/////////////////////////////////////////// // Running ProWRAS on folding_hypothyroid /////////////////////////////////////////// Load 'data_input/folding_hypothyroid' from pickle file non empty cut in data_input/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: 553, 50 LR fn, tp: 6, 25 LR f1 score: 0.472 LR cohens kappa score: 0.432 LR average precision score: 0.512 -> 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: 597, 6 GB fn, tp: 7, 24 GB f1 score: 0.787 GB cohens kappa score: 0.776 -> 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: 530, 73 LR fn, tp: 5, 26 LR f1 score: 0.400 LR cohens kappa score: 0.352 LR average precision score: 0.456 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 9, 22 RF f1 score: 0.772 RF cohens kappa score: 0.761 -> 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: 530, 73 LR fn, tp: 7, 24 LR f1 score: 0.375 LR cohens kappa score: 0.325 LR average precision score: 0.363 -> 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: 597, 6 GB fn, tp: 5, 26 GB f1 score: 0.825 GB cohens kappa score: 0.816 -> 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: 522, 81 LR fn, tp: 4, 27 LR f1 score: 0.388 LR cohens kappa score: 0.338 LR average precision score: 0.409 -> test with 'RF' RF tn, fp: 602, 1 RF fn, tp: 14, 17 RF f1 score: 0.694 RF cohens kappa score: 0.682 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 10, 21 GB f1 score: 0.750 GB cohens kappa score: 0.739 -> 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: 542, 58 LR fn, tp: 3, 24 LR f1 score: 0.440 LR cohens kappa score: 0.402 LR average precision score: 0.573 -> test with 'RF' RF tn, fp: 600, 0 RF fn, tp: 8, 19 RF f1 score: 0.826 RF cohens kappa score: 0.820 -> test with 'GB' GB tn, fp: 596, 4 GB fn, tp: 5, 22 GB f1 score: 0.830 GB cohens kappa score: 0.823 -> 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: 544, 59 LR fn, tp: 8, 23 LR f1 score: 0.407 LR cohens kappa score: 0.362 LR average precision score: 0.481 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 9, 22 RF f1 score: 0.772 RF cohens kappa score: 0.761 -> test with 'GB' GB tn, fp: 596, 7 GB fn, tp: 9, 22 GB f1 score: 0.733 GB cohens kappa score: 0.720 -> 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 2/5: 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: 5, 26 LR f1 score: 0.452 LR cohens kappa score: 0.410 LR average precision score: 0.495 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 15, 16 RF f1 score: 0.653 RF cohens kappa score: 0.640 -> test with 'GB' GB tn, fp: 596, 7 GB fn, tp: 3, 28 GB f1 score: 0.848 GB cohens kappa score: 0.840 -> 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: 527, 76 LR fn, tp: 5, 26 LR f1 score: 0.391 LR cohens kappa score: 0.342 LR average precision score: 0.581 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 14, 17 RF f1 score: 0.667 RF cohens kappa score: 0.653 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 10, 21 GB f1 score: 0.737 GB cohens kappa score: 0.725 -> 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 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 526, 77 LR fn, tp: 7, 24 LR f1 score: 0.364 LR cohens kappa score: 0.312 LR average precision score: 0.313 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 10, 21 RF f1 score: 0.750 RF cohens kappa score: 0.739 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 7, 24 GB f1 score: 0.814 GB cohens kappa score: 0.804 -> 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: 0.526 -> test with 'RF' RF tn, fp: 595, 5 RF fn, tp: 4, 23 RF f1 score: 0.836 RF cohens kappa score: 0.829 -> test with 'GB' GB tn, fp: 593, 7 GB fn, tp: 3, 24 GB f1 score: 0.828 GB cohens kappa score: 0.819 -> 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: 0.494 -> test with 'RF' RF tn, fp: 602, 1 RF fn, tp: 14, 17 RF f1 score: 0.694 RF cohens kappa score: 0.682 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 8, 23 GB f1 score: 0.807 GB cohens kappa score: 0.798 -> 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: 546, 57 LR fn, tp: 12, 19 LR f1 score: 0.355 LR cohens kappa score: 0.307 LR average precision score: 0.335 -> test with 'RF' RF tn, fp: 596, 7 RF fn, tp: 9, 22 RF f1 score: 0.733 RF cohens kappa score: 0.720 -> 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: 569, 34 KNN fn, tp: 6, 25 KNN f1 score: 0.556 KNN cohens kappa score: 0.525 ------ 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: 0.599 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 8, 23 RF f1 score: 0.807 RF cohens kappa score: 0.798 -> 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: 513, 90 LR fn, tp: 3, 28 LR f1 score: 0.376 LR cohens kappa score: 0.323 LR average precision score: 0.487 -> test with 'RF' RF tn, fp: 597, 6 RF fn, tp: 13, 18 RF f1 score: 0.655 RF cohens kappa score: 0.639 -> test with 'GB' GB tn, fp: 591, 12 GB fn, tp: 10, 21 GB f1 score: 0.656 GB cohens