/////////////////////////////////////////// // Running CTGAN 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: 503, 100 LR fn, tp: 3, 28 LR f1 score: 0.352 LR cohens kappa score: 0.297 LR average precision score: 0.346 -> 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: 593, 10 GB fn, tp: 2, 29 GB f1 score: 0.829 GB cohens kappa score: 0.819 -> 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: 481, 122 LR fn, tp: 2, 29 LR f1 score: 0.319 LR cohens kappa score: 0.259 LR average precision score: 0.299 -> test with 'RF' RF tn, fp: 584, 19 RF fn, tp: 1, 30 RF f1 score: 0.750 RF cohens kappa score: 0.734 -> test with 'GB' GB tn, fp: 581, 22 GB fn, tp: 1, 30 GB f1 score: 0.723 GB cohens kappa score: 0.705 -> test with 'KNN' KNN tn, fp: 578, 25 KNN fn, tp: 4, 27 KNN f1 score: 0.651 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: 480, 123 LR fn, tp: 6, 25 LR f1 score: 0.279 LR cohens kappa score: 0.216 LR average precision score: 0.215 -> test with 'RF' RF tn, fp: 591, 12 RF fn, tp: 1, 30 RF f1 score: 0.822 RF cohens kappa score: 0.811 -> test with 'GB' GB tn, fp: 586, 17 GB fn, tp: 1, 30 GB f1 score: 0.769 GB cohens kappa score: 0.755 -> test with 'KNN' KNN tn, fp: 580, 23 KNN fn, tp: 10, 21 KNN f1 score: 0.560 KNN cohens kappa score: 0.533 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 489, 114 LR fn, tp: 2, 29 LR f1 score: 0.333 LR cohens kappa score: 0.275 LR average precision score: 0.262 -> test with 'RF' RF tn, fp: 590, 13 RF fn, tp: 4, 27 RF f1 score: 0.761 RF cohens kappa score: 0.747 -> test with 'GB' GB tn, fp: 591, 12 GB fn, tp: 3, 28 GB f1 score: 0.789 GB cohens kappa score: 0.776 -> test with 'KNN' KNN tn, fp: 582, 21 KNN fn, tp: 10, 21 KNN f1 score: 0.575 KNN cohens kappa score: 0.550 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 484, 116 LR fn, tp: 3, 24 LR f1 score: 0.287 LR cohens kappa score: 0.232 LR average precision score: 0.391 -> test with 'RF' RF tn, fp: 590, 10 RF fn, tp: 4, 23 RF f1 score: 0.767 RF cohens kappa score: 0.755 -> test with 'GB' GB tn, fp: 588, 12 GB fn, tp: 4, 23 GB f1 score: 0.742 GB cohens kappa score: 0.729 -> test with 'KNN' KNN tn, fp: 585, 15 KNN fn, tp: 7, 20 KNN f1 score: 0.645 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: 500, 103 LR fn, tp: 4, 27 LR f1 score: 0.335 LR cohens kappa score: 0.278 LR average precision score: 0.331 -> 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: 591, 12 GB fn, tp: 2, 29 GB f1 score: 0.806 GB cohens kappa score: 0.794 -> test with 'KNN' KNN tn, fp: 586, 17 KNN fn, tp: 6, 25 KNN f1 score: 0.685 KNN cohens kappa score: 0.666 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 500, 103 LR fn, tp: 7, 24 LR f1 score: 0.304 LR cohens kappa score: 0.244 LR average precision score: 0.328 -> test with 'RF' RF tn, fp: 586, 17 RF fn, tp: 2, 29 RF f1 score: 0.753 RF cohens kappa score: 0.738 -> 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: 586, 17 KNN fn, tp: 4, 27 KNN f1 score: 0.720 KNN cohens kappa score: 0.703 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 472, 131 LR fn, tp: 0, 31 LR f1 score: 0.321 LR cohens kappa score: 0.261 LR average precision score: 0.436 -> 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: 588, 15 GB fn, tp: 4, 27 GB f1 score: 0.740 GB cohens kappa score: 0.724 -> test with 'KNN' KNN tn, fp: 579, 24 KNN fn, tp: 9, 22 KNN f1 score: 0.571 KNN cohens kappa score: 0.545 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 489, 114 LR fn, tp: 7, 24 LR f1 score: 0.284 LR cohens kappa score: 0.222 LR average precision score: 0.189 -> test with 'RF' RF tn, fp: 594, 9 RF fn, tp: 5, 26 RF f1 score: 0.788 RF cohens kappa score: 0.776 -> test with 'GB' GB tn, fp: 589, 14 GB fn, tp: 5, 26 GB f1 score: 0.732 GB cohens kappa score: 0.717 -> 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 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 477, 123 LR fn, tp: 1, 26 LR f1 score: 0.295 LR cohens kappa score: 0.240 LR average