/////////////////////////////////////////// // 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: 179, 26 LR fn, tp: 6, 3 LR f1 score: 0.158 LR cohens kappa score: 0.100 LR average precision score: 0.079 -> test with 'GB' GB tn, fp: 182, 23 GB fn, tp: 6, 3 GB f1 score: 0.171 GB cohens kappa score: 0.116 -> 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 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: 2, 7 LR f1 score: 0.359 LR cohens kappa score: 0.315 LR average precision score: 0.393 -> test with 'GB' GB tn, fp: 187, 18 GB fn, tp: 3, 6 GB f1 score: 0.364 GB cohens kappa score: 0.322 -> test with 'KNN' KNN tn, fp: 192, 13 KNN fn, tp: 3, 6 KNN f1 score: 0.429 KNN cohens kappa score: 0.394 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 183, 22 LR fn, tp: 2, 7 LR f1 score: 0.368 LR cohens kappa score: 0.325 LR average precision score: 0.295 -> test with 'GB' GB tn, fp: 186, 19 GB fn, tp: 4, 5 GB f1 score: 0.303 GB cohens kappa score: 0.258 -> test with 'KNN' KNN tn, fp: 188, 17 KNN fn, tp: 4, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.280 ------ Step 1/5: 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: 0, 9 LR f1 score: 0.514 LR cohens kappa score: 0.482 LR average precision score: 0.669 -> test with 'GB' GB tn, fp: 191, 14 GB fn, tp: 2, 7 GB f1 score: 0.467 GB cohens kappa score: 0.433 -> test with 'KNN' KNN tn, fp: 194, 11 KNN fn, tp: 5, 4 KNN f1 score: 0.333 KNN cohens kappa score: 0.296 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 181, 22 LR fn, tp: 3, 4 LR f1 score: 0.242 LR cohens kappa score: 0.200 LR average precision score: 0.169 -> test with 'GB' GB tn, fp: 186, 17 GB fn, tp: 3, 4 GB f1 score: 0.286 GB cohens kappa score: 0.248 -> test with 'KNN' KNN tn, fp: 184, 19 KNN fn, tp: 2, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.286 ====== 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: 185, 20 LR fn, tp: 3, 6 LR f1 score: 0.343 LR cohens kappa score: 0.299 LR average precision score: 0.415 -> test with 'GB' GB tn, fp: 191, 14 GB fn, tp: 6, 3 GB f1 score: 0.231 GB cohens kappa score: 0.186 -> test with 'KNN' KNN tn, fp: 190, 15 KNN fn, tp: 5, 4 KNN f1 score: 0.286 KNN cohens kappa score: 0.242 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 185, 20 LR fn, tp: 3, 6 LR f1 score: 0.343 LR cohens kappa score: 0.299 LR average precision score: 0.260 -> test with 'GB' GB tn, fp: 188, 17 GB fn, tp: 3, 6 GB f1 score: 0.375 GB cohens kappa score: 0.335 -> test with 'KNN' KNN tn, fp: 179, 26 KNN fn, tp: 5, 4 KNN f1 score: 0.205 KNN cohens kappa score: 0.150 ------ 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: 0.262 -> 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: 190, 15 KNN fn, tp: 6, 3 KNN f1 score: 0.222 KNN cohens kappa score: 0.176 ------ Step 2/5: 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: 4, 5 LR f1 score: 0.312 LR cohens kappa score: 0.268 LR average precision score: 0.231 -> test with 'GB' GB tn, fp: 189, 16 GB fn, tp: 5, 4 GB f1 score: 0.276 GB cohens kappa score: 0.231 -> test with 'KNN' KNN tn, fp: 192, 13 KNN fn, tp: 5, 4 KNN f1 score: 0.308 KNN cohens kappa score: 0.267 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 183, 20 LR fn, tp: 1, 6 LR f1 score: 0.364 LR cohens kappa score: 0.328 LR average precision score: 0.335 -> test with 'GB' GB tn, fp: 187, 16 GB fn, tp: 2, 5 GB f1 score: 0.357 GB cohens kappa score: 0.323 -> test with 'KNN' KNN tn, fp: 185, 18 KNN fn, tp: 3, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.237 ====== 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: 189, 16 LR fn, tp: 1, 8 LR f1 score: 0.485 LR cohens kappa score: 0.451 LR average precision score: 0.545 -> test with 'GB' GB tn, fp: 191, 14 GB fn, tp: 0, 9 GB f1 score: 0.562 GB cohens kappa score: 0.534 -> test with 'KNN' KNN tn, fp: 193, 12 KNN fn, tp: 3, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.411 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 176, 29 LR fn, tp: 4, 5 LR f1 score: 0.233 LR cohens kappa score: 0.178 LR average precision score: 0.238 -> 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: 177, 28 KNN fn, tp: 4, 5 KNN f1 score: 0.238 KNN cohens kappa score: 0.184 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 188, 17 LR fn, tp: 3, 6 LR f1 score: 0.375 LR cohens kappa score: 0.335 LR average precision score: 0.242 -> test with 'GB' GB tn, fp: 197, 8 GB fn, tp: 4, 5 GB f1 score: 0.455 GB cohens kappa score: 0.426 -> test with 'KNN' KNN tn, fp: 192, 13 KNN fn, tp: 5, 4 KNN f1 score: 0.308 KNN cohens kappa score: 0.267 ------ Step 3/5: 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: 4, 5 LR f1 score: 0.357 LR cohens kappa score: 0.318 LR average precision score: 0.228 -> test with 'GB' GB tn, fp: 194, 11 GB fn, tp: 7, 2 GB f1 score: 0.182 GB cohens