/////////////////////////////////////////// // Running convGAN-proximary-5 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 GAN.predict GAN tn, fp: 6228, 532 GAN fn, tp: 35, 162 GAN f1 score: 0.364 GAN cohens kappa score: 0.334 -> test with 'LR' LR tn, fp: 6351, 409 LR fn, tp: 25, 172 LR f1 score: 0.442 LR cohens kappa score: 0.418 LR average precision score: 0.766 -> test with 'GB' GB tn, fp: 6400, 360 GB fn, tp: 94, 103 GB f1 score: 0.312 GB cohens kappa score: 0.284 -> test with 'KNN' KNN tn, fp: 6264, 496 KNN fn, tp: 15, 182 KNN f1 score: 0.416 KNN cohens kappa score: 0.389 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 5812, 948 GAN fn, tp: 38, 159 GAN f1 score: 0.244 GAN cohens kappa score: 0.206 -> test with 'LR' LR tn, fp: 6403, 357 LR fn, tp: 22, 175 LR f1 score: 0.480 LR cohens kappa score: 0.458 LR average precision score: 0.788 -> test with 'GB' GB tn, fp: 6298, 462 GB fn, tp: 90, 107 GB f1 score: 0.279 GB cohens kappa score: 0.248 -> test with 'KNN' KNN tn, fp: 6344, 416 KNN fn, tp: 33, 164 KNN f1 score: 0.422 KNN cohens kappa score: 0.397 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 5952, 808 GAN fn, tp: 28, 169 GAN f1 score: 0.288 GAN cohens kappa score: 0.253 -> test with 'LR' LR tn, fp: 6385, 375 LR fn, tp: 15, 182 LR f1 score: 0.483 LR cohens kappa score: 0.460 LR average precision score: 0.838 -> test with 'GB' GB tn, fp: 6353, 407 GB fn, tp: 87, 110 GB f1 score: 0.308 GB cohens kappa score: 0.279 -> test with 'KNN' KNN tn, fp: 6217, 543 KNN fn, tp: 23, 174 KNN f1 score: 0.381 KNN cohens kappa score: 0.352 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 5778, 982 GAN fn, tp: 43, 154 GAN f1 score: 0.231 GAN cohens kappa score: 0.192 -> test with 'LR' LR tn, fp: 6370, 390 LR fn, tp: 17, 180 LR f1 score: 0.469 LR cohens kappa score: 0.446 LR average precision score: 0.754 -> test with 'GB' GB tn, fp: 6352, 408 GB fn, tp: 90, 107 GB f1 score: 0.301 GB cohens kappa score: 0.271 -> test with 'KNN' KNN tn, fp: 6261, 499 KNN fn, tp: 27, 170 KNN f1 score: 0.393 KNN cohens kappa score: 0.365 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with GAN.predict GAN tn, fp: 6428, 331 GAN fn, tp: 42, 151 GAN f1 score: 0.447 GAN cohens kappa score: 0.425 -> test with 'LR' LR tn, fp: 6397, 362 LR fn, tp: 32, 161 LR f1 score: 0.450 LR cohens kappa score: 0.426 LR average precision score: 0.741 -> test with 'GB' GB tn, fp: 6376, 383 GB fn, tp: 93, 100 GB f1 score: 0.296 GB cohens kappa score: 0.267 -> test with 'KNN' KNN tn, fp: 6246, 513 KNN fn, tp: 30, 163 KNN f1 score: 0.375 KNN cohens kappa score: 0.347 ====== 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 GAN.predict GAN tn, fp: 5571, 1189 GAN fn, tp: 35, 162 GAN f1 score: 0.209 GAN cohens kappa score: 0.168 -> test with 'LR' LR tn, fp: 6377, 383 LR fn, tp: 23, 174 LR f1 score: 0.462 LR cohens kappa score: 0.438 LR average precision score: 0.794 -> test with 'GB' GB tn, fp: 6353, 407 GB fn, tp: 92, 105 GB f1 score: 0.296 GB cohens kappa score: 0.266 -> test with 'KNN' KNN tn, fp: 6192, 568 KNN fn, tp: 28, 169 KNN f1 score: 0.362 KNN cohens kappa score: 0.332 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 6194, 566 GAN fn, tp: 35, 162 GAN f1 score: 0.350 GAN cohens kappa score: 0.320 -> test with 'LR' LR tn, fp: 6391, 369 LR fn, tp: 21, 176 LR f1 score: 0.474 LR cohens kappa score: 0.452 LR average precision score: 0.796 -> test with 'GB' GB tn, fp: 6383, 377 GB fn, tp: 92, 105 GB f1 score: 0.309 GB cohens kappa score: 0.280 -> test with 'KNN' KNN tn, fp: 6231, 