/////////////////////////////////////////// // 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: 551, 52 LR fn, tp: 6, 25 LR f1 score: 0.463 LR cohens kappa score: 0.423 LR average precision score: 0.511 -> 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: 529, 74 LR fn, tp: 5, 26 LR f1 score: 0.397 LR cohens kappa score: 0.348 LR average precision score: 0.469 -> test with 'GB' GB tn, fp: 594, 9 GB fn, tp: 3, 28 GB f1 score: 0.824 GB cohens kappa score: 0.814 -> test with 'KNN' KNN tn, fp: 582, 21 KNN fn, tp: 6, 25 KNN f1 score: 0.649 KNN cohens kappa score: 0.628 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 532, 71 LR fn, tp: 7, 24 LR f1 score: 0.381 LR cohens kappa score: 0.332 LR average precision score: 0.363 -> 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: 521, 82 LR fn, tp: 5, 26 LR f1 score: 0.374 LR cohens kappa score: 0.323 LR average precision score: 0.393 -> 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: 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: 540, 60 LR fn, tp: 3, 24 LR f1 score: 0.432 LR cohens kappa score: 0.393 LR average precision score: 0.559 -> 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: 543, 60 LR fn, tp: 8, 23 LR f1 score: 0.404 LR cohens kappa score: 0.358 LR average precision score: 0.484 -> 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: 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: 547, 56 LR fn, tp: 6, 25 LR f1 score: 0.446 LR cohens kappa score: 0.404 LR average precision score: 0.491 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 2, 29 GB f1 score: 0.879 GB cohens kappa score: 0.872 -> test with 'KNN' KNN tn, fp: 581, 22 KNN fn, tp: 7, 24 KNN f1 score: 0.623 KNN cohens kappa score: 0.600 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 526, 77 LR fn, tp: 4, 27 LR f1 score: 0.400 LR cohens kappa score: 0.351 LR average precision score: 0.593 -> 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: 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: 529, 74 LR fn, tp: 7, 24 LR f1 score: 0.372 LR cohens kappa score: 0.322 LR average precision score: 0.312 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 9, 22 GB f1 score: 0.759 GB cohens kappa score: 0.747 -> 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 'GB' GB tn, fp: 594, 6 GB fn, tp: 4, 23 GB f1 score: 0.821 GB cohens kappa score: 0.813 -> test with 'KNN' KNN tn, fp: 584, 16 KNN fn, tp: 5, 22 KNN f1 score: 0.677 KNN cohens kappa score: 0.660 ====== 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: 533, 70 LR fn, tp: 6, 25 LR f1 score: 0.397 LR cohens kappa score: 0.349 LR average precision score: 0.499 -> 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: 549, 54 LR fn, tp: 12, 19 LR f1 score: 0.365 LR cohens kappa score: 0.319 LR average precision score: 0.333 -> test with 'GB' GB tn, fp: 593, 10 GB fn, tp: 5, 26 GB f1 score: 0.776 GB cohens kappa score: 0.764 -> 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: 532, 71 LR fn, tp: 1, 30 LR f1 score: 0.455 LR cohens kappa score: 0.410 LR average precision score: 0.600 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 6, 25 GB f1 score: 0.781 GB cohens kappa score: 0.770 -> 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: 516, 87 LR fn, tp: 3, 28 LR f1 score: 0.384 LR cohens kappa score: 0.332 LR average precision score: 0.483 -> 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: 0.361 -> test with 'GB' GB tn, fp: 597, 3 GB fn, tp: 4, 23 GB f1 score: 0.868 GB cohens kappa score: 0.862 -> 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: 532, 71 LR fn, tp: 5, 26 LR f1 score: 0.406 LR cohens kappa score: 0.359 LR average precision score: 0.418 -> 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: 6, 25 LR f1 score: 0.446 LR cohens kappa score: 0.404 LR average precision score: 0.458 -> 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: 523, 80 LR fn, tp: 3, 28 LR f1 score: 0.403 LR cohens kappa score: 0.354 LR average precision score: 0.590 -> test with 'GB' GB tn, fp: 601, 2 GB fn, tp: 7, 24 GB f1 score: 0.842 GB cohens kappa score: 0.835 -> 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: 0.456 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 7, 24 GB f1 score: 0.828 GB cohens kappa score: 0.819 -> 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: 538, 62 LR fn, tp: 9, 18 LR f1 score: 0.336 LR cohens kappa score: 0.291 LR average precision score: 0.410 -> test with 'GB' GB tn, fp: 598, 2 GB fn, tp: 9, 18 GB f1 score: 0.766 GB cohens kappa score: 0.757 -> 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: 539, 64 LR fn, tp: 6, 25 LR f1 score: 0.417 LR cohens kappa score: 0.371 LR average precision score: 0.458 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 10, 21 GB f1 score: 0.700 GB cohens kappa score: 0.685 -> test with 'KNN' KNN tn, fp: 582, 21 KNN fn, tp: 9, 22 KNN f1 score: 0.595 KNN cohens kappa score: 0.570 ------ 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: 0.518 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 5, 26 GB f1 score: 0.867 GB cohens kappa score: 0.860 -> 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: 524, 79 LR fn, tp: 4, 27 LR f1 score: 0.394 LR cohens kappa score: 0.345 LR average precision score: 0.517 -> 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: 528, 75 LR fn, tp: 4, 27 LR f1 score: 0.406 LR cohens kappa score: 0.358 LR average precision score: 0.549 -> 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: 533, 67 LR fn, tp: 4, 23 LR f1 score: 0.393 LR cohens kappa score: 0.350 LR average precision score: 0.370 -> test with 'GB' GB tn, fp: 596, 4 GB fn, tp: 10, 17 GB f1 score: 0.708 GB cohens kappa score: 0.697 -> test with 'KNN' KNN tn, fp: 582, 18 KNN fn, tp: 10, 17 KNN f1 score: 0.548 KNN cohens kappa score: 0.525 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 551, 87 LR fn, tp: 12, 30 LR f1 score: 0.463 LR cohens kappa score: 0.423 LR average precision score: 0.600 average: LR tn, fp: 532.48, 69.92 LR fn, tp: 5.24, 24.96 LR f1 score: 0.400 LR cohens kappa score: 0.353 LR average precision score: 0.469 minimum: LR tn, fp: 514, 52 LR fn, tp: 1, 18 LR f1 score: 0.336 LR cohens kappa score: 0.291 LR average precision score: 0.312 -----[ GB ]----- maximum: GB tn, fp: 601, 12 GB fn, tp: 11, 29 GB f1 score: 0.879 GB cohens kappa score: 0.872 average: GB tn, fp: 596.96, 5.44 GB fn, tp: 6.92, 23.28 GB f1 score: 0.789 GB cohens kappa score: 0.779 minimum: GB tn, fp: 591, 2 GB fn, tp: 2, 17 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: 582.08, 20.32 KNN fn, tp: 7.84, 22.36 KNN f1 score: 0.616 KNN cohens kappa score: 0.593 minimum: KNN tn, fp: 569, 12 KNN fn, tp: 4, 17 KNN f1 score: 0.521 KNN cohens kappa score: 0.492