/////////////////////////////////////////// // Running Repeater on imblearn_webpage /////////////////////////////////////////// Load 'data_input/imblearn_webpage' from imblearn non empty cut in data_input/imblearn_webpage! (76 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 26255 synthetic samples -> test with 'LR' LR tn, fp: 6389, 371 LR fn, tp: 21, 176 LR f1 score: 0.473 LR cohens kappa score: 0.450 LR average precision score: 0.761 -> test with 'GB' GB tn, fp: 6418, 342 GB fn, tp: 24, 173 GB f1 score: 0.486 GB cohens kappa score: 0.464 -> test with 'KNN' KNN tn, fp: 6155, 605 KNN fn, tp: 16, 181 KNN f1 score: 0.368 KNN cohens kappa score: 0.338 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6449, 311 LR fn, tp: 24, 173 LR f1 score: 0.508 LR cohens kappa score: 0.487 LR average precision score: 0.766 -> test with 'GB' GB tn, fp: 6446, 314 GB fn, tp: 28, 169 GB f1 score: 0.497 GB cohens kappa score: 0.476 -> test with 'KNN' KNN tn, fp: 6175, 585 KNN fn, tp: 30, 167 KNN f1 score: 0.352 KNN cohens kappa score: 0.322 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6426, 334 LR fn, tp: 13, 184 LR f1 score: 0.515 LR cohens kappa score: 0.494 LR average precision score: 0.827 -> test with 'GB' GB tn, fp: 6433, 327 GB fn, tp: 22, 175 GB f1 score: 0.501 GB cohens kappa score: 0.480 -> test with 'KNN' KNN tn, fp: 6122, 638 KNN fn, tp: 20, 177 KNN f1 score: 0.350 KNN cohens kappa score: 0.319 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6412, 348 LR fn, tp: 18, 179 LR f1 score: 0.494 LR cohens kappa score: 0.473 LR average precision score: 0.753 -> test with 'GB' GB tn, fp: 6433, 327 GB fn, tp: 26, 171 GB f1 score: 0.492 GB cohens kappa score: 0.471 -> test with 'KNN' KNN tn, fp: 6071, 689 KNN fn, tp: 24, 173 KNN f1 score: 0.327 KNN cohens kappa score: 0.294 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with 'LR' LR tn, fp: 6472, 287 LR fn, tp: 29, 164 LR f1 score: 0.509 LR cohens kappa score: 0.489 LR average precision score: 0.757 -> test with 'GB' GB tn, fp: 6479, 280 GB fn, tp: 31, 162 GB f1 score: 0.510 GB cohens kappa score: 0.491 -> test with 'KNN' KNN tn, fp: 6046, 713 KNN fn, tp: 25, 168 KNN f1 score: 0.313 KNN cohens kappa score: 0.280 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6406, 354 LR fn, tp: 15, 182 LR f1 score: 0.497 LR cohens kappa score: 0.475 LR average precision score: 0.793 -> test with 'GB' GB tn, fp: 6454, 306 GB fn, tp: 28, 169 GB f1 score: 0.503 GB cohens kappa score: 0.482 -> test with 'KNN' KNN tn, fp: 6135, 625 KNN fn, tp: 20, 177 KNN f1 score: 0.354 KNN cohens kappa score: 0.324 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6457, 303 LR fn, tp: 25, 172 LR f1 score: 0.512 LR cohens kappa score: 0.492 LR average precision score: 0.792 -> test with 'GB' GB tn, fp: 6455, 305 GB fn, tp: 24, 173 GB f1 score: 0.513 GB cohens kappa score: 0.492 -> test with 'KNN' KNN tn, fp: 6076, 684 KNN fn, tp: 18, 179 KNN f1 score: 0.338 KNN cohens kappa score: 0.306 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6439, 321 LR fn, tp: 27, 170 LR f1 score: 0.494 LR cohens kappa score: 0.473 LR average precision score: 0.756 -> test with 'GB' GB tn, fp: 6433, 327 GB fn, tp: 28, 169 GB f1 score: 0.488 GB cohens kappa score: 0.466 -> test with 'KNN' KNN tn, fp: 6141, 619 KNN fn, tp: 25, 172 KNN f1 score: 0.348 KNN cohens kappa score: 0.317 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6381, 379 LR fn, tp: 21, 176 LR f1 score: 0.468 LR cohens kappa score: 0.445 LR average precision score: 0.746 -> test with 'GB' GB tn, fp: 6453, 307 GB fn, tp: 28, 169 GB f1 score: 0.502 GB cohens kappa score: 0.481 -> test with 'KNN' KNN tn, fp: 6108, 652 KNN