/////////////////////////////////////////// // Running convGAN-proximary-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: 1976, 209 GAN fn, tp: 3, 49 GAN f1 score: 0.316 GAN cohens kappa score: 0.289 -> test with 'LR' LR tn, fp: 1990, 195 LR fn, tp: 6, 46 LR f1 score: 0.314 LR cohens kappa score: 0.287 LR average precision score: 0.606 -> test with 'GB' GB tn, fp: 2117, 68 GB fn, tp: 11, 41 GB f1 score: 0.509 GB cohens kappa score: 0.493 -> test with 'KNN' KNN tn, fp: 2113, 72 KNN fn, tp: 10, 42 KNN f1 score: 0.506 KNN cohens kappa score: 0.490 ------ 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: 2043, 142 GAN fn, tp: 8, 44 GAN f1 score: 0.370 GAN cohens kappa score: 0.346 -> test with 'LR' LR tn, fp: 1931, 254 LR fn, tp: 7, 45 LR f1 score: 0.256 LR cohens kappa score: 0.226 LR average precision score: 0.592 -> test with 'GB' GB tn, fp: 2123, 62 GB fn, tp: 9, 43 GB f1 score: 0.548 GB cohens kappa score: 0.533 -> test with 'KNN' KNN tn, fp: 2125, 60 KNN fn, tp: 10, 42 KNN f1 score: 0.545 KNN cohens kappa score: 0.531 ------ 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: 2066, 119 GAN fn, tp: 9, 43 GAN f1 score: 0.402 GAN cohens kappa score: 0.380 -> 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: 0.670 -> test with 'GB' GB tn, fp: 2118, 67 GB fn, tp: 10, 42 GB f1 score: 0.522 GB cohens kappa score: 0.506 -> test with 'KNN' KNN tn, fp: 1448, 737 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 GAN.predict GAN tn, fp: 1986, 199 GAN fn, tp: 7, 45 GAN f1 score: 0.304 GAN cohens kappa score: 0.276 -> test with 'LR' LR tn, fp: 1937, 248 LR fn, tp: 6, 46 LR f1 score: 0.266 LR cohens kappa score: 0.236 LR average precision score: 0.496 -> 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: 2112, 73 KNN fn, tp: 9, 43 KNN f1 score: 0.512 KNN cohens kappa score: 0.496 ------ 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: 2024, 159 GAN fn, tp: 9, 43 GAN f1 score: 0.339 GAN cohens kappa score: 0.313 -> test with 'LR' LR tn, fp: 1941, 242 LR fn, tp: 6, 46 LR f1 score: 0.271 LR cohens kappa score: 0.241 LR average precision score: 0.585 -> test with 'GB' GB tn, fp: 2124, 59 GB fn, tp: 7, 45 GB f1 score: 0.577 GB cohens kappa score: 0.563 -> test with 'KNN' KNN tn, fp: 2118, 65 KNN fn, tp: 12, 40 KNN f1 score: 0.510 KNN cohens kappa score: 0.494 ====== 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: 1940, 245 GAN fn, tp: 5, 47 GAN f1 score: 0.273 GAN cohens kappa score: 0.243 -> test with 'LR' LR tn, fp: 1931, 254 LR fn, tp: 6, 46 LR f1 score: 0.261 LR cohens kappa score: 0.231 LR average precision score: 0.594 -> test with 'GB' GB tn, fp: 2086, 99 GB fn, tp: 11, 41 GB f1 score: 0.427 GB cohens kappa score: 0.407 -> test with 'KNN' KNN tn, fp: 2098, 87 KNN fn, tp: 9, 43 KNN f1 score: 0.473 KNN cohens kappa score: 0.454 ------ 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: 1983, 202 GAN fn, tp: 7, 45 GAN f1 score: 0.301 GAN cohens kappa score: 0.273 -> test with 'LR' LR tn, fp: 1940, 245 LR fn, tp: 6, 46 LR f1 score: 0.268 LR cohens kappa score: 0.238 LR average precision score: 0.590 -> test with 'GB' GB tn, fp: 2091, 94 GB fn, tp: 9, 43 GB f1 score: 0.455 GB cohens kappa score: 0.436 -> test with 'KNN' KNN tn, fp: 2094, 91 KNN fn, tp: 9, 43 KNN f1 score: 0.462 KNN cohens kappa score: 0.444 ------ 