/////////////////////////////////////////// // Running ctGAN 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: 484, 119 LR fn, tp: 7, 24 LR f1 score: 0.276 LR cohens kappa score: 0.213 LR average precision score: 0.246 -> test with 'RF' RF tn, fp: 593, 10 RF fn, tp: 3, 28 RF f1 score: 0.812 RF cohens kappa score: 0.801 -> test with 'GB' GB tn, fp: 589, 14 GB fn, tp: 2, 29 GB f1 score: 0.784 GB cohens kappa score: 0.771 -> 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 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 489, 114 LR fn, tp: 9, 22 LR f1 score: 0.263 LR cohens kappa score: 0.200 LR average precision score: 0.169 -> test with 'RF' RF tn, fp: 587, 16 RF fn, tp: 2, 29 RF f1 score: 0.763 RF cohens kappa score: 0.749 -> test with 'GB' GB tn, fp: 584, 19 GB fn, tp: 2, 29 GB f1 score: 0.734 GB cohens kappa score: 0.717 -> test with 'KNN' KNN tn, fp: 580, 23 KNN fn, tp: 4, 27 KNN f1 score: 0.667 KNN cohens kappa score: 0.645 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 481, 122 LR fn, tp: 7, 24 LR f1 score: 0.271 LR cohens kappa score: 0.207 LR average precision score: 0.200 -> test with 'RF' RF tn, fp: 586, 17 RF fn, tp: 1, 30 RF f1 score: 0.769 RF cohens kappa score: 0.755 -> test with 'GB' GB tn, fp: 585, 18 GB fn, tp: 1, 30 GB f1 score: 0.759 GB cohens kappa score: 0.744 -> test with 'KNN' KNN tn, fp: 578, 25 KNN fn, tp: 12, 19 KNN f1 score: 0.507 KNN cohens kappa score: 0.477 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 498, 105 LR fn, tp: 6, 25 LR f1 score: 0.311 LR cohens kappa score: 0.251 LR average precision score: 0.198 -> test with 'RF' RF tn, fp: 592, 11 RF fn, tp: 3, 28 RF f1 score: 0.800 RF cohens kappa score: 0.788 -> 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: 583, 20 KNN fn, tp: 9, 22 KNN f1 score: 0.603 KNN cohens kappa score: 0.579 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 492, 108 LR fn, tp: 5, 22 LR f1 score: 0.280 LR cohens kappa score: 0.225 LR average precision score: 0.286 -> test with 'RF' RF tn, fp: 589, 11 RF fn, tp: 3, 24 RF f1 score: 0.774 RF cohens kappa score: 0.763 -> test with 'GB' GB tn, fp: 587, 13 GB fn, tp: 3, 24 GB f1 score: 0.750 GB cohens kappa score: 0.737 -> test with 'KNN' KNN tn, fp: 581, 19 KNN fn, tp: 8, 19 KNN f1 score: 0.585 KNN cohens kappa score: 0.563 ====== 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: 476, 127 LR fn, tp: 7, 24 LR f1 score: 0.264 LR cohens kappa score: 0.199 LR average precision score: 0.194 -> test with 'RF' RF tn, fp: 589, 14 RF fn, tp: 2, 29 RF f1 score: 0.784 RF cohens kappa score: 0.771 -> 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: 583, 20 KNN fn, tp: 10, 21 KNN f1 score: 0.583 KNN cohens kappa score: 0.559 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 494, 109 LR fn, tp: 7, 24 LR f1 score: 0.293 LR cohens kappa score: 0.232 LR average precision score: 0.260 -> test with 'RF' RF tn, fp: 581, 22 RF fn, tp: 1, 30 RF f1 score: 0.723 RF cohens kappa score: 0.705 -> test with 'GB' GB tn, fp: 586, 17 GB fn, tp: 0, 31 GB f1 score: 0.785 GB cohens kappa score: 0.771 -> test with 'KNN' KNN tn, fp: 