/////////////////////////////////////////// // Running ctGAN on folding_car-vgood /////////////////////////////////////////// Load 'data_input/folding_car-vgood' from pickle file 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 1278 synthetic samples -> test with 'LR' LR tn, fp: 284, 49 LR fn, tp: 0, 13 LR f1 score: 0.347 LR cohens kappa score: 0.303 LR average precision score: 0.335 -> test with 'GB' GB tn, fp: 330, 3 GB fn, tp: 0, 13 GB f1 score: 0.897 GB cohens kappa score: 0.892 -> test with 'KNN' KNN tn, fp: 278, 55 KNN fn, tp: 0, 13 KNN f1 score: 0.321 KNN cohens kappa score: 0.275 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 272, 61 LR fn, tp: 1, 12 LR f1 score: 0.279 LR cohens kappa score: 0.230 LR average precision score: 0.281 -> test with 'GB' GB tn, fp: 326, 7 GB fn, tp: 0, 13 GB f1 score: 0.788 GB cohens kappa score: 0.778 -> test with 'KNN' KNN tn, fp: 275, 58 KNN fn, tp: 0, 13 KNN f1 score: 0.310 KNN cohens kappa score: 0.263 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 263, 70 LR fn, tp: 0, 13 LR f1 score: 0.271 LR cohens kappa score: 0.220 LR average precision score: 0.359 -> test with 'GB' GB tn, fp: 325, 8 GB fn, tp: 0, 13 GB f1 score: 0.765 GB cohens kappa score: 0.753 -> test with 'KNN' KNN tn, fp: 263, 70 KNN fn, tp: 0, 13 KNN f1 score: 0.271 KNN cohens kappa score: 0.220 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 280, 53 LR fn, tp: 0, 13 LR f1 score: 0.329 LR cohens kappa score: 0.284 LR average precision score: 0.412 -> test with 'GB' GB tn, fp: 327, 6 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 279, 54 KNN fn, tp: 0, 13 KNN f1 score: 0.325 KNN cohens kappa score: 0.280 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 281, 50 LR fn, tp: 0, 13 LR f1 score: 0.342 LR cohens kappa score: 0.298 LR average precision score: 0.410 -> test with 'GB' GB tn, fp: 324, 7 GB fn, tp: 0, 13 GB f1 score: 0.788 GB cohens kappa score: 0.778 -> test with 'KNN' KNN tn, fp: 277, 54 KNN fn, tp: 0, 13 KNN f1 score: 0.325 KNN cohens kappa score: 0.279 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 273, 60 LR fn, tp: 0, 13 LR f1 score: 0.302 LR cohens kappa score: 0.255 LR average precision score: 0.274 -> test with 'GB' GB tn, fp: 325, 8 GB fn, tp: 0, 13 GB f1 score: 0.765 GB cohens kappa score: 0.753 -> test with 'KNN' KNN tn, fp: 272, 61 KNN fn, tp: 0, 13 KNN f1 score: 0.299 KNN cohens kappa score: 0.251 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 264, 69 LR fn, tp: 0, 13 LR f1 score: 0.274 LR cohens kappa score: 0.223 LR average precision score: 0.309 -> test with 'GB' GB tn, fp: 322, 11 GB fn, tp: 0, 13 GB f1 score: 0.703 GB cohens kappa score: 0.687 -> test with 'KNN' KNN tn, fp: 266, 67 KNN fn, tp: 0, 13 KNN f1 score: 0.280 KNN cohens kappa score: 0.230 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 285, 48 LR fn, tp: 0, 13 LR f1 score: 0.351 LR cohens kappa score: 0.309 LR average precision score: 0.352 -> test with 'GB' GB tn, fp: 328, 5 GB fn, tp: 0, 13 GB f1 score: 0.839 GB cohens kappa score: 0.831 -> test with 'KNN' KNN tn, fp: 271, 62 KNN fn, tp: 0, 13 KNN f1 score: 0.295 KNN cohens kappa score: 0.247 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 286, 47 LR fn, tp: 0, 13 LR f1 score: 0.356 LR cohens kappa score: 0.314 LR average precision score: 0.345 -> test with 'GB' GB tn, fp: 328, 5 GB fn, tp: 0, 13 GB f1 score: 0.839 GB cohens kappa score: 0.831 -> test