/////////////////////////////////////////// // Running Repeater 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: 1897, 288 LR fn, tp: 7, 45 LR f1 score: 0.234 LR cohens kappa score: 0.202 LR average precision score: 0.558 -> test with 'GB' GB tn, fp: 2110, 75 GB fn, tp: 10, 42 GB f1 score: 0.497 GB cohens kappa score: 0.480 -> test with 'KNN' KNN tn, fp: 1452, 733 KNN fn, tp: 9, 43 KNN f1 score: 0.104 KNN cohens kappa score: 0.063 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1888, 297 LR fn, tp: 6, 46 LR f1 score: 0.233 LR cohens kappa score: 0.201 LR average precision score: 0.506 -> test with 'GB' GB tn, fp: 2119, 66 GB fn, tp: 10, 42 GB f1 score: 0.525 GB cohens kappa score: 0.510 -> test with 'KNN' KNN tn, fp: 1432, 753 KNN fn, tp: 8, 44 KNN f1 score: 0.104 KNN cohens kappa score: 0.063 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1893, 292 LR fn, tp: 5, 47 LR f1 score: 0.240 LR cohens kappa score: 0.209 LR average precision score: 0.601 -> test with 'GB' GB tn, fp: 2116, 69 GB fn, tp: 9, 43 GB f1 score: 0.524 GB cohens kappa score: 0.509 -> test with 'KNN' KNN tn, fp: 1450, 735 KNN fn, tp: 8, 44 KNN f1 score: 0.106 KNN cohens kappa score: 0.065 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1905, 280 LR fn, tp: 8, 44 LR f1 score: 0.234 LR cohens kappa score: 0.202 LR average precision score: 0.373 -> test with 'GB' GB tn, fp: 2106, 79 GB fn, tp: 10, 42 GB f1 score: 0.486 GB cohens kappa score: 0.468 -> test with 'KNN' KNN tn, fp: 1430, 755 KNN fn, tp: 7, 45 KNN f1 score: 0.106 KNN cohens kappa score: 0.065 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1895, 288 LR fn, tp: 6, 46 LR f1 score: 0.238 LR cohens kappa score: 0.206 LR average precision score: 0.555 -> test with 'GB' GB tn, fp: 2111, 72 GB fn, tp: 6, 46 GB f1 score: 0.541 GB cohens kappa score: 0.526 -> test with 'KNN' KNN tn, fp: 1508, 675 KNN fn, tp: 12, 40 KNN f1 score: 0.104 KNN cohens kappa score: 0.064 ====== 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: 1877, 308 LR fn, tp: 7, 45 LR f1 score: 0.222 LR cohens kappa score: 0.189 LR average precision score: 0.530 -> test with 'GB' GB tn, fp: 2100, 85 GB fn, tp: 10, 42 GB f1 score: 0.469 GB cohens kappa score: 0.451 -> test with 'KNN' KNN tn, fp: 1459, 726 KNN fn, tp: 7, 45 KNN f1 score: 0.109 KNN cohens kappa score: 0.069 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1880, 305 LR fn, tp: 8, 44 LR f1 score: 0.219 LR cohens kappa score: 0.187 LR average precision score: 0.499 -> test with 'GB' GB tn, fp: 2107, 78 GB fn, tp: 6, 46 GB f1 score: 0.523 GB cohens kappa score: 0.507 -> test with 'KNN' KNN tn, fp: 1442, 743 KNN fn, tp: 8, 44 KNN f1 score: 0.105 KNN cohens kappa score: 0.064 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1911, 274 LR fn, tp: 8, 44 LR f1 score: 0.238 LR cohens kappa score: 0.206 LR average precision score: 0.521 -> test with 'GB' GB tn, fp: 2129, 56 GB fn, tp: 9, 43 GB f1 score: 0.570 GB cohens kappa score: 0.556 -> test with 'KNN' KNN tn, fp: 1467, 718 KNN fn, tp: 10, 42 KNN f1 score: 0.103 KNN cohens kappa score: 0.063 ------ Step 2/5: 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: 4, 48 LR f1 score: 0.257 LR cohens kappa score: 0.226 LR average precision score: 0.533 -> test with 'GB' GB tn, fp: 2116, 69 GB fn, tp: 4, 48 GB f1 score: 0.568 GB cohens kappa score: 0.554 -> test with 'KNN' KNN tn, fp: 1454, 731 KNN fn, tp: 5, 47 KNN f1 score: 0.113 KNN cohens kappa score: 0.073 