/////////////////////////////////////////// // Running Repeater 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: 27649, 1242 LR fn, tp: 17, 243 LR f1 score: 0.279 LR cohens kappa score: 0.267 LR average precision score: 0.863 -> test with 'GB' GB tn, fp: 28386, 505 GB fn, tp: 19, 241 GB f1 score: 0.479 GB cohens kappa score: 0.472 -> test with 'KNN' KNN tn, fp: 28546, 345 KNN fn, tp: 96, 164 KNN f1 score: 0.427 KNN cohens kappa score: 0.420 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27695, 1196 LR fn, tp: 14, 246 LR f1 score: 0.289 LR cohens kappa score: 0.278 LR average precision score: 0.883 -> test with 'GB' GB tn, fp: 28384, 507 GB fn, tp: 14, 246 GB f1 score: 0.486 GB cohens kappa score: 0.479 -> test with 'KNN' KNN tn, fp: 28529, 362 KNN fn, tp: 81, 179 KNN f1 score: 0.447 KNN cohens kappa score: 0.440 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27630, 1261 LR fn, tp: 6, 254 LR f1 score: 0.286 LR cohens kappa score: 0.275 LR average precision score: 0.887 -> test with 'GB' GB tn, fp: 28342, 549 GB fn, tp: 11, 249 GB f1 score: 0.471 GB cohens kappa score: 0.463 -> test with 'KNN' KNN tn, fp: 28493, 398 KNN fn, tp: 110, 150 KNN f1 score: 0.371 KNN cohens kappa score: 0.364 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27663, 1228 LR fn, tp: 17, 243 LR f1 score: 0.281 LR cohens kappa score: 0.270 LR average precision score: 0.855 -> test with 'GB' GB tn, fp: 28406, 485 GB fn, tp: 19, 241 GB f1 score: 0.489 GB cohens kappa score: 0.482 -> test with 'KNN' KNN tn, fp: 28499, 392 KNN fn, tp: 94, 166 KNN f1 score: 0.406 KNN cohens kappa score: 0.399 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with 'LR' LR tn, fp: 27824, 1067 LR fn, tp: 22, 234 LR f1 score: 0.301 LR cohens kappa score: 0.290 LR average precision score: 0.812 -> test with 'GB' GB tn, fp: 28481, 410 GB fn, tp: 26, 230 GB f1 score: 0.513 GB cohens kappa score: 0.507 -> test with 'KNN' KNN tn, fp: 28504, 387 KNN fn, tp: 113, 143 KNN f1 score: 0.364 KNN cohens kappa score: 0.356 ====== 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: 27706, 1185 LR fn, tp: 12, 248 LR f1 score: 0.293 LR cohens kappa score: 0.282 LR average precision score: 0.870 -> test with 'GB' GB tn, fp: 28416, 475 GB fn, tp: 17, 243 GB f1 score: 0.497 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 28549, 342 KNN fn, tp: 100, 160 KNN f1 score: 0.420 KNN cohens kappa score: 0.413 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27692, 1199 LR fn, tp: 10, 250 LR f1 score: 0.293 LR cohens kappa score: 0.282 LR average precision score: 0.892 -> test with 'GB' GB tn, fp: 28359, 532 GB fn, tp: 17, 243 GB f1 score: 0.470 GB cohens kappa score: 0.462 -> test with 'KNN' KNN tn, fp: 28527, 364 KNN fn, tp: 93, 167 KNN f1 score: 0.422 KNN cohens kappa score: 0.415 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27688, 1203 LR fn, tp: 18, 242 LR f1 score: 0.284 LR cohens kappa score: 0.273 LR average precision score: 0.830 -> test with 'GB' GB tn, fp: 28423, 468 GB fn, tp: 19, 241 GB f1 score: 0.497 GB cohens kappa score: 0.491 -> test with 'KNN' KNN tn, fp: 28497, 394 KNN fn, tp: 99, 161 KNN f1 score: 0.395 KNN cohens kappa score: 0.388 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27688, 1203 LR fn, tp: 13, 247 LR f1 score: 0.289 LR cohens kappa score: 0.278 LR average precision score: 0.864 -> test with 'GB' GB tn, fp: 28399, 492 GB fn, tp: 16, 244 GB f1 score: 0.490 GB cohens kappa score: 0.483 -> test with 'KNN' KNN tn, fp: 28502, 389 KNN fn, tp: 90, 170 KNN f1 score: 0.415 KNN cohens kappa score: 0.408 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with 'LR' LR tn, fp: 27631, 1260 LR