/////////////////////////////////////////// // Running ProWRAS on imblearn_ozone_level /////////////////////////////////////////// Load 'data_input/imblearn_ozone_level' from imblearn 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 1912 synthetic samples -> test with 'LR' LR tn, fp: 459, 34 LR fn, tp: 2, 13 LR f1 score: 0.419 LR cohens kappa score: 0.392 LR average precision score: 0.394 -> test with 'GB' GB tn, fp: 482, 11 GB fn, tp: 11, 4 GB f1 score: 0.267 GB cohens kappa score: 0.244 -> test with 'KNN' KNN tn, fp: 424, 69 KNN fn, tp: 11, 4 KNN f1 score: 0.091 KNN cohens kappa score: 0.044 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 452, 41 LR fn, tp: 5, 10 LR f1 score: 0.303 LR cohens kappa score: 0.270 LR average precision score: 0.237 -> test with 'GB' GB tn, fp: 490, 3 GB fn, tp: 11, 4 GB f1 score: 0.364 GB cohens kappa score: 0.351 -> test with 'KNN' KNN tn, fp: 426, 67 KNN fn, tp: 10, 5 KNN f1 score: 0.115 KNN cohens kappa score: 0.069 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 451, 42 LR fn, tp: 5, 10 LR f1 score: 0.299 LR cohens kappa score: 0.265 LR average precision score: 0.168 -> test with 'GB' GB tn, fp: 485, 8 GB fn, tp: 11, 4 GB f1 score: 0.296 GB cohens kappa score: 0.277 -> test with 'KNN' KNN tn, fp: 421, 72 KNN fn, tp: 9, 6 KNN f1 score: 0.129 KNN cohens kappa score: 0.084 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 444, 49 LR fn, tp: 7, 8 LR f1 score: 0.222 LR cohens kappa score: 0.184 LR average precision score: 0.220 -> test with 'GB' GB tn, fp: 487, 6 GB fn, tp: 12, 3 GB f1 score: 0.250 GB cohens kappa score: 0.233 -> test with 'KNN' KNN tn, fp: 449, 44 KNN fn, tp: 8, 7 KNN f1 score: 0.212 KNN cohens kappa score: 0.174 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 443, 48 LR fn, tp: 3, 10 LR f1 score: 0.282 LR cohens kappa score: 0.250 LR average precision score: 0.144 -> test with 'GB' GB tn, fp: 484, 7 GB fn, tp: 10, 3 GB f1 score: 0.261 GB cohens kappa score: 0.244 -> test with 'KNN' KNN tn, fp: 425, 66 KNN fn, tp: 9, 4 KNN f1 score: 0.096 KNN cohens kappa score: 0.055 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 453, 40 LR fn, tp: 8, 7 LR f1 score: 0.226 LR cohens kappa score: 0.190 LR average precision score: 0.203 -> test with 'GB' GB tn, fp: 484, 9 GB fn, tp: 12, 3 GB f1 score: 0.222 GB cohens kappa score: 0.201 -> test with 'KNN' KNN tn, fp: 428, 65 KNN fn, tp: 11, 4 KNN f1 score: 0.095 KNN cohens kappa score: 0.049 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 461, 32 LR fn, tp: 5, 10 LR f1 score: 0.351 LR cohens kappa score: 0.321 LR average precision score: 0.180 -> test with 'GB' GB tn, fp: 485, 8 GB fn, tp: 13, 2 GB f1 score: 0.160 GB cohens kappa score: 0.140 -> test with 'KNN' KNN tn, fp: 428, 65 KNN fn, tp: 6, 9 KNN f1 score: 0.202 KNN cohens kappa score: 0.161 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 454, 39 LR fn, tp: 2, 13 LR f1 score: 0.388 LR cohens kappa score: 0.359 LR average precision score: 0.404 -> test with 'GB' GB tn, fp: 487, 6 GB fn, tp: 11, 4 GB f1 score: 0.320 GB cohens kappa score: 0.304 -> test with 'KNN' KNN tn, fp: 427, 66 KNN fn, tp: 10, 5 KNN f1 score: 0.116 KNN cohens kappa score: 0.071 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 458, 35 LR fn, tp: 5, 10 LR f1 score: 0.333 LR cohens kappa score: 0.302 LR average precision score: 0.165 -> test with 'GB' GB tn, fp: 485, 8 GB fn, tp: 12, 3 GB f1 score: 0.231 GB cohens kappa score: 0.211 -> test with 'KNN' KNN tn, fp: 441, 52 KNN fn, tp: 