588, 15 KNN fn, tp: 4, 27 KNN f1 score: 0.740 KNN cohens kappa score: 0.724 ------ Step 2/5: Slice 3/5 ------- -> 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: 0.289 -> test with 'RF' RF tn, fp: 595, 8 RF fn, tp: 2, 29 RF f1 score: 0.853 RF cohens kappa score: 0.845 -> test with 'GB' GB tn, fp: 589, 14 GB fn, tp: 2, 29 GB f1 score: 0.784 GB cohens kappa score: 0.771 -> 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 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 509, 94 LR fn, tp: 11, 20 LR f1 score: 0.276 LR cohens kappa score: 0.216 LR average precision score: 0.191 -> test with 'RF' RF tn, fp: 586, 17 RF fn, tp: 5, 26 RF f1 score: 0.703 RF cohens kappa score: 0.685 -> test with 'GB' GB tn, fp: 585, 18 GB fn, tp: 5, 26 GB f1 score: 0.693 GB cohens kappa score: 0.675 -> test with 'KNN' KNN tn, fp: 579, 24 KNN fn, tp: 11, 20 KNN f1 score: 0.533 KNN cohens kappa score: 0.505 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 498, 102 LR fn, tp: 5, 22 LR f1 score: 0.291 LR cohens kappa score: 0.237 LR average precision score: 0.218 -> test with 'RF' RF tn, fp: 585, 15 RF fn, tp: 2, 25 RF f1 score: 0.746 RF cohens kappa score: 0.733 -> test with 'GB' GB tn, fp: 587, 13 GB fn, tp: 2, 25 GB f1 score: 0.769 GB cohens kappa score: 0.757 -> test with 'KNN' KNN tn, fp: 580, 20 KNN fn, tp: 5, 22 KNN f1 score: 0.638 KNN cohens kappa score: 0.618 ====== 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: 7, 24 LR f1 score: 0.300 LR cohens kappa score: 0.240 LR average precision score: 0.221 -> test with 'RF' RF tn, fp: 594, 9 RF fn, tp: 0, 31 RF f1 score: 0.873 RF cohens kappa score: 0.866 -> test with 'GB' GB tn, fp: 593, 10 GB fn, tp: 1, 30 GB f1 score: 0.845 GB cohens kappa score: 0.836 -> test with 'KNN' KNN tn, fp: 594, 9 KNN fn, tp: 10, 21 KNN f1 score: 0.689 KNN cohens kappa score: 0.673 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 508, 95 LR fn, tp: 8, 23 LR f1 score: 0.309 LR cohens kappa score: 0.251 LR average precision score: 0.212 -> test with 'RF' RF tn, fp: 580, 23 RF fn, tp: 3, 28 RF f1 score: 0.683 RF cohens kappa score: 0.662 -> test with 'GB' GB tn, fp: 576, 27 GB fn, tp: 3, 28 GB f1 score: 0.651 GB cohens kappa score: 0.628 -> 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: 494, 109 LR fn, tp: 5, 26 LR f1 score: 0.313 LR cohens kappa score: 0.254 LR average precision score: 0.306 -> test with 'RF' RF tn, fp: 585, 18 RF fn, tp: 6, 25 RF f1 score: 0.676 RF cohens kappa score: 0.656 -> test with 'GB' GB tn, fp: 581, 22 GB fn, tp: 5, 26 GB f1 score: 0.658 GB cohens kappa score: 0.637 -> test with 'KNN' KNN tn, fp: 575, 28 KNN fn, tp: 10, 21 KNN f1 score: 0.525 KNN cohens kappa score: 0.495 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 512, 91 LR fn, tp: 6, 25 LR f1 score: 0.340 LR cohens kappa score: 0.285 LR average precision score: 0.307 -> test with 'RF' RF tn, fp: 588, 15 RF fn, tp: 2, 29 RF f1 score: 0.773 RF cohens kappa score: 0.760 -> 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: 575, 28 KNN fn, tp: 7, 24 KNN f1 score: 0.578 KNN cohens kappa score: 0.551 