fn, tp: 13, 243 LR f1 score: 0.276 LR cohens kappa score: 0.265 LR average precision score: 0.840 -> test with 'GB' GB tn, fp: 28376, 515 GB fn, tp: 20, 236 GB f1 score: 0.469 GB cohens kappa score: 0.462 -> test with 'KNN' KNN tn, fp: 28518, 373 KNN fn, tp: 97, 159 KNN f1 score: 0.404 KNN cohens kappa score: 0.396 ====== 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: 27777, 1114 LR fn, tp: 16, 244 LR f1 score: 0.302 LR cohens kappa score: 0.291 LR average precision score: 0.868 -> test with 'GB' GB tn, fp: 28417, 474 GB fn, tp: 19, 241 GB f1 score: 0.494 GB cohens kappa score: 0.488 -> test with 'KNN' KNN tn, fp: 28501, 390 KNN fn, tp: 91, 169 KNN f1 score: 0.413 KNN cohens kappa score: 0.405 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27678, 1213 LR fn, tp: 13, 247 LR f1 score: 0.287 LR cohens kappa score: 0.276 LR average precision score: 0.865 -> test with 'GB' GB tn, fp: 28404, 487 GB fn, tp: 18, 242 GB f1 score: 0.489 GB cohens kappa score: 0.483 -> test with 'KNN' KNN tn, fp: 28539, 352 KNN fn, tp: 108, 152 KNN f1 score: 0.398 KNN cohens kappa score: 0.391 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27722, 1169 LR fn, tp: 17, 243 LR f1 score: 0.291 LR cohens kappa score: 0.280 LR average precision score: 0.834 -> test with 'GB' GB tn, fp: 28409, 482 GB fn, tp: 21, 239 GB f1 score: 0.487 GB cohens kappa score: 0.480 -> test with 'KNN' KNN tn, fp: 28523, 368 KNN fn, tp: 101, 159 KNN f1 score: 0.404 KNN cohens kappa score: 0.397 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27635, 1256 LR fn, tp: 14, 246 LR f1 score: 0.279 LR cohens kappa score: 0.268 LR average precision score: 0.855 -> test with 'GB' GB tn, fp: 28359, 532 GB fn, tp: 12, 248 GB f1 score: 0.477 GB cohens kappa score: 0.470 -> test with 'KNN' KNN tn, fp: 28495, 396 KNN fn, tp: 99, 161 KNN f1 score: 0.394 KNN cohens kappa score: 0.387 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with 'LR' LR tn, fp: 27643, 1248 LR fn, tp: 11, 245 LR f1 score: 0.280 LR cohens kappa score: 0.269 LR average precision score: 0.882 -> test with 'GB' GB tn, fp: 28384, 507 GB fn, tp: 16, 240 GB f1 score: 0.479 GB cohens kappa score: 0.472 -> test with 'KNN' KNN tn, fp: 28505, 386 KNN fn, tp: 91, 165 KNN f1 score: 0.409 KNN cohens kappa score: 0.402 ====== 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: 27672, 1219 LR fn, tp: 13, 247 LR f1 score: 0.286 LR cohens kappa score: 0.275 LR average precision score: 0.873 -> test with 'GB' GB tn, fp: 28378, 513 GB fn, tp: 15, 245 GB f1 score: 0.481 GB cohens kappa score: 0.474 -> test with 'KNN' KNN tn, fp: 28514, 377 KNN fn, tp: 96, 164 KNN f1 score: 0.409 KNN cohens kappa score: 0.402 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27703, 1188 LR fn, tp: 16, 244 LR f1 score: 0.288 LR cohens kappa score: 0.278 LR average precision score: 0.835 -> test with 'GB' GB tn, fp: 28382, 509 GB fn, tp: 18, 242 GB f1 score: 0.479 GB cohens kappa score: 0.472 -> test with 'KNN' KNN tn, fp: 28527, 364 KNN fn, tp: 105, 155 KNN f1 score: 0.398 KNN cohens kappa score: 0.391 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27707, 1184 LR fn, tp: 17, 243 LR f1 score: 0.288 LR cohens kappa score: 0.277 LR average precision score: 0.859 -> test with 'GB' GB tn, fp: 28415, 476 GB fn, tp: 20, 240 GB f1 score: 0.492 GB cohens kappa score: 0.485 -> test with 'KNN' KNN tn, fp: 28501, 390 KNN fn, tp: 89, 171 KNN f1 score: 0.417 KNN cohens kappa score: 0.409 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27671, 1220 LR fn, tp: 12, 248 LR f1 score: 0.287 LR cohens kappa score: 0.276 LR average precision score: 0.878 -> test with 'GB' GB tn, fp: 28427, 464 