fn, tp: 17, 180 KNN f1 score: 0.350 KNN cohens kappa score: 0.319 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with 'LR' LR tn, fp: 6410, 349 LR fn, tp: 18, 175 LR f1 score: 0.488 LR cohens kappa score: 0.466 LR average precision score: 0.801 -> test with 'GB' GB tn, fp: 6420, 339 GB fn, tp: 22, 171 GB f1 score: 0.486 GB cohens kappa score: 0.465 -> test with 'KNN' KNN tn, fp: 6097, 662 KNN fn, tp: 28, 165 KNN f1 score: 0.324 KNN cohens kappa score: 0.292 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6383, 377 LR fn, tp: 26, 171 LR f1 score: 0.459 LR cohens kappa score: 0.436 LR average precision score: 0.729 -> test with 'GB' GB tn, fp: 6427, 333 GB fn, tp: 27, 170 GB f1 score: 0.486 GB cohens kappa score: 0.464 -> test with 'KNN' KNN tn, fp: 6158, 602 KNN fn, tp: 24, 173 KNN f1 score: 0.356 KNN cohens kappa score: 0.326 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6462, 298 LR fn, tp: 21, 176 LR f1 score: 0.525 LR cohens kappa score: 0.505 LR average precision score: 0.795 -> test with 'GB' GB tn, fp: 6456, 304 GB fn, tp: 27, 170 GB f1 score: 0.507 GB cohens kappa score: 0.486 -> test with 'KNN' KNN tn, fp: 6118, 642 KNN fn, tp: 18, 179 KNN f1 score: 0.352 KNN cohens kappa score: 0.321 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6446, 314 LR fn, tp: 26, 171 LR f1 score: 0.501 LR cohens kappa score: 0.481 LR average precision score: 0.723 -> test with 'GB' GB tn, fp: 6450, 310 GB fn, tp: 32, 165 GB f1 score: 0.491 GB cohens kappa score: 0.470 -> test with 'KNN' KNN tn, fp: 6071, 689 KNN fn, tp: 34, 163 KNN f1 score: 0.311 KNN cohens kappa score: 0.278 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6382, 378 LR fn, tp: 11, 186 LR f1 score: 0.489 LR cohens kappa score: 0.466 LR average precision score: 0.809 -> test with 'GB' GB tn, fp: 6415, 345 GB fn, tp: 23, 174 GB f1 score: 0.486 GB cohens kappa score: 0.464 -> test with 'KNN' KNN tn, fp: 6045, 715 KNN fn, tp: 18, 179 KNN f1 score: 0.328 KNN cohens kappa score: 0.295 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with 'LR' LR tn, fp: 6422, 337 LR fn, tp: 21, 172 LR f1 score: 0.490 LR cohens kappa score: 0.469 LR average precision score: 0.757 -> test with 'GB' GB tn, fp: 6424, 335 GB fn, tp: 23, 170 GB f1 score: 0.487 GB cohens kappa score: 0.466 -> test with 'KNN' KNN tn, fp: 6139, 620 KNN fn, tp: 13, 180 KNN f1 score: 0.363 KNN cohens kappa score: 0.333 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6420, 340 LR fn, tp: 26, 171 LR f1 score: 0.483 LR cohens kappa score: 0.461 LR average precision score: 0.751 -> test with 'GB' GB tn, fp: 6421, 339 GB fn, tp: 28, 169 GB f1 score: 0.479 GB cohens kappa score: 0.457 -> test with 'KNN' KNN tn, fp: 6149, 611 KNN fn, tp: 30, 167 KNN f1 score: 0.343 KNN cohens kappa score: 0.311 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6452, 308 LR fn, tp: 26, 171 LR f1 score: 0.506 LR cohens kappa score: 0.485 LR average precision score: 0.749 -> test with 'GB' GB tn, fp: 6479, 281 GB fn, tp: 31, 166 GB f1 score: 0.516 GB cohens kappa score: 0.496 -> test with 'KNN' KNN tn, fp: 6086, 674 KNN fn, tp: 18, 179 KNN f1 score: 0.341 KNN cohens kappa score: 0.309 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6427, 333 LR fn, tp: 15, 182 LR f1 score: 0.511 LR cohens kappa score: 0.490 LR average precision score: 0.800 -> test with 'GB' GB tn, fp: 6420, 340 GB fn, tp: 23, 174 GB f1 score: 0.489 GB cohens kappa score: 0.468 -> test with 'KNN' KNN tn, fp: 6145, 615 KNN fn, tp: 20, 177 KNN f1 score: 0.358 KNN cohens kappa score: 0.327 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6418, 342 LR fn, tp: 19, 178 LR f1 score: 0.497 LR cohens kappa score: 0.475 