529 KNN fn, tp: 27, 170 KNN f1 score: 0.379 KNN cohens kappa score: 0.351 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 6185, 575 GAN fn, tp: 36, 161 GAN f1 score: 0.345 GAN cohens kappa score: 0.314 -> test with 'LR' LR tn, fp: 6399, 361 LR fn, tp: 28, 169 LR f1 score: 0.465 LR cohens kappa score: 0.442 LR average precision score: 0.758 -> test with 'GB' GB tn, fp: 6325, 435 GB fn, tp: 94, 103 GB f1 score: 0.280 GB cohens kappa score: 0.249 -> test with 'KNN' KNN tn, fp: 6243, 517 KNN fn, tp: 27, 170 KNN f1 score: 0.385 KNN cohens kappa score: 0.356 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 6011, 749 GAN fn, tp: 34, 163 GAN f1 score: 0.294 GAN cohens kappa score: 0.259 -> test with 'LR' LR tn, fp: 6329, 431 LR fn, tp: 20, 177 LR f1 score: 0.440 LR cohens kappa score: 0.415 LR average precision score: 0.755 -> test with 'GB' GB tn, fp: 6441, 319 GB fn, tp: 93, 104 GB f1 score: 0.335 GB cohens kappa score: 0.309 -> test with 'KNN' KNN tn, fp: 6339, 421 KNN fn, tp: 19, 178 KNN f1 score: 0.447 KNN cohens kappa score: 0.423 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with GAN.predict GAN tn, fp: 5813, 946 GAN fn, tp: 34, 159 GAN f1 score: 0.245 GAN cohens kappa score: 0.208 -> test with 'LR' LR tn, fp: 6372, 387 LR fn, tp: 19, 174 LR f1 score: 0.462 LR cohens kappa score: 0.438 LR average precision score: 0.800 -> test with 'GB' GB tn, fp: 6355, 404 GB fn, tp: 105, 88 GB f1 score: 0.257 GB cohens kappa score: 0.226 -> test with 'KNN' KNN tn, fp: 6287, 472 KNN fn, tp: 31, 162 KNN f1 score: 0.392 KNN cohens kappa score: 0.365 ====== 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 GAN.predict GAN tn, fp: 6043, 717 GAN fn, tp: 40, 157 GAN f1 score: 0.293 GAN cohens kappa score: 0.259 -> test with 'LR' LR tn, fp: 6359, 401 LR fn, tp: 30, 167 LR f1 score: 0.437 LR cohens kappa score: 0.412 LR average precision score: 0.733 -> test with 'GB' GB tn, fp: 6366, 394 GB fn, tp: 92, 105 GB f1 score: 0.302 GB cohens kappa score: 0.272 -> test with 'KNN' KNN tn, fp: 6341, 419 KNN fn, tp: 29, 168 KNN f1 score: 0.429 KNN cohens kappa score: 0.403 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 6180, 580 GAN fn, tp: 35, 162 GAN f1 score: 0.345 GAN cohens kappa score: 0.314 -> test with 'LR' LR tn, fp: 6410, 350 LR fn, tp: 18, 179 LR f1 score: 0.493 LR cohens kappa score: 0.471 LR average precision score: 0.799 -> test with 'GB' GB tn, fp: 6344, 416 GB fn, tp: 93, 104 GB f1 score: 0.290 GB cohens kappa score: 0.260 -> test with 'KNN' KNN tn, fp: 6233, 527 KNN fn, tp: 24, 173 KNN f1 score: 0.386 KNN cohens kappa score: 0.357 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 6239, 521 GAN fn, tp: 49, 148 GAN f1 score: 0.342 GAN cohens kappa score: 0.312 -> test with 'LR' LR tn, fp: 6399, 361 LR fn, tp: 32, 165 LR f1 score: 0.456 LR cohens kappa score: 0.433 LR average precision score: 0.713 -> test with 'GB' GB tn, fp: 6401, 359 GB fn, tp: 100, 97 GB f1 score: 0.297 GB cohens kappa score: 0.268 -> test with 'KNN' KNN tn, fp: 6297, 463 KNN fn, tp: 41, 156 KNN f1 score: 0.382 KNN cohens kappa score: 0.355 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 6238, 522 GAN fn, tp: 29, 168 GAN f1 score: 0.379 GAN cohens kappa score: 0.350 -> test with 'LR' LR tn, fp: 6359, 401 LR fn, tp: 17, 180 LR f1 score: 0.463 LR cohens kappa score: 0.439 LR average precision score: 0.811 -> test with 'GB' GB tn, fp: 6320, 440 GB fn, tp: 88, 109 GB f1 score: 0.292 GB cohens kappa score: 0.261 -> test with 'KNN' KNN tn, fp: 6258, 502 KNN fn, tp: 21, 176 KNN