precision score: 0.339 -> test with 'RF' RF tn, fp: 586, 14 RF fn, tp: 2, 25 RF f1 score: 0.758 RF cohens kappa score: 0.745 -> test with 'GB' GB tn, fp: 583, 17 GB fn, tp: 3, 24 GB f1 score: 0.706 GB cohens kappa score: 0.690 -> 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: 488, 115 LR fn, tp: 5, 26 LR f1 score: 0.302 LR cohens kappa score: 0.242 LR average precision score: 0.345 -> test with 'RF' RF tn, fp: 596, 7 RF fn, tp: 0, 31 RF f1 score: 0.899 RF cohens kappa score: 0.893 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 1, 30 GB f1 score: 0.923 GB cohens kappa score: 0.919 -> test with 'KNN' KNN tn, fp: 593, 10 KNN fn, tp: 10, 21 KNN f1 score: 0.677 KNN cohens kappa score: 0.661 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 500, 103 LR fn, tp: 5, 26 LR f1 score: 0.325 LR cohens kappa score: 0.267 LR average precision score: 0.215 -> 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: 584, 19 GB fn, tp: 3, 28 GB f1 score: 0.718 GB cohens kappa score: 0.700 -> 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: 491, 112 LR fn, tp: 3, 28 LR f1 score: 0.327 LR cohens kappa score: 0.269 LR average precision score: 0.410 -> test with 'RF' RF tn, fp: 584, 19 RF fn, tp: 4, 27 RF f1 score: 0.701 RF cohens kappa score: 0.683 -> test with 'GB' GB tn, fp: 583, 20 GB fn, tp: 4, 27 GB f1 score: 0.692 GB cohens kappa score: 0.673 -> test with 'KNN' KNN tn, fp: 578, 25 KNN fn, tp: 10, 21 KNN f1 score: 0.545 KNN cohens kappa score: 0.517 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 490, 113 LR fn, tp: 2, 29 LR f1 score: 0.335 LR cohens kappa score: 0.277 LR average precision score: 0.382 -> 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: 588, 15 GB fn, tp: 2, 29 GB f1 score: 0.773 GB cohens kappa score: 0.760 -> 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 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: 2, 25 LR f1 score: 0.311 LR cohens kappa score: 0.257 LR average precision score: 0.210 -> test with 'RF' RF tn, fp: 587, 13 RF fn, tp: 1, 26 RF f1 score: 0.788 RF cohens kappa score: 0.777 -> test with 'GB' GB tn, fp: 588, 12 GB fn, tp: 1, 26 GB f1 score: 0.800 GB cohens kappa score: 0.789 -> test with 'KNN' KNN tn, fp: 581, 19 KNN fn, tp: 3, 24 KNN f1 score: 0.686 KNN cohens kappa score: 0.668 ====== 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: 502, 101 LR fn, tp: 6, 25 LR f1 score: 0.318 LR cohens kappa score: 0.260 LR average precision score: 0.273 -> test with 'RF' RF tn, fp: 585, 18 RF fn, tp: 4, 27 RF f1 score: 0.711 RF cohens kappa score: 0.693 -> 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: 585, 18 KNN fn, tp: 8, 23 KNN f1 score: 0.639 KNN cohens kappa score: 0.618 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 498, 105 LR fn, tp: 4, 27 LR f1 score: 0.331 LR cohens kappa score: 0.274 LR average precision score: 0.321 -> test with 'RF' RF tn, fp: 594, 9 RF fn, tp: 1, 30 RF f1 score: 0.857 RF cohens kappa score: 0.849 -> test with 'GB' GB tn, fp: 590, 13 GB fn, tp: 1, 30 GB f1 score: 0.811 GB cohens kappa score: 0.799 -> test with 'KNN' KNN tn, fp: 590, 13 KNN fn, tp: 6, 25 KNN f1 score: 0.725 KNN cohens kappa score: 0.709 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 480, 123 LR fn, tp: 3, 28 LR f1 score: 0.308 LR cohens kappa score: 0.247 LR average precision score: 0.412 -> 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: 590, 13 GB fn, tp: 4, 27 GB f1 score: 0.761 GB cohens kappa score: 0.747 -> test with 'KNN' KNN tn, fp: 578, 25 KNN fn, tp: 6, 25 KNN f1 score: 0.617 KNN cohens kappa score: 0.593 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 480, 123 LR fn, tp: 3, 28 LR f1 score: 0.308 LR cohens kappa score: 0.247 LR average precision score: 0.239 -> test with 'RF' RF tn, fp: 591, 12 RF fn, tp: 0, 31 RF f1 score: 0.838 RF cohens kappa score: 0.828 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 0, 31 GB f1 score: 0.886 GB cohens kappa score: 0.879 -> test with 'KNN' KNN