/////////////////////////////////////////// // Running Repeater 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: 520, 83 LR fn, tp: 4, 27 LR f1 score: 0.383 LR cohens kappa score: 0.332 LR average precision score: 0.473 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 6, 25 RF f1 score: 0.833 RF cohens kappa score: 0.825 -> test with 'GB' GB tn, fp: 592, 11 GB fn, tp: 3, 28 GB f1 score: 0.800 GB cohens kappa score: 0.788 -> test with 'KNN' KNN tn, fp: 574, 29 KNN fn, tp: 2, 29 KNN f1 score: 0.652 KNN cohens kappa score: 0.628 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 507, 96 LR fn, tp: 2, 29 LR f1 score: 0.372 LR cohens kappa score: 0.318 LR average precision score: 0.467 -> test with 'RF' RF tn, fp: 597, 6 RF fn, tp: 3, 28 RF f1 score: 0.862 RF cohens kappa score: 0.854 -> test with 'GB' GB tn, fp: 586, 17 GB fn, tp: 2, 29 GB f1 score: 0.753 GB cohens kappa score: 0.738 -> test with 'KNN' KNN tn, fp: 566, 37 KNN fn, tp: 6, 25 KNN f1 score: 0.538 KNN cohens kappa score: 0.505 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 503, 100 LR fn, tp: 4, 27 LR f1 score: 0.342 LR cohens kappa score: 0.286 LR average precision score: 0.346 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 4, 27 RF f1 score: 0.885 RF cohens kappa score: 0.879 -> test with 'GB' GB tn, fp: 587, 16 GB fn, tp: 2, 29 GB f1 score: 0.763 GB cohens kappa score: 0.749 -> test with 'KNN' KNN tn, fp: 572, 31 KNN fn, tp: 6, 25 KNN f1 score: 0.575 KNN cohens kappa score: 0.546 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 499, 104 LR fn, tp: 3, 28 LR f1 score: 0.344 LR cohens kappa score: 0.287 LR average precision score: 0.420 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 13, 18 RF f1 score: 0.692 RF cohens kappa score: 0.680 -> 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: 571, 32 KNN fn, tp: 11, 20 KNN f1 score: 0.482 KNN cohens kappa score: 0.448 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 509, 91 LR fn, tp: 3, 24 LR f1 score: 0.338 LR cohens kappa score: 0.288 LR average precision score: 0.524 -> test with 'RF' RF tn, fp: 597, 3 RF fn, tp: 7, 20 RF f1 score: 0.800 RF cohens kappa score: 0.792 -> test with 'GB' GB tn, fp: 592, 8 GB fn, tp: 3, 24 GB f1 score: 0.814 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 568, 32 KNN fn, tp: 3, 24 KNN f1 score: 0.578 KNN cohens kappa score: 0.552 ====== 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: 521, 82 LR fn, tp: 3, 28 LR f1 score: 0.397 LR cohens kappa score: 0.347 LR average precision score: 0.482 -> test with 'RF' RF tn, fp: 598, 5 RF fn, tp: 8, 23 RF f1 score: 0.780 RF cohens kappa score: 0.769 -> test with 'GB' GB tn, fp: 590, 13 GB fn, tp: 2, 29 GB f1 score: 0.795 GB cohens kappa score: 0.782 -> test with 'KNN' KNN tn, fp: 580, 23 KNN fn, tp: 6, 25 KNN f1 score: 0.633 KNN cohens kappa score: 0.610 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 520, 83 LR fn, tp: 5, 26 LR f1 score: 0.371 LR cohens kappa score: 0.320 LR average precision score: 0.411 -> test with 'RF' RF tn, fp: 596, 7 RF fn, tp: 7, 24 RF f1 score: 0.774 RF cohens kappa score: 0.763 -> test with 'GB' GB tn, fp: 588, 15 GB fn, tp: 3, 28 GB f1 score: 0.757 GB cohens kappa score: 0.742 -> test with 'KNN' KNN tn, fp: 573, 30 KNN