/////////////////////////////////////////// // Running convGAN-full 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: 181, 24 LR fn, tp: 7, 2 LR f1 score: 0.114 LR cohens kappa score: 0.055 LR average precision score: 0.086 -> test with 'GB' GB tn, fp: 198, 7 GB fn, tp: 8, 1 GB f1 score: 0.118 GB cohens kappa score: 0.081 -> test with 'KNN' KNN tn, fp: 179, 26 KNN fn, tp: 4, 5 KNN f1 score: 0.250 KNN cohens kappa score: 0.198 ------ Step 1/5: Slice 2/5 ------- -> 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: 0.322 -> test with 'GB' GB tn, fp: 202, 3 GB fn, tp: 8, 1 GB f1 score: 0.154 GB cohens kappa score: 0.131 -> test with 'KNN' KNN tn, fp: 170, 35 KNN fn, tp: 2, 7 KNN f1 score: 0.275 KNN cohens kappa score: 0.221 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 180, 25 LR fn, tp: 4, 5 LR f1 score: 0.256 LR cohens kappa score: 0.205 LR average precision score: 0.433 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 175, 30 KNN fn, tp: 4, 5 KNN f1 score: 0.227 KNN cohens kappa score: 0.172 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 186, 19 LR fn, tp: 0, 9 LR f1 score: 0.486 LR cohens kappa score: 0.452 LR average precision score: 0.718 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 7, 2 GB f1 score: 0.364 GB cohens kappa score: 0.354 -> test with 'KNN' KNN tn, fp: 187, 18 KNN fn, tp: 5, 4 KNN f1 score: 0.258 KNN cohens kappa score: 0.211 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 176, 27 LR fn, tp: 3, 4 LR f1 score: 0.211 LR cohens kappa score: 0.165 LR average precision score: 0.267 -> test with 'GB' GB tn, fp: 201, 2 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.015 -> test with 'KNN' KNN tn, fp: 172, 31 KNN fn, tp: 1, 6 KNN f1 score: 0.273 KNN cohens kappa score: 0.230 ====== 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: 176, 29 LR fn, tp: 2, 7 LR f1 score: 0.311 LR cohens kappa score: 0.261 LR average precision score: 0.485 -> test with 'GB' GB tn, fp: 201, 4 GB fn, tp: 8, 1 GB f1 score: 0.143 GB cohens kappa score: 0.116 -> test with 'KNN' KNN tn, fp: 178, 27 KNN fn, tp: 2, 7 KNN f1 score: 0.326 KNN cohens kappa score: 0.278 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 177, 28 LR fn, tp: 5, 4 LR f1 score: 0.195 LR cohens kappa score: 0.139 LR average precision score: 0.405 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 178, 27 KNN fn, tp: 4, 5 KNN f1 score: 0.244 KNN cohens kappa score: 0.191 ------ Step 2/5: Slice 3/5 ------- -> 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: 0.375 -> test with 'GB' GB tn, fp: 202, 3 GB fn, tp: 8, 1 GB f1 score: 0.154 GB cohens kappa score: 0.131 -> test with 'KNN' KNN tn, fp: 182, 23 KNN fn, tp: 5, 4 KNN f1 score: 0.222 KNN cohens kappa score: 0.170 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 190, 15 LR fn, tp: 4, 5 LR f1 score: 0.345 LR cohens kappa score: 0.304 LR average precision score: 0.288 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.149 -> 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 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 174, 29 LR fn, tp: 0, 7 LR f1 score: 0.326 LR cohens kappa score: 0.286 LR average precision score: 0.374 -> test with 'GB' GB tn, fp: 201, 2 GB fn, tp: 6, 1 GB f1 score: 0.200 GB cohens kappa score: 0.184 -> test with 'KNN' KNN tn, fp: 174, 29 KNN fn, tp: 1, 6 KNN f1 score: 0.286 KNN cohens kappa score: 0.244 ====== 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: 188, 17 LR fn, tp: 2, 7 LR f1 score: 0.424 LR cohens kappa score: 0.387 LR average precision score: 0.787 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 189, 16 KNN fn, tp: 3, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.348 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 174, 31 LR fn, tp: 2, 7 LR f1 score: 0.298 LR cohens kappa score: 0.247 LR average precision score: 0.288 -> test with 'GB' GB tn, fp: 194, 11 GB fn, tp: 5, 4 GB f1 score: 0.333 GB cohens kappa score: 0.296 -> 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 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 184, 21 LR fn, tp: 3, 6 LR f1 score: 0.333 LR cohens kappa score: 0.288 LR average precision score: 0.432 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.008 -> test with 'KNN' KNN tn, fp: 172, 33 KNN fn, tp: 2, 7 KNN f1 score: 0.286 KNN cohens kappa score: 0.233 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 188, 17 LR fn, tp: 4, 5 LR f1 score: 0.323 LR cohens kappa score: 0.280 LR average precision score: 0.233 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 184, 21 KNN fn, tp: 3, 6 KNN f1 score: 0.333 KNN cohens kappa score: 0.288 ------ Step 3/5: 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: 1, 6 LR