/////////////////////////////////////////// // Running Repeater on folding_flare-F /////////////////////////////////////////// Load 'data_input/folding_flare-F' from pickle file non empty cut in data_input/folding_flare-F! (23 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 784 synthetic samples -> test with 'LR' LR tn, fp: 165, 40 LR fn, tp: 5, 4 LR f1 score: 0.151 LR cohens kappa score: 0.087 LR average precision score: 0.070 -> test with 'GB' GB tn, fp: 177, 28 GB fn, tp: 6, 3 GB f1 score: 0.150 GB cohens kappa score: 0.091 -> test with 'KNN' KNN tn, fp: 168, 37 KNN fn, tp: 4, 5 KNN f1 score: 0.196 KNN cohens kappa score: 0.136 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 153, 52 LR fn, tp: 0, 9 LR f1 score: 0.257 LR cohens kappa score: 0.198 LR average precision score: 0.371 -> test with 'GB' GB tn, fp: 175, 30 GB fn, tp: 3, 6 GB f1 score: 0.267 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 161, 44 KNN fn, tp: 1, 8 KNN f1 score: 0.262 KNN cohens kappa score: 0.205 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 167, 38 LR fn, tp: 2, 7 LR f1 score: 0.259 LR cohens kappa score: 0.203 LR average precision score: 0.397 -> test with 'GB' GB tn, fp: 184, 21 GB fn, tp: 3, 6 GB f1 score: 0.333 GB cohens kappa score: 0.288 -> test with 'KNN' KNN tn, fp: 178, 27 KNN fn, tp: 5, 4 KNN f1 score: 0.200 KNN cohens kappa score: 0.144 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 179, 26 LR fn, tp: 0, 9 LR f1 score: 0.409 LR cohens kappa score: 0.367 LR average precision score: 0.768 -> test with 'GB' GB tn, fp: 181, 24 GB fn, tp: 2, 7 GB f1 score: 0.350 GB cohens kappa score: 0.305 -> test with 'KNN' KNN tn, fp: 175, 30 KNN fn, tp: 2, 7 KNN f1 score: 0.304 KNN cohens kappa score: 0.254 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 168, 35 LR fn, tp: 2, 5 LR f1 score: 0.213 LR cohens kappa score: 0.165 LR average precision score: 0.222 -> test with 'GB' GB tn, fp: 175, 28 GB fn, tp: 1, 6 GB f1 score: 0.293 GB cohens kappa score: 0.251 -> test with 'KNN' KNN tn, fp: 170, 33 KNN fn, tp: 2, 5 KNN f1 score: 0.222 KNN cohens kappa score: 0.176 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 166, 39 LR fn, tp: 1, 8 LR f1 score: 0.286 LR cohens kappa score: 0.231 LR average precision score: 0.401 -> test with 'GB' GB tn, fp: 179, 26 GB fn, tp: 2, 7 GB f1 score: 0.333 GB cohens kappa score: 0.286 -> test with 'KNN' KNN tn, fp: 169, 36 KNN fn, tp: 2, 7 KNN f1 score: 0.269 KNN cohens kappa score: 0.215 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 166, 39 LR fn, tp: 2, 7 LR f1 score: 0.255 LR cohens kappa score: 0.198 LR average precision score: 0.368 -> test with 'GB' GB tn, fp: 175, 30 GB fn, tp: 1, 8 GB f1 score: 0.340 GB cohens kappa score: 0.292 -> test with 'KNN' KNN tn, fp: 169, 36 KNN fn, tp: 2, 7 KNN f1 score: 0.269 KNN cohens kappa score: 0.215 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 165, 40 LR fn, tp: 1, 8 LR f1 score: 0.281 LR cohens kappa score: 0.226 LR average precision score: 0.322 -> test with 'GB' GB tn, fp: 174, 31 GB fn, tp: 4, 5 GB f1 score: 0.222 GB cohens kappa score: 0.166 -> test with 'KNN' KNN tn, fp: 168, 37 KNN fn, tp: 3, 6 KNN f1 score: 0.231 KNN cohens kappa score: 0.173 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 177, 28 LR fn, tp: 3, 6 LR f1 score: 0.279 LR cohens kappa score: 0.228 LR average precision score: 0.310 -> test with 'GB' GB tn, fp: 187, 18 GB fn, tp: 4, 5 GB f1 score: 0.312 GB cohens kappa score: 0.268 -> test with 'KNN' KNN tn, fp: 