/////////////////////////////////////////// // Running Repeater 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: 424, 69 LR fn, tp: 2, 13 LR f1 score: 0.268 LR cohens kappa score: 0.230 LR average precision score: 0.348 -> test with 'GB' GB tn, fp: 480, 13 GB fn, tp: 8, 7 GB f1 score: 0.400 GB cohens kappa score: 0.379 -> test with 'KNN' KNN tn, fp: 434, 59 KNN fn, tp: 11, 4 KNN f1 score: 0.103 KNN cohens kappa score: 0.058 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 431, 62 LR fn, tp: 4, 11 LR f1 score: 0.250 LR cohens kappa score: 0.211 LR average precision score: 0.211 -> test with 'GB' GB tn, fp: 487, 6 GB fn, tp: 7, 8 GB f1 score: 0.552 GB cohens kappa score: 0.539 -> test with 'KNN' KNN tn, fp: 446, 47 KNN fn, tp: 13, 2 KNN f1 score: 0.062 KNN cohens kappa score: 0.018 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 432, 61 LR fn, tp: 4, 11 LR f1 score: 0.253 LR cohens kappa score: 0.214 LR average precision score: 0.124 -> test with 'GB' GB tn, fp: 477, 16 GB fn, tp: 9, 6 GB f1 score: 0.324 GB cohens kappa score: 0.300 -> 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 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 424, 69 LR fn, tp: 5, 10 LR f1 score: 0.213 LR cohens kappa score: 0.172 LR average precision score: 0.213 -> test with 'GB' GB tn, fp: 477, 16 GB fn, tp: 8, 7 GB f1 score: 0.368 GB cohens kappa score: 0.345 -> test with 'KNN' KNN tn, fp: 443, 50 KNN fn, tp: 14, 1 KNN f1 score: 0.030 KNN cohens kappa score: -0.016 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 418, 73 LR fn, tp: 3, 10 LR f1 score: 0.208 LR cohens kappa score: 0.171 LR average precision score: 0.188 -> test with 'GB' GB tn, fp: 480, 11 GB fn, tp: 7, 6 GB f1 score: 0.400 GB cohens kappa score: 0.382 -> test with 'KNN' KNN tn, fp: 430, 61 KNN fn, tp: 9, 4 KNN f1 score: 0.103 KNN cohens kappa score: 0.062 ====== 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: 421, 72 LR fn, tp: 5, 10 LR f1 score: 0.206 LR cohens kappa score: 0.164 LR average precision score: 0.257 -> test with 'GB' GB tn, fp: 481, 12 GB fn, tp: 12, 3 GB f1 score: 0.200 GB cohens kappa score: 0.176 -> test with 'KNN' KNN tn, fp: 431, 62 KNN fn, tp: 12, 3 KNN f1 score: 0.075 KNN cohens kappa score: 0.028 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 440, 53 LR fn, tp: 5, 10 LR f1 score: 0.256 LR cohens kappa score: 0.219 LR average precision score: 0.229 -> test with 'GB' GB tn, fp: 479, 14 GB fn, tp: 6, 9 GB f1 score: 0.474 GB cohens kappa score: 0.454 -> 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 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 427, 66 LR fn, tp: 1, 14 LR f1 score: 0.295 LR cohens kappa score: 0.258 LR average precision score: 0.350 -> test with 'GB' GB tn, fp: 484, 9 GB fn, tp: 9, 6 GB f1 score: 0.400 GB cohens kappa score: 0.382 -> test with 'KNN' KNN tn, fp: 430, 63 KNN fn, tp: 13, 2 KNN f1 score: 0.050 KNN cohens kappa score: 0.002 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 424, 69 LR fn, tp: 5, 10 LR f1 score: 0.213 LR cohens kappa score: 0.172 LR average precision score: 0.151 -> test with 'GB' GB tn, fp: 481, 12 GB fn, tp: 11, 4 GB f1 score: 0.258 GB cohens kappa score: 0.235 -> test with 'KNN' KNN tn, fp: 448, 45 KNN fn, tp: 13, 2 KNN f1 score: 0.065 KNN cohens kappa score: 0.021 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 415, 76 LR fn, tp: 3, 10 LR f1 score: 0.202 LR cohens kappa