/////////////////////////////////////////// // Running CTAB-GAN 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 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 571, 32 LR fn, tp: 20, 11 LR f1 score: 0.297 LR cohens kappa score: 0.255 LR average precision score: 0.234 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 7, 24 GB f1 score: 0.814 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 589, 14 KNN fn, tp: 9, 22 KNN f1 score: 0.657 KNN cohens kappa score: 0.638 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 557, 46 LR fn, tp: 24, 7 LR f1 score: 0.167 LR cohens kappa score: 0.112 LR average precision score: 0.126 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 8, 23 GB f1 score: 0.742 GB cohens kappa score: 0.729 -> test with 'KNN' KNN tn, fp: 587, 16 KNN fn, tp: 6, 25 KNN f1 score: 0.694 KNN cohens kappa score: 0.676 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 590, 13 LR fn, tp: 26, 5 LR f1 score: 0.204 LR cohens kappa score: 0.174 LR average precision score: 0.207 -> 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: 592, 11 KNN fn, tp: 14, 17 KNN f1 score: 0.576 KNN cohens kappa score: 0.556 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 596, 7 LR fn, tp: 24, 7 LR f1 score: 0.311 LR cohens kappa score: 0.289 LR average precision score: 0.328 -> test with 'GB' GB tn, fp: 601, 2 GB fn, tp: 11, 20 GB f1 score: 0.755 GB cohens kappa score: 0.744 -> test with 'KNN' KNN tn, fp: 590, 13 KNN fn, tp: 13, 18 KNN f1 score: 0.581 KNN cohens kappa score: 0.559 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2288 synthetic samples -> test with 'LR' LR tn, fp: 542, 58 LR fn, tp: 16, 11 LR f1 score: 0.229 LR cohens kappa score: 0.178 LR average precision score: 0.155 -> test with 'GB' GB tn, fp: 596, 4 GB fn, tp: 6, 21 GB f1 score: 0.808 GB cohens kappa score: 0.799 -> test with 'KNN' KNN tn, fp: 583, 17 KNN fn, tp: 11, 16 KNN f1 score: 0.533 KNN cohens kappa score: 0.510 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 568, 35 LR fn, tp: 24, 7 LR f1 score: 0.192 LR cohens kappa score: 0.144 LR average precision score: 0.155 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 10, 21 GB f1 score: 0.724 GB cohens kappa score: 0.711 -> test with 'KNN' KNN tn, fp: 594, 9 KNN fn, tp: 12, 19 KNN f1 score: 0.644 KNN cohens kappa score: 0.627 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 557, 46 LR fn, tp: 20, 11 LR f1 score: 0.250 LR cohens kappa score: 0.199 LR average precision score: 0.167 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 5, 26 GB f1 score: 0.825 GB cohens kappa score: 0.816 -> test with 'KNN' KNN tn, fp: 592, 11 KNN fn, tp: 9, 22 KNN f1 score: 0.688 KNN cohens kappa score: 0.671 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 576, 27 LR fn, tp: 22, 9 LR f1 score: 0.269 LR cohens kappa score: 0.228 LR average precision score: 0.257 -> 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: 593, 10 KNN fn, tp: 12, 19 KNN f1 score: 0.633 KNN cohens kappa score: 0.615 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 574, 29 LR fn, tp: 19, 12 LR f1 score: 0.333 LR cohens kappa score: 0.294 LR average precision score: 0.208 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 9, 22 GB f1 score: 0.772 GB cohens kappa score: 0.761 -> test with 'KNN' KNN tn, fp: 585, 18 KNN fn, tp: 14, 17 KNN f1 score: 0.515 KNN cohens kappa score: 0.489 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2288 