/////////////////////////////////////////// // Running convGAN-majority-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: 6714, 46 GAN fn, tp: 42, 155 GAN f1 score: 0.779 GAN cohens kappa score: 0.772 -> 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: 0.765 -> test with 'GB' GB tn, fp: 6399, 361 GB fn, tp: 91, 106 GB f1 score: 0.319 GB cohens kappa score: 0.291 -> test with 'KNN' KNN tn, fp: 6247, 513 KNN fn, tp: 14, 183 KNN f1 score: 0.410 KNN cohens kappa score: 0.383 ------ 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: 6717, 43 GAN fn, tp: 48, 149 GAN f1 score: 0.766 GAN cohens kappa score: 0.759 -> test with 'LR' LR tn, fp: 6401, 359 LR fn, tp: 21, 176 LR f1 score: 0.481 LR cohens kappa score: 0.458 LR average precision score: 0.790 -> test with 'GB' GB tn, fp: 6326, 434 GB fn, tp: 91, 106 GB f1 score: 0.288 GB cohens kappa score: 0.257 -> test with 'KNN' KNN tn, fp: 6290, 470 KNN fn, tp: 31, 166 KNN f1 score: 0.399 KNN cohens kappa score: 0.371 ------ 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: 6719, 41 GAN fn, tp: 35, 162 GAN f1 score: 0.810 GAN cohens kappa score: 0.804 -> 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: 0.839 -> test with 'GB' GB tn, fp: 6356, 404 GB fn, tp: 95, 102 GB f1 score: 0.290 GB cohens kappa score: 0.260 -> test with 'KNN' KNN tn, fp: 6217, 543 KNN fn, tp: 25, 172 KNN f1 score: 0.377 KNN cohens kappa score: 0.348 ------ 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: 6698, 62 GAN fn, tp: 47, 150 GAN f1 score: 0.733 GAN cohens kappa score: 0.725 -> 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: 0.753 -> test with 'GB' GB tn, fp: 6353, 407 GB fn, tp: 96, 101 GB f1 score: 0.287 GB cohens kappa score: 0.256 -> test with 'KNN' KNN tn, fp: 6259, 501 KNN fn, tp: 27, 170 KNN f1 score: 0.392 KNN cohens kappa score: 0.364 ------ 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: 6713, 46 GAN fn, tp: 59, 134 GAN f1 score: 0.718 GAN cohens kappa score: 0.711 -> test with 'LR' LR tn, fp: 6398, 361 LR fn, tp: 33, 160 LR f1 score: 0.448 LR cohens kappa score: 0.425 LR average precision score: 0.741 -> test with 'GB' GB tn, fp: 6364, 395 GB fn, tp: 91, 102 GB f1 score: 0.296 GB cohens kappa score: 0.266 -> 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: 6723, 37 GAN fn, tp: 59, 138 GAN f1 score: 0.742 GAN cohens kappa score: 0.735 -> test with 'LR' LR tn, fp: 6376, 384 LR fn, tp: 24, 173 LR f1 score: 0.459 LR cohens kappa score: 0.435 LR average precision score: 0.793 -> test with 'GB' GB tn, fp: 6354, 406 GB fn, tp: 96, 101 GB f1 score: 0.287 GB cohens kappa score: 0.257 -> test with 'KNN' KNN tn, fp: 6212, 548 KNN fn, tp: 30, 167 KNN f1 score: 0.366 KNN cohens kappa score: 0.337 ------ 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: 6709, 51 GAN fn, tp: 55, 142 GAN f1 score: 0.728 GAN cohens kappa score: 0.720 -> test with 'LR' LR tn, fp: 6390, 370 LR fn, tp: 22, 175 LR f1 score: 0.472 LR cohens kappa score: 0.449 LR average precision score: 0.795 -> test with 'GB' GB tn, fp: 6393, 367 GB fn, tp: 92, 105 GB f1 score: 0.314 GB cohens kappa score: 0.285 -> test with 'KNN' KNN tn, fp: 6224, 536 KNN fn, tp: 27, 170 KNN f1 score: 0.377 KNN cohens kappa score: 0.348 ------ 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: 6710, 50 GAN fn, tp: 51, 146 GAN