/////////////////////////////////////////// // 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: 559, 44 LR fn, tp: 19, 12 LR f1 score: 0.276 LR cohens kappa score: 0.227 LR average precision score: 0.207 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 9, 22 RF f1 score: 0.772 RF cohens kappa score: 0.761 -> 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: 587, 16 KNN fn, tp: 10, 21 KNN f1 score: 0.618 KNN cohens kappa score: 0.596 ------ 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: 564, 39 LR fn, tp: 23, 8 LR f1 score: 0.205 LR cohens kappa score: 0.155 LR average precision score: 0.181 -> 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: 595, 8 GB fn, tp: 6, 25 GB f1 score: 0.781 GB cohens kappa score: 0.770 -> 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 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: 528, 75 LR fn, tp: 19, 12 LR f1 score: 0.203 LR cohens kappa score: 0.141 LR average precision score: 0.136 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 5, 26 RF f1 score: 0.867 RF cohens kappa score: 0.860 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 6, 25 GB f1 score: 0.820 GB cohens kappa score: 0.811 -> test with 'KNN' KNN tn, fp: 591, 12 KNN fn, tp: 15, 16 KNN f1 score: 0.542 KNN cohens kappa score: 0.520 ------ 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: 558, 45 LR fn, tp: 22, 9 LR f1 score: 0.212 LR cohens kappa score: 0.160 LR average precision score: 0.143 -> test with 'RF' RF tn, fp: 603, 0 RF fn, tp: 13, 18 RF f1 score: 0.735 RF cohens kappa score: 0.725 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 12, 19 GB f1 score: 0.717 GB cohens kappa score: 0.705 -> 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 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: 0.208 -> 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: 596, 4 GB fn, tp: 4, 23 GB f1 score: 0.852 GB cohens kappa score: 0.845 -> test with 'KNN' KNN tn, fp: 582, 18 KNN fn, tp: 10, 17 KNN f1 score: 0.548 KNN cohens kappa score: 0.525 ====== 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: 559, 44 LR fn, tp: 23, 8 LR f1 score: 0.193 LR cohens kappa score: 0.140 LR average precision score: 0.121 -> test with 'RF' RF tn, fp: 597, 6 RF fn, tp: 11, 20 RF f1 score: 0.702 RF cohens kappa score: 0.688 -> test with 'GB' GB tn, fp: 596, 7 GB fn, tp: 10, 21 GB f1 score: 0.712 GB cohens kappa score: 0.698 -> test with 'KNN' KNN tn, fp: 589, 14 KNN fn, tp: 15, 16 KNN f1 score: 0.525 KNN cohens kappa score: 0.501 ------ 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: 570, 33 LR fn, tp: 22, 9 LR f1 score: 0.247 LR cohens kappa score: 0.202 LR average precision score: 0.185 -> 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: 597, 6 GB fn, tp: 4, 27 GB f1 score: 0.844 GB cohens kappa score: 0.835 -> test with 'KNN' KNN tn, fp: 589, 14 KNN fn, tp: 8, 23 KNN f1 score: 0.676 KNN cohens kappa score: 0.658 ------ 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: 561, 42 LR fn, tp: 22, 9 LR f1 score: 0.220 LR cohens kappa score: 0.169 LR average precision score: 0.169 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 11, 20 RF f1 score: 0.755 RF cohens kappa score: 0.744 -> 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: 593, 10 KNN fn, tp: 13, 18 KNN f1 score: 0.610 KNN cohens kappa score: 0.591 ------ 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: 553, 50 LR fn, tp: 19, 12 LR f1 score: 0.258 LR cohens kappa score: 0.206 LR average precision score: 0.237 -> 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: 600, 3 GB fn, tp: 8, 23 GB f1 score: 0.807 GB cohens kappa score: 0.798 -> test with 'KNN' KNN tn, fp: 592, 11 KNN fn, tp: 10, 21 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ------ 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: 559, 41 LR fn, tp: 24, 3 LR f1 score: 0.085 LR cohens kappa score: 0.033 LR average precision score: 0.123 -> test with 'RF' RF tn, fp: 598, 2 RF