kappa score: 0.638 -> 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: 532, 68 LR fn, tp: 6, 21 LR f1 score: 0.362 LR cohens kappa score: 0.317 LR average precision score: 0.336 -> test with 'RF' RF tn, fp: 599, 1 RF fn, tp: 7, 20 RF f1 score: 0.833 RF cohens kappa score: 0.827 -> 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: 586, 14 KNN fn, tp: 4, 23 KNN f1 score: 0.719 KNN cohens kappa score: 0.704 ====== 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: 533, 70 LR fn, tp: 5, 26 LR f1 score: 0.409 LR cohens kappa score: 0.362 LR average precision score: 0.427 -> 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: 595, 8 GB fn, tp: 3, 28 GB f1 score: 0.836 GB cohens kappa score: 0.827 -> 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: 546, 57 LR fn, tp: 7, 24 LR f1 score: 0.429 LR cohens kappa score: 0.385 LR average precision score: 0.461 -> 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: 598, 5 GB fn, tp: 7, 24 GB f1 score: 0.800 GB cohens kappa score: 0.790 -> 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: 2, 29 LR f1 score: 0.417 LR cohens kappa score: 0.369 LR average precision score: 0.590 -> test with 'RF' RF tn, fp: 603, 0 RF fn, tp: 12, 19 RF f1 score: 0.760 RF cohens kappa score: 0.751 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 8, 23 GB f1 score: 0.807 GB cohens kappa score: 0.798 -> 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: 521, 82 LR fn, tp: 4, 27 LR f1 score: 0.386 LR cohens kappa score: 0.335 LR average precision score: 0.471 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 14, 17 RF f1 score: 0.680 RF cohens kappa score: 0.668 -> 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: 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: 540, 60 LR fn, tp: 9, 18 LR f1 score: 0.343 LR cohens kappa score: 0.298 LR average precision score: 0.422 -> test with 'RF' RF tn, fp: 596, 4 RF fn, tp: 8, 19 RF f1 score: 0.760 RF cohens kappa score: 0.750 -> test with 'GB' GB tn, fp: 598, 2 GB fn, tp: 8, 19 GB f1 score: 0.792 GB cohens kappa score: 0.784 -> 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: 0.455 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 10, 21 RF f1 score: 0.750 RF cohens kappa score: 0.739 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 9, 22 GB f1 score: 0.721 GB cohens kappa score: 0.707 -> 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: 533, 70 LR fn, tp: 6, 25 LR f1 score: 0.397 LR cohens kappa score: 0.349 LR average precision score: 0.522 -> test with 'RF' RF tn, fp: 603, 0 RF fn, tp: 13, 18 RF f1 score: 0.735 RF cohens kappa score: 0.725 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 7, 24 GB f1 score: 0.800 GB cohens kappa score: 0.790 -> 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: 525, 78 LR fn, tp: 4, 27 LR f1 score: 0.397 LR cohens kappa score: 0.348 LR average precision score: 0.517 -> 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: 576, 27 KNN fn, tp: 9, 22 KNN f1 score: 0.550 KNN cohens kappa score: 0.521 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 531, 72 LR fn, tp: 4, 27 LR f1 score: 0.415 LR cohens kappa score: 0.368 LR average precision score: 0.549 -> 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: 598, 5 GB fn, tp: 4, 27 GB f1 score: 0.857 GB cohens kappa score: 0.850 -> 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 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 533, 67 LR fn, tp: 4, 23 LR f1 score: 0.393 LR cohens kappa score: 0.350 LR average precision score: 0.376 -> 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: 553, 90 LR fn, tp: 12, 30 LR f1 score: 0.472 LR cohens kappa score: 0.432 LR average precision score: 0.599 average: LR tn, fp: 532.44, 69.96 LR fn, tp: 5.24, 24.96 LR f1 score: 0.400 LR cohens kappa score: 0.353 LR average precision score: 0.470 minimum: LR tn, fp: 513, 50 LR fn, tp: 1, 18 LR f1 score: 0.343 LR cohens kappa score: 0.298 LR average precision score: 0.313 -----[ RF ]----- maximum: RF tn, fp: 603, 7 RF fn, tp: 16, 23 RF f1 score: 0.836 RF cohens kappa score: 0.829 average: RF tn, fp: 599.6, 2.8 RF fn, tp: 10.88, 19.32 RF f1 score: 0.737 RF cohens kappa score: 0.726 minimum: RF tn, fp: 595, 0 RF fn, tp: 4, 15 RF f1 score: 0.600 RF cohens kappa score: 0.585 -----[ GB ]----- maximum: GB tn, fp: 600, 12 GB fn, tp: 11, 28 GB f1 score: 0.857 GB cohens kappa score: 0.850 average: GB tn, fp: 596.6, 5.8 GB fn, tp: 6.76, 23.44 GB f1 score: 0.788 GB cohens kappa score: 0.777 minimum: GB tn, fp: 591, 2 GB fn, tp: 3, 16 GB f1 score: 0.656 GB cohens kappa score: 0.638 -----[ KNN ]----- maximum: KNN tn, fp: 591, 34 KNN fn, tp: 12, 25 KNN f1 score: 0.719 KNN cohens kappa score: 0.704 average: KNN tn, fp: 581.92, 20.48 KNN fn, tp: 7.8, 22.4 KNN f1 score: 0.615 KNN cohens kappa score: 0.592 minimum: KNN tn, fp: 569, 12 KNN fn, tp: 4, 17 KNN f1 score: 0.521 KNN cohens kappa score: 0.492