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1906, 277 LR fn, tp: 8, 44 LR f1 score: 0.236 LR cohens kappa score: 0.204 LR average precision score: 0.564 -> test with 'GB' GB tn, fp: 2126, 57 GB fn, tp: 11, 41 GB f1 score: 0.547 GB cohens kappa score: 0.532 -> test with 'KNN' KNN tn, fp: 1446, 737 KNN fn, tp: 12, 40 KNN f1 score: 0.097 KNN cohens kappa score: 0.055 ====== 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: 1888, 297 LR fn, tp: 8, 44 LR f1 score: 0.224 LR cohens kappa score: 0.191 LR average precision score: 0.604 -> test with 'GB' GB tn, fp: 2106, 79 GB fn, tp: 8, 44 GB f1 score: 0.503 GB cohens kappa score: 0.486 -> test with 'KNN' KNN tn, fp: 1475, 710 KNN fn, tp: 5, 47 KNN f1 score: 0.116 KNN cohens kappa score: 0.076 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1907, 278 LR fn, tp: 7, 45 LR f1 score: 0.240 LR cohens kappa score: 0.208 LR average precision score: 0.420 -> test with 'GB' GB tn, fp: 2123, 62 GB fn, tp: 12, 40 GB f1 score: 0.519 GB cohens kappa score: 0.504 -> test with 'KNN' KNN tn, fp: 1446, 739 KNN fn, tp: 10, 42 KNN f1 score: 0.101 KNN cohens kappa score: 0.060 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1886, 299 LR fn, tp: 2, 50 LR f1 score: 0.249 LR cohens kappa score: 0.218 LR average precision score: 0.506 -> test with 'GB' GB tn, fp: 2110, 75 GB fn, tp: 4, 48 GB f1 score: 0.549 GB cohens kappa score: 0.533 -> test with 'KNN' KNN tn, fp: 1447, 738 KNN fn, tp: 7, 45 KNN f1 score: 0.108 KNN cohens kappa score: 0.067 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1902, 283 LR fn, tp: 9, 43 LR f1 score: 0.228 LR cohens kappa score: 0.195 LR average precision score: 0.501 -> test with 'GB' GB tn, fp: 2113, 72 GB fn, tp: 10, 42 GB f1 score: 0.506 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 1470, 715 KNN fn, tp: 11, 41 KNN f1 score: 0.101 KNN cohens kappa score: 0.061 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1894, 289 LR fn, tp: 7, 45 LR f1 score: 0.233 LR cohens kappa score: 0.201 LR average precision score: 0.609 -> test with 'GB' GB tn, fp: 2117, 66 GB fn, tp: 9, 43 GB f1 score: 0.534 GB cohens kappa score: 0.519 -> test with 'KNN' KNN tn, fp: 1448, 735 KNN fn, tp: 9, 43 KNN f1 score: 0.104 KNN cohens kappa score: 0.063 ====== 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: 1917, 268 LR fn, tp: 8, 44 LR f1 score: 0.242 LR cohens kappa score: 0.210 LR average precision score: 0.589 -> test with 'GB' GB tn, fp: 2124, 61 GB fn, tp: 17, 35 GB f1 score: 0.473 GB cohens kappa score: 0.457 -> test with 'KNN' KNN tn, fp: 1491, 694 KNN fn, tp: 12, 40 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: 1899, 286 LR fn, tp: 6, 46 LR f1 score: 0.240 LR cohens kappa score: 0.208 LR average precision score: 0.442 -> test with 'GB' GB tn, fp: 2116, 69 GB fn, tp: 8, 44 GB f1 score: 0.533 GB cohens kappa score: 0.518 -> test with 'KNN' KNN tn, fp: 1500, 685 KNN fn, tp: 7, 45 KNN f1 score: 0.115 KNN cohens kappa score: 0.075 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1902, 283 LR fn, tp: 7, 45 LR f1 score: 0.237 LR cohens kappa score: 0.205 LR average precision score: 0.569 -> test with 'GB' GB tn, fp: 2110, 75 GB fn, tp: 7, 45 GB f1 score: 0.523 GB cohens kappa score: 0.507 -> test with 'KNN' KNN tn, fp: 1417, 768 KNN fn, tp: 7, 45 KNN f1 score: 0.104 KNN cohens kappa score: 0.063 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1901, 284 LR fn, tp: 9, 43 LR f1 score: 0.227 LR cohens kappa score: 0.195 LR average precision score: 0.477 -> test with 'GB' GB tn, fp: 2115, 70 GB fn, tp: 9, 43 GB f1 score: 0.521 GB cohens kappa score: 0.505 -> test with 'KNN' KNN tn, fp: 1413, 772 KNN fn, tp: 10, 42 KNN f1 score: 0.097 KNN cohens kappa score: 0.056 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1868, 315 LR fn, tp: 3, 49 LR f1 score: 0.236 LR cohens kappa score: 0.203 LR average precision score: 0.538 -> test with 'GB' GB tn, fp: 2118, 65 GB fn, tp: 6, 46 GB f1 score: 0.564 GB cohens kappa score: 0.550 -> test with 'KNN' KNN tn, fp: 1464, 719 KNN fn, tp: 10, 42 KNN f1 score: 0.103 KNN cohens kappa score: 0.062 ====== 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: 1890, 295 LR fn, tp: 3, 49 LR f1 score: 0.247 LR cohens kappa score: 0.216 LR average precision score: 0.533 -> test with 'GB' GB tn, fp: 2121, 64 GB fn, tp: 4, 48 GB f1 score: 0.585 GB cohens kappa score: 0.572 -> test with 'KNN' KNN tn, fp: 1469, 716 KNN fn, tp: 9, 43 KNN f1 score: 0.106 KNN cohens kappa score: 0.065 ------ Step 5/5: Slice 2/5 ------- -> 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: 0.482 -> test with 'GB' GB tn, fp: 2107, 78 GB fn, tp: 7, 45 GB f1 score: 0.514 GB cohens kappa score: 0.498 -> test with 'KNN' KNN tn, fp: 1448, 737 KNN fn, tp: 7, 45 KNN f1 score: 0.108 KNN cohens kappa score: 0.067 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1899, 286 LR fn, tp: 10, 42 LR f1 score: 0.221 LR cohens kappa score: 0.188 LR average precision score: 0.529 -> test with 'GB' GB tn, fp: 2114, 71 GB fn, tp: 11, 41 GB f1 score: 0.500 GB cohens kappa score: 0.484 -> test with 'KNN' KNN tn, fp: 1464, 721 KNN fn, tp: 11, 41 KNN f1 score: 0.101 KNN cohens kappa score: 0.060 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1888, 297 LR fn, tp: 4, 48 LR f1 score: 0.242 LR cohens kappa score: 0.210 LR average precision score: 0.521 -> test with 'GB' GB tn, fp: 2109, 76 GB fn, tp: 9, 43 GB f1 score: 0.503 GB cohens kappa score: 0.486 -> test with 'KNN' KNN tn, fp: 1470, 715 KNN fn, tp: 7, 45 KNN f1 score: 0.111 KNN cohens kappa score: 0.070 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1900, 283 LR fn, tp: 9, 43 LR f1 score: 0.228 LR cohens kappa score: 0.195 LR average precision score: 0.598 -> test with 'GB' GB tn, fp: 2115, 68 GB fn, tp: 10, 42 GB f1 score: 0.519 GB cohens kappa score: 0.503 -> test with 'KNN' KNN tn, fp: 1432, 751 KNN fn, tp: 12, 40 KNN f1 score: 0.095 KNN cohens kappa score: 0.054 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 1917, 315 LR fn, tp: 10, 50 LR f1 score: 0.257 LR cohens kappa score: 0.226 LR average precision score: 0.609 average: LR tn, fp: 1896.36, 288.24 LR fn, tp: 6.6, 45.4 LR f1 score: 0.236 LR cohens kappa score: 0.203 LR average precision score: 0.526 minimum: LR tn, fp: 1868, 268 LR fn, tp: 2, 42 LR f1 score: 0.219 LR cohens kappa score: 0.187 LR average precision score: 0.373 -----[ GB ]----- maximum: GB tn, fp: 2129, 85 GB fn, tp: 17, 48 GB f1 score: 0.585 GB cohens kappa score: 0.572 average: GB tn, fp: 2114.32, 70.28 GB fn, tp: 8.64, 43.36 GB f1 score: 0.524 GB cohens kappa score: 0.508 minimum: GB tn, fp: 2100, 56 GB fn, tp: 4, 35 GB f1 score: 0.469 GB cohens kappa score: 0.451 -----[ KNN ]----- maximum: KNN tn, fp: 1508, 772 KNN fn, tp: 12, 47 KNN f1 score: 0.116 KNN cohens kappa score: 0.076 average: KNN tn, fp: 1455.76, 728.84 KNN fn, tp: 8.8, 43.2 KNN f1 score: 0.105 KNN cohens kappa score: 0.064 minimum: KNN tn, fp: 1413, 675 KNN fn, tp: 5, 40 KNN f1 score: 0.095 KNN cohens kappa score: 0.054