/////////////////////////////////////////// // Running ProWRAS 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: 1977, 208 LR fn, tp: 7, 45 LR f1 score: 0.295 LR cohens kappa score: 0.267 LR average precision score: 0.572 -> 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: 2132, 53 KNN fn, tp: 9, 43 KNN f1 score: 0.581 KNN cohens kappa score: 0.568 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1975, 210 LR fn, tp: 7, 45 LR f1 score: 0.293 LR cohens kappa score: 0.265 LR average precision score: 0.483 -> test with 'GB' GB tn, fp: 2154, 31 GB fn, tp: 15, 37 GB f1 score: 0.617 GB cohens kappa score: 0.606 -> test with 'KNN' KNN tn, fp: 2151, 34 KNN fn, tp: 13, 39 KNN f1 score: 0.624 KNN cohens kappa score: 0.614 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1966, 219 LR fn, tp: 9, 43 LR f1 score: 0.274 LR cohens kappa score: 0.245 LR average precision score: 0.593 -> test with 'GB' GB tn, fp: 2146, 39 GB fn, tp: 11, 41 GB f1 score: 0.621 GB cohens kappa score: 0.610 -> test with 'KNN' KNN tn, fp: 1459, 726 KNN fn, tp: 11, 41 KNN f1 score: 0.100 KNN cohens kappa score: 0.059 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1965, 220 LR fn, tp: 8, 44 LR f1 score: 0.278 LR cohens kappa score: 0.249 LR average precision score: 0.328 -> test with 'GB' GB tn, fp: 2144, 41 GB fn, tp: 15, 37 GB f1 score: 0.569 GB cohens kappa score: 0.557 -> test with 'KNN' KNN tn, fp: 2137, 48 KNN fn, tp: 12, 40 KNN f1 score: 0.571 KNN cohens kappa score: 0.559 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1969, 214 LR fn, tp: 8, 44 LR f1 score: 0.284 LR cohens kappa score: 0.255 LR average precision score: 0.563 -> 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: 2147, 36 KNN fn, tp: 15, 37 KNN f1 score: 0.592 KNN cohens kappa score: 0.581 ====== 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: 1954, 231 LR fn, tp: 7, 45 LR f1 score: 0.274 LR cohens kappa score: 0.245 LR average precision score: 0.521 -> test with 'GB' GB tn, fp: 2141, 44 GB fn, tp: 11, 41 GB f1 score: 0.599 GB cohens kappa score: 0.587 -> test with 'KNN' KNN tn, fp: 2126, 59 KNN fn, tp: 11, 41 KNN f1 score: 0.539 KNN cohens kappa score: 0.525 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1948, 237 LR fn, tp: 9, 43 LR f1 score: 0.259 LR cohens kappa score: 0.229 LR average precision score: 0.468 -> test with 'GB' GB tn, fp: 2135, 50 GB fn, tp: 15, 37 GB f1 score: 0.532 GB cohens kappa score: 0.518 -> test with 'KNN' KNN tn, fp: 2138, 47 KNN fn, tp: 9, 43 KNN f1 score: 0.606 KNN cohens kappa score: 0.594 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 2010, 175 LR fn, tp: 8, 44 LR f1 score: 0.325 LR cohens kappa score: 0.298 LR average precision score: 0.536 -> test with 'GB' GB tn, fp: 2154, 31 GB fn, tp: 16, 36 GB f1 score: 0.605 GB cohens kappa score: 0.594 -> test with 'KNN' KNN tn, fp: 2142, 43 KNN fn, tp: 13, 39 KNN f1 score: 0.582 KNN cohens kappa score: 0.570 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1967, 218 LR fn, tp: 5, 47 LR f1 score: 0.297 LR cohens kappa score: 0.268 LR average precision score: 0.533 -> test with 'GB' GB tn, fp: 2152, 33 GB fn, tp: 8, 44 GB f1 score: 0.682 GB cohens kappa score: 0.673 -> test with 'KNN' KNN tn, fp: 2136, 49 KNN fn, tp: 10, 42 KNN f1 score: 0.587 KNN cohens kappa score: 0.575 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1994, 189 LR fn, tp: 9, 43 LR f1 score: 0.303 LR cohens kappa score: 0.275 LR average precision score: 0.562 -> test with 