fn, tp: 4, 27 KNN f1 score: 0.614 KNN cohens kappa score: 0.588 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 501, 102 LR fn, tp: 2, 29 LR f1 score: 0.358 LR cohens kappa score: 0.303 LR average precision score: 0.574 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 10, 21 RF f1 score: 0.764 RF cohens kappa score: 0.753 -> test with 'GB' GB tn, fp: 594, 9 GB fn, tp: 6, 25 GB f1 score: 0.769 GB cohens kappa score: 0.757 -> test with 'KNN' KNN tn, fp: 569, 34 KNN fn, tp: 7, 24 KNN f1 score: 0.539 KNN cohens kappa score: 0.508 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 508, 95 LR fn, tp: 5, 26 LR f1 score: 0.342 LR cohens kappa score: 0.287 LR average precision score: 0.310 -> test with 'RF' RF tn, fp: 597, 6 RF fn, tp: 7, 24 RF f1 score: 0.787 RF cohens kappa score: 0.776 -> test with 'GB' GB tn, fp: 589, 14 GB fn, tp: 4, 27 GB f1 score: 0.750 GB cohens kappa score: 0.735 -> test with 'KNN' KNN tn, fp: 569, 34 KNN fn, tp: 7, 24 KNN f1 score: 0.539 KNN cohens kappa score: 0.508 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 493, 107 LR fn, tp: 1, 26 LR f1 score: 0.325 LR cohens kappa score: 0.273 LR average precision score: 0.477 -> test with 'RF' RF tn, fp: 596, 4 RF fn, tp: 3, 24 RF f1 score: 0.873 RF cohens kappa score: 0.867 -> test with 'GB' GB tn, fp: 591, 9 GB fn, tp: 2, 25 GB f1 score: 0.820 GB cohens kappa score: 0.811 -> test with 'KNN' KNN tn, fp: 565, 35 KNN fn, tp: 5, 22 KNN f1 score: 0.524 KNN cohens kappa score: 0.494 ====== 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: 498, 105 LR fn, tp: 1, 30 LR f1 score: 0.361 LR cohens kappa score: 0.306 LR average precision score: 0.478 -> 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: 595, 8 GB fn, tp: 3, 28 GB f1 score: 0.836 GB cohens kappa score: 0.827 -> test with 'KNN' KNN tn, fp: 579, 24 KNN fn, tp: 7, 24 KNN f1 score: 0.608 KNN cohens kappa score: 0.583 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 525, 78 LR fn, tp: 10, 21 LR f1 score: 0.323 LR cohens kappa score: 0.269 LR average precision score: 0.294 -> test with 'RF' RF tn, fp: 596, 7 RF fn, tp: 4, 27 RF f1 score: 0.831 RF cohens kappa score: 0.822 -> test with 'GB' GB tn, fp: 588, 15 GB fn, tp: 3, 28 GB f1 score: 0.757 GB cohens kappa score: 0.742 -> test with 'KNN' KNN tn, fp: 572, 31 KNN fn, tp: 6, 25 KNN f1 score: 0.575 KNN cohens kappa score: 0.546 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 510, 93 LR fn, tp: 1, 30 LR f1 score: 0.390 LR cohens kappa score: 0.338 LR average precision score: 0.547 -> test with 'RF' RF tn, fp: 598, 5 RF fn, tp: 6, 25 RF f1 score: 0.820 RF cohens kappa score: 0.811 -> test with 'GB' GB tn, fp: 585, 18 GB fn, tp: 3, 28 GB f1 score: 0.727 GB cohens kappa score: 0.710 -> test with 'KNN' KNN tn, fp: 566, 37 KNN fn, tp: 7, 24 KNN f1 score: 0.522 KNN cohens kappa score: 0.489 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 507, 96 LR fn, tp: 2, 29 LR f1 score: 0.372 LR cohens kappa score: 0.318 LR average precision score: 0.457 -> test with 'RF' RF tn, fp: 598, 5 RF fn, tp: 10, 21 RF f1 score: 0.737 RF cohens kappa score: 0.725 -> test with 'GB' GB tn, fp: 592, 11 GB fn, tp: 4, 27 GB f1 score: 0.783 GB cohens kappa score: 0.770 -> test with 'KNN' KNN tn, fp: 570, 33 KNN fn, tp: 7, 