f1 score: 0.743 GAN cohens kappa score: 0.736 -> test with 'LR' LR tn, fp: 6401, 359 LR fn, tp: 28, 169 LR f1 score: 0.466 LR cohens kappa score: 0.443 LR average precision score: 0.759 -> test with 'GB' GB tn, fp: 6312, 448 GB fn, tp: 93, 104 GB f1 score: 0.278 GB cohens kappa score: 0.246 -> test with 'KNN' KNN tn, fp: 6248, 512 KNN fn, tp: 26, 171 KNN f1 score: 0.389 KNN cohens kappa score: 0.361 ------ 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: 6714, 46 GAN fn, tp: 52, 145 GAN f1 score: 0.747 GAN cohens kappa score: 0.740 -> test with 'LR' LR tn, fp: 6331, 429 LR fn, tp: 20, 177 LR f1 score: 0.441 LR cohens kappa score: 0.416 LR average precision score: 0.756 -> test with 'GB' GB tn, fp: 6417, 343 GB fn, tp: 92, 105 GB f1 score: 0.326 GB cohens kappa score: 0.298 -> test with 'KNN' KNN tn, fp: 6334, 426 KNN fn, tp: 19, 178 KNN f1 score: 0.444 KNN cohens kappa score: 0.420 ------ 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: 6722, 37 GAN fn, tp: 51, 142 GAN f1 score: 0.763 GAN cohens kappa score: 0.757 -> test with 'LR' LR tn, fp: 6371, 388 LR fn, tp: 19, 174 LR f1 score: 0.461 LR cohens kappa score: 0.438 LR average precision score: 0.786 -> test with 'GB' GB tn, fp: 6358, 401 GB fn, tp: 108, 85 GB f1 score: 0.250 GB cohens kappa score: 0.219 -> test with 'KNN' KNN tn, fp: 6267, 492 KNN fn, tp: 30, 163 KNN f1 score: 0.384 KNN cohens kappa score: 0.357 ====== 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: 6709, 51 GAN fn, tp: 48, 149 GAN f1 score: 0.751 GAN cohens kappa score: 0.743 -> test with 'LR' LR tn, fp: 6360, 400 LR fn, tp: 30, 167 LR f1 score: 0.437 LR cohens kappa score: 0.412 LR average precision score: 0.734 -> test with 'GB' GB tn, fp: 6345, 415 GB fn, tp: 93, 104 GB f1 score: 0.291 GB cohens kappa score: 0.260 -> test with 'KNN' KNN tn, fp: 6335, 425 KNN fn, tp: 29, 168 KNN f1 score: 0.425 KNN cohens kappa score: 0.400 ------ 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: 6723, 37 GAN fn, tp: 57, 140 GAN f1 score: 0.749 GAN cohens kappa score: 0.742 -> 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: 6338, 422 GB fn, tp: 93, 104 GB f1 score: 0.288 GB cohens kappa score: 0.257 -> test with 'KNN' KNN tn, fp: 6202, 558 KNN fn, tp: 23, 174 KNN f1 score: 0.375 KNN cohens kappa score: 0.345 ------ 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: 6718, 42 GAN fn, tp: 62, 135 GAN f1 score: 0.722 GAN cohens kappa score: 0.714 -> test with 'LR' LR tn, fp: 6400, 360 LR fn, tp: 32, 165 LR f1 score: 0.457 LR cohens kappa score: 0.434 LR average precision score: 0.708 -> test with 'GB' GB tn, fp: 6399, 361 GB fn, tp: 95, 102 GB f1 score: 0.309 GB cohens kappa score: 0.281 -> test with 'KNN' KNN tn, fp: 6301, 459 KNN fn, tp: 40, 157 KNN f1 score: 0.386 KNN cohens kappa score: 0.359 ------ 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: 6707, 53 GAN fn, tp: 38, 159 GAN f1 score: 0.778 GAN cohens kappa score: 0.771 -> test with 'LR' LR tn, fp: 6358, 402 LR fn, tp: 17, 180 LR f1 score: 0.462 LR cohens kappa score: 0.438 LR average precision score: 0.802 -> test with 'GB' GB tn, fp: 6329, 431 GB fn, tp: 89, 108 GB f1 score: 0.293 GB cohens kappa score: 0.263 -> test with 'KNN' KNN tn, fp: 6252, 508 KNN fn, tp: 21, 176 KNN f1 score: 0.400 KNN cohens kappa score: 0.372 ------ 