/////////////////////////////////////////// // Running convGAN-proximary-5 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 GAN.predict GAN tn, fp: 1180, 1005 GAN fn, tp: 12, 40 GAN f1 score: 0.073 GAN cohens kappa score: 0.030 -> 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: 0.568 -> test with 'GB' GB tn, fp: 2134, 51 GB fn, tp: 17, 35 GB f1 score: 0.507 GB cohens kappa score: 0.493 -> test with 'KNN' KNN tn, fp: 2086, 99 KNN fn, tp: 7, 45 KNN f1 score: 0.459 KNN cohens kappa score: 0.440 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 1816, 369 GAN fn, tp: 19, 33 GAN f1 score: 0.145 GAN cohens kappa score: 0.109 -> test with 'LR' LR tn, fp: 1911, 274 LR fn, tp: 6, 46 LR f1 score: 0.247 LR cohens kappa score: 0.216 LR average precision score: 0.476 -> test with 'GB' GB tn, fp: 2132, 53 GB fn, tp: 11, 41 GB f1 score: 0.562 GB cohens kappa score: 0.548 -> test with 'KNN' KNN tn, fp: 2102, 83 KNN fn, tp: 8, 44 KNN f1 score: 0.492 KNN cohens kappa score: 0.474 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 853, 1332 GAN fn, tp: 12, 40 GAN f1 score: 0.056 GAN cohens kappa score: 0.012 -> test with 'LR' LR tn, fp: 1771, 414 LR fn, tp: 5, 47 LR f1 score: 0.183 LR cohens kappa score: 0.148 LR average precision score: 0.602 -> 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: 1432, 753 KNN fn, tp: 9, 43 KNN f1 score: 0.101 KNN cohens kappa score: 0.060 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 2096, 89 GAN fn, tp: 17, 35 GAN f1 score: 0.398 GAN cohens kappa score: 0.377 -> test with 'LR' LR tn, fp: 1925, 260 LR fn, tp: 6, 46 LR f1 score: 0.257 LR cohens kappa score: 0.226 LR average precision score: 0.329 -> test with 'GB' GB tn, fp: 2140, 45 GB fn, tp: 17, 35 GB f1 score: 0.530 GB cohens kappa score: 0.517 -> test with 'KNN' KNN tn, fp: 2090, 95 KNN fn, tp: 9, 43 KNN f1 score: 0.453 KNN cohens kappa score: 0.434 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with GAN.predict GAN tn, fp: 1857, 326 GAN fn, tp: 18, 34 GAN f1 score: 0.165 GAN cohens kappa score: 0.130 -> test with 'LR' LR tn, fp: 1898, 285 LR fn, tp: 6, 46 LR f1 score: 0.240 LR cohens kappa score: 0.208 LR average precision score: 0.562 -> test with 'GB' GB tn, fp: 2143, 40 GB fn, tp: 14, 38 GB f1 score: 0.585 GB cohens kappa score: 0.573 -> test with 'KNN' KNN tn, fp: 2106, 77 KNN fn, tp: 12, 40 KNN f1 score: 0.473 KNN cohens kappa score: 0.456 ====== 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 GAN.predict GAN tn, fp: 2031, 154 GAN fn, tp: 14, 38 GAN f1 score: 0.311 GAN cohens kappa score: 0.285 -> test with 'LR' LR tn, fp: 1866, 319 LR fn, tp: 6, 46 LR f1 score: 0.221 LR cohens kappa score: 0.188 LR average precision score: 0.511 -> test with 'GB' GB tn, fp: 2112, 73 GB fn, tp: 11, 41 GB f1 score: 0.494 GB cohens kappa score: 0.477 -> test with 'KNN' KNN tn, fp: 2088, 97 KNN fn, tp: 8, 44 KNN f1 score: 0.456 KNN cohens kappa score: 0.437 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 1725, 460 GAN fn, tp: 16, 36 GAN f1 score: 0.131 GAN cohens kappa score: 0.093 -> test with 'LR' LR tn, fp: 1858, 327 LR fn, tp: 6, 46 LR f1 score: 0.216 LR cohens kappa score: 0.183 LR average precision score: 0.478 -> test with 'GB' GB tn, fp: 2132, 53 GB fn, tp: 16, 36 GB f1 score: 0.511 GB cohens kappa score: 0.496 -> test with 'KNN' KNN tn, fp: 1421, 764 KNN fn, tp: 5, 47 KNN f1 score: 0.109 KNN