9, 6 KNN f1 score: 0.164 KNN cohens kappa score: 0.123 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 442, 49 LR fn, tp: 5, 8 LR f1 score: 0.229 LR cohens kappa score: 0.195 LR average precision score: 0.195 -> test with 'GB' GB tn, fp: 488, 3 GB fn, tp: 11, 2 GB f1 score: 0.222 GB cohens kappa score: 0.211 -> test with 'KNN' KNN tn, fp: 413, 78 KNN fn, tp: 7, 6 KNN f1 score: 0.124 KNN cohens kappa score: 0.083 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 450, 43 LR fn, tp: 4, 11 LR f1 score: 0.319 LR cohens kappa score: 0.286 LR average precision score: 0.236 -> test with 'GB' GB tn, fp: 485, 8 GB fn, tp: 14, 1 GB f1 score: 0.083 GB cohens kappa score: 0.063 -> test with 'KNN' KNN tn, fp: 445, 48 KNN fn, tp: 11, 4 KNN f1 score: 0.119 KNN cohens kappa score: 0.077 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 448, 45 LR fn, tp: 5, 10 LR f1 score: 0.286 LR cohens kappa score: 0.251 LR average precision score: 0.168 -> test with 'GB' GB tn, fp: 482, 11 GB fn, tp: 10, 5 GB f1 score: 0.323 GB cohens kappa score: 0.301 -> test with 'KNN' KNN tn, fp: 443, 50 KNN fn, tp: 11, 4 KNN f1 score: 0.116 KNN cohens kappa score: 0.073 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 453, 40 LR fn, tp: 5, 10 LR f1 score: 0.308 LR cohens kappa score: 0.275 LR average precision score: 0.251 -> test with 'GB' GB tn, fp: 485, 8 GB fn, tp: 12, 3 GB f1 score: 0.231 GB cohens kappa score: 0.211 -> test with 'KNN' KNN tn, fp: 433, 60 KNN fn, tp: 10, 5 KNN f1 score: 0.125 KNN cohens kappa score: 0.081 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 463, 30 LR fn, tp: 7, 8 LR f1 score: 0.302 LR cohens kappa score: 0.271 LR average precision score: 0.183 -> test with 'GB' GB tn, fp: 490, 3 GB fn, tp: 12, 3 GB f1 score: 0.286 GB cohens kappa score: 0.273 -> test with 'KNN' KNN tn, fp: 425, 68 KNN fn, tp: 9, 6 KNN f1 score: 0.135 KNN cohens kappa score: 0.090 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 443, 48 LR fn, tp: 4, 9 LR f1 score: 0.257 LR cohens kappa score: 0.225 LR average precision score: 0.347 -> test with 'GB' GB tn, fp: 485, 6 GB fn, tp: 8, 5 GB f1 score: 0.417 GB cohens kappa score: 0.403 -> test with 'KNN' KNN tn, fp: 409, 82 KNN fn, tp: 7, 6 KNN f1 score: 0.119 KNN cohens kappa score: 0.077 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 449, 44 LR fn, tp: 4, 11 LR f1 score: 0.314 LR cohens kappa score: 0.281 LR average precision score: 0.273 -> test with 'GB' GB tn, fp: 485, 8 GB fn, tp: 12, 3 GB f1 score: 0.231 GB cohens kappa score: 0.211 -> test with 'KNN' KNN tn, fp: 426, 67 KNN fn, tp: 12, 3 KNN f1 score: 0.071 KNN cohens kappa score: 0.023 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 457, 36 LR fn, tp: 4, 11 LR f1 score: 0.355 LR cohens kappa score: 0.325 LR average precision score: 0.239 -> test with 'GB' GB tn, fp: 487, 6 GB fn, tp: 12, 3 GB f1 score: 0.250 GB cohens kappa score: 0.233 -> test with 'KNN' KNN tn, fp: 436, 57 KNN fn, tp: 9, 6 KNN f1 score: 0.154 KNN cohens kappa score: 0.111 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 461, 32 LR fn, tp: 7, 8 LR f1 score: 0.291 LR cohens kappa score: 0.259 LR average precision score: 0.201 -> test with 'GB' GB tn, fp: 491, 2 GB fn, tp: 10, 5 GB f1 score: 0.455 GB cohens kappa score: 0.444 -> test with 'KNN' KNN tn, fp: 428, 65 KNN fn, tp: 11, 4 KNN f1 score: 0.095 KNN cohens kappa score: 0.049 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 447, 46 LR fn, tp: 3, 12 LR f1 score: 0.329 LR cohens kappa score: 0.296 