172, 33 KNN fn, tp: 3, 6 KNN f1 score: 0.250 KNN cohens kappa score: 0.195 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 162, 41 LR fn, tp: 0, 7 LR f1 score: 0.255 LR cohens kappa score: 0.208 LR average precision score: 0.416 -> test with 'GB' GB tn, fp: 173, 30 GB fn, tp: 3, 4 GB f1 score: 0.195 GB cohens kappa score: 0.148 -> test with 'KNN' KNN tn, fp: 164, 39 KNN fn, tp: 0, 7 KNN f1 score: 0.264 KNN cohens kappa score: 0.219 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 180, 25 LR fn, tp: 0, 9 LR f1 score: 0.419 LR cohens kappa score: 0.377 LR average precision score: 0.779 -> test with 'GB' GB tn, fp: 191, 14 GB fn, tp: 3, 6 GB f1 score: 0.414 GB cohens kappa score: 0.378 -> test with 'KNN' KNN tn, fp: 186, 19 KNN fn, tp: 3, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.310 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 159, 46 LR fn, tp: 2, 7 LR f1 score: 0.226 LR cohens kappa score: 0.166 LR average precision score: 0.243 -> test with 'GB' GB tn, fp: 173, 32 GB fn, tp: 3, 6 GB f1 score: 0.255 GB cohens kappa score: 0.201 -> test with 'KNN' KNN tn, fp: 156, 49 KNN fn, tp: 2, 7 KNN f1 score: 0.215 KNN cohens kappa score: 0.154 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 162, 43 LR fn, tp: 2, 7 LR f1 score: 0.237 LR cohens kappa score: 0.179 LR average precision score: 0.353 -> test with 'GB' GB tn, fp: 180, 25 GB fn, tp: 2, 7 GB f1 score: 0.341 GB cohens kappa score: 0.295 -> test with 'KNN' KNN tn, fp: 157, 48 KNN fn, tp: 1, 8 KNN f1 score: 0.246 KNN cohens kappa score: 0.187 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 172, 33 LR fn, tp: 3, 6 LR f1 score: 0.250 LR cohens kappa score: 0.195 LR average precision score: 0.294 -> test with 'GB' GB tn, fp: 184, 21 GB fn, tp: 3, 6 GB f1 score: 0.333 GB cohens kappa score: 0.288 -> test with 'KNN' KNN tn, fp: 173, 32 KNN fn, tp: 3, 6 KNN f1 score: 0.255 KNN cohens kappa score: 0.201 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 159, 44 LR fn, tp: 1, 6 LR f1 score: 0.211 LR cohens kappa score: 0.161 LR average precision score: 0.224 -> test with 'GB' GB tn, fp: 169, 34 GB fn, tp: 4, 3 GB f1 score: 0.136 GB cohens kappa score: 0.085 -> test with 'KNN' KNN tn, fp: 166, 37 KNN fn, tp: 4, 3 KNN f1 score: 0.128 KNN cohens kappa score: 0.075 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 163, 42 LR fn, tp: 1, 8 LR f1 score: 0.271 LR cohens kappa score: 0.215 LR average precision score: 0.186 -> test with 'GB' GB tn, fp: 176, 29 GB fn, tp: 3, 6 GB f1 score: 0.273 GB cohens kappa score: 0.221 -> test with 'KNN' KNN tn, fp: 165, 40 KNN fn, tp: 2, 7 KNN f1 score: 0.250 KNN cohens kappa score: 0.193 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 172, 33 LR fn, tp: 2, 7 LR f1 score: 0.286 LR cohens kappa score: 0.233 LR average precision score: 0.537 -> test with 'GB' GB tn, fp: 188, 17 GB fn, tp: 4, 5 GB f1 score: 0.323 GB cohens kappa score: 0.280 -> test with 'KNN' KNN tn, fp: 180, 25 KNN fn, tp: 2, 7 KNN f1 score: 0.341 KNN cohens kappa score: 0.295 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 165, 40 LR fn, tp: 3, 6 LR f1 score: 0.218 LR cohens kappa score: 0.159 LR average precision score: 0.244 -> test with 'GB' GB tn, fp: 168, 37 GB fn, tp: 4, 5 GB f1 score: 0.196 GB cohens kappa score: 0.136 -> test with 'KNN' KNN tn, fp: 159, 46 KNN fn, tp: 4, 5 KNN f1 score: 0.167 KNN cohens kappa score: 0.102 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 168, 37 LR fn, tp: 0, 9 LR f1 score: 0.327 LR cohens kappa