/////////////////////////////////////////// // Running convGAN-full on imblearn_mammography /////////////////////////////////////////// Load 'data_input/imblearn_mammography' from imblearn non empty cut in data_input/imblearn_mammography! (7 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 8530 synthetic samples -> test with 'LR' LR tn, fp: 1904, 281 LR fn, tp: 6, 46 LR f1 score: 0.243 LR cohens kappa score: 0.211 LR average precision score: 0.560 -> test with 'GB' GB tn, fp: 2129, 56 GB fn, tp: 12, 40 GB f1 score: 0.541 GB cohens kappa score: 0.526 -> test with 'KNN' KNN tn, fp: 1429, 756 KNN fn, tp: 6, 46 KNN f1 score: 0.108 KNN cohens kappa score: 0.067 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1915, 270 LR fn, tp: 6, 46 LR f1 score: 0.250 LR cohens kappa score: 0.219 LR average precision score: 0.481 -> test with 'GB' GB tn, fp: 2141, 44 GB fn, tp: 12, 40 GB f1 score: 0.588 GB cohens kappa score: 0.576 -> test with 'KNN' KNN tn, fp: 2093, 92 KNN fn, tp: 9, 43 KNN f1 score: 0.460 KNN cohens kappa score: 0.441 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1916, 269 LR fn, tp: 6, 46 LR f1 score: 0.251 LR cohens kappa score: 0.220 LR average precision score: 0.606 -> test with 'GB' GB tn, fp: 2153, 32 GB fn, tp: 12, 40 GB f1 score: 0.645 GB cohens kappa score: 0.635 -> test with 'KNN' KNN tn, fp: 1408, 777 KNN fn, tp: 6, 46 KNN f1 score: 0.105 KNN cohens kappa score: 0.064 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1918, 267 LR fn, tp: 7, 45 LR f1 score: 0.247 LR cohens kappa score: 0.216 LR average precision score: 0.326 -> test with 'GB' GB tn, fp: 2119, 66 GB fn, tp: 13, 39 GB f1 score: 0.497 GB cohens kappa score: 0.481 -> test with 'KNN' KNN tn, fp: 2092, 93 KNN fn, tp: 8, 44 KNN f1 score: 0.466 KNN cohens kappa score: 0.447 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1917, 266 LR fn, tp: 5, 47 LR f1 score: 0.258 LR cohens kappa score: 0.227 LR average precision score: 0.569 -> test with 'GB' GB tn, fp: 2139, 44 GB fn, tp: 9, 43 GB f1 score: 0.619 GB cohens kappa score: 0.607 -> test with 'KNN' KNN tn, fp: 1484, 699 KNN fn, tp: 9, 43 KNN f1 score: 0.108 KNN cohens kappa score: 0.068 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1879, 306 LR fn, tp: 6, 46 LR f1 score: 0.228 LR cohens kappa score: 0.195 LR average precision score: 0.487 -> test with 'GB' GB tn, fp: 2114, 71 GB fn, tp: 10, 42 GB f1 score: 0.509 GB cohens kappa score: 0.493 -> test with 'KNN' KNN tn, fp: 2078, 107 KNN fn, tp: 8, 44 KNN f1 score: 0.433 KNN cohens kappa score: 0.413 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1881, 304 LR fn, tp: 7, 45 LR f1 score: 0.224 LR cohens kappa score: 0.192 LR average precision score: 0.410 -> test with 'GB' GB tn, fp: 2126, 59 GB fn, tp: 10, 42 GB f1 score: 0.549 GB cohens kappa score: 0.535 -> test with 'KNN' KNN tn, fp: 2069, 116 KNN fn, tp: 7, 45 KNN f1 score: 0.423 KNN cohens kappa score: 0.402 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1936, 249 LR fn, tp: 7, 45 LR f1 score: 0.260 LR cohens kappa score: 0.230 LR average precision score: 0.513 -> test with 'GB' GB tn, fp: 2135, 50 GB fn, tp: 13, 39 GB f1 score: 0.553 GB cohens kappa score: 0.540 -> test with 'KNN' KNN tn, fp: 2084, 101 KNN fn, tp: 10, 42 KNN f1 score: 0.431 KNN cohens kappa score: 0.411 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1911, 274 LR fn, tp: 4, 48 LR f1 score: 0.257 LR cohens kappa score: 0.226 LR average precision score: 0.490 -> test with 'GB' GB tn, fp: 2140, 45 GB fn, tp: 9, 43 GB f1 score: 0.614 GB cohens kappa score: 0.603 -> test with 'KNN' KNN