/////////////////////////////////////////// // Running convGAN-proxymary-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 GAN.predict GAN tn, fp: 2002, 183 GAN fn, tp: 13, 39 GAN f1 score: 0.285 GAN cohens kappa score: 0.257 -> 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: 0.573 -> 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: 2093, 92 KNN fn, tp: 8, 44 KNN f1 score: 0.468 KNN cohens kappa score: 0.450 ------ 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: 1357, 828 GAN fn, tp: 19, 33 GAN f1 score: 0.072 GAN cohens kappa score: 0.030 -> test with 'LR' LR tn, fp: 1917, 268 LR fn, tp: 6, 46 LR f1 score: 0.251 LR cohens kappa score: 0.220 LR average precision score: 0.482 -> test with 'GB' GB tn, fp: 2138, 47 GB fn, tp: 12, 40 GB f1 score: 0.576 GB cohens kappa score: 0.563 -> test with 'KNN' KNN tn, fp: 2091, 94 KNN fn, tp: 8, 44 KNN f1 score: 0.463 KNN cohens kappa score: 0.444 ------ 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: 1015, 1170 GAN fn, tp: 12, 40 GAN f1 score: 0.063 GAN cohens kappa score: 0.020 -> test with 'LR' LR tn, fp: 1903, 282 LR fn, tp: 6, 46 LR f1 score: 0.242 LR cohens kappa score: 0.210 LR average precision score: 0.603 -> test with 'GB' GB tn, fp: 2156, 29 GB fn, tp: 12, 40 GB f1 score: 0.661 GB cohens kappa score: 0.652 -> test with 'KNN' KNN tn, fp: 2105, 80 KNN fn, tp: 8, 44 KNN f1 score: 0.500 KNN cohens kappa score: 0.483 ------ 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: 2093, 92 GAN fn, tp: 16, 36 GAN f1 score: 0.400 GAN cohens kappa score: 0.379 -> test with 'LR' LR tn, fp: 1928, 257 LR fn, tp: 6, 46 LR f1 score: 0.259 LR cohens kappa score: 0.229 LR average precision score: 0.340 -> test with 'GB' GB tn, fp: 2134, 51 GB fn, tp: 14, 38 GB f1 score: 0.539 GB cohens kappa score: 0.525 -> test with 'KNN' KNN tn, fp: 2081, 104 KNN fn, tp: 9, 43 KNN f1 score: 0.432 KNN cohens kappa score: 0.412 ------ 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: 1957, 226 GAN fn, tp: 16, 36 GAN f1 score: 0.229 GAN cohens kappa score: 0.198 -> test with 'LR' LR tn, fp: 1913, 270 LR fn, tp: 6, 46 LR f1 score: 0.250 LR cohens kappa score: 0.219 LR average precision score: 0.563 -> test with 'GB' GB tn, fp: 2145, 38 GB fn, tp: 11, 41 GB f1 score: 0.626 GB cohens kappa score: 0.615 -> test with 'KNN' KNN tn, fp: 2087, 96 KNN fn, tp: 9, 43 KNN f1 score: 0.450 KNN cohens kappa score: 0.431 ====== 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: 1945, 240 GAN fn, tp: 10, 42 GAN f1 score: 0.251 GAN cohens kappa score: 0.221 -> 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: 0.486 -> test with 'GB' GB tn, fp: 2116, 69 GB fn, tp: 11, 41 GB f1 score: 0.506 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 2066, 119 KNN fn, tp: 8, 44 KNN f1 score: 0.409 KNN cohens kappa score: 0.388 ------ 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: 1752, 433 GAN fn, tp: 18, 34 GAN f1 score: 0.131 GAN cohens kappa score: 0.093 -> 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: 0.432 -> 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: 2071, 114 KNN fn, tp: 8, 44 KNN f1 score: 0.419 KNN cohens kappa score: 0.398 ------ 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: 