score: 0.165 LR average precision score: 0.203 -> test with 'GB' GB tn, fp: 481, 10 GB fn, tp: 7, 6 GB f1 score: 0.414 GB cohens kappa score: 0.397 -> test with 'KNN' KNN tn, fp: 431, 60 KNN fn, tp: 7, 6 KNN f1 score: 0.152 KNN cohens kappa score: 0.114 ====== 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: 427, 66 LR fn, tp: 3, 12 LR f1 score: 0.258 LR cohens kappa score: 0.219 LR average precision score: 0.311 -> test with 'GB' GB tn, fp: 478, 15 GB fn, tp: 7, 8 GB f1 score: 0.421 GB cohens kappa score: 0.400 -> test with 'KNN' KNN tn, fp: 439, 54 KNN fn, tp: 13, 2 KNN f1 score: 0.056 KNN cohens kappa score: 0.010 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 424, 69 LR fn, tp: 4, 11 LR f1 score: 0.232 LR cohens kappa score: 0.191 LR average precision score: 0.138 -> test with 'GB' GB tn, fp: 482, 11 GB fn, tp: 5, 10 GB f1 score: 0.556 GB cohens kappa score: 0.540 -> test with 'KNN' KNN tn, fp: 439, 54 KNN fn, tp: 13, 2 KNN f1 score: 0.056 KNN cohens kappa score: 0.010 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 445, 48 LR fn, tp: 4, 11 LR f1 score: 0.297 LR cohens kappa score: 0.263 LR average precision score: 0.177 -> test with 'GB' GB tn, fp: 484, 9 GB fn, tp: 9, 6 GB f1 score: 0.400 GB cohens kappa score: 0.382 -> test with 'KNN' KNN tn, fp: 429, 64 KNN fn, tp: 12, 3 KNN f1 score: 0.073 KNN cohens kappa score: 0.026 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 427, 66 LR fn, tp: 5, 10 LR f1 score: 0.220 LR cohens kappa score: 0.179 LR average precision score: 0.171 -> test with 'GB' GB tn, fp: 482, 11 GB fn, tp: 8, 7 GB f1 score: 0.424 GB cohens kappa score: 0.405 -> test with 'KNN' KNN tn, fp: 436, 57 KNN fn, tp: 13, 2 KNN f1 score: 0.054 KNN cohens kappa score: 0.007 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 418, 73 LR fn, tp: 3, 10 LR f1 score: 0.208 LR cohens kappa score: 0.171 LR average precision score: 0.345 -> test with 'GB' GB tn, fp: 476, 15 GB fn, tp: 8, 5 GB f1 score: 0.303 GB cohens kappa score: 0.281 -> 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 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: 420, 73 LR fn, tp: 3, 12 LR f1 score: 0.240 LR cohens kappa score: 0.200 LR average precision score: 0.276 -> test with 'GB' GB tn, fp: 477, 16 GB fn, tp: 6, 9 GB f1 score: 0.450 GB cohens kappa score: 0.429 -> test with 'KNN' KNN tn, fp: 419, 74 KNN fn, tp: 11, 4 KNN f1 score: 0.086 KNN cohens kappa score: 0.038 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 434, 59 LR fn, tp: 4, 11 LR f1 score: 0.259 LR cohens kappa score: 0.221 LR average precision score: 0.230 -> test with 'GB' GB tn, fp: 483, 10 GB fn, tp: 9, 6 GB f1 score: 0.387 GB cohens kappa score: 0.368 -> test with 'KNN' KNN tn, fp: 444, 49 KNN fn, tp: 11, 4 KNN f1 score: 0.118 KNN cohens kappa score: 0.075 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 436, 57 LR fn, tp: 3, 12 LR f1 score: 0.286 LR cohens kappa score: 0.249 LR average precision score: 0.211 -> test with 'GB' GB tn, fp: 484, 9 GB fn, tp: 6, 9 GB f1 score: 0.545 GB cohens kappa score: 0.530 -> test with 'KNN' KNN tn, fp: 449, 44 KNN fn, tp: 14, 1 KNN f1 score: 0.033 KNN cohens kappa score: -0.011 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 418, 75 LR fn, tp: 3, 12 LR f1 score: 0.235 LR cohens kappa score: 0.195 LR average precision score: 0.277 -> test with 'GB' GB tn, fp: 480, 13 GB fn, tp: 7, 8 GB f1 score: 0.444 GB