/////////////////////////////////////////// // 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: 0.256 -> 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: 584, 19 KNN fn, tp: 6, 25 KNN f1 score: 0.667 KNN cohens kappa score: 0.646 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 487, 116 LR fn, tp: 9, 22 LR f1 score: 0.260 LR cohens kappa score: 0.196 LR average precision score: 0.191 -> test with 'GB' GB tn, fp: 582, 21 GB fn, tp: 2, 29 GB f1 score: 0.716 GB cohens kappa score: 0.698 -> test with 'KNN' KNN tn, fp: 582, 21 KNN fn, tp: 4, 27 KNN f1 score: 0.684 KNN cohens kappa score: 0.664 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 493, 110 LR fn, tp: 8, 23 LR f1 score: 0.280 LR cohens kappa score: 0.219 LR average precision score: 0.202 -> test with 'GB' GB tn, fp: 587, 16 GB fn, tp: 1, 30 GB f1 score: 0.779 GB cohens kappa score: 0.766 -> 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 1/5: 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: 7, 24 LR f1 score: 0.318 LR cohens kappa score: 0.260 LR average precision score: 0.196 -> test with 'GB' GB tn, fp: 582, 21 GB fn, tp: 3, 28 GB f1 score: 0.700 GB cohens kappa score: 0.681 -> 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: 0.295 -> test with 'GB' GB tn, fp: 586, 14 GB fn, tp: 3, 24 GB f1 score: 0.738 GB cohens kappa score: 0.725 -> 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: 482, 121 LR fn, tp: 8, 23 LR f1 score: 0.263 LR cohens kappa score: 0.198 LR average precision score: 0.206 -> 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: 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: 0.251 -> test with 'GB' GB tn, fp: 589, 14 GB fn, tp: 0, 31 GB f1 score: 0.816 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 587, 16 KNN fn, tp: 4, 27 KNN f1 score: 0.730 KNN cohens kappa score: 0.713 ------ Step 2/5: 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: 3, 28 LR f1 score: 0.346 LR cohens kappa score: 0.289 LR average precision score: 0.284 -> 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: 579, 24 KNN fn, tp: 11, 20 KNN f1 score: 0.533 KNN cohens kappa score: 0.505 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 510, 93 LR fn, tp: 12, 19 LR f1 score: 0.266 LR cohens kappa score: 0.205 LR average precision score: 0.192 -> test with 'GB' GB tn, fp: 582, 21 GB fn, tp: 5, 26 GB f1 score: 0.667 GB cohens kappa score: 0.646 -> test with 'KNN' KNN tn, fp: 583, 20 KNN fn, tp: 11, 20 KNN f1 score: 0.563 KNN cohens kappa score: 0.538 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 489, 111 LR fn, tp: 4, 23 LR f1 score: 0.286 LR cohens kappa score: 0.231 LR average precision score: 0.185 -> test with 'GB' GB tn, fp: 579, 21 GB fn, tp: 3, 24 GB f1 score: 0.667 GB cohens kappa score: 0.648 -> test with 'KNN' KNN tn, fp: 577, 23 KNN fn, tp: 4, 23 KNN f1 score: 0.630 KNN cohens kappa score: 0.609 ====== 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: 501, 102 LR fn, tp: 8, 23 LR f1 score: 0.295 LR cohens kappa score: 0.235 LR average precision score: 0.222 -> 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: 594, 9 KNN fn, tp: 9, 22 KNN f1 score: 0.710 KNN cohens kappa score: 0.695 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 513, 90 LR fn, tp: 9, 22 LR f1 score: 0.308 LR cohens kappa score: 0.250 LR average precision score: 0.206 -> test with 'GB' GB tn, fp: 580, 23 GB fn, tp: 3, 28 GB f1 score: 0.683 GB cohens kappa score: 0.662 -> test with 'KNN' KNN tn, fp: 564, 39 KNN fn, tp: 6, 25 KNN f1 score: 0.526 KNN cohens kappa score: 0.493 ------ Step 3/5: Slice 3/5 ------- -> 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: 0.265 -> test with 'GB' GB tn, fp: 583, 20 GB fn, tp: 5, 26 GB f1 score: 0.675 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 576, 27 KNN fn, tp: 10, 21 KNN f1 score: 0.532 KNN cohens kappa score: 0.502 ------ Step 3/5: 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: 4, 27 LR f1 score: 0.320 LR cohens kappa score: 0.260 LR average precision score: 0.284 -> test with 'GB' GB tn, fp: 583, 20 GB fn, tp: 3, 28 GB f1 score: 0.709 GB cohens kappa score: 0.690 -> 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 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 496, 