1817, 368 GAN fn, tp: 21, 31 GAN f1 score: 0.137 GAN cohens kappa score: 0.100 -> test with 'LR' LR tn, fp: 1934, 251 LR fn, tp: 8, 44 LR f1 score: 0.254 LR cohens kappa score: 0.223 LR average precision score: 0.511 -> 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: 2098, 87 KNN fn, tp: 8, 44 KNN f1 score: 0.481 KNN cohens kappa score: 0.463 ------ 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: 2036, 149 GAN fn, tp: 24, 28 GAN f1 score: 0.245 GAN cohens kappa score: 0.216 -> test with 'LR' LR tn, fp: 1903, 282 LR fn, tp: 5, 47 LR f1 score: 0.247 LR cohens kappa score: 0.215 LR average precision score: 0.493 -> test with 'GB' GB tn, fp: 2143, 42 GB fn, tp: 13, 39 GB f1 score: 0.586 GB cohens kappa score: 0.574 -> test with 'KNN' KNN tn, fp: 2080, 105 KNN fn, tp: 6, 46 KNN f1 score: 0.453 KNN cohens kappa score: 0.434 ------ 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: 1791, 392 GAN fn, tp: 17, 35 GAN f1 score: 0.146 GAN cohens kappa score: 0.109 -> test with 'LR' LR tn, fp: 1915, 268 LR fn, tp: 8, 44 LR f1 score: 0.242 LR cohens kappa score: 0.210 LR average precision score: 0.531 -> test with 'GB' GB tn, fp: 2155, 28 GB fn, tp: 15, 37 GB f1 score: 0.632 GB cohens kappa score: 0.623 -> test with 'KNN' KNN tn, fp: 2109, 74 KNN fn, tp: 11, 41 KNN f1 score: 0.491 KNN cohens kappa score: 0.474 ====== 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: 1120, 1065 GAN fn, tp: 10, 42 GAN f1 score: 0.072 GAN cohens kappa score: 0.029 -> 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: 0.595 -> test with 'GB' GB tn, fp: 2147, 38 GB fn, tp: 13, 39 GB f1 score: 0.605 GB cohens kappa score: 0.593 -> 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 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with GAN.predict GAN tn, fp: 1313, 872 GAN fn, tp: 18, 34 GAN f1 score: 0.071 GAN cohens kappa score: 0.028 -> test with 'LR' LR tn, fp: 1921, 264 LR fn, tp: 7, 45 LR f1 score: 0.249 LR cohens kappa score: 0.218 LR average precision score: 0.393 -> test with 'GB' GB tn, fp: 2145, 40 GB fn, tp: 18, 34 GB f1 score: 0.540 GB cohens kappa score: 0.527 -> test with 'KNN' KNN tn, fp: 2088, 97 KNN fn, tp: 10, 42 KNN f1 score: 0.440 KNN cohens kappa score: 0.420 ------ 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: 1190, 995 GAN fn, tp: 10, 42 GAN f1 score: 0.077 GAN cohens kappa score: 0.034 -> test with 'LR' LR tn, fp: 1904, 281 LR fn, tp: 3, 49 LR f1 score: 0.257 LR cohens kappa score: 0.225 LR average precision score: 0.449 -> test with 'GB' GB tn, fp: 2134, 51 GB fn, tp: 8, 44 GB f1 score: 0.599 GB cohens kappa score: 0.586 -> test with 'KNN' KNN tn, fp: 1425, 760 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: 1461, 724 GAN fn, tp: 19, 33 GAN f1 score: 0.082 GAN cohens kappa score: 0.040 -> test with 'LR' LR tn, fp: 1918, 267 LR fn, tp: 9, 43 LR f1 score: 0.238 LR cohens kappa score: 0.206 LR average precision score: 0.484 -> test with 'GB' GB tn, fp: 2143, 42 GB fn, tp: 16, 36 GB f1 score: 0.554 GB cohens kappa score: 0.541 -> test with 'KNN' KNN tn, fp: 2095, 90 KNN fn, tp: 11, 41 KNN f1 score: 0.448 KNN cohens kappa score: 0.429 ------ 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: 