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: 6719, 40 GAN fn, tp: 42, 151 GAN f1 score: 0.786 GAN cohens kappa score: 0.780 -> test with 'LR' LR tn, fp: 6372, 387 LR fn, tp: 16, 177 LR f1 score: 0.468 LR cohens kappa score: 0.445 LR average precision score: 0.775 -> test with 'GB' GB tn, fp: 6385, 374 GB fn, tp: 94, 99 GB f1 score: 0.297 GB cohens kappa score: 0.268 -> test with 'KNN' KNN tn, fp: 6197, 562 KNN fn, tp: 15, 178 KNN f1 score: 0.382 KNN cohens kappa score: 0.353 ====== 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: 6716, 44 GAN fn, tp: 61, 136 GAN f1 score: 0.721 GAN cohens kappa score: 0.714 -> 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: 6390, 370 GB fn, tp: 98, 99 GB f1 score: 0.297 GB cohens kappa score: 0.268 -> test with 'KNN' KNN tn, fp: 6256, 504 KNN fn, tp: 36, 161 KNN f1 score: 0.374 KNN cohens kappa score: 0.345 ------ 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: 6710, 50 GAN fn, tp: 54, 143 GAN f1 score: 0.733 GAN cohens kappa score: 0.726 -> test with 'LR' LR tn, fp: 6352, 408 LR fn, tp: 22, 175 LR f1 score: 0.449 LR cohens kappa score: 0.424 LR average precision score: 0.751 -> test with 'GB' GB tn, fp: 6346, 414 GB fn, tp: 97, 100 GB f1 score: 0.281 GB cohens kappa score: 0.251 -> test with 'KNN' KNN tn, fp: 6207, 553 KNN fn, tp: 26, 171 KNN f1 score: 0.371 KNN cohens kappa score: 0.342 ------ 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: 6725, 35 GAN fn, tp: 41, 156 GAN f1 score: 0.804 GAN cohens kappa score: 0.799 -> test with 'LR' LR tn, fp: 6377, 383 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: 6399, 361 GB fn, tp: 81, 116 GB f1 score: 0.344 GB cohens kappa score: 0.317 -> test with 'KNN' KNN tn, fp: 6323, 437 KNN fn, tp: 19, 178 KNN f1 score: 0.438 KNN cohens kappa score: 0.413 ------ 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: 6727, 33 GAN fn, tp: 71, 126 GAN f1 score: 0.708 GAN cohens kappa score: 0.700 -> test with 'LR' LR tn, fp: 6380, 380 LR fn, tp: 22, 175 LR f1 score: 0.465 LR cohens kappa score: 0.442 LR average precision score: 0.753 -> test with 'GB' GB tn, fp: 6312, 448 GB fn, tp: 88, 109 GB f1 score: 0.289 GB cohens kappa score: 0.258 -> test with 'KNN' KNN tn, fp: 6195, 565 KNN fn, tp: 29, 168 KNN f1 score: 0.361 KNN cohens kappa score: 0.331 ------ 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: 6730, 29 GAN fn, tp: 42, 151 GAN f1 score: 0.810 GAN cohens kappa score: 0.804 -> test with 'LR' LR tn, fp: 6355, 404 LR fn, tp: 20, 173 LR f1 score: 0.449 LR cohens kappa score: 0.425 LR average precision score: 0.792 -> test with 'GB' GB tn, fp: 6336, 423 GB fn, tp: 88, 105 GB f1 score: 0.291 GB cohens kappa score: 0.261 -> test with 'KNN' KNN tn, fp: 6234, 525 KNN fn, tp: 21, 172 KNN f1 score: 0.387 KNN cohens kappa score: 0.359 ====== 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: 6707, 53 GAN fn, tp: 48, 149 GAN f1 score: 0.747 GAN cohens kappa score: 0.739 -> test with 'LR' LR tn, fp: 6413, 347 LR fn, tp: 22, 175 LR f1 score: 0.487 LR cohens kappa score: 0.465 LR average precision score: 0.757 -> test with 'GB' GB tn, fp: 6383, 377 GB fn, tp: 85, 112 GB f1 score: 0.327 GB cohens kappa score: 0.298 -> test with 'KNN' KNN tn, fp: 6295, 465 KNN fn, tp: 22, 175 KNN f1 score: 0.418 KNN cohens kappa score: 0.392 ------ 