tn, fp: 2079, 106 KNN fn, tp: 6, 46 KNN f1 score: 0.451 KNN cohens kappa score: 0.431 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1927, 256 LR fn, tp: 7, 45 LR f1 score: 0.255 LR cohens kappa score: 0.224 LR average precision score: 0.529 -> test with 'GB' GB tn, fp: 2161, 22 GB fn, tp: 14, 38 GB f1 score: 0.679 GB cohens kappa score: 0.670 -> test with 'KNN' KNN tn, fp: 2110, 73 KNN fn, tp: 10, 42 KNN f1 score: 0.503 KNN cohens kappa score: 0.487 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1920, 265 LR fn, tp: 6, 46 LR f1 score: 0.253 LR cohens kappa score: 0.222 LR average precision score: 0.579 -> test with 'GB' GB tn, fp: 2141, 44 GB fn, tp: 10, 42 GB f1 score: 0.609 GB cohens kappa score: 0.597 -> test with 'KNN' KNN tn, fp: 2083, 102 KNN fn, tp: 7, 45 KNN f1 score: 0.452 KNN cohens kappa score: 0.433 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1874, 311 LR fn, tp: 6, 46 LR f1 score: 0.225 LR cohens kappa score: 0.192 LR average precision score: 0.434 -> test with 'GB' GB tn, fp: 2139, 46 GB fn, tp: 15, 37 GB f1 score: 0.548 GB cohens kappa score: 0.535 -> test with 'KNN' KNN tn, fp: 2100, 85 KNN fn, tp: 10, 42 KNN f1 score: 0.469 KNN cohens kappa score: 0.451 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1907, 278 LR fn, tp: 3, 49 LR f1 score: 0.259 LR cohens kappa score: 0.228 LR average precision score: 0.462 -> test with 'GB' GB tn, fp: 2133, 52 GB fn, tp: 9, 43 GB f1 score: 0.585 GB cohens kappa score: 0.572 -> test with 'KNN' KNN tn, fp: 1417, 768 KNN fn, tp: 4, 48 KNN f1 score: 0.111 KNN cohens kappa score: 0.070 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1913, 272 LR fn, tp: 9, 43 LR f1 score: 0.234 LR cohens kappa score: 0.203 LR average precision score: 0.489 -> test with 'GB' GB tn, fp: 2141, 44 GB fn, tp: 15, 37 GB f1 score: 0.556 GB cohens kappa score: 0.543 -> test with 'KNN' KNN tn, fp: 2095, 90 KNN fn, tp: 10, 42 KNN f1 score: 0.457 KNN cohens kappa score: 0.438 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1909, 274 LR fn, tp: 7, 45 LR f1 score: 0.243 LR cohens kappa score: 0.211 LR average precision score: 0.567 -> test with 'GB' GB tn, fp: 2150, 33 GB fn, tp: 16, 36 GB f1 score: 0.595 GB cohens kappa score: 0.584 -> test with 'KNN' KNN tn, fp: 2096, 87 KNN fn, tp: 9, 43 KNN f1 score: 0.473 KNN cohens kappa score: 0.454 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1912, 273 LR fn, tp: 7, 45 LR f1 score: 0.243 LR cohens kappa score: 0.212 LR average precision score: 0.565 -> test with 'GB' GB tn, fp: 2137, 48 GB fn, tp: 18, 34 GB f1 score: 0.507 GB cohens kappa score: 0.493 -> test with 'KNN' KNN tn, fp: 1456, 729 KNN fn, tp: 10, 42 KNN f1 score: 0.102 KNN cohens kappa score: 0.061 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1894, 291 LR fn, tp: 5, 47 LR f1 score: 0.241 LR cohens kappa score: 0.209 LR average precision score: 0.400 -> test with 'GB' GB tn, fp: 2134, 51 GB fn, tp: 16, 36 GB f1 score: 0.518 GB cohens kappa score: 0.504 -> test with 'KNN' KNN tn, fp: 1460, 725 KNN fn, tp: 5, 47 KNN f1 score: 0.114 KNN cohens kappa score: 0.074 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1920, 265 LR fn, tp: 7, 45 LR f1 score: 0.249 LR cohens kappa score: 0.217 LR average precision score: 0.474 -> test with 'GB' GB tn, fp: 2138, 47 GB fn, tp: 8, 44 GB f1 score: 0.615 GB cohens kappa score: 0.604 -> test with 'KNN' KNN tn, fp: 2090, 95 KNN fn, tp: 8, 44 KNN f1 score: 0.461 KNN cohens kappa score: 0.442 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1915, 270 LR fn, tp: 9, 43 LR f1 score: 0.236 LR cohens kappa