1367, 816 GAN fn, tp: 18, 34 GAN f1 score: 0.075 GAN cohens kappa score: 0.033 -> test with 'LR' LR tn, fp: 1899, 284 LR fn, tp: 7, 45 LR f1 score: 0.236 LR cohens kappa score: 0.204 LR average precision score: 0.567 -> test with 'GB' GB tn, fp: 2138, 45 GB fn, tp: 14, 38 GB f1 score: 0.563 GB cohens kappa score: 0.550 -> test with 'KNN' KNN tn, fp: 2094, 89 KNN fn, tp: 10, 42 KNN f1 score: 0.459 KNN cohens kappa score: 0.440 ====== 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: 1152, 1033 GAN fn, tp: 14, 38 GAN f1 score: 0.068 GAN cohens kappa score: 0.024 -> test with 'LR' LR tn, fp: 1919, 266 LR fn, tp: 7, 45 LR f1 score: 0.248 LR cohens kappa score: 0.217 LR average precision score: 0.561 -> 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: 2118, 67 KNN fn, tp: 11, 41 KNN f1 score: 0.513 KNN cohens kappa score: 0.497 ------ 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: 2040, 145 GAN fn, tp: 14, 38 GAN f1 score: 0.323 GAN cohens kappa score: 0.298 -> 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: 0.419 -> 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: 2098, 87 KNN fn, tp: 7, 45 KNN f1 score: 0.489 KNN cohens kappa score: 0.472 ------ 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: 1976, 209 GAN fn, tp: 20, 32 GAN f1 score: 0.218 GAN cohens kappa score: 0.187 -> test with 'LR' LR tn, fp: 1922, 263 LR fn, tp: 7, 45 LR f1 score: 0.250 LR cohens kappa score: 0.219 LR average precision score: 0.458 -> test with 'GB' GB tn, fp: 2149, 36 GB fn, tp: 10, 42 GB f1 score: 0.646 GB cohens kappa score: 0.636 -> test with 'KNN' KNN tn, fp: 2091, 94 KNN fn, tp: 8, 44 KNN f1 score: 0.463 KNN cohens kappa score: 0.444 ------ 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: 1218, 967 GAN fn, tp: 15, 37 GAN f1 score: 0.070 GAN cohens kappa score: 0.027 -> 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: 0.483 -> test with 'GB' GB tn, fp: 2142, 43 GB fn, tp: 14, 38 GB f1 score: 0.571 GB cohens kappa score: 0.559 -> test with 'KNN' KNN tn, fp: 2080, 105 KNN fn, tp: 8, 44 KNN f1 score: 0.438 KNN cohens kappa score: 0.418 ------ 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: 1959, 224 GAN fn, tp: 18, 34 GAN f1 score: 0.219 GAN cohens kappa score: 0.188 -> test with 'LR' LR tn, fp: 1880, 303 LR fn, tp: 1, 51 LR f1 score: 0.251 LR cohens kappa score: 0.220 LR average precision score: 0.494 -> test with 'GB' GB tn, fp: 2141, 42 GB fn, tp: 11, 41 GB f1 score: 0.607 GB cohens kappa score: 0.596 -> test with 'KNN' KNN tn, fp: 2083, 100 KNN fn, tp: 7, 45 KNN f1 score: 0.457 KNN cohens kappa score: 0.438 ====== 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: 1408, 777 GAN fn, tp: 18, 34 GAN f1 score: 0.079 GAN cohens kappa score: 0.037 -> test with 'LR' LR tn, fp: 1898, 287 LR fn, tp: 3, 49 LR f1 score: 0.253 LR cohens kappa score: 0.221 LR average precision score: 0.473 -> test with 'GB' GB tn, fp: 2135, 50 GB fn, tp: 10, 42 GB f1 score: 0.583 GB cohens kappa score: 0.571 -> test with 'KNN' KNN tn, fp: 2098, 87 KNN fn, tp: 6, 46 KNN f1 score: 0.497 KNN cohens kappa score: 0.480 ------ 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: 1119, 1066 GAN fn, tp: 12, 40 GAN