/////////////////////////////////////////// // Running ctGAN 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: 176, 29 LR fn, tp: 6, 3 LR f1 score: 0.146 LR cohens kappa score: 0.086 LR average precision score: 0.084 -> test with 'RF' RF tn, fp: 178, 27 RF fn, tp: 6, 3 RF f1 score: 0.154 RF cohens kappa score: 0.095 -> test with 'GB' GB tn, fp: 180, 25 GB fn, tp: 7, 2 GB f1 score: 0.111 GB cohens kappa score: 0.051 -> test with 'KNN' KNN tn, fp: 174, 31 KNN fn, tp: 5, 4 KNN f1 score: 0.182 KNN cohens kappa score: 0.123 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 191, 14 LR fn, tp: 2, 7 LR f1 score: 0.467 LR cohens kappa score: 0.433 LR average precision score: 0.387 -> test with 'RF' RF tn, fp: 186, 19 RF fn, tp: 5, 4 RF f1 score: 0.250 RF cohens kappa score: 0.202 -> test with 'GB' GB tn, fp: 196, 9 GB fn, tp: 3, 6 GB f1 score: 0.500 GB cohens kappa score: 0.472 -> test with 'KNN' KNN tn, fp: 186, 19 KNN fn, tp: 1, 8 KNN f1 score: 0.444 KNN cohens kappa score: 0.407 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 186, 19 LR fn, tp: 2, 7 LR f1 score: 0.400 LR cohens kappa score: 0.360 LR average precision score: 0.316 -> test with 'RF' RF tn, fp: 189, 16 RF fn, tp: 5, 4 RF f1 score: 0.276 RF cohens kappa score: 0.231 -> test with 'GB' GB tn, fp: 188, 17 GB fn, tp: 5, 4 GB f1 score: 0.267 GB cohens kappa score: 0.221 -> test with 'KNN' KNN tn, fp: 182, 23 KNN fn, tp: 2, 7 KNN f1 score: 0.359 KNN cohens kappa score: 0.315 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 191, 14 LR fn, tp: 2, 7 LR f1 score: 0.467 LR cohens kappa score: 0.433 LR average precision score: 0.695 -> test with 'RF' RF tn, fp: 193, 12 RF fn, tp: 5, 4 RF f1 score: 0.320 RF cohens kappa score: 0.281 -> test with 'GB' GB tn, fp: 193, 12 GB fn, tp: 4, 5 GB f1 score: 0.385 GB cohens kappa score: 0.349 -> test with 'KNN' KNN tn, fp: 195, 10 KNN fn, tp: 4, 5 KNN f1 score: 0.417 KNN cohens kappa score: 0.384 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 184, 19 LR fn, tp: 3, 4 LR f1 score: 0.267 LR cohens kappa score: 0.227 LR average precision score: 0.166 -> test with 'RF' RF tn, fp: 181, 22 RF fn, tp: 4, 3 RF f1 score: 0.188 RF cohens kappa score: 0.143 -> test with 'GB' GB tn, fp: 187, 16 GB fn, tp: 3, 4 GB f1 score: 0.296 GB cohens kappa score: 0.260 -> test with 'KNN' KNN tn, fp: 181, 22 KNN fn, tp: 4, 3 KNN f1 score: 0.188 KNN cohens kappa score: 0.143 ====== 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: 186, 19 LR fn, tp: 3, 6 LR f1 score: 0.353 LR cohens kappa score: 0.310 LR average precision score: 0.411 -> test with 'RF' RF tn, fp: 186, 19 RF fn, tp: 5, 4 RF f1 score: 0.250 RF cohens kappa score: 0.202 -> test with 'GB' GB tn, fp: 192, 13 GB fn, tp: 6, 3 GB f1 score: 0.240 GB cohens kappa score: 0.197 -> test with 'KNN' KNN tn, fp: 188, 17 KNN fn, tp: 6, 3 KNN f1 score: 0.207 KNN cohens kappa score: 0.158 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 182, 23 LR fn, tp: 3, 6 LR f1 score: 0.316 LR cohens kappa score: 0.269 LR average precision score: 0.240 -> test with 'RF' RF tn, fp: 185, 20 RF fn, tp: 4, 5 RF f1 score: 0.294 RF cohens kappa score: 0.248 -> 