LR average precision score: 0.318 -> test with 'GB' GB tn, fp: 489, 4 GB fn, tp: 11, 4 GB f1 score: 0.348 GB cohens kappa score: 0.334 -> test with 'KNN' KNN tn, fp: 424, 69 KNN fn, tp: 10, 5 KNN f1 score: 0.112 KNN cohens kappa score: 0.067 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 456, 35 LR fn, tp: 4, 9 LR f1 score: 0.316 LR cohens kappa score: 0.287 LR average precision score: 0.203 -> test with 'GB' GB tn, fp: 484, 7 GB fn, tp: 10, 3 GB f1 score: 0.261 GB cohens kappa score: 0.244 -> test with 'KNN' KNN tn, fp: 421, 70 KNN fn, tp: 7, 6 KNN f1 score: 0.135 KNN cohens kappa score: 0.095 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 461, 32 LR fn, tp: 4, 11 LR f1 score: 0.379 LR cohens kappa score: 0.351 LR average precision score: 0.263 -> test with 'GB' GB tn, fp: 486, 7 GB fn, tp: 6, 9 GB f1 score: 0.581 GB cohens kappa score: 0.567 -> test with 'KNN' KNN tn, fp: 419, 74 KNN fn, tp: 10, 5 KNN f1 score: 0.106 KNN cohens kappa score: 0.060 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 443, 50 LR fn, tp: 5, 10 LR f1 score: 0.267 LR cohens kappa score: 0.230 LR average precision score: 0.206 -> test with 'GB' GB tn, fp: 483, 10 GB fn, tp: 12, 3 GB f1 score: 0.214 GB cohens kappa score: 0.192 -> test with 'KNN' KNN tn, fp: 426, 67 KNN fn, tp: 11, 4 KNN f1 score: 0.093 KNN cohens kappa score: 0.047 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 470, 23 LR fn, tp: 8, 7 LR f1 score: 0.311 LR cohens kappa score: 0.283 LR average precision score: 0.179 -> test with 'GB' GB tn, fp: 487, 6 GB fn, tp: 13, 2 GB f1 score: 0.174 GB cohens kappa score: 0.157 -> test with 'KNN' KNN tn, fp: 419, 74 KNN fn, tp: 7, 8 KNN f1 score: 0.165 KNN cohens kappa score: 0.121 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 444, 49 LR fn, tp: 3, 12 LR f1 score: 0.316 LR cohens kappa score: 0.282 LR average precision score: 0.226 -> test with 'GB' GB tn, fp: 487, 6 GB fn, tp: 9, 6 GB f1 score: 0.444 GB cohens kappa score: 0.429 -> test with 'KNN' KNN tn, fp: 438, 55 KNN fn, tp: 9, 6 KNN f1 score: 0.158 KNN cohens kappa score: 0.116 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 442, 49 LR fn, tp: 3, 10 LR f1 score: 0.278 LR cohens kappa score: 0.246 LR average precision score: 0.230 -> test with 'GB' GB tn, fp: 484, 7 GB fn, tp: 9, 4 GB f1 score: 0.333 GB cohens kappa score: 0.317 -> test with 'KNN' KNN tn, fp: 423, 68 KNN fn, tp: 10, 3 KNN f1 score: 0.071 KNN cohens kappa score: 0.029 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 470, 50 LR fn, tp: 8, 13 LR f1 score: 0.419 LR cohens kappa score: 0.392 LR average precision score: 0.404 average: LR tn, fp: 452.16, 40.44 LR fn, tp: 4.68, 9.92 LR f1 score: 0.307 LR cohens kappa score: 0.275 LR average precision score: 0.233 minimum: LR tn, fp: 442, 23 LR fn, tp: 2, 7 LR f1 score: 0.222 LR cohens kappa score: 0.184 LR average precision score: 0.144 -----[ GB ]----- maximum: GB tn, fp: 491, 11 GB fn, tp: 14, 9 GB f1 score: 0.581 GB cohens kappa score: 0.567 average: GB tn, fp: 485.88, 6.72 GB fn, tp: 10.96, 3.64 GB f1 score: 0.289 GB cohens kappa score: 0.272 minimum: GB tn, fp: 482, 2 GB fn, tp: 6, 1 GB f1 score: 0.083 GB cohens kappa score: 0.063 -----[ KNN ]----- maximum: KNN tn, fp: 449, 82 KNN fn, tp: 12, 9 KNN f1 score: 0.212 KNN cohens kappa score: 0.174 average: KNN tn, fp: 427.88, 64.72 KNN fn, tp: 9.36, 5.24 KNN f1 score: 0.125 KNN cohens kappa score: 0.081 minimum: KNN tn, fp: 409, 44 KNN fn, tp: 6, 3 KNN f1 score: 0.071 KNN cohens kappa score: 0.023