f1 score: 0.402 KNN cohens kappa score: 0.375 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with GAN.predict GAN tn, fp: 5751, 1008 GAN fn, tp: 37, 156 GAN f1 score: 0.230 GAN cohens kappa score: 0.191 -> test with 'LR' LR tn, fp: 6374, 385 LR fn, tp: 16, 177 LR f1 score: 0.469 LR cohens kappa score: 0.446 LR average precision score: 0.775 -> test with 'GB' GB tn, fp: 6393, 366 GB fn, tp: 95, 98 GB f1 score: 0.298 GB cohens kappa score: 0.270 -> test with 'KNN' KNN tn, fp: 6241, 518 KNN fn, tp: 16, 177 KNN f1 score: 0.399 KNN cohens kappa score: 0.371 ====== 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 GAN.predict GAN tn, fp: 5798, 962 GAN fn, tp: 39, 158 GAN f1 score: 0.240 GAN cohens kappa score: 0.201 -> test with 'LR' LR tn, fp: 6397, 363 LR fn, tp: 27, 170 LR f1 score: 0.466 LR cohens kappa score: 0.443 LR average precision score: 0.742 -> test with 'GB' GB tn, fp: 6389, 371 GB fn, tp: 100, 97 GB f1 score: 0.292 GB cohens kappa score: 0.262 -> test with 'KNN' KNN tn, fp: 6277, 483 KNN fn, tp: 37, 160 KNN f1 score: 0.381 KNN cohens kappa score: 0.353 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 6150, 610 GAN fn, tp: 42, 155 GAN f1 score: 0.322 GAN cohens kappa score: 0.290 -> test with 'LR' LR tn, fp: 6353, 407 LR fn, tp: 22, 175 LR f1 score: 0.449 LR cohens kappa score: 0.425 LR average precision score: 0.748 -> test with 'GB' GB tn, fp: 6342, 418 GB fn, tp: 96, 101 GB f1 score: 0.282 GB cohens kappa score: 0.251 -> test with 'KNN' KNN tn, fp: 6235, 525 KNN fn, tp: 26, 171 KNN f1 score: 0.383 KNN cohens kappa score: 0.354 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 5932, 828 GAN fn, tp: 26, 171 GAN f1 score: 0.286 GAN cohens kappa score: 0.250 -> test with 'LR' LR tn, fp: 6378, 382 LR fn, tp: 17, 180 LR f1 score: 0.474 LR cohens kappa score: 0.451 LR average precision score: 0.808 -> test with 'GB' GB tn, fp: 6430, 330 GB fn, tp: 79, 118 GB f1 score: 0.366 GB cohens kappa score: 0.340 -> test with 'KNN' KNN tn, fp: 6331, 429 KNN fn, tp: 19, 178 KNN f1 score: 0.443 KNN cohens kappa score: 0.418 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 6109, 651 GAN fn, tp: 45, 152 GAN f1 score: 0.304 GAN cohens kappa score: 0.271 -> test with 'LR' LR tn, fp: 6377, 383 LR fn, tp: 21, 176 LR f1 score: 0.466 LR cohens kappa score: 0.442 LR average precision score: 0.752 -> test with 'GB' GB tn, fp: 6322, 438 GB fn, tp: 88, 109 GB f1 score: 0.293 GB cohens kappa score: 0.262 -> test with 'KNN' KNN tn, fp: 6238, 522 KNN fn, tp: 29, 168 KNN f1 score: 0.379 KNN cohens kappa score: 0.350 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with GAN.predict GAN tn, fp: 6057, 702 GAN fn, tp: 28, 165 GAN f1 score: 0.311 GAN cohens kappa score: 0.279 -> test with 'LR' LR tn, fp: 6357, 402 LR fn, tp: 20, 173 LR f1 score: 0.451 LR cohens kappa score: 0.427 LR average precision score: 0.792 -> test with 'GB' GB tn, fp: 6349, 410 GB fn, tp: 91, 102 GB f1 score: 0.289 GB cohens kappa score: 0.260 -> test with 'KNN' KNN tn, fp: 6232, 527 KNN fn, tp: 21, 172 KNN f1 score: 0.386 KNN cohens kappa score: 0.358 ====== 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 GAN.predict GAN tn, fp: 5930, 830 GAN fn, tp: 33, 164 GAN f1 score: 0.275 GAN cohens kappa score: 0.239 -> test with 'LR' LR tn, fp: 6412, 348 LR fn, tp: 22, 175 LR f1 score: 0.486 LR cohens kappa score: 0.464 LR average precision score: 0.766 -> test with 'GB' GB tn, fp: 6380, 380 GB fn, tp: 85, 112 GB f1 score: 0.325 GB cohens kappa score: 0.297 -> test with 'KNN' KNN tn, fp: 