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: 180, 25 KNN fn, tp: 3, 6 KNN f1 score: 0.300 KNN cohens kappa score: 0.251 ------ Step 2/5: 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: 2, 7 LR f1 score: 0.378 LR cohens kappa score: 0.336 LR average precision score: 0.293 -> test with 'RF' RF tn, fp: 188, 17 RF fn, tp: 5, 4 RF f1 score: 0.267 RF cohens kappa score: 0.221 -> test with 'GB' GB tn, fp: 191, 14 GB fn, tp: 4, 5 GB f1 score: 0.357 GB cohens kappa score: 0.318 -> 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 2/5: 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: 5, 4 LR f1 score: 0.250 LR cohens kappa score: 0.202 LR average precision score: 0.262 -> test with 'RF' RF tn, fp: 186, 19 RF fn, tp: 6, 3 RF f1 score: 0.194 RF cohens kappa score: 0.142 -> test with 'GB' GB tn, fp: 187, 18 GB fn, tp: 5, 4 GB f1 score: 0.258 GB cohens kappa score: 0.211 -> test with 'KNN' KNN tn, fp: 185, 20 KNN fn, tp: 4, 5 KNN f1 score: 0.294 KNN cohens kappa score: 0.248 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 178, 25 LR fn, tp: 1, 6 LR f1 score: 0.316 LR cohens kappa score: 0.276 LR average precision score: 0.325 -> test with 'RF' RF tn, fp: 179, 24 RF fn, tp: 3, 4 RF f1 score: 0.229 RF cohens kappa score: 0.185 -> test with 'GB' GB tn, fp: 183, 20 GB fn, tp: 1, 6 GB f1 score: 0.364 GB cohens kappa score: 0.328 -> test with 'KNN' KNN tn, fp: 181, 22 KNN fn, tp: 2, 5 KNN f1 score: 0.294 KNN cohens kappa score: 0.255 ====== 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: 190, 15 LR fn, tp: 1, 8 LR f1 score: 0.500 LR cohens kappa score: 0.468 LR average precision score: 0.510 -> test with 'RF' RF tn, fp: 187, 18 RF fn, tp: 3, 6 RF f1 score: 0.364 RF cohens kappa score: 0.322 -> test with 'GB' GB tn, fp: 190, 15 GB fn, tp: 1, 8 GB f1 score: 0.500 GB cohens kappa score: 0.468 -> test with 'KNN' KNN tn, fp: 193, 12 KNN fn, tp: 2, 7 KNN f1 score: 0.500 KNN cohens kappa score: 0.470 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 171, 34 LR fn, tp: 3, 6 LR f1 score: 0.245 LR cohens kappa score: 0.189 LR average precision score: 0.247 -> test with 'RF' RF tn, fp: 175, 30 RF fn, tp: 6, 3 RF f1 score: 0.143 RF cohens kappa score: 0.082 -> test with 'GB' GB tn, fp: 179, 26 GB fn, tp: 4, 5 GB f1 score: 0.250 GB cohens kappa score: 0.198 -> test with 'KNN' KNN tn, fp: 169, 36 KNN fn, tp: 3, 6 KNN f1 score: 0.235 KNN cohens kappa score: 0.178 ------ 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: 0.239 -> test with 'RF' RF tn, fp: 179, 26 RF fn, tp: 5, 4 RF f1 score: 0.205 RF cohens kappa score: 0.150 -> test with 'GB' GB tn, fp: 192, 13 GB fn, tp: 4, 5 GB f1 score: 0.370 GB cohens kappa score: 0.333 -> 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 3/5: 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: 5, 4 LR f1 score: 0.286 LR cohens kappa score: 0.242 LR average precision score: 0.211 -> test with 'RF' RF tn, fp: 186, 19 RF fn, tp: 4, 5 RF f1 score: 0.303 RF cohens kappa score: 0.258 -> test with 'GB' GB tn, fp: 194, 11 GB fn, tp: 6, 3 GB f1 score: 0.261 GB cohens kappa score: 0.221 -> test with 'KNN' KNN tn, fp: 193, 12 KNN fn, tp: 5, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.281 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 167, 36 LR fn, tp: 2, 5 LR f1 score: 0.208 LR cohens kappa score: 0.161 LR average precision score: 0.263 -> test with 'RF' RF tn, fp: 179, 24 RF fn, tp: 3, 4 RF f1 score: 0.229 RF cohens kappa score: 0.185 -> test with 'GB' GB tn, fp: 188, 15 GB fn, tp: 3, 4 GB f1 score: 0.308 GB cohens kappa score: 0.272 -> test with 'KNN' KNN tn, fp: 179, 24 KNN fn, tp: 4, 3 KNN f1 score: 0.176 KNN cohens kappa score: 0.130 ====== 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: 186, 19 LR fn, tp: 5, 4 LR f1 score: 0.250 LR cohens kappa score: 0.202 LR average precision score: 0.145 -> test with 'RF' RF tn, fp: 187, 18 RF fn, tp: 8, 1 RF f1 score: 0.071 RF cohens kappa score: 0.015 -> test with 'GB' GB tn, fp: 188, 17 GB fn, tp: 7, 2 GB f1 score: 0.143 GB cohens kappa score: 0.091 -> test with 'KNN' KNN tn, fp: 185, 20 KNN fn, tp: 7, 2 KNN f1 score: 0.129 KNN cohens kappa score: 0.074 ------ 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: 0.513 -> test with 'RF' RF tn, fp: 182, 23 RF fn, tp: 4, 5 RF f1 score: 0.270 RF cohens kappa score: 0.221 -> test with 'GB' GB tn, fp: 186, 19 GB fn, tp: 3, 6 GB f1 score: 0.353 GB cohens kappa score: 0.310 -> test with 'KNN' KNN tn, fp: 184, 21 KNN fn, tp: 5, 4 KNN f1 score: 0.235 KNN cohens kappa score: 0.185 ------ Step 4/5: Slice 3/5 ------- -> 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: 0.235 -> test with 'RF' RF tn, fp: 179, 26 RF fn, tp: 4, 5 RF f1 score: 0.250 RF cohens kappa score: 0.198 -> test with 'GB' GB tn, fp: 186, 19 GB fn, tp: 5, 4 GB f1 score: 0.250 GB cohens kappa score: 0.202 -> 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 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 185, 20 LR fn, tp: 2, 7 LR f1 score: 0.389 LR cohens kappa score: 0.348 LR average precision score: 0.340 -> test with 'RF' RF tn, fp: 187, 18 RF fn, tp: 4, 5 RF f1 score: 0.312 RF cohens kappa score: 0.268 -> test with 'GB' GB tn, fp: 189, 16 GB fn, tp: 3, 6 GB f1 score: 0.387 GB cohens kappa score: 0.348 -> test with 'KNN' KNN tn, fp: 188, 17 KNN fn, tp: 2, 7 KNN f1 score: 0.424 KNN cohens kappa score: 0.387 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 184, 19 LR fn, tp: 0, 7 LR f1 score: 0.424 LR cohens kappa score: 0.392 LR average precision score: 0.529 -> test with 'RF' RF tn, fp: 183, 20 RF fn, tp: 0, 7 RF f1 score: 0.412 RF cohens kappa score: 0.379 -> test with 'GB' GB tn, fp: 184, 19 GB fn, tp: 0, 7 GB f1 score: 0.424 GB cohens kappa score: 0.392 -> test with 'KNN' KNN tn, fp: 181, 22 KNN fn, tp: 1, 6 KNN f1 score: 0.343 KNN cohens kappa score: 0.306 ====== 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: 185, 20 LR fn, tp: 5, 4 LR f1 score: 0.242 LR cohens kappa score: 0.193 LR average precision score: 0.244 -> test with 'RF' RF tn, fp: 189, 16 RF fn, tp: 3, 6 RF f1 score: 0.387 RF cohens kappa score: 0.348 -> test with 'GB' GB tn, fp: 189, 16 GB fn, tp: 4, 5 GB f1 score: 0.333 GB cohens kappa score: 0.292 -> test with 'KNN' KNN tn, fp: 185, 20 KNN fn, tp: 4, 5 KNN f1 score: 0.294 KNN cohens kappa score: 0.248 