24 KNN f1 score: 0.545 KNN cohens kappa score: 0.515 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 509, 91 LR fn, tp: 4, 23 LR f1 score: 0.326 LR cohens kappa score: 0.276 LR average precision score: 0.283 -> test with 'RF' RF tn, fp: 597, 3 RF fn, tp: 3, 24 RF f1 score: 0.889 RF cohens kappa score: 0.884 -> test with 'GB' GB tn, fp: 591, 9 GB fn, tp: 1, 26 GB f1 score: 0.839 GB cohens kappa score: 0.830 -> test with 'KNN' KNN tn, fp: 574, 26 KNN fn, tp: 2, 25 KNN f1 score: 0.641 KNN cohens kappa score: 0.620 ====== 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: 519, 84 LR fn, tp: 3, 28 LR f1 score: 0.392 LR cohens kappa score: 0.341 LR average precision score: 0.362 -> test with 'RF' RF tn, fp: 597, 6 RF fn, tp: 7, 24 RF f1 score: 0.787 RF cohens kappa score: 0.776 -> test with 'GB' GB tn, fp: 588, 15 GB fn, tp: 4, 27 GB f1 score: 0.740 GB cohens kappa score: 0.724 -> test with 'KNN' KNN tn, fp: 570, 33 KNN fn, tp: 5, 26 KNN f1 score: 0.578 KNN cohens kappa score: 0.549 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 518, 85 LR fn, tp: 5, 26 LR f1 score: 0.366 LR cohens kappa score: 0.314 LR average precision score: 0.423 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 7, 24 RF f1 score: 0.814 RF cohens kappa score: 0.804 -> test with 'GB' GB tn, fp: 589, 14 GB fn, tp: 3, 28 GB f1 score: 0.767 GB cohens kappa score: 0.753 -> test with 'KNN' KNN tn, fp: 574, 29 KNN fn, tp: 4, 27 KNN f1 score: 0.621 KNN cohens kappa score: 0.595 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 505, 98 LR fn, tp: 3, 28 LR f1 score: 0.357 LR cohens kappa score: 0.302 LR average precision score: 0.585 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 8, 23 RF f1 score: 0.793 RF cohens kappa score: 0.783 -> 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: 575, 28 KNN fn, tp: 5, 26 KNN f1 score: 0.612 KNN cohens kappa score: 0.586 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 492, 111 LR fn, tp: 2, 29 LR f1 score: 0.339 LR cohens kappa score: 0.282 LR average precision score: 0.436 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 3, 28 RF f1 score: 0.918 RF cohens kappa score: 0.914 -> test with 'GB' GB tn, fp: 592, 11 GB fn, tp: 2, 29 GB f1 score: 0.817 GB cohens kappa score: 0.806 -> test with 'KNN' KNN tn, fp: 570, 33 KNN fn, tp: 7, 24 KNN f1 score: 0.545 KNN cohens kappa score: 0.515 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 506, 94 LR fn, tp: 3, 24 LR f1 score: 0.331 LR cohens kappa score: 0.281 LR average precision score: 0.421 -> test with 'RF' RF tn, fp: 594, 6 RF fn, tp: 8, 19 RF f1 score: 0.731 RF cohens kappa score: 0.719 -> test with 'GB' GB tn, fp: 586, 14 GB fn, tp: 4, 23 GB f1 score: 0.719 GB cohens kappa score: 0.704 -> test with 'KNN' KNN tn, fp: 565, 35 KNN fn, tp: 6, 21 KNN f1 score: 0.506 KNN cohens kappa score: 0.476 ====== 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: 508, 95 LR fn, tp: 3, 28 LR f1 score: 0.364 LR cohens kappa score: 0.310 LR average precision score: 0.435 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 6, 25 RF f1 score: 0.833 RF cohens kappa score: 0.825 -> test with 'GB' GB tn, fp: 587, 16 GB fn, tp: 3, 28 GB f1 score: 0.747 GB cohens kappa score: 0.731 -> test with 'KNN' KNN tn, fp: 575, 28 