 -> test with 'GB' GB tn, fp: 598, 2 GB fn, tp: 3, 24 GB f1 score: 0.906 GB cohens kappa score: 0.901 -> test with 'KNN' KNN tn, fp: 585, 15 KNN fn, tp: 4, 23 KNN f1 score: 0.708 KNN cohens kappa score: 0.692 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 531, 72 LR fn, tp: 5, 26 LR f1 score: 0.403 LR cohens kappa score: 0.355 LR average precision score: 0.425 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 11, 20 RF f1 score: 0.727 RF cohens kappa score: 0.715 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 4, 27 GB f1 score: 0.818 GB cohens kappa score: 0.808 -> test with 'KNN' KNN tn, fp: 577, 26 KNN fn, tp: 9, 22 KNN f1 score: 0.557 KNN cohens kappa score: 0.529 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 547, 56 LR fn, tp: 7, 24 LR f1 score: 0.432 LR cohens kappa score: 0.389 LR average precision score: 0.460 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 11, 20 RF f1 score: 0.727 RF cohens kappa score: 0.715 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 8, 23 GB f1 score: 0.767 GB cohens kappa score: 0.755 -> test with 'KNN' KNN tn, fp: 585, 18 KNN fn, tp: 8, 23 KNN f1 score: 0.639 KNN cohens kappa score: 0.618 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 524, 79 LR fn, tp: 3, 28 LR f1 score: 0.406 LR cohens kappa score: 0.357 LR average precision score: 0.585 -> test with 'RF' RF tn, fp: 602, 1 RF fn, tp: 11, 20 RF f1 score: 0.769 RF cohens kappa score: 0.760 -> test with 'GB' GB tn, fp: 601, 2 GB fn, tp: 8, 23 GB f1 score: 0.821 GB cohens kappa score: 0.813 -> test with 'KNN' KNN tn, fp: 589, 14 KNN fn, tp: 6, 25 KNN f1 score: 0.714 KNN cohens kappa score: 0.698 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 520, 83 LR fn, tp: 4, 27 LR f1 score: 0.383 LR cohens kappa score: 0.332 LR average precision score: 0.462 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 13, 18 RF f1 score: 0.706 RF cohens kappa score: 0.694 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 5, 26 GB f1 score: 0.839 GB cohens kappa score: 0.830 -> test with 'KNN' KNN tn, fp: 579, 24 KNN fn, tp: 8, 23 KNN f1 score: 0.590 KNN cohens kappa score: 0.564 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 539, 61 LR fn, tp: 9, 18 LR f1 score: 0.340 LR cohens kappa score: 0.294 LR average precision score: 0.415 -> test with 'RF' RF tn, fp: 597, 3 RF fn, tp: 10, 17 RF f1 score: 0.723 RF cohens kappa score: 0.713 -> test with 'GB' GB tn, fp: 596, 4 GB fn, tp: 8, 19 GB f1 score: 0.760 GB cohens kappa score: 0.750 -> test with 'KNN' KNN tn, fp: 573, 27 KNN fn, tp: 5, 22 KNN f1 score: 0.579 KNN cohens kappa score: 0.554 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 540, 63 LR fn, tp: 6, 25 LR f1 score: 0.420 LR cohens kappa score: 0.375 LR average precision score: 0.460 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 12, 19 RF f1 score: 0.717 RF cohens kappa score: 0.705 -> test with 'GB' GB tn, fp: 596, 7 GB fn, tp: 10, 21 GB f1 score: 0.712 GB cohens kappa score: 0.698 -> test with 'KNN' KNN tn, fp: 581, 22 KNN fn, tp: 9, 22 KNN f1 score: 0.587 KNN cohens kappa score: 0.562 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 534, 69 LR fn, tp: 6, 25 LR f1 score: 0.400 LR cohens kappa score: 