/////////////////////////////////////////// // Running CTAB-GAN 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 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 486, 117 LR fn, tp: 4, 27 LR f1 score: 0.309 LR cohens kappa score: 0.248 LR average precision score: 0.417 -> 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: 600, 3 GB fn, tp: 7, 24 GB f1 score: 0.828 GB cohens kappa score: 0.819 -> 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 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 504, 99 LR fn, tp: 4, 27 LR f1 score: 0.344 LR cohens kappa score: 0.288 LR average precision score: 0.382 -> test with 'RF' RF tn, fp: 595, 8 RF fn, tp: 10, 21 RF f1 score: 0.700 RF cohens kappa score: 0.685 -> 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: 563, 40 KNN fn, tp: 5, 26 KNN f1 score: 0.536 KNN cohens kappa score: 0.503 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 498, 105 LR fn, tp: 9, 22 LR f1 score: 0.278 LR cohens kappa score: 0.217 LR average precision score: 0.236 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 6, 25 RF f1 score: 0.862 RF cohens kappa score: 0.855 -> 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: 576, 27 KNN fn, tp: 8, 23 KNN f1 score: 0.568 KNN cohens kappa score: 0.540 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 472, 131 LR fn, tp: 3, 28 LR f1 score: 0.295 LR cohens kappa score: 0.232 LR average precision score: 0.253 -> 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: 601, 2 GB fn, tp: 9, 22 GB f1 score: 0.800 GB cohens kappa score: 0.791 -> test with 'KNN' KNN tn, fp: 578, 25 KNN fn, tp: 7, 24 KNN f1 score: 0.600 KNN cohens kappa score: 0.575 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2288 synthetic samples -> test with 'LR' LR tn, fp: 530, 70 LR fn, tp: 7, 20 LR f1 score: 0.342 LR cohens kappa score: 0.295 LR average precision score: 0.348 -> test with 'RF' RF tn, fp: 597, 3 RF fn, tp: 8, 19 RF f1 score: 0.776 RF cohens kappa score: 0.766 -> 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: 584, 16 KNN fn, tp: 8, 19 KNN f1 score: 0.613 KNN cohens kappa score: 0.593 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 505, 98 LR fn, tp: 4, 27 LR f1 score: 0.346 LR cohens kappa score: 0.291 LR average precision score: 0.352 -> test with 'RF' RF tn, fp: 598, 5 RF fn, tp: 13, 18 RF f1 score: 0.667 RF cohens kappa score: 0.652 -> 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: 574, 29 KNN fn, tp: 7, 24 KNN f1 score: 0.571 KNN cohens kappa score: 0.543 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 516, 87 LR fn, tp: 10, 21 LR f1 score: 0.302 LR cohens kappa score: 0.245 LR average precision score: 0.284 -> 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: 597, 6 GB fn, tp: 6, 25 GB f1 score: 0.806 GB cohens kappa score: 0.797 -> test with 'KNN' KNN tn, fp: 574, 29 KNN fn, tp: 6, 25 KNN f1 score: 0.588 KNN cohens kappa score: 0.561 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 494, 109 LR fn, tp: 0, 31 LR f1 score: 0.363 LR cohens kappa score: 0.307 LR average precision score: 0.446 -> 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: 600, 3 GB fn, tp: 10, 21 GB f1 score: 0.764 GB cohens kappa score: 0.753 -> test with 'KNN' KNN tn, fp: 572, 31 KNN fn, tp: 9, 22 KNN f1 score: 0.524 KNN cohens kappa score: 0.492 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 505, 98 LR fn, tp: 7, 24 LR f1 score: 0.314 LR cohens kappa score: 0.256 LR average precision score: 0.255 -> 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: 600, 3 GB fn, tp: 6, 25 GB f1 score: 0.847 GB cohens kappa score: 0.840 -> 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 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2288 synthetic samples -> test with 'LR' LR tn, fp: 484, 116 LR fn, tp: 2, 25 LR f1 score: 0.298 LR cohens kappa score: 0.243 LR average precision score: 0.441 -> test with 'RF' RF tn, fp: 598, 2 RF fn, tp: 5, 22 RF f1 score: 0.863 RF cohens kappa score: 0.857 -> test with 'GB' GB tn, fp: 