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 180, 25 LR fn, tp: 2, 7 LR f1 score: 0.341 LR cohens kappa score: 0.295 LR average precision score: 0.289 -> test with 'RF' RF tn, fp: 179, 26 RF fn, tp: 5, 4 RF f1 score: 0.205 RF cohens kappa score: 0.150 -> test with 'GB' GB tn, fp: 189, 16 GB fn, tp: 3, 6 GB f1 score: 0.387 GB cohens kappa score: 0.348 -> 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 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 175, 30 LR fn, tp: 0, 9 LR f1 score: 0.375 LR cohens kappa score: 0.329 LR average precision score: 0.387 -> test with 'RF' RF tn, fp: 180, 25 RF fn, tp: 1, 8 RF f1 score: 0.381 RF cohens kappa score: 0.337 -> test with 'GB' GB tn, fp: 183, 22 GB fn, tp: 2, 7 GB f1 score: 0.368 GB cohens kappa score: 0.325 -> test with 'KNN' KNN tn, fp: 174, 31 KNN fn, tp: 1, 8 KNN f1 score: 0.333 KNN cohens kappa score: 0.284 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 191, 14 LR fn, tp: 6, 3 LR f1 score: 0.231 LR cohens kappa score: 0.186 LR average precision score: 0.207 -> test with 'RF' RF tn, fp: 192, 13 RF fn, tp: 5, 4 RF f1 score: 0.308 RF cohens kappa score: 0.267 -> test with 'GB' GB tn, fp: 196, 9 GB fn, tp: 6, 3 GB f1 score: 0.286 GB cohens kappa score: 0.250 -> test with 'KNN' KNN tn, fp: 193, 12 KNN fn, tp: 5, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.281 ------ Step 5/5: 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: 2, 5 LR f1 score: 0.263 LR cohens kappa score: 0.221 LR average precision score: 0.268 -> test with 'RF' RF tn, fp: 178, 25 RF fn, tp: 2, 5 RF f1 score: 0.270 RF cohens kappa score: 0.229 -> test with 'GB' GB tn, fp: 186, 17 GB fn, tp: 2, 5 GB f1 score: 0.345 GB cohens kappa score: 0.310 -> test with 'KNN' KNN tn, fp: 178, 25 KNN fn, tp: 2, 5 KNN f1 score: 0.270 KNN cohens kappa score: 0.229 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 191, 36 LR fn, tp: 6, 9 LR f1 score: 0.500 LR cohens kappa score: 0.468 LR average precision score: 0.695 average: LR tn, fp: 182.88, 21.72 LR fn, tp: 2.88, 5.72 LR f1 score: 0.321 LR cohens kappa score: 0.277 LR average precision score: 0.312 minimum: LR tn, fp: 167, 14 LR fn, tp: 0, 3 LR f1 score: 0.146 LR cohens kappa score: 0.086 LR average precision score: 0.084 -----[ RF ]----- maximum: RF tn, fp: 193, 30 RF fn, tp: 8, 8 RF f1 score: 0.412 RF cohens kappa score: 0.379 average: RF tn, fp: 183.72, 20.88 RF fn, tp: 4.2, 4.4 RF f1 score: 0.261 RF cohens kappa score: 0.214 minimum: RF tn, fp: 175, 12 RF fn, tp: 0, 1 RF f1 score: 0.071 RF cohens kappa score: 0.015 -----[ GB ]----- maximum: GB tn, fp: 196, 26 GB fn, tp: 7, 8 GB f1 score: 0.500 GB cohens kappa score: 0.472 average: GB tn, fp: 188.28, 16.32 GB fn, tp: 3.76, 4.84 GB f1 score: 0.326 GB cohens kappa score: 0.286 minimum: GB tn, fp: 179, 9 GB fn, tp: 0, 2 GB f1 score: 0.111 GB cohens kappa score: 0.051 -----[ KNN ]----- maximum: KNN tn, fp: 195, 36 KNN fn, tp: 7, 8 KNN f1 score: 0.500 KNN cohens kappa score: 0.470 average: KNN tn, fp: 183.04, 21.56 KNN fn, tp: 3.4, 5.2 KNN f1 score: 0.298 KNN cohens kappa score: 0.253 minimum: KNN tn, fp: 169, 10 KNN fn, tp: 1, 2 KNN f1 score: 0.129 KNN cohens kappa score: 0.074