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: 2004, 181 GAN fn, tp: 8, 44 GAN f1 score: 0.318 GAN cohens kappa score: 0.291 -> test with 'LR' LR tn, fp: 1993, 192 LR fn, tp: 7, 45 LR f1 score: 0.311 LR cohens kappa score: 0.284 LR average precision score: 0.581 -> test with 'GB' GB tn, fp: 2127, 58 GB fn, tp: 12, 40 GB f1 score: 0.533 GB cohens kappa score: 0.519 -> test with 'KNN' KNN tn, fp: 2119, 66 KNN fn, tp: 11, 41 KNN f1 score: 0.516 KNN cohens kappa score: 0.500 ------ 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: 2013, 172 GAN fn, tp: 6, 46 GAN f1 score: 0.341 GAN cohens kappa score: 0.315 -> test with 'LR' LR tn, fp: 1938, 247 LR fn, tp: 4, 48 LR f1 score: 0.277 LR cohens kappa score: 0.247 LR average precision score: 0.630 -> test with 'GB' GB tn, fp: 2113, 72 GB fn, tp: 6, 46 GB f1 score: 0.541 GB cohens kappa score: 0.526 -> test with 'KNN' KNN tn, fp: 2106, 79 KNN fn, tp: 5, 47 KNN f1 score: 0.528 KNN cohens kappa score: 0.512 ------ 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: 2057, 126 GAN fn, tp: 10, 42 GAN f1 score: 0.382 GAN cohens kappa score: 0.359 -> test with 'LR' LR tn, fp: 1969, 214 LR fn, tp: 9, 43 LR f1 score: 0.278 LR cohens kappa score: 0.249 LR average precision score: 0.596 -> test with 'GB' GB tn, fp: 2129, 54 GB fn, tp: 13, 39 GB f1 score: 0.538 GB cohens kappa score: 0.524 -> test with 'KNN' KNN tn, fp: 2137, 46 KNN fn, tp: 12, 40 KNN f1 score: 0.580 KNN cohens kappa score: 0.567 ====== 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: 1992, 193 GAN fn, tp: 8, 44 GAN f1 score: 0.304 GAN cohens kappa score: 0.277 -> test with 'LR' LR tn, fp: 1933, 252 LR fn, tp: 5, 47 LR f1 score: 0.268 LR cohens kappa score: 0.238 LR average precision score: 0.688 -> test with 'GB' GB tn, fp: 2114, 71 GB fn, tp: 6, 46 GB f1 score: 0.544 GB cohens kappa score: 0.529 -> test with 'KNN' KNN tn, fp: 2126, 59 KNN fn, tp: 7, 45 KNN f1 score: 0.577 KNN cohens kappa score: 0.563 ------ 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: 1997, 188 GAN fn, tp: 5, 47 GAN f1 score: 0.328 GAN cohens kappa score: 0.301 -> test with 'LR' LR tn, fp: 1956, 229 LR fn, tp: 6, 46 LR f1 score: 0.281 LR cohens kappa score: 0.252 LR average precision score: 0.502 -> test with 'GB' GB tn, fp: 2122, 63 GB fn, tp: 13, 39 GB f1 score: 0.506 GB cohens kappa score: 0.491 -> test with 'KNN' KNN tn, fp: 2131, 54 KNN fn, tp: 10, 42 KNN f1 score: 0.568 KNN cohens kappa score: 0.554 ------ 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: 1995, 190 GAN fn, tp: 5, 47 GAN f1 score: 0.325 GAN cohens kappa score: 0.299 -> test with 'LR' LR tn, fp: 1935, 250 LR fn, tp: 2, 50 LR f1 score: 0.284 LR cohens kappa score: 0.255 LR average precision score: 0.583 -> test with 'GB' GB tn, fp: 2104, 81 GB fn, tp: 4, 48 GB f1 score: 0.530 GB cohens kappa score: 0.514 -> test with 'KNN' KNN tn, fp: 2119, 66 KNN fn, tp: 6, 46 KNN f1 score: 0.561 KNN cohens kappa score: 0.547 ------ 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: 1925, 260 GAN fn, tp: 11, 41 GAN f1 score: 0.232 GAN cohens kappa score: 0.201 -> test with 'LR' LR tn, fp: 1984, 201 LR fn, tp: 11, 41 LR f1 score: 0.279 LR cohens kappa score: 0.250 LR average precision score: 0.556 -> test with 'GB' GB tn, fp: 2101, 84 GB fn, tp: 12, 40 GB f1 score: 0.455 GB cohens kappa score: 0.436 -> test with 'KNN' KNN tn, fp: 