/////////////////////////////////////////// // Running ProWRAS on imblearn_protein_homo /////////////////////////////////////////// Load 'data_input/imblearn_protein_homo' from imblearn 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 114528 synthetic samples -> test with 'LR' LR tn, fp: 28420, 471 LR fn, tp: 23, 237 LR f1 score: 0.490 LR cohens kappa score: 0.483 LR average precision score: 0.865 -> test with 'GB' GB tn, fp: 28766, 125 GB fn, tp: 46, 214 GB f1 score: 0.715 GB cohens kappa score: 0.712 -> test with 'KNN' KNN tn, fp: 28330, 561 KNN fn, tp: 94, 166 KNN f1 score: 0.336 KNN cohens kappa score: 0.328 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28467, 424 LR fn, tp: 25, 235 LR f1 score: 0.511 LR cohens kappa score: 0.505 LR average precision score: 0.889 -> test with 'GB' GB tn, fp: 28808, 83 GB fn, tp: 41, 219 GB f1 score: 0.779 GB cohens kappa score: 0.777 -> test with 'KNN' KNN tn, fp: 28260, 631 KNN fn, tp: 75, 185 KNN f1 score: 0.344 KNN cohens kappa score: 0.335 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28373, 518 LR fn, tp: 9, 251 LR f1 score: 0.488 LR cohens kappa score: 0.481 LR average precision score: 0.891 -> test with 'GB' GB tn, fp: 28787, 104 GB fn, tp: 51, 209 GB f1 score: 0.729 GB cohens kappa score: 0.727 -> test with 'KNN' KNN tn, fp: 28282, 609 KNN fn, tp: 98, 162 KNN f1 score: 0.314 KNN cohens kappa score: 0.305 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28474, 417 LR fn, tp: 26, 234 LR f1 score: 0.514 LR cohens kappa score: 0.507 LR average precision score: 0.858 -> test with 'GB' GB tn, fp: 28774, 117 GB fn, tp: 51, 209 GB f1 score: 0.713 GB cohens kappa score: 0.710 -> test with 'KNN' KNN tn, fp: 28299, 592 KNN fn, tp: 96, 164 KNN f1 score: 0.323 KNN cohens kappa score: 0.314 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with 'LR' LR tn, fp: 28479, 412 LR fn, tp: 35, 221 LR f1 score: 0.497 LR cohens kappa score: 0.491 LR average precision score: 0.815 -> test with 'GB' GB tn, fp: 28793, 98 GB fn, tp: 63, 193 GB f1 score: 0.706 GB cohens kappa score: 0.703 -> test with 'KNN' KNN tn, fp: 28361, 530 KNN fn, tp: 105, 151 KNN f1 score: 0.322 KNN cohens kappa score: 0.314 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28460, 431 LR fn, tp: 25, 235 LR f1 score: 0.508 LR cohens kappa score: 0.501 LR average precision score: 0.864 -> test with 'GB' GB tn, fp: 28793, 98 GB fn, tp: 53, 207 GB f1 score: 0.733 GB cohens kappa score: 0.730 -> test with 'KNN' KNN tn, fp: 28272, 619 KNN fn, tp: 99, 161 KNN f1 score: 0.310 KNN cohens kappa score: 0.300 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28432, 459 LR fn, tp: 18, 242 LR f1 score: 0.504 LR cohens kappa score: 0.497 LR average precision score: 0.896 -> test with 'GB' GB tn, fp: 28753, 138 GB fn, tp: 43, 217 GB f1 score: 0.706 GB cohens kappa score: 0.703 -> test with 'KNN' KNN tn, fp: 28275, 616 KNN fn, tp: 85, 175 KNN f1 score: 0.333 KNN cohens kappa score: 0.324 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28481, 410 LR fn, tp: 28, 232 LR f1 score: 0.514 LR cohens kappa score: 0.508 LR average precision score: 0.846 -> test with 'GB' GB tn, fp: 28801, 90 GB fn, tp: 50, 210 GB f1 score: 0.750 GB cohens kappa score: 0.748 -> test with 'KNN' KNN tn, fp: 28338, 553 KNN fn, tp: 92, 168 KNN f1 score: 0.343 KNN cohens kappa score: 0.334 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28435, 456 LR fn, tp: 23, 237 LR f1 score: 0.497 LR cohens kappa score: 0.491 LR average precision score: 0.867 -> test with 'GB' GB tn, fp: 28787, 104 GB fn, tp: 51, 209 GB f1 score: 0.729 GB cohens kappa score: 0.727 -> test with 'KNN' KNN tn, fp: 28321, 570 KNN fn, tp: 88, 172 KNN f1 score: 