/////////////////////////////////////////// // Running convGAN 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: 1899, 286 LR fn, tp: 6, 46 LR f1 score: 0.240 LR cohens kappa score: 0.208 LR average precision score: 0.557 -> test with 'GB' GB tn, fp: 2124, 61 GB fn, tp: 12, 40 GB f1 score: 0.523 GB cohens kappa score: 0.508 -> test with 'KNN' KNN tn, fp: 2091, 94 KNN fn, tp: 7, 45 KNN f1 score: 0.471 KNN cohens kappa score: 0.453 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1906, 279 LR fn, tp: 6, 46 LR f1 score: 0.244 LR cohens kappa score: 0.212 LR average precision score: 0.489 -> test with 'GB' GB tn, fp: 2142, 43 GB fn, tp: 12, 40 GB f1 score: 0.593 GB cohens kappa score: 0.581 -> test with 'KNN' KNN tn, fp: 2106, 79 KNN fn, tp: 7, 45 KNN f1 score: 0.511 KNN cohens kappa score: 0.495 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1906, 279 LR fn, tp: 6, 46 LR f1 score: 0.244 LR cohens kappa score: 0.212 LR average precision score: 0.606 -> test with 'GB' GB tn, fp: 2155, 30 GB fn, tp: 15, 37 GB f1 score: 0.622 GB cohens kappa score: 0.612 -> test with 'KNN' KNN tn, fp: 2106, 79 KNN fn, tp: 7, 45 KNN f1 score: 0.511 KNN cohens kappa score: 0.495 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1891, 294 LR fn, tp: 6, 46 LR f1 score: 0.235 LR cohens kappa score: 0.203 LR average precision score: 0.335 -> test with 'GB' GB tn, fp: 2136, 49 GB fn, tp: 16, 36 GB f1 score: 0.526 GB cohens kappa score: 0.511 -> test with 'KNN' KNN tn, fp: 2083, 102 KNN fn, tp: 8, 44 KNN f1 score: 0.444 KNN cohens kappa score: 0.425 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1922, 261 LR fn, tp: 6, 46 LR f1 score: 0.256 LR cohens kappa score: 0.225 LR average precision score: 0.555 -> test with 'GB' GB tn, fp: 2140, 43 GB fn, tp: 12, 40 GB f1 score: 0.593 GB cohens kappa score: 0.581 -> test with 'KNN' KNN tn, fp: 2096, 87 KNN fn, tp: 12, 40 KNN f1 score: 0.447 KNN cohens kappa score: 0.428 ====== 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: 1880, 305 LR fn, tp: 6, 46 LR f1 score: 0.228 LR cohens kappa score: 0.196 LR average precision score: 0.484 -> test with 'GB' GB tn, fp: 2130, 55 GB fn, tp: 15, 37 GB f1 score: 0.514 GB cohens kappa score: 0.499 -> test with 'KNN' KNN tn, fp: 2081, 104 KNN fn, tp: 8, 44 KNN f1 score: 0.440 KNN cohens kappa score: 0.420 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1870, 315 LR fn, tp: 7, 45 LR f1 score: 0.218 LR cohens kappa score: 0.185 LR average precision score: 0.461 -> test with 'GB' GB tn, fp: 2120, 65 GB fn, tp: 12, 40 GB f1 score: 0.510 GB cohens kappa score: 0.494 -> test with 'KNN' KNN tn, fp: 2074, 111 KNN fn, tp: 6, 46 KNN f1 score: 0.440 KNN cohens kappa score: 0.420 ------ Step 2/5: 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: 0.495 -> test with 'GB' GB tn, fp: 2145, 40 GB fn, tp: 16, 36 GB f1 score: 0.562 GB cohens kappa score: 0.550 -> test with 'KNN' KNN tn, fp: 2098, 87 KNN fn, tp: 11, 41 KNN f1 score: 0.456 KNN cohens kappa score: 0.437 ------ Step 2/5: 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: 4, 48 LR f1 score: 0.258 LR cohens kappa score: 0.227 LR average precision score: 0.476 -> 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: 2089, 96 KNN fn, tp: 6, 46 KNN f1 score: 0.474 KNN cohens kappa score: 0.456 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1874, 309 LR fn, tp: 8, 44 LR f1 score: 0.217 LR cohens kappa