cohens kappa score: 0.068 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 1824, 361 GAN fn, tp: 20, 32 GAN f1 score: 0.144 GAN cohens kappa score: 0.107 -> test with 'LR' LR tn, fp: 1918, 267 LR fn, tp: 8, 44 LR f1 score: 0.242 LR cohens kappa score: 0.211 LR average precision score: 0.509 -> test with 'GB' GB tn, fp: 2140, 45 GB fn, tp: 17, 35 GB f1 score: 0.530 GB cohens kappa score: 0.517 -> test with 'KNN' KNN tn, fp: 2102, 83 KNN fn, tp: 11, 41 KNN f1 score: 0.466 KNN cohens kappa score: 0.448 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 1777, 408 GAN fn, tp: 19, 33 GAN f1 score: 0.134 GAN cohens kappa score: 0.096 -> test with 'LR' LR tn, fp: 1906, 279 LR fn, tp: 4, 48 LR f1 score: 0.253 LR cohens kappa score: 0.222 LR average precision score: 0.485 -> test with 'GB' GB tn, fp: 2146, 39 GB fn, tp: 12, 40 GB f1 score: 0.611 GB cohens kappa score: 0.599 -> test with 'KNN' KNN tn, fp: 2090, 95 KNN fn, tp: 6, 46 KNN f1 score: 0.477 KNN cohens kappa score: 0.458 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with GAN.predict GAN tn, fp: 859, 1324 GAN fn, tp: 18, 34 GAN f1 score: 0.048 GAN cohens kappa score: 0.004 -> test with 'LR' LR tn, fp: 1769, 414 LR fn, tp: 8, 44 LR f1 score: 0.173 LR cohens kappa score: 0.136 LR average precision score: 0.534 -> test with 'GB' GB tn, fp: 2156, 27 GB fn, tp: 15, 37 GB f1 score: 0.638 GB cohens kappa score: 0.628 -> test with 'KNN' KNN tn, fp: 2117, 66 KNN fn, tp: 11, 41 KNN f1 score: 0.516 KNN cohens kappa score: 0.500 ====== 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 GAN.predict GAN tn, fp: 1235, 950 GAN fn, tp: 8, 44 GAN f1 score: 0.084 GAN cohens kappa score: 0.042 -> test with 'LR' LR tn, fp: 1913, 272 LR fn, tp: 6, 46 LR f1 score: 0.249 LR cohens kappa score: 0.217 LR average precision score: 0.549 -> test with 'GB' GB tn, fp: 2148, 37 GB fn, tp: 10, 42 GB f1 score: 0.641 GB cohens kappa score: 0.631 -> test with 'KNN' KNN tn, fp: 2100, 85 KNN fn, tp: 6, 46 KNN f1 score: 0.503 KNN cohens kappa score: 0.486 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 1223, 962 GAN fn, tp: 19, 33 GAN f1 score: 0.063 GAN cohens kappa score: 0.020 -> test with 'LR' LR tn, fp: 1916, 269 LR fn, tp: 8, 44 LR f1 score: 0.241 LR cohens kappa score: 0.210 LR average precision score: 0.398 -> 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: 2104, 81 KNN fn, tp: 9, 43 KNN f1 score: 0.489 KNN cohens kappa score: 0.471 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 1211, 974 GAN fn, tp: 13, 39 GAN f1 score: 0.073 GAN cohens kappa score: 0.030 -> test with 'LR' LR tn, fp: 1913, 272 LR fn, tp: 3, 49 LR f1 score: 0.263 LR cohens kappa score: 0.232 LR average precision score: 0.447 -> test with 'GB' GB tn, fp: 2140, 45 GB fn, tp: 8, 44 GB f1 score: 0.624 GB cohens kappa score: 0.613 -> test with 'KNN' KNN tn, fp: 1428, 757 KNN fn, tp: 4, 48 KNN f1 score: 0.112 KNN cohens kappa score: 0.071 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 1386, 799 GAN fn, tp: 18, 34 GAN f1 score: 0.077 GAN cohens kappa score: 0.035 -> test with 'LR' LR tn, fp: 1919, 266 LR fn, tp: 9, 43 LR f1 score: 0.238 LR cohens kappa score: 0.207 LR average precision score: 0.486 -> test with 'GB' GB tn, fp: 2143, 42 GB fn, tp: 17, 35 GB f1 score: 0.543 GB cohens kappa score: 0.530 -> test with 'KNN' KNN tn, fp: 2093, 92 KNN fn, tp: 12, 40 KNN f1 score: 0.435 