fn, tp: 6, 21 RF f1 score: 0.840 RF cohens kappa score: 0.833 -> 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: 584, 16 KNN fn, tp: 10, 17 KNN f1 score: 0.567 KNN cohens kappa score: 0.545 ====== 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: 570, 33 LR fn, tp: 21, 10 LR f1 score: 0.270 LR cohens kappa score: 0.226 LR average precision score: 0.229 -> 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: 602, 1 GB fn, tp: 8, 23 GB f1 score: 0.836 GB cohens kappa score: 0.829 -> test with 'KNN' KNN tn, fp: 598, 5 KNN fn, tp: 15, 16 KNN f1 score: 0.615 KNN cohens kappa score: 0.600 ------ 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: 26, 5 LR f1 score: 0.222 LR cohens kappa score: 0.198 LR average precision score: 0.269 -> test with 'RF' RF tn, fp: 595, 8 RF fn, tp: 5, 26 RF f1 score: 0.800 RF cohens kappa score: 0.789 -> test with 'GB' GB tn, fp: 594, 9 GB fn, tp: 4, 27 GB f1 score: 0.806 GB cohens kappa score: 0.795 -> test with 'KNN' KNN tn, fp: 580, 23 KNN fn, tp: 14, 17 KNN f1 score: 0.479 KNN cohens kappa score: 0.448 ------ 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: 570, 33 LR fn, tp: 24, 7 LR f1 score: 0.197 LR cohens kappa score: 0.150 LR average precision score: 0.173 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 8, 23 RF f1 score: 0.793 RF cohens kappa score: 0.783 -> 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: 586, 17 KNN fn, tp: 10, 21 KNN f1 score: 0.609 KNN cohens kappa score: 0.586 ------ 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: 556, 47 LR fn, tp: 18, 13 LR f1 score: 0.286 LR cohens kappa score: 0.236 LR average precision score: 0.230 -> test with 'RF' RF tn, fp: 597, 6 RF fn, tp: 9, 22 RF f1 score: 0.746 RF cohens kappa score: 0.733 -> 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: 589, 14 KNN fn, tp: 11, 20 KNN f1 score: 0.615 KNN cohens kappa score: 0.595 ------ 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: 539, 61 LR fn, tp: 20, 7 LR f1 score: 0.147 LR cohens kappa score: 0.091 LR average precision score: 0.139 -> test with 'RF' RF tn, fp: 598, 2 RF fn, tp: 6, 21 RF f1 score: 0.840 RF cohens kappa score: 0.833 -> test with 'GB' GB tn, fp: 598, 2 GB fn, tp: 6, 21 GB f1 score: 0.840 GB cohens kappa score: 0.833 -> test with 'KNN' KNN tn, fp: 587, 13 KNN fn, tp: 10, 17 KNN f1 score: 0.596 KNN cohens kappa score: 0.577 ====== 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: 551, 52 LR fn, tp: 21, 10 LR f1 score: 0.215 LR cohens kappa score: 0.160 LR average precision score: 0.136 -> test with 'RF' RF tn, fp: 597, 6 RF fn, tp: 9, 22 RF f1 score: 0.746 RF cohens kappa score: 0.733 -> test with 'GB' GB tn, fp: 596, 7 GB fn, tp: 4, 27 GB f1 score: 0.831 GB cohens kappa score: 0.822 -> test with 'KNN' KNN tn, fp: 586, 17 KNN fn, tp: 10, 21 KNN f1 score: 0.609 KNN cohens kappa score: 0.586 ------ 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: 562, 41 LR fn, tp: 26, 5 LR f1 score: 0.130 LR cohens kappa score: 0.076 LR average precision score: 0.113 -> test with 'RF' RF tn, fp: 600, 3 RF fn, tp: 9, 22 RF f1 score: 0.786 RF cohens kappa score: 0.776 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 7, 24 GB f1 score: 0.800 GB cohens kappa score: 0.790 -> 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 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: 545, 58 LR fn, tp: 21, 10 LR f1 score: 0.202 LR cohens kappa score: 0.145 LR average precision score: 0.150 -> test with 'RF' RF tn, fp: 602, 1 RF fn, tp: 8, 23 RF f1 score: 0.836 RF cohens kappa score: 0.829 -> 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: 594, 9 KNN fn, tp: 10, 21 KNN f1 score: 0.689 KNN cohens kappa score: 0.673 ------ 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: 559, 44 LR fn, tp: 20, 11 LR f1 score: 0.256 LR cohens kappa score: 0.206 LR average precision score: 0.181 -> 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: 601, 2 GB fn, tp: 4, 27 GB f1 score: 0.900 GB cohens