0.343 KNN cohens kappa score: 0.335 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with 'LR' LR tn, fp: 28425, 466 LR fn, tp: 23, 233 LR f1 score: 0.488 LR cohens kappa score: 0.481 LR average precision score: 0.850 -> test with 'GB' GB tn, fp: 28796, 95 GB fn, tp: 57, 199 GB f1 score: 0.724 GB cohens kappa score: 0.721 -> test with 'KNN' KNN tn, fp: 28293, 598 KNN fn, tp: 97, 159 KNN f1 score: 0.314 KNN cohens kappa score: 0.305 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28429, 462 LR fn, tp: 23, 237 LR f1 score: 0.494 LR cohens kappa score: 0.488 LR average precision score: 0.872 -> test with 'GB' GB tn, fp: 28791, 100 GB fn, tp: 48, 212 GB f1 score: 0.741 GB cohens kappa score: 0.739 -> test with 'KNN' KNN tn, fp: 28282, 609 KNN fn, tp: 89, 171 KNN f1 score: 0.329 KNN cohens kappa score: 0.320 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28432, 459 LR fn, tp: 23, 237 LR f1 score: 0.496 LR cohens kappa score: 0.489 LR average precision score: 0.868 -> test with 'GB' GB tn, fp: 28795, 96 GB fn, tp: 53, 207 GB f1 score: 0.735 GB cohens kappa score: 0.733 -> test with 'KNN' KNN tn, fp: 28331, 560 KNN fn, tp: 103, 157 KNN f1 score: 0.321 KNN cohens kappa score: 0.312 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28440, 451 LR fn, tp: 33, 227 LR f1 score: 0.484 LR cohens kappa score: 0.477 LR average precision score: 0.836 -> test with 'GB' GB tn, fp: 28778, 113 GB fn, tp: 50, 210 GB f1 score: 0.720 GB cohens kappa score: 0.718 -> test with 'KNN' KNN tn, fp: 28298, 593 KNN fn, tp: 103, 157 KNN f1 score: 0.311 KNN cohens kappa score: 0.302 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28434, 457 LR fn, tp: 25, 235 LR f1 score: 0.494 LR cohens kappa score: 0.487 LR average precision score: 0.868 -> test with 'GB' GB tn, fp: 28783, 108 GB fn, tp: 53, 207 GB f1 score: 0.720 GB cohens kappa score: 0.717 -> test with 'KNN' KNN tn, fp: 28322, 569 KNN fn, tp: 95, 165 KNN f1 score: 0.332 KNN cohens kappa score: 0.323 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with 'LR' LR tn, fp: 28416, 475 LR fn, tp: 19, 237 LR f1 score: 0.490 LR cohens kappa score: 0.483 LR average precision score: 0.887 -> test with 'GB' GB tn, fp: 28780, 111 GB fn, tp: 47, 209 GB f1 score: 0.726 GB cohens kappa score: 0.723 -> test with 'KNN' KNN tn, fp: 28299, 592 KNN fn, tp: 84, 172 KNN f1 score: 0.337 KNN cohens kappa score: 0.328 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28437, 454 LR fn, tp: 20, 240 LR f1 score: 0.503 LR cohens kappa score: 0.497 LR average precision score: 0.880 -> test with 'GB' GB tn, fp: 28787, 104 GB fn, tp: 47, 213 GB f1 score: 0.738 GB cohens kappa score: 0.736 -> test with 'KNN' KNN tn, fp: 28301, 590 KNN fn, tp: 86, 174 KNN f1 score: 0.340 KNN cohens kappa score: 0.331 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28450, 441 LR fn, tp: 22, 238 LR f1 score: 0.507 LR cohens kappa score: 0.500 LR average precision score: 0.846 -> test with 'GB' GB tn, fp: 28802, 89 GB fn, tp: 63, 197 GB f1 score: 0.722 GB cohens kappa score: 0.719 -> test with 'KNN' KNN tn, fp: 28323, 568 KNN fn, tp: 104, 156 KNN f1 score: 0.317 KNN cohens kappa score: 0.308 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28493, 398 LR fn, tp: 27, 233 LR f1 score: 0.523 LR cohens kappa score: 0.517 LR average precision score: 0.859 -> test with 'GB' GB tn, fp: 28803, 88 GB fn, tp: 48, 212 GB f1 score: 0.757 GB cohens kappa score: 0.755 -> test with 'KNN' KNN tn, fp: 28281, 610 KNN fn, tp: 87, 173 KNN f1 score: 0.332 KNN cohens kappa score: 0.323 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28423, 468 LR fn, tp: 24, 236 LR f1 score: 0.490 LR cohens kappa score: 0.483 LR average