LR average precision score: 0.740 -> test with 'GB' GB tn, fp: 6453, 307 GB fn, tp: 26, 171 GB f1 score: 0.507 GB cohens kappa score: 0.486 -> test with 'KNN' KNN tn, fp: 6117, 643 KNN fn, tp: 21, 176 KNN f1 score: 0.346 KNN cohens kappa score: 0.315 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with 'LR' LR tn, fp: 6392, 367 LR fn, tp: 16, 177 LR f1 score: 0.480 LR cohens kappa score: 0.458 LR average precision score: 0.796 -> test with 'GB' GB tn, fp: 6409, 350 GB fn, tp: 18, 175 GB f1 score: 0.487 GB cohens kappa score: 0.466 -> test with 'KNN' KNN tn, fp: 6104, 655 KNN fn, tp: 12, 181 KNN f1 score: 0.352 KNN cohens kappa score: 0.321 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6435, 325 LR fn, tp: 22, 175 LR f1 score: 0.502 LR cohens kappa score: 0.481 LR average precision score: 0.761 -> test with 'GB' GB tn, fp: 6469, 291 GB fn, tp: 27, 170 GB f1 score: 0.517 GB cohens kappa score: 0.497 -> test with 'KNN' KNN tn, fp: 6158, 602 KNN fn, tp: 16, 181 KNN f1 score: 0.369 KNN cohens kappa score: 0.340 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6431, 329 LR fn, tp: 30, 167 LR f1 score: 0.482 LR cohens kappa score: 0.460 LR average precision score: 0.728 -> test with 'GB' GB tn, fp: 6413, 347 GB fn, tp: 27, 170 GB f1 score: 0.476 GB cohens kappa score: 0.454 -> test with 'KNN' KNN tn, fp: 6158, 602 KNN fn, tp: 30, 167 KNN f1 score: 0.346 KNN cohens kappa score: 0.315 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6360, 400 LR fn, tp: 26, 171 LR f1 score: 0.445 LR cohens kappa score: 0.421 LR average precision score: 0.731 -> test with 'GB' GB tn, fp: 6400, 360 GB fn, tp: 23, 174 GB f1 score: 0.476 GB cohens kappa score: 0.453 -> test with 'KNN' KNN tn, fp: 6074, 686 KNN fn, tp: 23, 174 KNN f1 score: 0.329 KNN cohens kappa score: 0.297 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6430, 330 LR fn, tp: 17, 180 LR f1 score: 0.509 LR cohens kappa score: 0.488 LR average precision score: 0.819 -> test with 'GB' GB tn, fp: 6469, 291 GB fn, tp: 24, 173 GB f1 score: 0.523 GB cohens kappa score: 0.504 -> test with 'KNN' KNN tn, fp: 6086, 674 KNN fn, tp: 24, 173 KNN f1 score: 0.331 KNN cohens kappa score: 0.299 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with 'LR' LR tn, fp: 6409, 350 LR fn, tp: 18, 175 LR f1 score: 0.487 LR cohens kappa score: 0.466 LR average precision score: 0.764 -> test with 'GB' GB tn, fp: 6476, 283 GB fn, tp: 27, 166 GB f1 score: 0.517 GB cohens kappa score: 0.498 -> test with 'KNN' KNN tn, fp: 6094, 665 KNN fn, tp: 23, 170 KNN f1 score: 0.331 KNN cohens kappa score: 0.299 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 6472, 400 LR fn, tp: 30, 186 LR f1 score: 0.525 LR cohens kappa score: 0.505 LR average precision score: 0.827 average: LR tn, fp: 6420.4, 339.4 LR fn, tp: 21.24, 174.96 LR f1 score: 0.493 LR cohens kappa score: 0.471 LR average precision score: 0.768 minimum: LR tn, fp: 6360, 287 LR fn, tp: 11, 164 LR f1 score: 0.445 LR cohens kappa score: 0.421 LR average precision score: 0.723 -----[ GB ]----- maximum: GB tn, fp: 6479, 360 GB fn, tp: 32, 175 GB f1 score: 0.523 GB cohens kappa score: 0.504 average: GB tn, fp: 6440.2, 319.6 GB fn, tp: 25.88, 170.32 GB f1 score: 0.497 GB cohens kappa score: 0.476 minimum: GB tn, fp: 6400, 280 GB fn, tp: 18, 162 GB f1 score: 0.476 GB cohens kappa score: 0.453 -----[ KNN ]----- maximum: KNN tn, fp: 6175, 715 KNN fn, tp: 34, 181 KNN f1 score: 0.369 KNN cohens kappa score: 0.340 average: KNN tn, fp: 6113.12, 646.68 KNN fn, tp: 21.88, 174.32 KNN f1 score: 0.343 KNN cohens kappa score: 0.312 minimum: KNN tn, fp: 6045, 585 KNN fn, tp: 12, 163 KNN f1 score: 0.311 KNN cohens kappa score: 0.278