104 LR fn, tp: 6, 21 LR f1 score: 0.276 LR cohens kappa score: 0.221 LR average precision score: 0.189 -> test with 'GB' GB tn, fp: 589, 11 GB fn, tp: 1, 26 GB f1 score: 0.812 GB cohens kappa score: 0.803 -> test with 'KNN' KNN tn, fp: 582, 18 KNN fn, tp: 3, 24 KNN f1 score: 0.696 KNN cohens kappa score: 0.679 ====== 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: 492, 111 LR fn, tp: 10, 21 LR f1 score: 0.258 LR cohens kappa score: 0.194 LR average precision score: 0.189 -> 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: 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: 481, 122 LR fn, tp: 8, 23 LR f1 score: 0.261 LR cohens kappa score: 0.197 LR average precision score: 0.200 -> 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: 589, 14 KNN fn, tp: 7, 24 KNN f1 score: 0.696 KNN cohens kappa score: 0.678 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 483, 120 LR fn, tp: 3, 28 LR f1 score: 0.313 LR cohens kappa score: 0.252 LR average precision score: 0.295 -> 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: 583, 20 KNN fn, tp: 6, 25 KNN f1 score: 0.658 KNN cohens kappa score: 0.637 ------ 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: 8, 23 LR f1 score: 0.258 LR cohens kappa score: 0.193 LR average precision score: 0.188 -> 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: 569, 34 KNN fn, tp: 10, 21 KNN f1 score: 0.488 KNN cohens kappa score: 0.454 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 510, 90 LR fn, tp: 8, 19 LR f1 score: 0.279 LR cohens kappa score: 0.226 LR average precision score: 0.226 -> test with 'GB' GB tn, fp: 580, 20 GB fn, tp: 5, 22 GB f1 score: 0.638 GB cohens kappa score: 0.618 -> test with 'KNN' KNN tn, fp: 575, 25 KNN fn, tp: 6, 21 KNN f1 score: 0.575 KNN cohens kappa score: 0.551 ====== 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: 487, 116 LR fn, tp: 6, 25 LR f1 score: 0.291 LR cohens kappa score: 0.229 LR average precision score: 0.180 -> test with 'GB' GB tn, fp: 585, 18 GB fn, tp: 0, 31 GB f1 score: 0.775 GB cohens kappa score: 0.761 -> 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 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 509, 94 LR fn, tp: 8, 23 LR f1 score: 0.311 LR cohens kappa score: 0.253 LR average precision score: 0.252 -> 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: 581, 22 KNN fn, tp: 9, 22 KNN f1 score: 0.587 KNN cohens kappa score: 0.562 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 467, 136 LR fn, tp: 4, 27 LR f1 score: 0.278 LR cohens kappa score: 0.214 LR average precision score: 0.240 -> test with 'GB' GB tn, fp: 586, 17 GB fn, tp: 5, 26 GB f1 score: 0.703 GB cohens kappa score: 0.685 -> 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: 0.254 -> test with 'GB' GB tn, fp: 585, 18 GB fn, tp: 0, 31 GB f1 score: 0.775 GB cohens kappa score: 0.761 -> 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 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: 10, 17 LR f1 score: 0.272 LR cohens kappa score: 0.219 LR average precision score: 0.169 -> 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: 575, 25 KNN fn, tp: 5, 22 KNN f1 score: 0.595 KNN cohens kappa score: 0.571 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 519, 136 LR fn, tp: 12, 28 LR f1 score: 0.346 LR cohens kappa score: 0.289 LR average precision score: 0.295 average: LR tn, fp: 494.56, 107.84 LR fn, tp: 6.92, 23.28 LR f1 score: 0.289 LR cohens kappa score: 0.229 LR average precision score: 0.225 minimum: LR tn, fp: 467, 81 LR fn, tp: 3, 17 LR f1 score: 0.258 LR cohens kappa score: 0.193 LR average precision score: 0.169 -----[ GB ]----- maximum: GB tn, fp: 595, 23 GB fn, tp: 5, 31 GB f1 score: 0.886 GB cohens kappa score: 0.879 average: GB tn, fp: 585.6, 16.8 GB fn, tp: 2.52, 27.68 GB f1 score: 0.742 GB cohens kappa score: 0.727 minimum: GB tn, fp: 579, 8 GB fn, tp: 0, 22 GB f1 score: 0.638 GB cohens kappa score: 0.618 -----[ KNN ]----- maximum: KNN tn, fp: 594, 39 KNN fn, tp: 11, 27 KNN f1 score: 0.730 KNN cohens kappa score: 0.713 average: KNN tn, fp: 580.12, 22.28 KNN fn, tp: 7.32, 22.88 KNN f1 score: 0.610 KNN cohens kappa score: 0.586 minimum: KNN tn, fp: 564, 9 KNN fn, tp: 3, 19 KNN f1 score: 0.488 KNN cohens kappa score: 0.454