precision score: 0.877 -> test with 'GB' GB tn, fp: 28799, 92 GB fn, tp: 51, 209 GB f1 score: 0.745 GB cohens kappa score: 0.743 -> test with 'KNN' KNN tn, fp: 28327, 564 KNN fn, tp: 95, 165 KNN f1 score: 0.334 KNN cohens kappa score: 0.325 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with 'LR' LR tn, fp: 28487, 404 LR fn, tp: 27, 229 LR f1 score: 0.515 LR cohens kappa score: 0.509 LR average precision score: 0.860 -> test with 'GB' GB tn, fp: 28781, 110 GB fn, tp: 53, 203 GB f1 score: 0.714 GB cohens kappa score: 0.711 -> test with 'KNN' KNN tn, fp: 28280, 611 KNN fn, tp: 83, 173 KNN f1 score: 0.333 KNN cohens kappa score: 0.324 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28406, 485 LR fn, tp: 22, 238 LR f1 score: 0.484 LR cohens kappa score: 0.477 LR average precision score: 0.871 -> test with 'GB' GB tn, fp: 28770, 121 GB fn, tp: 47, 213 GB f1 score: 0.717 GB cohens kappa score: 0.714 -> test with 'KNN' KNN tn, fp: 28323, 568 KNN fn, tp: 102, 158 KNN f1 score: 0.320 KNN cohens kappa score: 0.311 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28486, 405 LR fn, tp: 28, 232 LR f1 score: 0.517 LR cohens kappa score: 0.511 LR average precision score: 0.866 -> test with 'GB' GB tn, fp: 28804, 87 GB fn, tp: 53, 207 GB f1 score: 0.747 GB cohens kappa score: 0.745 -> test with 'KNN' KNN tn, fp: 28316, 575 KNN fn, tp: 98, 162 KNN f1 score: 0.325 KNN cohens kappa score: 0.316 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28438, 453 LR fn, tp: 24, 236 LR f1 score: 0.497 LR cohens kappa score: 0.491 LR average precision score: 0.854 -> test with 'GB' GB tn, fp: 28813, 78 GB fn, tp: 59, 201 GB f1 score: 0.746 GB cohens kappa score: 0.743 -> test with 'KNN' KNN tn, fp: 28348, 543 KNN fn, tp: 102, 158 KNN f1 score: 0.329 KNN cohens kappa score: 0.320 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28453, 438 LR fn, tp: 20, 240 LR f1 score: 0.512 LR cohens kappa score: 0.505 LR average precision score: 0.872 -> test with 'GB' GB tn, fp: 28778, 113 GB fn, tp: 54, 206 GB f1 score: 0.712 GB cohens kappa score: 0.709 -> test with 'KNN' KNN tn, fp: 28282, 609 KNN fn, tp: 91, 169 KNN f1 score: 0.326 KNN cohens kappa score: 0.316 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with 'LR' LR tn, fp: 28436, 455 LR fn, tp: 28, 228 LR f1 score: 0.486 LR cohens kappa score: 0.479 LR average precision score: 0.855 -> test with 'GB' GB tn, fp: 28776, 115 GB fn, tp: 49, 207 GB f1 score: 0.716 GB cohens kappa score: 0.713 -> test with 'KNN' KNN tn, fp: 28316, 575 KNN fn, tp: 89, 167 KNN f1 score: 0.335 KNN cohens kappa score: 0.326 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 28493, 518 LR fn, tp: 35, 251 LR f1 score: 0.523 LR cohens kappa score: 0.517 LR average precision score: 0.896 average: LR tn, fp: 28444.24, 446.76 LR fn, tp: 24.0, 235.2 LR f1 score: 0.500 LR cohens kappa score: 0.494 LR average precision score: 0.865 minimum: LR tn, fp: 28373, 398 LR fn, tp: 9, 221 LR f1 score: 0.484 LR cohens kappa score: 0.477 LR average precision score: 0.815 -----[ GB ]----- maximum: GB tn, fp: 28813, 138 GB fn, tp: 63, 219 GB f1 score: 0.779 GB cohens kappa score: 0.777 average: GB tn, fp: 28787.92, 103.08 GB fn, tp: 51.24, 207.96 GB f1 score: 0.730 GB cohens kappa score: 0.727 minimum: GB tn, fp: 28753, 78 GB fn, tp: 41, 193 GB f1 score: 0.706 GB cohens kappa score: 0.703 -----[ KNN ]----- maximum: KNN tn, fp: 28361, 631 KNN fn, tp: 105, 185 KNN f1 score: 0.344 KNN cohens kappa score: 0.335 average: KNN tn, fp: 28306.4, 584.6 KNN fn, tp: 93.6, 165.6 KNN f1 score: 0.328 KNN cohens kappa score: 0.319 minimum: KNN tn, fp: 28260, 530 KNN fn, tp: 75, 151 KNN f1 score: 0.310 KNN cohens kappa score: 0.300