kappa score: 0.895 -> test with 'KNN' KNN tn, fp: 590, 13 KNN fn, tp: 12, 19 KNN f1 score: 0.603 KNN cohens kappa score: 0.582 ------ 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: 574, 26 LR fn, tp: 20, 7 LR f1 score: 0.233 LR cohens kappa score: 0.195 LR average precision score: 0.255 -> test with 'RF' RF tn, fp: 597, 3 RF fn, tp: 9, 18 RF f1 score: 0.750 RF cohens kappa score: 0.740 -> 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: 587, 13 KNN fn, tp: 10, 17 KNN f1 score: 0.596 KNN cohens kappa score: 0.577 ====== 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: 559, 44 LR fn, tp: 19, 12 LR f1 score: 0.276 LR cohens kappa score: 0.227 LR average precision score: 0.204 -> test with 'RF' RF tn, fp: 599, 4 RF fn, tp: 11, 20 RF f1 score: 0.727 RF cohens kappa score: 0.715 -> 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: 589, 14 KNN fn, tp: 9, 22 KNN f1 score: 0.657 KNN cohens kappa score: 0.638 ------ 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: 572, 31 LR fn, tp: 27, 4 LR f1 score: 0.121 LR cohens kappa score: 0.073 LR average precision score: 0.185 -> test with 'RF' RF tn, fp: 602, 1 RF fn, tp: 9, 22 RF f1 score: 0.815 RF cohens kappa score: 0.807 -> 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: 588, 15 KNN fn, tp: 14, 17 KNN f1 score: 0.540 KNN cohens kappa score: 0.516 ------ 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: 559, 44 LR fn, tp: 15, 16 LR f1 score: 0.352 LR cohens kappa score: 0.307 LR average precision score: 0.214 -> test with 'RF' RF tn, fp: 601, 2 RF fn, tp: 11, 20 RF f1 score: 0.755 RF cohens kappa score: 0.744 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 10, 21 GB f1 score: 0.737 GB cohens kappa score: 0.725 -> 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 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: 578, 25 LR fn, tp: 23, 8 LR f1 score: 0.250 LR cohens kappa score: 0.210 LR average precision score: 0.207 -> 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: 597, 6 GB fn, tp: 5, 26 GB f1 score: 0.825 GB cohens kappa score: 0.816 -> 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 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: 576, 24 LR fn, tp: 20, 7 LR f1 score: 0.241 LR cohens kappa score: 0.205 LR average precision score: 0.196 -> test with 'RF' RF tn, fp: 594, 6 RF fn, tp: 9, 18 RF f1 score: 0.706 RF cohens kappa score: 0.693 -> test with 'GB' GB tn, fp: 594, 6 GB fn, tp: 9, 18 GB f1 score: 0.706 GB cohens kappa score: 0.693 -> test with 'KNN' KNN tn, fp: 588, 12 KNN fn, tp: 14, 13 KNN f1 score: 0.500 KNN cohens kappa score: 0.478 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 594, 75 LR fn, tp: 27, 16 LR f1 score: 0.352 LR cohens kappa score: 0.307 LR average precision score: 0.269 average: LR tn, fp: 561.24, 41.16 LR fn, tp: 21.16, 9.04 LR f1 score: 0.223 LR cohens kappa score: 0.175 LR average precision score: 0.184 minimum: LR tn, fp: 528, 9 LR fn, tp: 15, 3 LR f1 score: 0.085 LR cohens kappa score: 0.033 LR average precision score: 0.113 -----[ RF ]----- maximum: RF tn, fp: 603, 8 RF fn, tp: 14, 26 RF f1 score: 0.867 RF cohens kappa score: 0.860 average: RF tn, fp: 598.8, 3.6 RF fn, tp: 8.6, 21.6 RF f1 score: 0.779 RF cohens kappa score: 0.769 minimum: RF tn, fp: 594, 0 RF fn, tp: 5, 17 RF f1 score: 0.694 RF cohens kappa score: 0.682 -----[ GB ]----- maximum: GB tn, fp: 603, 9 GB fn, tp: 12, 27 GB f1 score: 0.900 GB cohens kappa score: 0.895 average: GB tn, fp: 597.88, 4.52 GB fn, tp: 6.6, 23.6 GB f1 score: 0.809 GB cohens kappa score: 0.800 minimum: GB tn, fp: 594, 0 GB fn, tp: 4, 18 GB f1 score: 0.706 GB cohens kappa score: 0.693 -----[ KNN ]----- maximum: KNN tn, fp: 598, 23 KNN fn, tp: 15, 25 KNN f1 score: 0.689 KNN cohens kappa score: 0.673 average: KNN tn, fp: 588.4, 14.0 KNN fn, tp: 11.16, 19.04 KNN f1 score: 0.601 KNN cohens kappa score: 0.580 minimum: KNN tn, fp: 580, 5 KNN fn, tp: 6, 13 KNN f1 score: 0.479 KNN cohens kappa score: 0.448