596, 4 GB fn, tp: 3, 24 GB f1 score: 0.873 GB cohens kappa score: 0.867 -> test with 'KNN' KNN tn, fp: 567, 33 KNN fn, tp: 4, 23 KNN f1 score: 0.554 KNN cohens kappa score: 0.527 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 511, 92 LR fn, tp: 7, 24 LR f1 score: 0.327 LR cohens kappa score: 0.270 LR average precision score: 0.340 -> 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: 603, 0 GB fn, tp: 8, 23 GB f1 score: 0.852 GB cohens kappa score: 0.845 -> test with 'KNN' KNN tn, fp: 585, 18 KNN fn, tp: 12, 19 KNN f1 score: 0.559 KNN cohens kappa score: 0.534 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 513, 90 LR fn, tp: 7, 24 LR f1 score: 0.331 LR cohens kappa score: 0.275 LR average precision score: 0.214 -> 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: 594, 9 GB fn, tp: 7, 24 GB f1 score: 0.750 GB cohens kappa score: 0.737 -> test with 'KNN' KNN tn, fp: 548, 55 KNN fn, tp: 5, 26 KNN f1 score: 0.464 KNN cohens kappa score: 0.424 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 496, 107 LR fn, tp: 4, 27 LR f1 score: 0.327 LR cohens kappa score: 0.269 LR average precision score: 0.435 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 7, 24 RF f1 score: 0.828 RF cohens kappa score: 0.819 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 5, 26 GB f1 score: 0.839 GB cohens kappa score: 0.830 -> 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 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 495, 108 LR fn, tp: 2, 29 LR f1 score: 0.345 LR cohens kappa score: 0.288 LR average precision score: 0.395 -> test with 'RF' RF tn, fp: 596, 7 RF fn, tp: 11, 20 RF f1 score: 0.690 RF cohens kappa score: 0.675 -> 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: 567, 36 KNN fn, tp: 5, 26 KNN f1 score: 0.559 KNN cohens kappa score: 0.528 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2288 synthetic samples -> test with 'LR' LR tn, fp: 523, 77 LR fn, tp: 12, 15 LR f1 score: 0.252 LR cohens kappa score: 0.199 LR average precision score: 0.191 -> test with 'RF' RF tn, fp: 599, 1 RF fn, tp: 8, 19 RF f1 score: 0.809 RF cohens kappa score: 0.801 -> test with 'GB' GB tn, fp: 597, 3 GB fn, tp: 6, 21 GB f1 score: 0.824 GB cohens kappa score: 0.816 -> test with 'KNN' KNN tn, fp: 578, 22 KNN fn, tp: 5, 22 KNN f1 score: 0.620 KNN cohens kappa score: 0.598 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 516, 87 LR fn, tp: 7, 24 LR f1 score: 0.338 LR cohens kappa score: 0.283 LR average precision score: 0.284 -> test with 'RF' RF tn, fp: 597, 6 RF fn, tp: 8, 23 RF f1 score: 0.767 RF cohens kappa score: 0.755 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 4, 27 GB f1 score: 0.844 GB cohens kappa score: 0.835 -> 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 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 502, 101 LR fn, tp: 5, 26 LR f1 score: 0.329 LR cohens kappa score: 0.272 LR average precision score: 0.327 -> 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: 597, 6 GB fn, tp: 6, 25 GB f1 score: 0.806 GB cohens kappa score: 0.797 -> 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 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 501, 102 LR fn, tp: 3, 28 LR f1 score: 0.348 LR cohens kappa score: 0.292 LR average precision score: 0.401 -> test with 'RF' RF tn, fp: 602, 1 RF fn, tp: 7, 24 RF f1 score: 0.857 RF cohens kappa score: 0.851 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 6, 25 GB f1 score: 0.847 GB cohens kappa score: 0.840 -> 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 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 496, 107 LR fn, tp: 4, 27 LR f1 score: 0.327 LR cohens kappa score: 0.269 LR average precision score: 0.272 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 8, 23 RF f1 score: 0.821 RF cohens kappa score: 0.813 -> 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: 570, 33 KNN fn, tp: 9, 22 KNN f1 score: 0.512 KNN cohens kappa score: 0.479 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2288 