KNN fn, tp: 6, 25 KNN f1 score: 0.595 KNN cohens kappa score: 0.569 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 511, 92 LR fn, tp: 4, 27 LR f1 score: 0.360 LR cohens kappa score: 0.306 LR average precision score: 0.479 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 7, 24 RF f1 score: 0.842 RF cohens kappa score: 0.835 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 1, 30 GB f1 score: 0.870 GB cohens kappa score: 0.862 -> test with 'KNN' KNN tn, fp: 570, 33 KNN fn, tp: 8, 23 KNN f1 score: 0.529 KNN cohens kappa score: 0.497 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 500, 103 LR fn, tp: 2, 29 LR f1 score: 0.356 LR cohens kappa score: 0.300 LR average precision score: 0.496 -> 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: 588, 15 GB fn, tp: 7, 24 GB f1 score: 0.686 GB cohens kappa score: 0.668 -> test with 'KNN' KNN tn, fp: 569, 34 KNN fn, tp: 7, 24 KNN f1 score: 0.539 KNN cohens kappa score: 0.508 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 504, 99 LR fn, tp: 3, 28 LR f1 score: 0.354 LR cohens kappa score: 0.299 LR average precision score: 0.457 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 3, 28 RF f1 score: 0.889 RF cohens kappa score: 0.883 -> test with 'GB' GB tn, fp: 594, 9 GB fn, tp: 2, 29 GB f1 score: 0.841 GB cohens kappa score: 0.832 -> 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 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 519, 81 LR fn, tp: 2, 25 LR f1 score: 0.376 LR cohens kappa score: 0.330 LR average precision score: 0.311 -> test with 'RF' RF tn, fp: 594, 6 RF fn, tp: 10, 17 RF f1 score: 0.680 RF cohens kappa score: 0.667 -> test with 'GB' GB tn, fp: 590, 10 GB fn, tp: 5, 22 GB f1 score: 0.746 GB cohens kappa score: 0.733 -> test with 'KNN' KNN tn, fp: 569, 31 KNN fn, tp: 5, 22 KNN f1 score: 0.550 KNN cohens kappa score: 0.523 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 525, 111 LR fn, tp: 10, 30 LR f1 score: 0.397 LR cohens kappa score: 0.347 LR average precision score: 0.585 average: LR tn, fp: 508.48, 93.92 LR fn, tp: 3.2, 27.0 LR f1 score: 0.358 LR cohens kappa score: 0.305 LR average precision score: 0.438 minimum: LR tn, fp: 492, 78 LR fn, tp: 1, 21 LR f1 score: 0.323 LR cohens kappa score: 0.269 LR average precision score: 0.283 -----[ RF ]----- maximum: RF tn, fp: 601, 7 RF fn, tp: 13, 28 RF f1 score: 0.918 RF cohens kappa score: 0.914 average: RF tn, fp: 598.04, 4.36 RF fn, tp: 7.0, 23.2 RF f1 score: 0.800 RF cohens kappa score: 0.791 minimum: RF tn, fp: 594, 2 RF fn, tp: 3, 17 RF f1 score: 0.680 RF cohens kappa score: 0.667 -----[ GB ]----- maximum: GB tn, fp: 597, 18 GB fn, tp: 7, 30 GB f1 score: 0.870 GB cohens kappa score: 0.862 average: GB tn, fp: 590.44, 11.96 GB fn, tp: 3.28, 26.92 GB f1 score: 0.781 GB cohens kappa score: 0.768 minimum: GB tn, fp: 585, 6 GB fn, tp: 1, 22 GB f1 score: 0.686 GB cohens kappa score: 0.668 -----[ KNN ]----- maximum: KNN tn, fp: 580, 37 KNN fn, tp: 11, 29 KNN f1 score: 0.652 KNN cohens kappa score: 0.628 average: KNN tn, fp: 571.0, 31.4 KNN fn, tp: 5.8, 24.4 KNN f1 score: 0.568 KNN cohens kappa score: 0.540 minimum: KNN tn, fp: 565, 23 KNN fn, tp: 2, 20 KNN f1 score: 0.482 KNN cohens kappa score: 0.448 wall time: 00:02:55s, process time: 00:05:09s