score: 0.184 LR average precision score: 0.553 -> test with 'GB' GB tn, fp: 2144, 39 GB fn, tp: 16, 36 GB f1 score: 0.567 GB cohens kappa score: 0.555 -> test with 'KNN' KNN tn, fp: 2115, 68 KNN fn, tp: 10, 42 KNN f1 score: 0.519 KNN cohens kappa score: 0.503 ====== 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: 1909, 276 LR fn, tp: 6, 46 LR f1 score: 0.246 LR cohens kappa score: 0.215 LR average precision score: 0.541 -> test with 'GB' GB tn, fp: 2154, 31 GB fn, tp: 12, 40 GB f1 score: 0.650 GB cohens kappa score: 0.641 -> test with 'KNN' KNN tn, fp: 2101, 84 KNN fn, tp: 6, 46 KNN f1 score: 0.505 KNN cohens kappa score: 0.489 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1890, 295 LR fn, tp: 6, 46 LR f1 score: 0.234 LR cohens kappa score: 0.202 LR average precision score: 0.413 -> test with 'GB' GB tn, fp: 2136, 49 GB fn, tp: 14, 38 GB f1 score: 0.547 GB cohens kappa score: 0.533 -> test with 'KNN' KNN tn, fp: 2084, 101 KNN fn, tp: 11, 41 KNN f1 score: 0.423 KNN cohens kappa score: 0.402 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1869, 316 LR fn, tp: 2, 50 LR f1 score: 0.239 LR cohens kappa score: 0.207 LR average precision score: 0.464 -> 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: 2105, 80 KNN fn, tp: 5, 47 KNN f1 score: 0.525 KNN cohens kappa score: 0.509 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1912, 273 LR fn, tp: 9, 43 LR f1 score: 0.234 LR cohens kappa score: 0.202 LR average precision score: 0.489 -> test with 'GB' GB tn, fp: 2138, 47 GB fn, tp: 18, 34 GB f1 score: 0.511 GB cohens kappa score: 0.497 -> test with 'KNN' KNN tn, fp: 2101, 84 KNN fn, tp: 11, 41 KNN f1 score: 0.463 KNN cohens kappa score: 0.445 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1911, 272 LR fn, tp: 7, 45 LR f1 score: 0.244 LR cohens kappa score: 0.212 LR average precision score: 0.570 -> test with 'GB' GB tn, fp: 2145, 38 GB fn, tp: 17, 35 GB f1 score: 0.560 GB cohens kappa score: 0.548 -> test with 'KNN' KNN tn, fp: 2098, 85 KNN fn, tp: 10, 42 KNN f1 score: 0.469 KNN cohens kappa score: 0.451 ====== 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: 1927, 258 LR fn, tp: 7, 45 LR f1 score: 0.254 LR cohens kappa score: 0.223 LR average precision score: 0.551 -> test with 'GB' GB tn, fp: 2143, 42 GB fn, tp: 19, 33 GB f1 score: 0.520 GB cohens kappa score: 0.506 -> test with 'KNN' KNN tn, fp: 2123, 62 KNN fn, tp: 11, 41 KNN f1 score: 0.529 KNN cohens kappa score: 0.514 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1899, 286 LR fn, tp: 5, 47 LR f1 score: 0.244 LR cohens kappa score: 0.212 LR average precision score: 0.413 -> test with 'GB' GB tn, fp: 2146, 39 GB fn, tp: 15, 37 GB f1 score: 0.578 GB cohens kappa score: 0.566 -> test with 'KNN' KNN tn, fp: 2104, 81 KNN fn, tp: 8, 44 KNN f1 score: 0.497 KNN cohens kappa score: 0.480 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1915, 270 LR fn, tp: 7, 45 LR f1 score: 0.245 LR cohens kappa score: 0.214 LR average precision score: 0.458 -> test with 'GB' GB tn, fp: 2150, 35 GB fn, tp: 11, 41 GB f1 score: 0.641 GB cohens kappa score: 0.630 -> 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 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1916, 269 LR fn, tp: 9, 43 LR f1 score: 0.236 LR cohens kappa score: 0.205 LR average precision score: 0.487 -> test with 'GB' GB tn, fp: 2133, 52 GB fn, tp: 12, 40 GB f1 score: 0.556 GB cohens kappa