2097, 88 KNN fn, tp: 11, 41 KNN f1 score: 0.453 KNN cohens kappa score: 0.434 ------ 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: 1982, 201 GAN fn, tp: 13, 39 GAN f1 score: 0.267 GAN cohens kappa score: 0.238 -> test with 'LR' LR tn, fp: 1941, 242 LR fn, tp: 7, 45 LR f1 score: 0.265 LR cohens kappa score: 0.235 LR average precision score: 0.651 -> test with 'GB' GB tn, fp: 2118, 65 GB fn, tp: 10, 42 GB f1 score: 0.528 GB cohens kappa score: 0.513 -> 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 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: 2054, 131 GAN fn, tp: 11, 41 GAN f1 score: 0.366 GAN cohens kappa score: 0.343 -> test with 'LR' LR tn, fp: 1996, 189 LR fn, tp: 8, 44 LR f1 score: 0.309 LR cohens kappa score: 0.281 LR average precision score: 0.637 -> test with 'GB' GB tn, fp: 2123, 62 GB fn, tp: 17, 35 GB f1 score: 0.470 GB cohens kappa score: 0.453 -> test with 'KNN' KNN tn, fp: 2126, 59 KNN fn, tp: 12, 40 KNN f1 score: 0.530 KNN cohens kappa score: 0.515 ------ 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: 2038, 147 GAN fn, tp: 7, 45 GAN f1 score: 0.369 GAN cohens kappa score: 0.345 -> test with 'LR' LR tn, fp: 1967, 218 LR fn, tp: 6, 46 LR f1 score: 0.291 LR cohens kappa score: 0.262 LR average precision score: 0.574 -> 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: 2123, 62 KNN fn, tp: 8, 44 KNN f1 score: 0.557 KNN cohens kappa score: 0.543 ------ 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: 2057, 128 GAN fn, tp: 7, 45 GAN f1 score: 0.400 GAN cohens kappa score: 0.378 -> test with 'LR' LR tn, fp: 1956, 229 LR fn, tp: 7, 45 LR f1 score: 0.276 LR cohens kappa score: 0.247 LR average precision score: 0.691 -> test with 'GB' GB tn, fp: 2119, 66 GB fn, tp: 8, 44 GB f1 score: 0.543 GB cohens kappa score: 0.528 -> test with 'KNN' KNN tn, fp: 2109, 76 KNN fn, tp: 8, 44 KNN f1 score: 0.512 KNN cohens kappa score: 0.495 ------ 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: 2015, 170 GAN fn, tp: 7, 45 GAN f1 score: 0.337 GAN cohens kappa score: 0.311 -> test with 'LR' LR tn, fp: 1934, 251 LR fn, tp: 9, 43 LR f1 score: 0.249 LR cohens kappa score: 0.218 LR average precision score: 0.502 -> 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: 2114, 71 KNN fn, tp: 12, 40 KNN f1 score: 0.491 KNN cohens kappa score: 0.474 ------ 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: 2003, 180 GAN fn, tp: 4, 48 GAN f1 score: 0.343 GAN cohens kappa score: 0.317 -> test with 'LR' LR tn, fp: 1946, 237 LR fn, tp: 2, 50 LR f1 score: 0.295 LR cohens kappa score: 0.266 LR average precision score: 0.592 -> test with 'GB' GB tn, fp: 2111, 72 GB fn, tp: 5, 47 GB f1 score: 0.550 GB cohens kappa score: 0.535 -> test with 'KNN' KNN tn, fp: 2099, 84 KNN fn, tp: 7, 45 KNN f1 score: 0.497 KNN cohens kappa score: 0.480 ====== 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: 2024, 161 GAN fn, tp: 8, 44 GAN f1 score: 0.342 GAN cohens kappa score: 0.317 -> test with 'LR' LR tn, fp: 1971, 214 LR fn, tp: 4, 48 LR f1 score: 0.306 LR cohens kappa score: 0.278 LR average precision score: 0.663 -> test with 'GB' GB tn, fp: 2124, 61 GB fn, tp: 7, 45 GB f1 score: 0.570 GB cohens kappa score: 0.556 -> test with 'KNN' KNN tn, fp: 1457, 728 KNN fn, tp: 9, 43 KNN f1 score: 0.104 KNN cohens kappa score: 0.064 ------ 