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: 6720, 40 GAN fn, tp: 65, 132 GAN f1 score: 0.715 GAN cohens kappa score: 0.708 -> 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: 0.743 -> test with 'GB' GB tn, fp: 6365, 395 GB fn, tp: 98, 99 GB f1 score: 0.287 GB cohens kappa score: 0.256 -> test with 'KNN' KNN tn, fp: 6229, 531 KNN fn, tp: 31, 166 KNN f1 score: 0.371 KNN cohens kappa score: 0.342 ------ 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: 6696, 64 GAN fn, tp: 45, 152 GAN f1 score: 0.736 GAN cohens kappa score: 0.728 -> test with 'LR' LR tn, fp: 6313, 447 LR fn, tp: 25, 172 LR f1 score: 0.422 LR cohens kappa score: 0.396 LR average precision score: 0.749 -> test with 'GB' GB tn, fp: 6346, 414 GB fn, tp: 80, 117 GB f1 score: 0.321 GB cohens kappa score: 0.292 -> test with 'KNN' KNN tn, fp: 6294, 466 KNN fn, tp: 21, 176 KNN f1 score: 0.420 KNN cohens kappa score: 0.393 ------ 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: 6713, 47 GAN fn, tp: 48, 149 GAN f1 score: 0.758 GAN cohens kappa score: 0.751 -> test with 'LR' LR tn, fp: 6374, 386 LR fn, tp: 18, 179 LR f1 score: 0.470 LR cohens kappa score: 0.447 LR average precision score: 0.826 -> test with 'GB' GB tn, fp: 6345, 415 GB fn, tp: 93, 104 GB f1 score: 0.291 GB cohens kappa score: 0.260 -> test with 'KNN' KNN tn, fp: 6234, 526 KNN fn, tp: 29, 168 KNN f1 score: 0.377 KNN cohens kappa score: 0.348 ------ 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: 6720, 39 GAN fn, tp: 63, 130 GAN f1 score: 0.718 GAN cohens kappa score: 0.711 -> test with 'LR' LR tn, fp: 6392, 367 LR fn, tp: 25, 168 LR f1 score: 0.462 LR cohens kappa score: 0.439 LR average precision score: 0.754 -> test with 'GB' GB tn, fp: 6374, 385 GB fn, tp: 100, 93 GB f1 score: 0.277 GB cohens kappa score: 0.247 -> test with 'KNN' KNN tn, fp: 6224, 535 KNN fn, tp: 29, 164 KNN f1 score: 0.368 KNN cohens kappa score: 0.339 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 6413, 447 LR fn, tp: 33, 182 LR f1 score: 0.493 LR cohens kappa score: 0.471 LR average precision score: 0.839 average: LR tn, fp: 6377.32, 382.48 LR fn, tp: 22.44, 173.76 LR f1 score: 0.462 LR cohens kappa score: 0.439 LR average precision score: 0.771 minimum: LR tn, fp: 6313, 347 LR fn, tp: 15, 160 LR f1 score: 0.422 LR cohens kappa score: 0.396 LR average precision score: 0.708 -----[ GB ]----- maximum: GB tn, fp: 6417, 448 GB fn, tp: 108, 117 GB f1 score: 0.344 GB cohens kappa score: 0.317 average: GB tn, fp: 6360.96, 398.84 GB fn, tp: 92.68, 103.52 GB f1 score: 0.297 GB cohens kappa score: 0.267 minimum: GB tn, fp: 6312, 343 GB fn, tp: 80, 85 GB f1 score: 0.250 GB cohens kappa score: 0.219 -----[ KNN ]----- maximum: KNN tn, fp: 6335, 565 KNN fn, tp: 40, 183 KNN f1 score: 0.444 KNN cohens kappa score: 0.420 average: KNN tn, fp: 6252.88, 506.92 KNN fn, tp: 26.0, 170.2 KNN f1 score: 0.391 KNN cohens kappa score: 0.363 minimum: KNN tn, fp: 6195, 425 KNN fn, tp: 14, 157 KNN f1 score: 0.361 KNN cohens kappa score: 0.331 -----[ GAN ]----- maximum: GAN tn, fp: 6730, 64 GAN fn, tp: 71, 162 GAN f1 score: 0.810 GAN cohens kappa score: 0.804 average: GAN tn, fp: 6715.16, 44.64 GAN fn, tp: 51.36, 144.84 GAN f1 score: 0.751 GAN cohens kappa score: 0.744 minimum: GAN tn, fp: 6696, 29 GAN fn, tp: 35, 126 GAN f1 score: 0.708 GAN cohens kappa score: 0.700