synthetic samples -> test with 'LR' LR tn, fp: 562, 38 LR fn, tp: 22, 5 LR f1 score: 0.143 LR cohens kappa score: 0.095 LR average precision score: 0.149 -> 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: 585, 15 KNN fn, tp: 9, 18 KNN f1 score: 0.600 KNN cohens kappa score: 0.580 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 571, 32 LR fn, tp: 21, 10 LR f1 score: 0.274 LR cohens kappa score: 0.231 LR average precision score: 0.258 -> test with 'GB' GB tn, fp: 602, 1 GB fn, tp: 8, 23 GB f1 score: 0.836 GB cohens kappa score: 0.829 -> test with 'KNN' KNN tn, fp: 597, 6 KNN fn, tp: 14, 17 KNN f1 score: 0.630 KNN cohens kappa score: 0.614 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 594, 9 LR fn, tp: 25, 6 LR f1 score: 0.261 LR cohens kappa score: 0.237 LR average precision score: 0.327 -> test with 'GB' GB tn, fp: 594, 9 GB fn, tp: 5, 26 GB f1 score: 0.788 GB cohens kappa score: 0.776 -> test with 'KNN' KNN tn, fp: 587, 16 KNN fn, tp: 10, 21 KNN f1 score: 0.618 KNN cohens kappa score: 0.596 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 592, 11 LR fn, tp: 26, 5 LR f1 score: 0.213 LR cohens kappa score: 0.186 LR average precision score: 0.218 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 5, 26 GB f1 score: 0.852 GB cohens kappa score: 0.845 -> test with 'KNN' KNN tn, fp: 588, 15 KNN fn, tp: 9, 22 KNN f1 score: 0.647 KNN cohens kappa score: 0.627 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 565, 38 LR fn, tp: 17, 14 LR f1 score: 0.337 LR cohens kappa score: 0.294 LR average precision score: 0.252 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 6, 25 GB f1 score: 0.781 GB cohens kappa score: 0.770 -> test with 'KNN' KNN tn, fp: 590, 13 KNN fn, tp: 10, 21 KNN f1 score: 0.646 KNN cohens kappa score: 0.627 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2288 synthetic samples -> test with 'LR' LR tn, fp: 571, 29 LR fn, tp: 19, 8 LR f1 score: 0.250 LR cohens kappa score: 0.211 LR average precision score: 0.186 -> test with 'GB' GB tn, fp: 598, 2 GB fn, tp: 5, 22 GB f1 score: 0.863 GB cohens kappa score: 0.857 -> test with 'KNN' KNN tn, fp: 582, 18 KNN fn, tp: 7, 20 KNN f1 score: 0.615 KNN cohens kappa score: 0.595 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 592, 11 LR fn, tp: 29, 2 LR f1 score: 0.091 LR cohens kappa score: 0.064 LR average precision score: 0.203 -> test with 'GB' GB tn, fp: 596, 7 GB fn, tp: 6, 25 GB f1 score: 0.794 GB cohens kappa score: 0.783 -> test with 'KNN' KNN tn, fp: 593, 10 KNN fn, tp: 16, 15 KNN f1 score: 0.536 KNN cohens kappa score: 0.515 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 577, 26 LR fn, tp: 24, 7 LR f1 score: 0.219 LR cohens kappa score: 0.177 LR average precision score: 0.162 -> 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: 589, 14 KNN fn, tp: 13, 18 KNN f1 score: 0.571 KNN cohens kappa score: 0.549 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 575, 28 LR fn, tp: 18, 13 LR f1 score: 0.361 LR cohens kappa score: 0.323 LR average precision score: 0.280 -> test with 'GB' GB tn, fp: 601, 2 GB fn, tp: 6, 25 GB f1 score: 0.862 GB cohens kappa score: 0.855 -> 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 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 562, 41 LR fn, tp: 20, 11 LR f1 score: 0.265 LR cohens kappa score: 0.217 LR average precision score: 0.197 -> test with 'GB' GB tn, fp: 601, 2 GB fn, tp: 5, 26 GB f1 score: 0.881 