cohens kappa score: 0.425 -> test with 'KNN' KNN tn, fp: 425, 68 KNN fn, tp: 11, 4 KNN f1 score: 0.092 KNN cohens kappa score: 0.045 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 419, 72 LR fn, tp: 3, 10 LR f1 score: 0.211 LR cohens kappa score: 0.174 LR average precision score: 0.214 -> test with 'GB' GB tn, fp: 480, 11 GB fn, tp: 8, 5 GB f1 score: 0.345 GB cohens kappa score: 0.326 -> test with 'KNN' KNN tn, fp: 426, 65 KNN fn, tp: 12, 1 KNN f1 score: 0.025 KNN cohens kappa score: -0.019 ====== 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: 430, 63 LR fn, tp: 2, 13 LR f1 score: 0.286 LR cohens kappa score: 0.249 LR average precision score: 0.276 -> test with 'GB' GB tn, fp: 480, 13 GB fn, tp: 6, 9 GB f1 score: 0.486 GB cohens kappa score: 0.468 -> test with 'KNN' KNN tn, fp: 421, 72 KNN fn, tp: 12, 3 KNN f1 score: 0.067 KNN cohens kappa score: 0.018 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 418, 75 LR fn, tp: 3, 12 LR f1 score: 0.235 LR cohens kappa score: 0.195 LR average precision score: 0.167 -> test with 'GB' GB tn, fp: 482, 11 GB fn, tp: 9, 6 GB f1 score: 0.375 GB cohens kappa score: 0.355 -> test with 'KNN' KNN tn, fp: 429, 64 KNN fn, tp: 12, 3 KNN f1 score: 0.073 KNN cohens kappa score: 0.026 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 449, 44 LR fn, tp: 5, 10 LR f1 score: 0.290 LR cohens kappa score: 0.255 LR average precision score: 0.187 -> test with 'GB' GB tn, fp: 483, 10 GB fn, tp: 10, 5 GB f1 score: 0.333 GB cohens kappa score: 0.313 -> test with 'KNN' KNN tn, fp: 433, 60 KNN fn, tp: 11, 4 KNN f1 score: 0.101 KNN cohens kappa score: 0.056 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 425, 68 LR fn, tp: 3, 12 LR f1 score: 0.253 LR cohens kappa score: 0.214 LR average precision score: 0.233 -> test with 'GB' GB tn, fp: 479, 14 GB fn, tp: 8, 7 GB f1 score: 0.389 GB cohens kappa score: 0.367 -> test with 'KNN' KNN tn, fp: 447, 46 KNN fn, tp: 11, 4 KNN f1 score: 0.123 KNN cohens kappa score: 0.081 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 410, 81 LR fn, tp: 2, 11 LR f1 score: 0.210 LR cohens kappa score: 0.172 LR average precision score: 0.261 -> test with 'GB' GB tn, fp: 482, 9 GB fn, tp: 8, 5 GB f1 score: 0.370 GB cohens kappa score: 0.353 -> test with 'KNN' KNN tn, fp: 421, 70 KNN fn, tp: 10, 3 KNN f1 score: 0.070 KNN cohens kappa score: 0.027 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 449, 81 LR fn, tp: 5, 14 LR f1 score: 0.297 LR cohens kappa score: 0.263 LR average precision score: 0.350 average: LR tn, fp: 426.24, 66.36 LR fn, tp: 3.48, 11.12 LR f1 score: 0.243 LR cohens kappa score: 0.205 LR average precision score: 0.230 minimum: LR tn, fp: 410, 44 LR fn, tp: 1, 10 LR f1 score: 0.202 LR cohens kappa score: 0.164 LR average precision score: 0.124 -----[ GB ]----- maximum: GB tn, fp: 487, 16 GB fn, tp: 12, 10 GB f1 score: 0.556 GB cohens kappa score: 0.540 average: GB tn, fp: 480.76, 11.84 GB fn, tp: 7.92, 6.68 GB f1 score: 0.401 GB cohens kappa score: 0.381 minimum: GB tn, fp: 476, 6 GB fn, tp: 5, 3 GB f1 score: 0.200 GB cohens kappa score: 0.176 -----[ KNN ]----- maximum: KNN tn, fp: 449, 74 KNN fn, tp: 14, 6 KNN f1 score: 0.154 KNN cohens kappa score: 0.114 average: KNN tn, fp: 433.48, 59.12 KNN fn, tp: 11.48, 3.12 KNN f1 score: 0.080 KNN cohens kappa score: 0.036 minimum: KNN tn, fp: 419, 44 KNN fn, tp: 7, 1 KNN f1 score: 0.025 KNN cohens kappa score: -0.019