GB fn, tp: 15, 245 GB f1 score: 0.506 GB cohens kappa score: 0.499 -> test with 'KNN' KNN tn, fp: 28523, 368 KNN fn, tp: 93, 167 KNN f1 score: 0.420 KNN cohens kappa score: 0.413 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with 'LR' LR tn, fp: 27674, 1217 LR fn, tp: 16, 240 LR f1 score: 0.280 LR cohens kappa score: 0.269 LR average precision score: 0.841 -> test with 'GB' GB tn, fp: 28383, 508 GB fn, tp: 16, 240 GB f1 score: 0.478 GB cohens kappa score: 0.471 -> test with 'KNN' KNN tn, fp: 28492, 399 KNN fn, tp: 89, 167 KNN f1 score: 0.406 KNN cohens kappa score: 0.399 ====== 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: 27688, 1203 LR fn, tp: 13, 247 LR f1 score: 0.289 LR cohens kappa score: 0.278 LR average precision score: 0.866 -> test with 'GB' GB tn, fp: 28385, 506 GB fn, tp: 15, 245 GB f1 score: 0.485 GB cohens kappa score: 0.478 -> test with 'KNN' KNN tn, fp: 28548, 343 KNN fn, tp: 100, 160 KNN f1 score: 0.419 KNN cohens kappa score: 0.412 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27724, 1167 LR fn, tp: 15, 245 LR f1 score: 0.293 LR cohens kappa score: 0.282 LR average precision score: 0.870 -> test with 'GB' GB tn, fp: 28424, 467 GB fn, tp: 19, 241 GB f1 score: 0.498 GB cohens kappa score: 0.491 -> test with 'KNN' KNN tn, fp: 28520, 371 KNN fn, tp: 100, 160 KNN f1 score: 0.405 KNN cohens kappa score: 0.397 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27654, 1237 LR fn, tp: 16, 244 LR f1 score: 0.280 LR cohens kappa score: 0.269 LR average precision score: 0.854 -> test with 'GB' GB tn, fp: 28444, 447 GB fn, tp: 18, 242 GB f1 score: 0.510 GB cohens kappa score: 0.504 -> test with 'KNN' KNN tn, fp: 28515, 376 KNN fn, tp: 106, 154 KNN f1 score: 0.390 KNN cohens kappa score: 0.382 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27677, 1214 LR fn, tp: 12, 248 LR f1 score: 0.288 LR cohens kappa score: 0.277 LR average precision score: 0.863 -> test with 'GB' GB tn, fp: 28386, 505 GB fn, tp: 15, 245 GB f1 score: 0.485 GB cohens kappa score: 0.478 -> test with 'KNN' KNN tn, fp: 28522, 369 KNN fn, tp: 95, 165 KNN f1 score: 0.416 KNN cohens kappa score: 0.409 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with 'LR' LR tn, fp: 27691, 1200 LR fn, tp: 13, 243 LR f1 score: 0.286 LR cohens kappa score: 0.275 LR average precision score: 0.851 -> test with 'GB' GB tn, fp: 28373, 518 GB fn, tp: 14, 242 GB f1 score: 0.476 GB cohens kappa score: 0.469 -> test with 'KNN' KNN tn, fp: 28520, 371 KNN fn, tp: 91, 165 KNN f1 score: 0.417 KNN cohens kappa score: 0.410 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 27824, 1261 LR fn, tp: 22, 254 LR f1 score: 0.302 LR cohens kappa score: 0.291 LR average precision score: 0.892 average: LR tn, fp: 27687.28, 1203.72 LR fn, tp: 14.24, 244.96 LR f1 score: 0.287 LR cohens kappa score: 0.276 LR average precision score: 0.860 minimum: LR tn, fp: 27630, 1067 LR fn, tp: 6, 234 LR f1 score: 0.276 LR cohens kappa score: 0.265 LR average precision score: 0.812 -----[ GB ]----- maximum: GB tn, fp: 28481, 549 GB fn, tp: 26, 249 GB f1 score: 0.513 GB cohens kappa score: 0.507 average: GB tn, fp: 28397.68, 493.32 GB fn, tp: 17.16, 242.04 GB f1 score: 0.487 GB cohens kappa score: 0.480 minimum: GB tn, fp: 28342, 410 GB fn, tp: 11, 230 GB f1 score: 0.469 GB cohens kappa score: 0.462 -----[ KNN ]----- maximum: KNN tn, fp: 28549, 399 KNN fn, tp: 113, 179 KNN f1 score: 0.447 KNN cohens kappa score: 0.440 average: KNN tn, fp: 28516.36, 374.64 KNN fn, tp: 97.08, 162.12 KNN f1 score: 0.407 KNN cohens kappa score: 0.400 minimum: KNN tn, fp: 28492, 342 KNN fn, tp: 81, 143 KNN f1 score: 0.364 KNN cohens kappa score: 0.356