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 491, 109 LR fn, tp: 6, 21 LR f1 score: 0.268 LR cohens kappa score: 0.211 LR average precision score: 0.199 -> test with 'RF' RF tn, fp: 582, 18 RF fn, tp: 1, 26 RF f1 score: 0.732 RF cohens kappa score: 0.717 -> test with 'GB' GB tn, fp: 583, 17 GB fn, tp: 1, 26 GB f1 score: 0.743 GB cohens kappa score: 0.728 -> test with 'KNN' KNN tn, fp: 582, 18 KNN fn, tp: 4, 23 KNN f1 score: 0.676 KNN cohens kappa score: 0.659 ====== 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: 501, 102 LR fn, tp: 10, 21 LR f1 score: 0.273 LR cohens kappa score: 0.211 LR average precision score: 0.178 -> test with 'RF' RF tn, fp: 587, 16 RF fn, tp: 4, 27 RF f1 score: 0.730 RF cohens kappa score: 0.713 -> test with 'GB' GB tn, fp: 581, 22 GB fn, tp: 3, 28 GB f1 score: 0.691 GB cohens kappa score: 0.672 -> test with 'KNN' KNN tn, fp: 585, 18 KNN fn, tp: 9, 22 KNN f1 score: 0.620 KNN cohens kappa score: 0.598 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 492, 111 LR fn, tp: 7, 24 LR f1 score: 0.289 LR cohens kappa score: 0.228 LR average precision score: 0.209 -> test with 'RF' RF tn, fp: 594, 9 RF fn, tp: 2, 29 RF f1 score: 0.841 RF cohens kappa score: 0.832 -> test with 'GB' GB tn, fp: 591, 12 GB fn, tp: 1, 30 GB f1 score: 0.822 GB cohens kappa score: 0.811 -> test with 'KNN' KNN tn, fp: 588, 15 KNN fn, tp: 8, 23 KNN f1 score: 0.667 KNN cohens kappa score: 0.648 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 487, 116 LR fn, tp: 4, 27 LR f1 score: 0.310 LR cohens kappa score: 0.250 LR average precision score: 0.270 -> test with 'RF' RF tn, fp: 591, 12 RF fn, tp: 4, 27 RF f1 score: 0.771 RF cohens kappa score: 0.758 -> test with 'GB' GB tn, fp: 590, 13 GB fn, tp: 3, 28 GB f1 score: 0.778 GB cohens kappa score: 0.765 -> 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 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 479, 124 LR fn, tp: 5, 26 LR f1 score: 0.287 LR cohens kappa score: 0.224 LR average precision score: 0.212 -> test with 'RF' RF tn, fp: 589, 14 RF fn, tp: 1, 30 RF f1 score: 0.800 RF cohens kappa score: 0.788 -> test with 'GB' GB tn, fp: 587, 16 GB fn, tp: 0, 31 GB f1 score: 0.795 GB cohens kappa score: 0.782 -> test with 'KNN' KNN tn, fp: 571, 32 KNN fn, tp: 9, 22 KNN f1 score: 0.518 KNN cohens kappa score: 0.486 ------ 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: 7, 20 LR f1 score: 0.284 LR cohens kappa score: 0.230 LR average precision score: 0.237 -> test with 'RF' RF tn, fp: 583, 17 RF fn, tp: 4, 23 RF f1 score: 0.687 RF cohens kappa score: 0.670 -> test with 'GB' GB tn, fp: 580, 20 GB fn, tp: 4, 23 GB f1 score: 0.657 GB cohens kappa score: 0.638 -> 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: 483, 120 LR fn, tp: 7, 24 LR f1 score: 0.274 LR cohens kappa score: 0.211 LR average precision score: 0.186 -> test with 'RF' RF tn, fp: 590, 13 RF fn, tp: 2, 29 RF f1 score: 0.795 RF cohens kappa score: 0.782 -> test with 'GB' GB tn, fp: 589, 14 GB fn, tp: 2, 29 GB f1 score: 0.784 GB cohens kappa score: 0.771 -> 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 5/5: 