6303, 457 KNN fn, tp: 23, 174 KNN f1 score: 0.420 KNN cohens kappa score: 0.394 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 6249, 511 GAN fn, tp: 51, 146 GAN f1 score: 0.342 GAN cohens kappa score: 0.312 -> test with 'LR' LR tn, fp: 6406, 354 LR fn, tp: 26, 171 LR f1 score: 0.474 LR cohens kappa score: 0.451 LR average precision score: 0.750 -> test with 'GB' GB tn, fp: 6351, 409 GB fn, tp: 100, 97 GB f1 score: 0.276 GB cohens kappa score: 0.245 -> test with 'KNN' KNN tn, fp: 6222, 538 KNN fn, tp: 31, 166 KNN f1 score: 0.368 KNN cohens kappa score: 0.339 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 6133, 627 GAN fn, tp: 35, 162 GAN f1 score: 0.329 GAN cohens kappa score: 0.297 -> test with 'LR' LR tn, fp: 6309, 451 LR fn, tp: 25, 172 LR f1 score: 0.420 LR cohens kappa score: 0.393 LR average precision score: 0.745 -> test with 'GB' GB tn, fp: 6328, 432 GB fn, tp: 80, 117 GB f1 score: 0.314 GB cohens kappa score: 0.284 -> test with 'KNN' KNN tn, fp: 6281, 479 KNN fn, tp: 21, 176 KNN f1 score: 0.413 KNN cohens kappa score: 0.386 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with GAN.predict GAN tn, fp: 5751, 1009 GAN fn, tp: 33, 164 GAN f1 score: 0.239 GAN cohens kappa score: 0.201 -> test with 'LR' LR tn, fp: 6376, 384 LR fn, tp: 17, 180 LR f1 score: 0.473 LR cohens kappa score: 0.450 LR average precision score: 0.824 -> test with 'GB' GB tn, fp: 6360, 400 GB fn, tp: 96, 101 GB f1 score: 0.289 GB cohens kappa score: 0.259 -> test with 'KNN' KNN tn, fp: 6275, 485 KNN fn, tp: 28, 169 KNN f1 score: 0.397 KNN cohens kappa score: 0.370 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with GAN.predict GAN tn, fp: 5570, 1189 GAN fn, tp: 34, 159 GAN f1 score: 0.206 GAN cohens kappa score: 0.166 -> test with 'LR' LR tn, fp: 6388, 371 LR fn, tp: 24, 169 LR f1 score: 0.461 LR cohens kappa score: 0.438 LR average precision score: 0.754 -> test with 'GB' GB tn, fp: 6378, 381 GB fn, tp: 104, 89 GB f1 score: 0.268 GB cohens kappa score: 0.239 -> test with 'KNN' KNN tn, fp: 6202, 557 KNN fn, tp: 31, 162 KNN f1 score: 0.355 KNN cohens kappa score: 0.326 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 6412, 451 LR fn, tp: 32, 182 LR f1 score: 0.493 LR cohens kappa score: 0.471 LR average precision score: 0.838 average: LR tn, fp: 6377.12, 382.68 LR fn, tp: 22.24, 173.96 LR f1 score: 0.463 LR cohens kappa score: 0.439 LR average precision score: 0.772 minimum: LR tn, fp: 6309, 348 LR fn, tp: 15, 161 LR f1 score: 0.420 LR cohens kappa score: 0.393 LR average precision score: 0.713 -----[ GB ]----- maximum: GB tn, fp: 6441, 462 GB fn, tp: 105, 118 GB f1 score: 0.366 GB cohens kappa score: 0.340 average: GB tn, fp: 6363.56, 396.24 GB fn, tp: 92.68, 103.52 GB f1 score: 0.298 GB cohens kappa score: 0.268 minimum: GB tn, fp: 6298, 319 GB fn, tp: 79, 88 GB f1 score: 0.257 GB cohens kappa score: 0.226 -----[ KNN ]----- maximum: KNN tn, fp: 6344, 568 KNN fn, tp: 41, 182 KNN f1 score: 0.447 KNN cohens kappa score: 0.423 average: KNN tn, fp: 6263.6, 496.2 KNN fn, tp: 26.28, 169.92 KNN f1 score: 0.395 KNN cohens kappa score: 0.367 minimum: KNN tn, fp: 6192, 416 KNN fn, tp: 15, 156 KNN f1 score: 0.355 KNN cohens kappa score: 0.326 -----[ GAN ]----- maximum: GAN tn, fp: 6428, 1189 GAN fn, tp: 51, 171 GAN f1 score: 0.447 GAN cohens kappa score: 0.425 average: GAN tn, fp: 6004.08, 755.72 GAN fn, tp: 36.64, 159.56 GAN f1 score: 0.298 GAN cohens kappa score: 0.264 minimum: GAN tn, fp: 5570, 331 GAN fn, tp: 26, 146 GAN f1 score: 0.206 GAN cohens kappa score: 0.166