score: 0.276 LR average precision score: 0.387 -> test with 'GB' GB tn, fp: 177, 28 GB fn, tp: 3, 6 GB f1 score: 0.279 GB cohens kappa score: 0.228 -> test with 'KNN' KNN tn, fp: 173, 32 KNN fn, tp: 2, 7 KNN f1 score: 0.292 KNN cohens kappa score: 0.240 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 165, 38 LR fn, tp: 0, 7 LR f1 score: 0.269 LR cohens kappa score: 0.224 LR average precision score: 0.592 -> test with 'GB' GB tn, fp: 179, 24 GB fn, tp: 2, 5 GB f1 score: 0.278 GB cohens kappa score: 0.237 -> test with 'KNN' KNN tn, fp: 164, 39 KNN fn, tp: 2, 5 KNN f1 score: 0.196 KNN cohens kappa score: 0.147 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 168, 37 LR fn, tp: 2, 7 LR f1 score: 0.264 LR cohens kappa score: 0.209 LR average precision score: 0.200 -> test with 'GB' GB tn, fp: 180, 25 GB fn, tp: 3, 6 GB f1 score: 0.300 GB cohens kappa score: 0.251 -> test with 'KNN' KNN tn, fp: 172, 33 KNN fn, tp: 3, 6 KNN f1 score: 0.250 KNN cohens kappa score: 0.195 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 158, 47 LR fn, tp: 1, 8 LR f1 score: 0.250 LR cohens kappa score: 0.192 LR average precision score: 0.418 -> test with 'GB' GB tn, fp: 180, 25 GB fn, tp: 3, 6 GB f1 score: 0.300 GB cohens kappa score: 0.251 -> test with 'KNN' KNN tn, fp: 165, 40 KNN fn, tp: 2, 7 KNN f1 score: 0.250 KNN cohens kappa score: 0.193 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 164, 41 LR fn, tp: 0, 9 LR f1 score: 0.305 LR cohens kappa score: 0.252 LR average precision score: 0.500 -> test with 'GB' GB tn, fp: 181, 24 GB fn, tp: 3, 6 GB f1 score: 0.308 GB cohens kappa score: 0.260 -> test with 'KNN' KNN tn, fp: 166, 39 KNN fn, tp: 1, 8 KNN f1 score: 0.286 KNN cohens kappa score: 0.231 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 175, 30 LR fn, tp: 4, 5 LR f1 score: 0.227 LR cohens kappa score: 0.172 LR average precision score: 0.187 -> test with 'GB' GB tn, fp: 183, 22 GB fn, tp: 3, 6 GB f1 score: 0.324 GB cohens kappa score: 0.278 -> test with 'KNN' KNN tn, fp: 182, 23 KNN fn, tp: 4, 5 KNN f1 score: 0.270 KNN cohens kappa score: 0.221 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 160, 43 LR fn, tp: 2, 5 LR f1 score: 0.182 LR cohens kappa score: 0.131 LR average precision score: 0.430 -> test with 'GB' GB tn, fp: 171, 32 GB fn, tp: 2, 5 GB f1 score: 0.227 GB cohens kappa score: 0.181 -> test with 'KNN' KNN tn, fp: 159, 44 KNN fn, tp: 3, 4 KNN f1 score: 0.145 KNN cohens kappa score: 0.093 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 180, 52 LR fn, tp: 5, 9 LR f1 score: 0.419 LR cohens kappa score: 0.377 LR average precision score: 0.779 average: LR tn, fp: 166.32, 38.28 LR fn, tp: 1.56, 7.04 LR f1 score: 0.263 LR cohens kappa score: 0.210 LR average precision score: 0.369 minimum: LR tn, fp: 153, 25 LR fn, tp: 0, 4 LR f1 score: 0.151 LR cohens kappa score: 0.087 LR average precision score: 0.070 -----[ GB ]----- maximum: GB tn, fp: 191, 37 GB fn, tp: 6, 8 GB f1 score: 0.414 GB cohens kappa score: 0.378 average: GB tn, fp: 178.4, 26.2 GB fn, tp: 2.96, 5.64 GB f1 score: 0.283 GB cohens kappa score: 0.235 minimum: GB tn, fp: 168, 14 GB fn, tp: 1, 3 GB f1 score: 0.136 GB cohens kappa score: 0.085 -----[ KNN ]----- maximum: KNN tn, fp: 186, 49 KNN fn, tp: 5, 8 KNN f1 score: 0.353 KNN cohens kappa score: 0.310 average: KNN tn, fp: 168.68, 35.92 KNN fn, tp: 2.48, 6.12 KNN f1 score: 0.245 KNN cohens kappa score: 0.191 minimum: KNN tn, fp: 156, 19 KNN fn, tp: 0, 3 KNN f1 score: 0.128 KNN cohens kappa score: 0.075