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: 8, 23 LR f1 score: 0.307 LR cohens kappa score: 0.248 LR average precision score: 0.229 -> test with 'RF' RF tn, fp: 590, 13 RF fn, tp: 2, 29 RF f1 score: 0.795 RF cohens kappa score: 0.782 -> test with 'GB' GB tn, fp: 585, 18 GB fn, tp: 2, 29 GB f1 score: 0.744 GB cohens kappa score: 0.728 -> test with 'KNN' KNN tn, fp: 580, 23 KNN fn, tp: 9, 22 KNN f1 score: 0.579 KNN cohens kappa score: 0.553 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 456, 147 LR fn, tp: 3, 28 LR f1 score: 0.272 LR cohens kappa score: 0.206 LR average precision score: 0.241 -> test with 'RF' RF tn, fp: 592, 11 RF fn, tp: 6, 25 RF f1 score: 0.746 RF cohens kappa score: 0.732 -> test with 'GB' GB tn, fp: 582, 21 GB fn, tp: 4, 27 GB f1 score: 0.684 GB cohens kappa score: 0.664 -> 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 5/5: 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: 4, 27 LR f1 score: 0.333 LR cohens kappa score: 0.276 LR average precision score: 0.277 -> test with 'RF' RF tn, fp: 585, 18 RF fn, tp: 0, 31 RF f1 score: 0.775 RF cohens kappa score: 0.761 -> test with 'GB' GB tn, fp: 586, 17 GB fn, tp: 0, 31 GB f1 score: 0.785 GB cohens kappa score: 0.771 -> 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 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 523, 77 LR fn, tp: 9, 18 LR f1 score: 0.295 LR cohens kappa score: 0.244 LR average precision score: 0.165 -> test with 'RF' RF tn, fp: 583, 17 RF fn, tp: 3, 24 RF f1 score: 0.706 RF cohens kappa score: 0.690 -> test with 'GB' GB tn, fp: 585, 15 GB fn, tp: 3, 24 GB f1 score: 0.727 GB cohens kappa score: 0.713 -> test with 'KNN' KNN tn, fp: 578, 22 KNN fn, tp: 9, 18 KNN f1 score: 0.537 KNN cohens kappa score: 0.512 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 523, 147 LR fn, tp: 11, 28 LR f1 score: 0.360 LR cohens kappa score: 0.306 LR average precision score: 0.307 average: LR tn, fp: 494.72, 107.68 LR fn, tp: 6.56, 23.64 LR f1 score: 0.294 LR cohens kappa score: 0.234 LR average precision score: 0.228 minimum: LR tn, fp: 456, 77 LR fn, tp: 3, 18 LR f1 score: 0.263 LR cohens kappa score: 0.199 LR average precision score: 0.165 -----[ RF ]----- maximum: RF tn, fp: 595, 23 RF fn, tp: 6, 31 RF f1 score: 0.873 RF cohens kappa score: 0.866 average: RF tn, fp: 587.84, 14.56 RF fn, tp: 2.56, 27.64 RF f1 score: 0.764 RF cohens kappa score: 0.750 minimum: RF tn, fp: 580, 8 RF fn, tp: 0, 23 RF f1 score: 0.676 RF cohens kappa score: 0.656 -----[ GB ]----- maximum: GB tn, fp: 593, 27 GB fn, tp: 5, 31 GB f1 score: 0.845 GB cohens kappa score: 0.836 average: GB tn, fp: 585.8, 16.6 GB fn, tp: 2.28, 27.92 GB f1 score: 0.748 GB cohens kappa score: 0.733 minimum: GB tn, fp: 576, 10 GB fn, tp: 0, 23 GB f1 score: 0.651 GB cohens kappa score: 0.628 -----[ KNN ]----- maximum: KNN tn, fp: 594, 33 KNN fn, tp: 12, 27 KNN f1 score: 0.740 KNN cohens kappa score: 0.724 average: KNN tn, fp: 580.28, 22.12 KNN fn, tp: 7.64, 22.56 KNN f1 score: 0.604 KNN cohens kappa score: 0.580 minimum: KNN tn, fp: 570, 9 KNN fn, tp: 4, 18 KNN f1 score: 0.507 KNN cohens kappa score: 0.477