KNN cohens kappa score: 0.415 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with GAN.predict GAN tn, fp: 1226, 957 GAN fn, tp: 18, 34 GAN f1 score: 0.065 GAN cohens kappa score: 0.022 -> test with 'LR' LR tn, fp: 1901, 282 LR fn, tp: 7, 45 LR f1 score: 0.237 LR cohens kappa score: 0.206 LR average precision score: 0.564 -> test with 'GB' GB tn, fp: 2143, 40 GB fn, tp: 17, 35 GB f1 score: 0.551 GB cohens kappa score: 0.538 -> test with 'KNN' KNN tn, fp: 2103, 80 KNN fn, tp: 10, 42 KNN f1 score: 0.483 KNN cohens kappa score: 0.465 ====== 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 GAN.predict GAN tn, fp: 1846, 339 GAN fn, tp: 15, 37 GAN f1 score: 0.173 GAN cohens kappa score: 0.138 -> test with 'LR' LR tn, fp: 1920, 265 LR fn, tp: 8, 44 LR f1 score: 0.244 LR cohens kappa score: 0.212 LR average precision score: 0.578 -> test with 'GB' GB tn, fp: 2148, 37 GB fn, tp: 17, 35 GB f1 score: 0.565 GB cohens kappa score: 0.552 -> test with 'KNN' KNN tn, fp: 2114, 71 KNN fn, tp: 11, 41 KNN f1 score: 0.500 KNN cohens kappa score: 0.484 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 1964, 221 GAN fn, tp: 16, 36 GAN f1 score: 0.233 GAN cohens kappa score: 0.202 -> test with 'LR' LR tn, fp: 1828, 357 LR fn, tp: 6, 46 LR f1 score: 0.202 LR cohens kappa score: 0.168 LR average precision score: 0.425 -> test with 'GB' GB tn, fp: 2150, 35 GB fn, tp: 15, 37 GB f1 score: 0.597 GB cohens kappa score: 0.586 -> test with 'KNN' KNN tn, fp: 1480, 705 KNN fn, tp: 6, 46 KNN f1 score: 0.115 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 GAN.predict GAN tn, fp: 1286, 899 GAN fn, tp: 18, 34 GAN f1 score: 0.069 GAN cohens kappa score: 0.026 -> test with 'LR' LR tn, fp: 1917, 268 LR fn, tp: 7, 45 LR f1 score: 0.247 LR cohens kappa score: 0.215 LR average precision score: 0.453 -> test with 'GB' GB tn, fp: 2132, 53 GB fn, tp: 9, 43 GB f1 score: 0.581 GB cohens kappa score: 0.568 -> test with 'KNN' KNN tn, fp: 2102, 83 KNN fn, tp: 8, 44 KNN f1 score: 0.492 KNN cohens kappa score: 0.474 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 1665, 520 GAN fn, tp: 18, 34 GAN f1 score: 0.112 GAN cohens kappa score: 0.073 -> test with 'LR' LR tn, fp: 1859, 326 LR fn, tp: 8, 44 LR f1 score: 0.209 LR cohens kappa score: 0.175 LR average precision score: 0.479 -> test with 'GB' GB tn, fp: 2136, 49 GB fn, tp: 13, 39 GB f1 score: 0.557 GB cohens kappa score: 0.544 -> test with 'KNN' KNN tn, fp: 2086, 99 KNN fn, tp: 10, 42 KNN f1 score: 0.435 KNN cohens kappa score: 0.415 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with GAN.predict GAN tn, fp: 2040, 143 GAN fn, tp: 17, 35 GAN f1 score: 0.304 GAN cohens kappa score: 0.278 -> test with 'LR' LR tn, fp: 1886, 297 LR fn, tp: 1, 51 LR f1 score: 0.255 LR cohens kappa score: 0.224 LR average precision score: 0.480 -> test with 'GB' GB tn, fp: 2129, 54 GB fn, tp: 10, 42 GB f1 score: 0.568 GB cohens kappa score: 0.554 -> test with 'KNN' KNN tn, fp: 2086, 97 KNN fn, tp: 10, 42 KNN f1 score: 0.440 KNN cohens kappa score: 0.420 ====== 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 GAN.predict GAN tn, fp: 1969, 216 GAN fn, tp: 17, 35 GAN f1 score: 0.231 GAN cohens kappa score: 0.200 -> test with 'LR' LR tn, fp: 1854, 331 LR fn, tp: 1, 51 LR f1 score: 0.235 LR cohens kappa score: 0.202 LR average precision score: 0.496 -> 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: 