'GB' GB tn, fp: 2162, 21 GB fn, tp: 19, 33 GB f1 score: 0.623 GB cohens kappa score: 0.613 -> test with 'KNN' KNN tn, fp: 2148, 35 KNN fn, tp: 16, 36 KNN f1 score: 0.585 KNN cohens kappa score: 0.574 ====== 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: 1979, 206 LR fn, tp: 8, 44 LR f1 score: 0.291 LR cohens kappa score: 0.263 LR average precision score: 0.588 -> test with 'GB' GB tn, fp: 2157, 28 GB fn, tp: 9, 43 GB f1 score: 0.699 GB cohens kappa score: 0.691 -> test with 'KNN' KNN tn, fp: 2136, 49 KNN fn, tp: 10, 42 KNN f1 score: 0.587 KNN cohens kappa score: 0.575 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1970, 215 LR fn, tp: 8, 44 LR f1 score: 0.283 LR cohens kappa score: 0.254 LR average precision score: 0.418 -> test with 'GB' GB tn, fp: 2155, 30 GB fn, tp: 17, 35 GB f1 score: 0.598 GB cohens kappa score: 0.588 -> test with 'KNN' KNN tn, fp: 1462, 723 KNN fn, tp: 12, 40 KNN f1 score: 0.098 KNN cohens kappa score: 0.057 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1960, 225 LR fn, tp: 4, 48 LR f1 score: 0.295 LR cohens kappa score: 0.267 LR average precision score: 0.486 -> test with 'GB' GB tn, fp: 2138, 47 GB fn, tp: 6, 46 GB f1 score: 0.634 GB cohens kappa score: 0.623 -> test with 'KNN' KNN tn, fp: 2134, 51 KNN fn, tp: 8, 44 KNN f1 score: 0.599 KNN cohens kappa score: 0.586 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1971, 214 LR fn, tp: 11, 41 LR f1 score: 0.267 LR cohens kappa score: 0.238 LR average precision score: 0.519 -> test with 'GB' GB tn, fp: 2149, 36 GB fn, tp: 17, 35 GB f1 score: 0.569 GB cohens kappa score: 0.557 -> test with 'KNN' KNN tn, fp: 2145, 40 KNN fn, tp: 15, 37 KNN f1 score: 0.574 KNN cohens kappa score: 0.561 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1958, 225 LR fn, tp: 7, 45 LR f1 score: 0.280 LR cohens kappa score: 0.250 LR average precision score: 0.586 -> test with 'GB' GB tn, fp: 2148, 35 GB fn, tp: 17, 35 GB f1 score: 0.574 GB cohens kappa score: 0.562 -> test with 'KNN' KNN tn, fp: 2143, 40 KNN fn, tp: 12, 40 KNN f1 score: 0.606 KNN cohens kappa score: 0.595 ====== 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: 1972, 213 LR fn, tp: 8, 44 LR f1 score: 0.285 LR cohens kappa score: 0.256 LR average precision score: 0.570 -> test with 'GB' GB tn, fp: 2152, 33 GB fn, tp: 19, 33 GB f1 score: 0.559 GB cohens kappa score: 0.548 -> test with 'KNN' KNN tn, fp: 2152, 33 KNN fn, tp: 13, 39 KNN f1 score: 0.629 KNN cohens kappa score: 0.619 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1968, 217 LR fn, tp: 8, 44 LR f1 score: 0.281 LR cohens kappa score: 0.252 LR average precision score: 0.424 -> test with 'GB' GB tn, fp: 2146, 39 GB fn, tp: 13, 39 GB f1 score: 0.600 GB cohens kappa score: 0.589 -> test with 'KNN' KNN tn, fp: 2141, 44 KNN fn, tp: 11, 41 KNN f1 score: 0.599 KNN cohens kappa score: 0.587 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1980, 205 LR fn, tp: 7, 45 LR f1 score: 0.298 LR cohens kappa score: 0.270 LR average precision score: 0.507 -> test with 'GB' GB tn, fp: 2152, 33 GB fn, tp: 9, 43 GB f1 score: 0.672 GB cohens kappa score: 0.663 -> test with 'KNN' KNN tn, fp: 2140, 45 KNN fn, tp: 9, 43 KNN f1 score: 0.614 KNN cohens kappa score: 0.603 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1974, 211 LR fn, tp: 10, 42 LR f1 score: 0.275 LR cohens kappa score: 0.246 LR average precision score: 0.481 -> test with 'GB' GB tn, fp: 2145, 40 GB fn, tp: 14, 38 GB f1 score: 0.585 GB cohens kappa score: 