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: 1878, 307 GAN fn, tp: 4, 48 GAN f1 score: 0.236 GAN cohens kappa score: 0.204 -> test with 'LR' LR tn, fp: 2005, 180 LR fn, tp: 8, 44 LR f1 score: 0.319 LR cohens kappa score: 0.292 LR average precision score: 0.554 -> test with 'GB' GB tn, fp: 2114, 71 GB fn, tp: 6, 46 GB f1 score: 0.544 GB cohens kappa score: 0.529 -> test with 'KNN' KNN tn, fp: 2107, 78 KNN fn, tp: 8, 44 KNN f1 score: 0.506 KNN cohens kappa score: 0.489 ------ 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: 1973, 212 GAN fn, tp: 10, 42 GAN f1 score: 0.275 GAN cohens kappa score: 0.245 -> test with 'LR' LR tn, fp: 1997, 188 LR fn, tp: 10, 42 LR f1 score: 0.298 LR cohens kappa score: 0.270 LR average precision score: 0.576 -> test with 'GB' GB tn, fp: 2120, 65 GB fn, tp: 14, 38 GB f1 score: 0.490 GB cohens kappa score: 0.474 -> test with 'KNN' KNN tn, fp: 2109, 76 KNN fn, tp: 13, 39 KNN f1 score: 0.467 KNN cohens kappa score: 0.449 ------ 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: 1982, 203 GAN fn, tp: 7, 45 GAN f1 score: 0.300 GAN cohens kappa score: 0.272 -> test with 'LR' LR tn, fp: 2003, 182 LR fn, tp: 6, 46 LR f1 score: 0.329 LR cohens kappa score: 0.302 LR average precision score: 0.619 -> test with 'GB' GB tn, fp: 2123, 62 GB fn, tp: 10, 42 GB f1 score: 0.538 GB cohens kappa score: 0.524 -> test with 'KNN' KNN tn, fp: 2115, 70 KNN fn, tp: 9, 43 KNN f1 score: 0.521 KNN cohens kappa score: 0.505 ------ 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: 2013, 170 GAN fn, tp: 11, 41 GAN f1 score: 0.312 GAN cohens kappa score: 0.285 -> test with 'LR' LR tn, fp: 1977, 206 LR fn, tp: 9, 43 LR f1 score: 0.286 LR cohens kappa score: 0.257 LR average precision score: 0.621 -> test with 'GB' GB tn, fp: 2118, 65 GB fn, tp: 14, 38 GB f1 score: 0.490 GB cohens kappa score: 0.474 -> test with 'KNN' KNN tn, fp: 2124, 59 KNN fn, tp: 12, 40 KNN f1 score: 0.530 KNN cohens kappa score: 0.515 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 2005, 254 LR fn, tp: 11, 50 LR f1 score: 0.329 LR cohens kappa score: 0.302 LR average precision score: 0.691 average: LR tn, fp: 1960.28, 224.32 LR fn, tp: 6.56, 45.44 LR f1 score: 0.284 LR cohens kappa score: 0.255 LR average precision score: 0.598 minimum: LR tn, fp: 1931, 180 LR fn, tp: 2, 41 LR f1 score: 0.249 LR cohens kappa score: 0.218 LR average precision score: 0.496 -----[ GB ]----- maximum: GB tn, fp: 2129, 99 GB fn, tp: 17, 48 GB f1 score: 0.577 GB cohens kappa score: 0.563 average: GB tn, fp: 2115.12, 69.48 GB fn, tp: 9.76, 42.24 GB f1 score: 0.517 GB cohens kappa score: 0.501 minimum: GB tn, fp: 2086, 54 GB fn, tp: 4, 35 GB f1 score: 0.427 GB cohens kappa score: 0.407 -----[ KNN ]----- maximum: KNN tn, fp: 2137, 737 KNN fn, tp: 13, 47 KNN f1 score: 0.580 KNN cohens kappa score: 0.567 average: KNN tn, fp: 2061.4, 123.2 KNN fn, tp: 9.52, 42.48 KNN f1 score: 0.484 KNN cohens kappa score: 0.466 minimum: KNN tn, fp: 1448, 46 KNN fn, tp: 5, 39 KNN f1 score: 0.104 KNN cohens kappa score: 0.064 -----[ GAN ]----- maximum: GAN tn, fp: 2066, 307 GAN fn, tp: 13, 49 GAN f1 score: 0.402 GAN cohens kappa score: 0.380 average: GAN tn, fp: 2000.8, 183.8 GAN fn, tp: 7.6, 44.4 GAN f1 score: 0.323 GAN cohens kappa score: 0.297 minimum: GAN tn, fp: 1878, 119 GAN fn, tp: 3, 39 GAN f1 score: 0.232 GAN cohens kappa score: 0.201