f1 score: 0.250 LR cohens kappa score: 0.205 LR average precision score: 0.269 -> test with 'GB' GB tn, fp: 197, 6 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.032 -> test with 'KNN' KNN tn, fp: 183, 20 KNN fn, tp: 4, 3 KNN f1 score: 0.200 KNN cohens kappa score: 0.157 ====== 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: 178, 27 LR fn, tp: 2, 7 LR f1 score: 0.326 LR cohens kappa score: 0.278 LR average precision score: 0.191 -> test with 'GB' GB tn, fp: 198, 7 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.038 -> test with 'KNN' KNN tn, fp: 179, 26 KNN fn, tp: 3, 6 KNN f1 score: 0.293 KNN cohens kappa score: 0.243 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 184, 21 LR fn, tp: 3, 6 LR f1 score: 0.333 LR cohens kappa score: 0.288 LR average precision score: 0.593 -> test with 'GB' GB tn, fp: 202, 3 GB fn, tp: 8, 1 GB f1 score: 0.154 GB cohens kappa score: 0.131 -> test with 'KNN' KNN tn, fp: 186, 19 KNN fn, tp: 4, 5 KNN f1 score: 0.303 KNN cohens kappa score: 0.258 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 173, 32 LR fn, tp: 4, 5 LR f1 score: 0.217 LR cohens kappa score: 0.161 LR average precision score: 0.273 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 8, 1 GB f1 score: 0.200 GB cohens kappa score: 0.193 -> test with 'KNN' KNN tn, fp: 181, 24 KNN fn, tp: 3, 6 KNN f1 score: 0.308 KNN cohens kappa score: 0.260 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 176, 29 LR fn, tp: 2, 7 LR f1 score: 0.311 LR cohens kappa score: 0.261 LR average precision score: 0.379 -> test with 'GB' GB tn, fp: 201, 4 GB fn, tp: 7, 2 GB f1 score: 0.267 GB cohens kappa score: 0.241 -> test with 'KNN' KNN tn, fp: 179, 26 KNN fn, tp: 2, 7 KNN f1 score: 0.333 KNN cohens kappa score: 0.286 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 177, 26 LR fn, tp: 1, 6 LR f1 score: 0.308 LR cohens kappa score: 0.268 LR average precision score: 0.397 -> test with 'GB' GB tn, fp: 201, 2 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.015 -> test with 'KNN' KNN tn, fp: 168, 35 KNN fn, tp: 3, 4 KNN f1 score: 0.174 KNN cohens kappa score: 0.124 ====== 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: 179, 26 LR fn, tp: 4, 5 LR f1 score: 0.250 LR cohens kappa score: 0.198 LR average precision score: 0.263 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 184, 21 KNN fn, tp: 3, 6 KNN f1 score: 0.333 KNN cohens kappa score: 0.288 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 175, 30 LR fn, tp: 2, 7 LR f1 score: 0.304 LR cohens kappa score: 0.254 LR average precision score: 0.418 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 167, 38 KNN fn, tp: 2, 7 KNN f1 score: 0.259 KNN cohens kappa score: 0.203 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 177, 28 LR fn, tp: 0, 9 LR f1 score: 0.391 LR cohens kappa score: 0.347 LR average precision score: 0.508 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 177, 28 KNN fn, tp: 2, 7 KNN f1 score: 0.318 KNN cohens kappa score: 0.269 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 187, 18 LR fn, tp: 5, 4 LR f1 score: 0.258 LR cohens kappa score: 0.211 LR average precision score: 0.194 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 189, 16 KNN fn, tp: 6, 3 KNN f1 score: 0.214 KNN cohens kappa score: 0.167 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 171, 32 LR fn, tp: 2, 5 LR f1 score: 0.227 LR cohens kappa score: 0.181 LR average precision score: 0.440 -> test with 'GB' GB tn, fp: 196, 7 GB fn, tp: 5, 2 GB f1 score: 0.250 GB cohens kappa score: 0.221 -> test with 'KNN' KNN tn, fp: 178, 25 KNN fn, tp: 3, 4 KNN f1 score: 0.222 KNN cohens kappa score: 0.178 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 190, 42 LR fn, tp: 7, 9 LR f1 score: 0.486 LR cohens kappa score: 0.452 LR average precision score: 0.787 average: LR tn, fp: 178.36, 26.24 LR fn, tp: 2.64, 5.96 LR f1 score: 0.294 LR cohens kappa score: 0.247 LR average precision score: 0.377 minimum: LR tn, fp: 163, 15 LR fn, tp: 0, 2 LR f1 score: 0.114 LR cohens kappa score: 0.055 LR average precision score: 0.086 -----[ GB ]----- maximum: GB tn, fp: 205, 11 GB fn, tp: 9, 4 GB f1 score: 0.364 GB cohens kappa score: 0.354 average: GB tn, fp: 201.6, 3.0 GB fn, tp: 7.72, 0.88 GB f1 score: 0.129 GB cohens kappa score: 0.109 minimum: GB tn, fp: 194, 0 GB fn, tp: 5, 0 GB f1 score: 0.000 GB cohens kappa score: -0.038 -----[ KNN ]----- maximum: KNN tn, fp: 189, 38 KNN fn, tp: 6, 7 KNN f1 score: 0.387 KNN cohens kappa score: 0.348 average: KNN tn, fp: 178.76, 25.84 KNN fn, tp: 3.08, 5.52 KNN f1 score: 0.277 KNN cohens kappa score: 0.229 minimum: KNN tn, fp: 167, 16 KNN fn, tp: 1, 3 KNN f1 score: 0.174 KNN cohens kappa score: 0.124