synthetic samples -> test with 'LR' LR tn, fp: 500, 100 LR fn, tp: 7, 20 LR f1 score: 0.272 LR cohens kappa score: 0.217 LR average precision score: 0.270 -> test with 'RF' RF tn, fp: 597, 3 RF fn, tp: 8, 19 RF f1 score: 0.776 RF cohens kappa score: 0.766 -> test with 'GB' GB tn, fp: 597, 3 GB fn, tp: 7, 20 GB f1 score: 0.800 GB cohens kappa score: 0.792 -> test with 'KNN' KNN tn, fp: 573, 27 KNN fn, tp: 7, 20 KNN f1 score: 0.541 KNN cohens kappa score: 0.514 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 503, 100 LR fn, tp: 5, 26 LR f1 score: 0.331 LR cohens kappa score: 0.274 LR average precision score: 0.279 -> 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: 6, 25 GB f1 score: 0.833 GB cohens kappa score: 0.825 -> 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 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 489, 114 LR fn, tp: 3, 28 LR f1 score: 0.324 LR cohens kappa score: 0.265 LR average precision score: 0.373 -> test with 'RF' RF tn, fp: 602, 1 RF fn, tp: 10, 21 RF f1 score: 0.792 RF cohens kappa score: 0.784 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 6, 25 GB f1 score: 0.847 GB cohens kappa score: 0.840 -> test with 'KNN' KNN tn, fp: 574, 29 KNN fn, tp: 9, 22 KNN f1 score: 0.537 KNN cohens kappa score: 0.507 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 470, 133 LR fn, tp: 2, 29 LR f1 score: 0.301 LR cohens kappa score: 0.238 LR average precision score: 0.405 -> 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: 598, 5 GB fn, tp: 11, 20 GB f1 score: 0.714 GB cohens kappa score: 0.701 -> test with 'KNN' KNN tn, fp: 561, 42 KNN fn, tp: 6, 25 KNN f1 score: 0.510 KNN cohens kappa score: 0.475 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 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: 0.445 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 6, 25 RF f1 score: 0.847 RF cohens kappa score: 0.840 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 5, 26 GB f1 score: 0.839 GB cohens kappa score: 0.830 -> test with 'KNN' KNN tn, fp: 572, 31 KNN fn, tp: 7, 24 KNN f1 score: 0.558 KNN cohens kappa score: 0.529 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/300 [00:00 create 2288 synthetic samples -> test with 'LR' LR tn, fp: 485, 115 LR fn, tp: 7, 20 LR f1 score: 0.247 LR cohens kappa score: 0.189 LR average precision score: 0.179 -> 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: 595, 5 GB fn, tp: 8, 19 GB f1 score: 0.745 GB cohens kappa score: 0.734 -> test with 'KNN' KNN tn, fp: 576, 24 KNN fn, tp: 6, 21 KNN f1 score: 0.583 KNN cohens kappa score: 0.560 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 530, 133 LR fn, tp: 12, 31 LR f1 score: 0.363 LR cohens kappa score: 0.307 LR average precision score: 0.446 average: LR tn, fp: 499.36, 103.04 LR fn, tp: 5.08, 25.12 LR f1 score: 0.317 LR cohens kappa score: 0.260 LR average precision score: 0.329 minimum: LR tn, fp: 470, 70 LR fn, tp: 0, 15 LR f1 score: 0.247 LR cohens kappa score: 0.189 LR average precision score: 0.179 -----[ RF ]----- maximum: RF tn, fp: 603, 8 RF fn, tp: 15, 25 RF f1 score: 0.863 RF cohens kappa score: 0.857 average: RF tn, fp: 599.08, 3.32 RF fn, tp: 8.84, 21.36 RF f1 score: 0.777 RF cohens kappa score: 0.767 minimum: RF tn, fp: 594, 0 RF fn, tp: 5, 16 RF f1 score: 0.653 RF cohens kappa score: 0.640 -----[ GB ]----- maximum: GB tn, fp: 603, 10 GB fn, tp: 11, 27 GB f1 score: 0.873 GB cohens kappa score: 0.867 average: GB tn, fp: 597.8, 4.6 GB fn, tp: 6.52, 23.68 GB f1 score: 0.810 GB cohens kappa score: 0.801 minimum: GB tn, fp: 593, 0 GB fn, tp: 3, 19 GB f1 score: 0.714 GB cohens kappa score: 0.701 -----[ KNN ]----- maximum: KNN tn, fp: 587, 55 KNN fn, tp: 12, 26 KNN f1 score: 0.657 KNN cohens kappa score: 0.637 average: KNN tn, fp: 573.2, 29.2 KNN fn, tp: 7.04, 23.16 KNN f1 score: 0.565 KNN cohens kappa score: 0.537 minimum: KNN tn, fp: 548, 16 KNN fn, tp: 4, 19 KNN f1 score: 0.464 KNN cohens kappa score: 0.424 wall time: 03:49:10s, process time: 1 days 05:30:22s