GB cohens kappa score: 0.876 -> test with 'KNN' KNN tn, fp: 584, 19 KNN fn, tp: 15, 16 KNN f1 score: 0.485 KNN cohens kappa score: 0.457 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2288 synthetic samples -> test with 'LR' LR tn, fp: 549, 51 LR fn, tp: 15, 12 LR f1 score: 0.267 LR cohens kappa score: 0.220 LR average precision score: 0.197 -> test with 'GB' GB tn, fp: 597, 3 GB fn, tp: 8, 19 GB f1 score: 0.776 GB cohens kappa score: 0.766 -> test with 'KNN' KNN tn, fp: 584, 16 KNN fn, tp: 10, 17 KNN f1 score: 0.567 KNN cohens kappa score: 0.545 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 560, 43 LR fn, tp: 16, 15 LR f1 score: 0.337 LR cohens kappa score: 0.292 LR average precision score: 0.205 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 8, 23 GB f1 score: 0.793 GB cohens kappa score: 0.783 -> test with 'KNN' KNN tn, fp: 586, 17 KNN fn, tp: 9, 22 KNN f1 score: 0.629 KNN cohens kappa score: 0.607 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 575, 28 LR fn, tp: 27, 4 LR f1 score: 0.127 LR cohens kappa score: 0.081 LR average precision score: 0.152 -> test with 'GB' GB tn, fp: 603, 0 GB fn, tp: 6, 25 GB f1 score: 0.893 GB cohens kappa score: 0.888 -> test with 'KNN' KNN tn, fp: 591, 12 KNN fn, tp: 14, 17 KNN f1 score: 0.567 KNN cohens kappa score: 0.545 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 544, 59 LR fn, tp: 20, 11 LR f1 score: 0.218 LR cohens kappa score: 0.161 LR average precision score: 0.159 -> 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: 590, 13 KNN fn, tp: 14, 17 KNN f1 score: 0.557 KNN cohens kappa score: 0.535 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2289 synthetic samples -> test with 'LR' LR tn, fp: 573, 30 LR fn, tp: 20, 11 LR f1 score: 0.306 LR cohens kappa score: 0.265 LR average precision score: 0.190 -> 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: 593, 10 KNN fn, tp: 10, 21 KNN f1 score: 0.677 KNN cohens kappa score: 0.661 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 2288 synthetic samples -> test with 'LR' LR tn, fp: 556, 44 LR fn, tp: 15, 12 LR f1 score: 0.289 LR cohens kappa score: 0.245 LR average precision score: 0.168 -> test with 'GB' GB tn, fp: 594, 6 GB fn, tp: 10, 17 GB f1 score: 0.680 GB cohens kappa score: 0.667 -> test with 'KNN' KNN tn, fp: 587, 13 KNN fn, tp: 7, 20 KNN f1 score: 0.667 KNN cohens kappa score: 0.650 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 596, 59 LR fn, tp: 29, 15 LR f1 score: 0.361 LR cohens kappa score: 0.323 LR average precision score: 0.328 average: LR tn, fp: 569.96, 32.44 LR fn, tp: 21.16, 9.04 LR f1 score: 0.248 LR cohens kappa score: 0.207 LR average precision score: 0.206 minimum: LR tn, fp: 542, 7 LR fn, tp: 15, 2 LR f1 score: 0.091 LR cohens kappa score: 0.064 LR average precision score: 0.126 -----[ GB ]----- maximum: GB tn, fp: 603, 9 GB fn, tp: 11, 27 GB f1 score: 0.893 GB cohens kappa score: 0.888 average: GB tn, fp: 598.16, 4.24 GB fn, tp: 7.04, 23.16 GB f1 score: 0.803 GB cohens kappa score: 0.794 minimum: GB tn, fp: 594, 0 GB fn, tp: 4, 17 GB f1 score: 0.680 GB cohens kappa score: 0.667 -----[ KNN ]----- maximum: KNN tn, fp: 597, 19 KNN fn, tp: 16, 25 KNN f1 score: 0.694 KNN cohens kappa score: 0.676 average: KNN tn, fp: 588.96, 13.44 KNN fn, tp: 11.08, 19.12 KNN f1 score: 0.608 KNN cohens kappa score: 0.588 minimum: KNN tn, fp: 582, 6 KNN fn, tp: 6, 15 KNN f1 score: 0.485 KNN cohens kappa score: 0.457