tn, fp: 568, 35 KNN fn, tp: 9, 22 KNN f1 score: 0.500 KNN cohens kappa score: 0.466 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 488, 112 LR fn, tp: 6, 21 LR f1 score: 0.263 LR cohens kappa score: 0.206 LR average precision score: 0.259 -> test with 'RF' RF tn, fp: 577, 23 RF fn, tp: 4, 23 RF f1 score: 0.630 RF cohens kappa score: 0.609 -> test with 'GB' GB tn, fp: 581, 19 GB fn, tp: 3, 24 GB f1 score: 0.686 GB cohens kappa score: 0.668 -> test with 'KNN' KNN tn, fp: 569, 31 KNN fn, tp: 2, 25 KNN f1 score: 0.602 KNN cohens kappa score: 0.578 ====== 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: 489, 114 LR fn, tp: 4, 27 LR f1 score: 0.314 LR cohens kappa score: 0.254 LR average precision score: 0.263 -> 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: 592, 11 GB fn, tp: 2, 29 GB f1 score: 0.817 GB cohens kappa score: 0.806 -> test with 'KNN' KNN tn, fp: 584, 19 KNN fn, tp: 7, 24 KNN f1 score: 0.649 KNN cohens kappa score: 0.627 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 493, 110 LR fn, tp: 5, 26 LR f1 score: 0.311 LR cohens kappa score: 0.252 LR average precision score: 0.397 -> 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: 591, 12 GB fn, tp: 2, 29 GB f1 score: 0.806 GB cohens kappa score: 0.794 -> 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 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 474, 129 LR fn, tp: 3, 28 LR f1 score: 0.298 LR cohens kappa score: 0.235 LR average precision score: 0.339 -> test with 'RF' RF tn, fp: 589, 14 RF fn, tp: 4, 27 RF f1 score: 0.750 RF cohens kappa score: 0.735 -> test with 'GB' GB tn, fp: 591, 12 GB fn, tp: 4, 27 GB f1 score: 0.771 GB cohens kappa score: 0.758 -> test with 'KNN' KNN tn, fp: 577, 26 KNN fn, tp: 7, 24 KNN f1 score: 0.593 KNN cohens kappa score: 0.566 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 482, 121 LR fn, tp: 2, 29 LR f1 score: 0.320 LR cohens kappa score: 0.261 LR average precision score: 0.420 -> test with 'RF' RF tn, fp: 591, 12 RF fn, tp: 0, 31 RF f1 score: 0.838 RF cohens kappa score: 0.828 -> test with 'GB' GB tn, fp: 590, 13 GB fn, tp: 1, 30 GB f1 score: 0.811 GB cohens kappa score: 0.799 -> 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: 508, 92 LR fn, tp: 7, 20 LR f1 score: 0.288 LR cohens kappa score: 0.235 LR average precision score: 0.190 -> 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: 583, 17 GB fn, tp: 4, 23 GB f1 score: 0.687 GB cohens kappa score: 0.670 -> test with 'KNN' KNN tn, fp: 581, 19 KNN fn, tp: 6, 21 KNN f1 score: 0.627 KNN cohens kappa score: 0.607 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 508, 131 LR fn, tp: 7, 31 LR f1 score: 0.352 LR cohens kappa score: 0.297 LR average precision score: 0.436 average: LR tn, fp: 489.16, 113.24 LR fn, tp: 3.8, 26.4 LR f1 score: 0.311 LR cohens kappa score: 0.252 LR average precision score: 0.312 minimum: LR tn, fp: 472, 92 LR fn, tp: 0, 20 LR f1 score: 0.263 LR cohens kappa score: 0.206 LR average precision score: 0.189 -----[ RF ]----- maximum: RF tn, fp: 596, 23 RF fn, tp: 5, 31 RF f1 score: 0.899 RF cohens kappa score: 0.893 average: RF tn, fp: 588.24, 14.16 RF fn, tp: 2.28, 27.92 RF f1 score: 0.774 RF cohens kappa score: 0.760 minimum: RF tn, fp: 577, 7 RF fn, tp: 0, 23 RF f1 score: 0.630 RF cohens kappa score: 0.609 -----[ GB ]----- maximum: GB tn, fp: 599, 22 GB fn, tp: 5, 31 GB f1 score: 0.923 GB cohens kappa score: 0.919 average: GB tn, fp: 588.44, 13.96 GB fn, tp: 2.4, 27.8 GB f1 score: 0.774 GB cohens kappa score: 0.760 minimum: GB tn, fp: 581, 4 GB fn, tp: 0, 23 GB f1 score: 0.686 GB cohens kappa score: 0.668 -----[ KNN ]----- maximum: KNN tn, fp: 593, 35 KNN fn, tp: 10, 27 KNN f1 score: 0.725 KNN cohens kappa score: 0.709 average: KNN tn, fp: 580.44, 21.96 KNN fn, tp: 7.0, 23.2 KNN f1 score: 0.618 KNN cohens kappa score: 0.595 minimum: KNN tn, fp: 568, 10 KNN fn, tp: 2, 20 KNN f1 score: 0.500 KNN cohens kappa score: 0.466 wall time: 00:16:05s, process time: 01:54:28s