0.352 LR average precision score: 0.522 -> test with 'RF' RF tn, fp: 602, 1 RF fn, tp: 10, 21 RF f1 score: 0.792 RF cohens kappa score: 0.784 -> test with 'GB' GB tn, fp: 601, 2 GB fn, tp: 5, 26 GB f1 score: 0.881 GB cohens kappa score: 0.876 -> test with 'KNN' KNN tn, fp: 586, 17 KNN fn, tp: 7, 24 KNN f1 score: 0.667 KNN cohens kappa score: 0.647 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 522, 81 LR fn, tp: 4, 27 LR f1 score: 0.388 LR cohens kappa score: 0.338 LR average precision score: 0.508 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 16, 15 RF f1 score: 0.600 RF cohens kappa score: 0.585 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 11, 20 GB f1 score: 0.714 GB cohens kappa score: 0.701 -> test with 'KNN' KNN tn, fp: 577, 26 KNN fn, tp: 9, 22 KNN f1 score: 0.557 KNN cohens kappa score: 0.529 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 534, 69 LR fn, tp: 5, 26 LR f1 score: 0.413 LR cohens kappa score: 0.366 LR average precision score: 0.559 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 9, 22 RF f1 score: 0.800 RF cohens kappa score: 0.791 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 4, 27 GB f1 score: 0.857 GB cohens kappa score: 0.850 -> test with 'KNN' KNN tn, fp: 580, 23 KNN fn, tp: 8, 23 KNN f1 score: 0.597 KNN cohens kappa score: 0.572 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 534, 66 LR fn, tp: 4, 23 LR f1 score: 0.397 LR cohens kappa score: 0.354 LR average precision score: 0.367 -> test with 'RF' RF tn, fp: 597, 3 RF fn, tp: 12, 15 RF f1 score: 0.667 RF cohens kappa score: 0.655 -> test with 'GB' GB tn, fp: 596, 4 GB fn, tp: 11, 16 GB f1 score: 0.681 GB cohens kappa score: 0.669 -> test with 'KNN' KNN tn, fp: 581, 19 KNN fn, tp: 10, 17 KNN f1 score: 0.540 KNN cohens kappa score: 0.516 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 550, 88 LR fn, tp: 12, 30 LR f1 score: 0.459 LR cohens kappa score: 0.418 LR average precision score: 0.595 average: LR tn, fp: 532.56, 69.84 LR fn, tp: 5.32, 24.88 LR f1 score: 0.399 LR cohens kappa score: 0.352 LR average precision score: 0.468 minimum: LR tn, fp: 514, 53 LR fn, tp: 1, 18 LR f1 score: 0.340 LR cohens kappa score: 0.294 LR average precision score: 0.313 -----[ RF ]----- maximum: RF tn, fp: 603, 8 RF fn, tp: 17, 22 RF f1 score: 0.875 RF cohens kappa score: 0.870 average: RF tn, fp: 599.44, 2.96 RF fn, tp: 11.28, 18.92 RF f1 score: 0.725 RF cohens kappa score: 0.714 minimum: RF tn, fp: 595, 0 RF fn, tp: 5, 14 RF f1 score: 0.600 RF cohens kappa score: 0.585 -----[ GB ]----- maximum: GB tn, fp: 601, 11 GB fn, tp: 12, 28 GB f1 score: 0.906 GB cohens kappa score: 0.901 average: GB tn, fp: 597.12, 5.28 GB fn, tp: 6.72, 23.48 GB f1 score: 0.795 GB cohens kappa score: 0.785 minimum: GB tn, fp: 592, 2 GB fn, tp: 3, 16 GB f1 score: 0.681 GB cohens kappa score: 0.669 -----[ KNN ]----- maximum: KNN tn, fp: 591, 33 KNN fn, tp: 12, 25 KNN f1 score: 0.714 KNN cohens kappa score: 0.698 average: KNN tn, fp: 582.0, 20.4 KNN fn, tp: 7.84, 22.36 KNN f1 score: 0.615 KNN cohens kappa score: 0.592 minimum: KNN tn, fp: 570, 12 KNN fn, tp: 4, 17 KNN f1 score: 0.521 KNN cohens kappa score: 0.492 wall time: 00:49:20s, process time: 11:53:00s