score: 0.204 LR average precision score: 0.483 -> test with 'GB' GB tn, fp: 2134, 51 GB fn, tp: 11, 41 GB f1 score: 0.569 GB cohens kappa score: 0.556 -> test with 'KNN' KNN tn, fp: 2085, 100 KNN fn, tp: 8, 44 KNN f1 score: 0.449 KNN cohens kappa score: 0.429 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1888, 295 LR fn, tp: 1, 51 LR f1 score: 0.256 LR cohens kappa score: 0.225 LR average precision score: 0.494 -> test with 'GB' GB tn, fp: 2133, 50 GB fn, tp: 10, 42 GB f1 score: 0.583 GB cohens kappa score: 0.571 -> test with 'KNN' KNN tn, fp: 2073, 110 KNN fn, tp: 8, 44 KNN f1 score: 0.427 KNN cohens kappa score: 0.407 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1896, 289 LR fn, tp: 3, 49 LR f1 score: 0.251 LR cohens kappa score: 0.220 LR average precision score: 0.467 -> test with 'GB' GB tn, fp: 2145, 40 GB fn, tp: 12, 40 GB f1 score: 0.606 GB cohens kappa score: 0.595 -> test with 'KNN' KNN tn, fp: 2100, 85 KNN fn, tp: 9, 43 KNN f1 score: 0.478 KNN cohens kappa score: 0.460 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1876, 309 LR fn, tp: 6, 46 LR f1 score: 0.226 LR cohens kappa score: 0.193 LR average precision score: 0.478 -> test with 'GB' GB tn, fp: 2128, 57 GB fn, tp: 9, 43 GB f1 score: 0.566 GB cohens kappa score: 0.552 -> test with 'KNN' KNN tn, fp: 1399, 786 KNN fn, tp: 5, 47 KNN f1 score: 0.106 KNN cohens kappa score: 0.065 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1907, 278 LR fn, tp: 9, 43 LR f1 score: 0.231 LR cohens kappa score: 0.198 LR average precision score: 0.512 -> test with 'GB' GB tn, fp: 2144, 41 GB fn, tp: 18, 34 GB f1 score: 0.535 GB cohens kappa score: 0.522 -> test with 'KNN' KNN tn, fp: 2083, 102 KNN fn, tp: 10, 42 KNN f1 score: 0.429 KNN cohens kappa score: 0.408 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1783, 402 LR fn, tp: 4, 48 LR f1 score: 0.191 LR cohens kappa score: 0.156 LR average precision score: 0.494 -> test with 'GB' GB tn, fp: 2136, 49 GB fn, tp: 11, 41 GB f1 score: 0.577 GB cohens kappa score: 0.565 -> test with 'KNN' KNN tn, fp: 2094, 91 KNN fn, tp: 7, 45 KNN f1 score: 0.479 KNN cohens kappa score: 0.461 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1853, 330 LR fn, tp: 7, 45 LR f1 score: 0.211 LR cohens kappa score: 0.177 LR average precision score: 0.589 -> test with 'GB' GB tn, fp: 2126, 57 GB fn, tp: 13, 39 GB f1 score: 0.527 GB cohens kappa score: 0.512 -> test with 'KNN' KNN tn, fp: 2099, 84 KNN fn, tp: 10, 42 KNN f1 score: 0.472 KNN cohens kappa score: 0.454 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 1936, 402 LR fn, tp: 9, 51 LR f1 score: 0.260 LR cohens kappa score: 0.230 LR average precision score: 0.606 average: LR tn, fp: 1898.84, 285.76 LR fn, tp: 6.0, 46.0 LR f1 score: 0.241 LR cohens kappa score: 0.209 LR average precision score: 0.498 minimum: LR tn, fp: 1783, 249 LR fn, tp: 1, 43 LR f1 score: 0.191 LR cohens kappa score: 0.156 LR average precision score: 0.326 -----[ GB ]----- maximum: GB tn, fp: 2161, 71 GB fn, tp: 18, 44 GB f1 score: 0.679 GB cohens kappa score: 0.670 average: GB tn, fp: 2136.64, 47.96 GB fn, tp: 12.2, 39.8 GB f1 score: 0.572 GB cohens kappa score: 0.559 minimum: GB tn, fp: 2114, 22 GB fn, tp: 8, 34 GB f1 score: 0.497 GB cohens kappa score: 0.481 -----[ KNN ]----- maximum: KNN tn, fp: 2110, 786 KNN fn, tp: 10, 48 KNN f1 score: 0.503 KNN cohens kappa score: 0.487 average: KNN tn, fp: 1906.24, 278.36 KNN fn, tp: 7.96, 44.04 KNN f1 score: 0.359 KNN cohens kappa score: 0.333 minimum: KNN tn, fp: 1399, 73 KNN fn, tp: 4, 42 KNN f1 score: 0.102 KNN cohens kappa score: 0.061