score: 0.542 -> test with 'KNN' KNN tn, fp: 2085, 100 KNN fn, tp: 9, 43 KNN f1 score: 0.441 KNN cohens kappa score: 0.421 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1850, 333 LR fn, tp: 1, 51 LR f1 score: 0.234 LR cohens kappa score: 0.201 LR average precision score: 0.510 -> test with 'GB' GB tn, fp: 2129, 54 GB fn, tp: 8, 44 GB f1 score: 0.587 GB cohens kappa score: 0.574 -> test with 'KNN' KNN tn, fp: 1447, 736 KNN fn, tp: 9, 43 KNN f1 score: 0.103 KNN cohens kappa score: 0.063 ====== 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: 1784, 401 LR fn, tp: 1, 51 LR f1 score: 0.202 LR cohens kappa score: 0.168 LR average precision score: 0.510 -> test with 'GB' GB tn, fp: 2153, 32 GB fn, tp: 15, 37 GB f1 score: 0.612 GB cohens kappa score: 0.601 -> test with 'KNN' KNN tn, fp: 2115, 70 KNN fn, tp: 8, 44 KNN f1 score: 0.530 KNN cohens kappa score: 0.515 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1905, 280 LR fn, tp: 7, 45 LR f1 score: 0.239 LR cohens kappa score: 0.207 LR average precision score: 0.436 -> test with 'GB' GB tn, fp: 2142, 43 GB fn, tp: 12, 40 GB f1 score: 0.593 GB cohens kappa score: 0.581 -> test with 'KNN' KNN tn, fp: 2078, 107 KNN fn, tp: 7, 45 KNN f1 score: 0.441 KNN cohens kappa score: 0.421 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1909, 276 LR fn, tp: 8, 44 LR f1 score: 0.237 LR cohens kappa score: 0.205 LR average precision score: 0.510 -> test with 'GB' GB tn, fp: 2143, 42 GB fn, tp: 19, 33 GB f1 score: 0.520 GB cohens kappa score: 0.506 -> 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 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1709, 476 LR fn, tp: 4, 48 LR f1 score: 0.167 LR cohens kappa score: 0.130 LR average precision score: 0.525 -> test with 'GB' GB tn, fp: 2136, 49 GB fn, tp: 12, 40 GB f1 score: 0.567 GB cohens kappa score: 0.554 -> test with 'KNN' KNN tn, fp: 2095, 90 KNN fn, tp: 9, 43 KNN f1 score: 0.465 KNN cohens kappa score: 0.446 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1832, 351 LR fn, tp: 6, 46 LR f1 score: 0.205 LR cohens kappa score: 0.171 LR average precision score: 0.589 -> test with 'GB' GB tn, fp: 2137, 46 GB fn, tp: 16, 36 GB f1 score: 0.537 GB cohens kappa score: 0.524 -> test with 'KNN' KNN tn, fp: 2098, 85 KNN fn, tp: 10, 42 KNN f1 score: 0.469 KNN cohens kappa score: 0.451 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 1927, 476 LR fn, tp: 9, 51 LR f1 score: 0.258 LR cohens kappa score: 0.227 LR average precision score: 0.606 average: LR tn, fp: 1884.72, 299.88 LR fn, tp: 5.88, 46.12 LR f1 score: 0.234 LR cohens kappa score: 0.202 LR average precision score: 0.499 minimum: LR tn, fp: 1709, 258 LR fn, tp: 1, 43 LR f1 score: 0.167 LR cohens kappa score: 0.130 LR average precision score: 0.335 -----[ GB ]----- maximum: GB tn, fp: 2155, 65 GB fn, tp: 19, 44 GB f1 score: 0.650 GB cohens kappa score: 0.641 average: GB tn, fp: 2140.28, 44.32 GB fn, tp: 13.88, 38.12 GB f1 score: 0.569 GB cohens kappa score: 0.556 minimum: GB tn, fp: 2120, 30 GB fn, tp: 8, 33 GB f1 score: 0.510 GB cohens kappa score: 0.494 -----[ KNN ]----- maximum: KNN tn, fp: 2123, 736 KNN fn, tp: 12, 47 KNN f1 score: 0.530 KNN cohens kappa score: 0.515 average: KNN tn, fp: 2070.64, 113.96 KNN fn, tp: 8.6, 43.4 KNN f1 score: 0.460 KNN cohens kappa score: 0.441 minimum: KNN tn, fp: 1447, 62 KNN fn, tp: 5, 40 KNN f1 score: 0.103 KNN cohens kappa score: 0.063