kappa score: 0.139 -> test with 'KNN' KNN tn, fp: 194, 11 KNN fn, tp: 5, 4 KNN f1 score: 0.333 KNN cohens kappa score: 0.296 ------ Step 3/5: 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: 0.251 -> test with 'GB' GB tn, fp: 191, 12 GB fn, tp: 3, 4 GB f1 score: 0.348 GB cohens kappa score: 0.316 -> test with 'KNN' KNN tn, fp: 186, 17 KNN fn, tp: 2, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.310 ====== 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: 184, 21 LR fn, tp: 5, 4 LR f1 score: 0.235 LR cohens kappa score: 0.185 LR average precision score: 0.134 -> test with 'GB' GB tn, fp: 187, 18 GB fn, tp: 7, 2 GB f1 score: 0.138 GB cohens kappa score: 0.085 -> test with 'KNN' KNN tn, fp: 182, 23 KNN fn, tp: 6, 3 KNN f1 score: 0.171 KNN cohens kappa score: 0.116 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 179, 26 LR fn, tp: 3, 6 LR f1 score: 0.293 LR cohens kappa score: 0.243 LR average precision score: 0.468 -> test with 'GB' GB tn, fp: 185, 20 GB fn, tp: 2, 7 GB f1 score: 0.389 GB cohens kappa score: 0.348 -> test with 'KNN' KNN tn, fp: 189, 16 KNN fn, tp: 4, 5 KNN f1 score: 0.333 KNN cohens kappa score: 0.292 ------ Step 4/5: 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: 4, 5 LR f1 score: 0.303 LR cohens kappa score: 0.258 LR average precision score: 0.229 -> test with 'GB' GB tn, fp: 191, 14 GB fn, tp: 5, 4 GB f1 score: 0.296 GB cohens kappa score: 0.254 -> test with 'KNN' KNN tn, fp: 189, 16 KNN fn, tp: 5, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.231 ------ Step 4/5: Slice 4/5 ------- -> 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: 0.350 -> test with 'GB' GB tn, fp: 187, 18 GB fn, tp: 2, 7 GB f1 score: 0.412 GB cohens kappa score: 0.373 -> 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 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 186, 17 LR fn, tp: 0, 7 LR f1 score: 0.452 LR cohens kappa score: 0.422 LR average precision score: 0.556 -> test with 'GB' GB tn, fp: 189, 14 GB fn, tp: 2, 5 GB f1 score: 0.385 GB cohens kappa score: 0.353 -> test with 'KNN' KNN tn, fp: 189, 14 KNN fn, tp: 3, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.286 ====== 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: 186, 19 LR fn, tp: 5, 4 LR f1 score: 0.250 LR cohens kappa score: 0.202 LR average precision score: 0.248 -> test with 'GB' GB tn, fp: 190, 15 GB fn, tp: 4, 5 GB f1 score: 0.345 GB cohens kappa score: 0.304 -> 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 5/5: 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: 2, 7 LR f1 score: 0.378 LR cohens kappa score: 0.336 LR average precision score: 0.266 -> test with 'GB' GB tn, fp: 185, 20 GB fn, tp: 3, 6 GB f1 score: 0.343 GB cohens kappa score: 0.299 -> test with 'KNN' KNN tn, fp: 185, 20 KNN fn, tp: 5, 4 KNN f1 score: 0.242 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: 182, 23 LR fn, tp: 0, 9 LR f1 score: 0.439 LR cohens kappa score: 0.400 LR average precision score: 0.390 -> 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: 182, 23 KNN fn, tp: 2, 7 KNN f1 score: 0.359 KNN cohens kappa score: 0.315 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 192, 13 LR fn, tp: 6, 3 LR f1 score: 0.240 LR cohens kappa score: 0.197 LR average precision score: 0.207 -> 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: 7, 2 KNN f1 score: 0.174 KNN cohens kappa score: 0.129 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 179, 24 LR fn, tp: 2, 5 LR f1 score: 0.278 LR cohens kappa score: 0.237 LR average precision score: 0.449 -> test with 'GB' GB tn, fp: 185, 18 GB fn, tp: 2, 5 GB f1 score: 0.333 GB cohens kappa score: 0.297 -> test with 'KNN' KNN tn, fp: 184, 19 KNN fn, tp: 3, 4 KNN f1 score: 0.267 KNN cohens kappa score: 0.227 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 192, 29 LR fn, tp: 6, 9 LR f1 score: 0.514 LR cohens kappa score: 0.482 LR average precision score: 0.669 average: LR tn, fp: 183.84, 20.76 LR fn, tp: 2.76, 5.84 LR f1 score: 0.332 LR cohens kappa score: 0.289 LR average precision score: 0.316 minimum: LR tn, fp: 176, 13 LR fn, tp: 0, 3 LR f1 score: 0.158 LR cohens kappa score: 0.100 LR average precision score: 0.079 -----[ GB ]----- maximum: GB tn, fp: 197, 26 GB fn, tp: 7, 9 GB f1 score: 0.562 GB cohens kappa score: 0.534 average: GB tn, fp: 188.24, 16.36 GB fn, tp: 3.72, 4.88 GB f1 score: 0.326 GB cohens kappa score: 0.285 minimum: GB tn, fp: 179, 8 GB fn, tp: 0, 2 GB f1 score: 0.138 GB cohens kappa score: 0.085 -----[ KNN ]----- maximum: KNN tn, fp: 194, 28 KNN fn, tp: 7, 7 KNN f1 score: 0.444 KNN cohens kappa score: 0.411 average: KNN tn, fp: 187.2, 17.4 KNN fn, tp: 4.2, 4.4 KNN f1 score: 0.293 KNN cohens kappa score: 0.250 minimum: KNN tn, fp: 177, 11 KNN fn, tp: 2, 2 KNN f1 score: 0.171 KNN cohens kappa score: 0.116