f1 score: 0.069 GAN cohens kappa score: 0.026 -> test with 'LR' LR tn, fp: 1904, 281 LR fn, tp: 7, 45 LR f1 score: 0.238 LR cohens kappa score: 0.206 LR average precision score: 0.448 -> test with 'GB' GB tn, fp: 2131, 54 GB fn, tp: 12, 40 GB f1 score: 0.548 GB cohens kappa score: 0.534 -> test with 'KNN' KNN tn, fp: 2065, 120 KNN fn, tp: 7, 45 KNN f1 score: 0.415 KNN cohens kappa score: 0.393 ------ 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: 1400, 785 GAN fn, tp: 18, 34 GAN f1 score: 0.078 GAN cohens kappa score: 0.036 -> 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: 0.515 -> test with 'GB' GB tn, fp: 2139, 46 GB fn, tp: 19, 33 GB f1 score: 0.504 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 2087, 98 KNN fn, tp: 11, 41 KNN f1 score: 0.429 KNN cohens kappa score: 0.409 ------ 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: 1294, 891 GAN fn, tp: 13, 39 GAN f1 score: 0.079 GAN cohens kappa score: 0.037 -> test with 'LR' LR tn, fp: 1891, 294 LR fn, tp: 4, 48 LR f1 score: 0.244 LR cohens kappa score: 0.212 LR average precision score: 0.459 -> test with 'GB' GB tn, fp: 2130, 55 GB fn, tp: 11, 41 GB f1 score: 0.554 GB cohens kappa score: 0.540 -> test with 'KNN' KNN tn, fp: 2095, 90 KNN fn, tp: 8, 44 KNN f1 score: 0.473 KNN cohens kappa score: 0.455 ------ 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: 1031, 1152 GAN fn, tp: 17, 35 GAN f1 score: 0.056 GAN cohens kappa score: 0.012 -> test with 'LR' LR tn, fp: 1935, 248 LR fn, tp: 8, 44 LR f1 score: 0.256 LR cohens kappa score: 0.225 LR average precision score: 0.578 -> test with 'GB' GB tn, fp: 2132, 51 GB fn, tp: 16, 36 GB f1 score: 0.518 GB cohens kappa score: 0.504 -> test with 'KNN' KNN tn, fp: 2093, 90 KNN fn, tp: 10, 42 KNN f1 score: 0.457 KNN cohens kappa score: 0.438 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 1935, 305 LR fn, tp: 9, 51 LR f1 score: 0.259 LR cohens kappa score: 0.229 LR average precision score: 0.603 average: LR tn, fp: 1908.12, 276.48 LR fn, tp: 6.2, 45.8 LR f1 score: 0.245 LR cohens kappa score: 0.213 LR average precision score: 0.496 minimum: LR tn, fp: 1880, 248 LR fn, tp: 1, 43 LR f1 score: 0.224 LR cohens kappa score: 0.192 LR average precision score: 0.340 -----[ GB ]----- maximum: GB tn, fp: 2156, 69 GB fn, tp: 19, 44 GB f1 score: 0.661 GB cohens kappa score: 0.652 average: GB tn, fp: 2138.12, 46.48 GB fn, tp: 13.24, 38.76 GB f1 score: 0.567 GB cohens kappa score: 0.554 minimum: GB tn, fp: 2116, 28 GB fn, tp: 8, 33 GB f1 score: 0.504 GB cohens kappa score: 0.490 -----[ KNN ]----- maximum: KNN tn, fp: 2118, 760 KNN fn, tp: 11, 48 KNN f1 score: 0.513 KNN cohens kappa score: 0.497 average: KNN tn, fp: 2063.28, 121.32 KNN fn, tp: 8.32, 43.68 KNN f1 score: 0.445 KNN cohens kappa score: 0.425 minimum: KNN tn, fp: 1425, 67 KNN fn, tp: 4, 41 KNN f1 score: 0.112 KNN cohens kappa score: 0.071 -----[ GAN ]----- maximum: GAN tn, fp: 2093, 1170 GAN fn, tp: 24, 42 GAN f1 score: 0.400 GAN cohens kappa score: 0.379 average: GAN tn, fp: 1552.52, 632.08 GAN fn, tp: 16.0, 36.0 GAN f1 score: 0.144 GAN cohens kappa score: 0.106 minimum: GAN tn, fp: 1015, 92 GAN fn, tp: 10, 28 GAN f1 score: 0.056 GAN cohens kappa score: 0.012