0.573 -> test with 'KNN' KNN tn, fp: 2135, 50 KNN fn, tp: 14, 38 KNN f1 score: 0.543 KNN cohens kappa score: 0.529 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1954, 229 LR fn, tp: 5, 47 LR f1 score: 0.287 LR cohens kappa score: 0.258 LR average precision score: 0.509 -> test with 'GB' GB tn, fp: 2144, 39 GB fn, tp: 10, 42 GB f1 score: 0.632 GB cohens kappa score: 0.621 -> test with 'KNN' KNN tn, fp: 2134, 49 KNN fn, tp: 12, 40 KNN f1 score: 0.567 KNN cohens kappa score: 0.554 ====== 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: 1973, 212 LR fn, tp: 4, 48 LR f1 score: 0.308 LR cohens kappa score: 0.280 LR average precision score: 0.542 -> test with 'GB' GB tn, fp: 2149, 36 GB fn, tp: 12, 40 GB f1 score: 0.625 GB cohens kappa score: 0.614 -> test with 'KNN' KNN tn, fp: 2149, 36 KNN fn, tp: 10, 42 KNN f1 score: 0.646 KNN cohens kappa score: 0.636 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1970, 215 LR fn, tp: 8, 44 LR f1 score: 0.283 LR cohens kappa score: 0.254 LR average precision score: 0.446 -> test with 'GB' GB tn, fp: 2146, 39 GB fn, tp: 16, 36 GB f1 score: 0.567 GB cohens kappa score: 0.555 -> test with 'KNN' KNN tn, fp: 2134, 51 KNN fn, tp: 10, 42 KNN f1 score: 0.579 KNN cohens kappa score: 0.566 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1980, 205 LR fn, tp: 11, 41 LR f1 score: 0.275 LR cohens kappa score: 0.246 LR average precision score: 0.540 -> test with 'GB' GB tn, fp: 2154, 31 GB fn, tp: 16, 36 GB f1 score: 0.605 GB cohens kappa score: 0.594 -> test with 'KNN' KNN tn, fp: 2139, 46 KNN fn, tp: 18, 34 KNN f1 score: 0.515 KNN cohens kappa score: 0.501 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8530 synthetic samples -> test with 'LR' LR tn, fp: 1960, 225 LR fn, tp: 4, 48 LR f1 score: 0.295 LR cohens kappa score: 0.267 LR average precision score: 0.525 -> 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: 2141, 44 KNN fn, tp: 9, 43 KNN f1 score: 0.619 KNN cohens kappa score: 0.607 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 8532 synthetic samples -> test with 'LR' LR tn, fp: 1987, 196 LR fn, tp: 9, 43 LR f1 score: 0.296 LR cohens kappa score: 0.268 LR average precision score: 0.600 -> test with 'GB' GB tn, fp: 2153, 30 GB fn, tp: 17, 35 GB f1 score: 0.598 GB cohens kappa score: 0.588 -> test with 'KNN' KNN tn, fp: 2141, 42 KNN fn, tp: 13, 39 KNN f1 score: 0.586 KNN cohens kappa score: 0.574 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 2010, 237 LR fn, tp: 11, 48 LR f1 score: 0.325 LR cohens kappa score: 0.298 LR average precision score: 0.600 average: LR tn, fp: 1971.24, 213.36 LR fn, tp: 7.56, 44.44 LR f1 score: 0.287 LR cohens kappa score: 0.259 LR average precision score: 0.516 minimum: LR tn, fp: 1948, 175 LR fn, tp: 4, 41 LR f1 score: 0.259 LR cohens kappa score: 0.229 LR average precision score: 0.328 -----[ GB ]----- maximum: GB tn, fp: 2162, 50 GB fn, tp: 19, 46 GB f1 score: 0.699 GB cohens kappa score: 0.691 average: GB tn, fp: 2148.76, 35.84 GB fn, tp: 13.76, 38.24 GB f1 score: 0.607 GB cohens kappa score: 0.596 minimum: GB tn, fp: 2135, 21 GB fn, tp: 6, 33 GB f1 score: 0.532 GB cohens kappa score: 0.518 -----[ KNN ]----- maximum: KNN tn, fp: 2152, 726 KNN fn, tp: 18, 44 KNN f1 score: 0.646 KNN cohens kappa score: 0.636 average: KNN tn, fp: 2085.68, 98.92 KNN fn, tp: 11.8, 40.2 KNN f1 score: 0.549 KNN cohens kappa score: 0.535 minimum: KNN tn, fp: 1459, 33 KNN fn, tp: 8, 34 KNN f1 score: 0.098 KNN cohens kappa score: 0.057