2098, 87 KNN fn, tp: 7, 45 KNN f1 score: 0.489 KNN cohens kappa score: 0.472 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 959, 1226 GAN fn, tp: 15, 37 GAN f1 score: 0.056 GAN cohens kappa score: 0.012 -> test with 'LR' LR tn, fp: 1906, 279 LR fn, tp: 7, 45 LR f1 score: 0.239 LR cohens kappa score: 0.208 LR average precision score: 0.428 -> test with 'GB' GB tn, fp: 2140, 45 GB fn, tp: 10, 42 GB f1 score: 0.604 GB cohens kappa score: 0.592 -> test with 'KNN' KNN tn, fp: 2074, 111 KNN fn, tp: 7, 45 KNN f1 score: 0.433 KNN cohens kappa score: 0.412 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 1254, 931 GAN fn, tp: 18, 34 GAN f1 score: 0.067 GAN cohens kappa score: 0.024 -> test with 'LR' LR tn, fp: 1914, 271 LR fn, tp: 8, 44 LR f1 score: 0.240 LR cohens kappa score: 0.208 LR average precision score: 0.510 -> test with 'GB' GB tn, fp: 2123, 62 GB fn, tp: 14, 38 GB f1 score: 0.500 GB cohens kappa score: 0.484 -> test with 'KNN' KNN tn, fp: 2091, 94 KNN fn, tp: 11, 41 KNN f1 score: 0.439 KNN cohens kappa score: 0.419 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 1191, 994 GAN fn, tp: 11, 41 GAN f1 score: 0.075 GAN cohens kappa score: 0.033 -> test with 'LR' LR tn, fp: 1885, 300 LR fn, tp: 4, 48 LR f1 score: 0.240 LR cohens kappa score: 0.208 LR average precision score: 0.487 -> 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: 2099, 86 KNN fn, tp: 8, 44 KNN f1 score: 0.484 KNN cohens kappa score: 0.466 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with GAN.predict GAN tn, fp: 1008, 1175 GAN fn, tp: 19, 33 GAN f1 score: 0.052 GAN cohens kappa score: 0.008 -> test with 'LR' LR tn, fp: 1868, 315 LR fn, tp: 8, 44 LR f1 score: 0.214 LR cohens kappa score: 0.181 LR average precision score: 0.582 -> test with 'GB' GB tn, fp: 2132, 51 GB fn, tp: 15, 37 GB f1 score: 0.529 GB cohens kappa score: 0.514 -> test with 'KNN' KNN tn, fp: 2104, 79 KNN fn, tp: 10, 42 KNN f1 score: 0.486 KNN cohens kappa score: 0.468 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 1925, 414 LR fn, tp: 9, 51 LR f1 score: 0.263 LR cohens kappa score: 0.232 LR average precision score: 0.602 average: LR tn, fp: 1885.0, 299.6 LR fn, tp: 6.08, 45.92 LR f1 score: 0.233 LR cohens kappa score: 0.201 LR average precision score: 0.497 minimum: LR tn, fp: 1769, 260 LR fn, tp: 1, 43 LR f1 score: 0.173 LR cohens kappa score: 0.136 LR average precision score: 0.329 -----[ GB ]----- maximum: GB tn, fp: 2156, 73 GB fn, tp: 17, 44 GB f1 score: 0.650 GB cohens kappa score: 0.641 average: GB tn, fp: 2138.6, 46.0 GB fn, tp: 13.36, 38.64 GB f1 score: 0.568 GB cohens kappa score: 0.555 minimum: GB tn, fp: 2112, 27 GB fn, tp: 8, 35 GB f1 score: 0.494 GB cohens kappa score: 0.477 -----[ KNN ]----- maximum: KNN tn, fp: 2117, 764 KNN fn, tp: 12, 48 KNN f1 score: 0.516 KNN cohens kappa score: 0.500 average: KNN tn, fp: 1991.84, 192.76 KNN fn, tp: 8.6, 43.4 KNN f1 score: 0.413 KNN cohens kappa score: 0.392 minimum: KNN tn, fp: 1421, 66 KNN fn, tp: 4, 40 KNN f1 score: 0.101 KNN cohens kappa score: 0.060 -----[ GAN ]----- maximum: GAN tn, fp: 2096, 1332 GAN fn, tp: 20, 44 GAN f1 score: 0.398 GAN cohens kappa score: 0.377 average: GAN tn, fp: 1499.24, 685.36 GAN fn, tp: 16.2, 35.8 GAN f1 score: 0.134 GAN cohens kappa score: 0.095 minimum: GAN tn, fp: 853, 89 GAN fn, tp: 8, 32 GAN f1 score: 0.048 GAN cohens kappa score: 0.004