with 'KNN' KNN tn, fp: 275, 58 KNN fn, tp: 0, 13 KNN f1 score: 0.310 KNN cohens kappa score: 0.263 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 274, 57 LR fn, tp: 0, 13 LR f1 score: 0.313 LR cohens kappa score: 0.266 LR average precision score: 0.552 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 281, 50 KNN fn, tp: 0, 13 KNN f1 score: 0.342 KNN cohens kappa score: 0.298 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 285, 48 LR fn, tp: 0, 13 LR f1 score: 0.351 LR cohens kappa score: 0.309 LR average precision score: 0.285 -> test with 'GB' GB tn, fp: 328, 5 GB fn, tp: 0, 13 GB f1 score: 0.839 GB cohens kappa score: 0.831 -> test with 'KNN' KNN tn, fp: 270, 63 KNN fn, tp: 0, 13 KNN f1 score: 0.292 KNN cohens kappa score: 0.244 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 285, 48 LR fn, tp: 0, 13 LR f1 score: 0.351 LR cohens kappa score: 0.309 LR average precision score: 0.426 -> test with 'GB' GB tn, fp: 328, 5 GB fn, tp: 0, 13 GB f1 score: 0.839 GB cohens kappa score: 0.831 -> test with 'KNN' KNN tn, fp: 270, 63 KNN fn, tp: 0, 13 KNN f1 score: 0.292 KNN cohens kappa score: 0.244 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 271, 62 LR fn, tp: 0, 13 LR f1 score: 0.295 LR cohens kappa score: 0.247 LR average precision score: 0.330 -> test with 'GB' GB tn, fp: 321, 12 GB fn, tp: 0, 13 GB f1 score: 0.684 GB cohens kappa score: 0.668 -> test with 'KNN' KNN tn, fp: 273, 60 KNN fn, tp: 0, 13 KNN f1 score: 0.302 KNN cohens kappa score: 0.255 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 280, 53 LR fn, tp: 0, 13 LR f1 score: 0.329 LR cohens kappa score: 0.284 LR average precision score: 0.450 -> test with 'GB' GB tn, fp: 328, 5 GB fn, tp: 0, 13 GB f1 score: 0.839 GB cohens kappa score: 0.831 -> test with 'KNN' KNN tn, fp: 271, 62 KNN fn, tp: 0, 13 KNN f1 score: 0.295 KNN cohens kappa score: 0.247 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 274, 57 LR fn, tp: 0, 13 LR f1 score: 0.313 LR cohens kappa score: 0.266 LR average precision score: 0.384 -> test with 'GB' GB tn, fp: 327, 4 GB fn, tp: 0, 13 GB f1 score: 0.867 GB cohens kappa score: 0.861 -> test with 'KNN' KNN tn, fp: 282, 49 KNN fn, tp: 0, 13 KNN f1 score: 0.347 KNN cohens kappa score: 0.303 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 282, 51 LR fn, tp: 0, 13 LR f1 score: 0.338 LR cohens kappa score: 0.294 LR average precision score: 0.431 -> test with 'GB' GB tn, fp: 327, 6 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 277, 56 KNN fn, tp: 0, 13 KNN f1 score: 0.317 KNN cohens kappa score: 0.271 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 275, 58 LR fn, tp: 0, 13 LR f1 score: 0.310 LR cohens kappa score: 0.263 LR average precision score: 0.477 -> test with 'GB' GB tn, fp: 328, 5 GB fn, tp: 0, 13 GB f1 score: 0.839 GB cohens kappa score: 0.831 -> test with 'KNN' KNN tn, fp: 274, 59 KNN fn, tp: 0, 13 KNN f1 score: 0.306 KNN cohens kappa score: 0.259 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 275, 58 LR fn, tp: 0, 13 LR f1 score: 0.310 LR cohens kappa score: 0.263 LR average precision score: 0.287 -> test with 'GB' GB tn, fp: 327, 6 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 276, 57 KNN fn, tp: 0, 13 KNN f1 score: 0.313 KNN cohens kappa score: 0.267 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 285, 48 LR fn, tp: 0, 13 LR f1 score: 0.351 LR cohens kappa score: 0.309 LR average precision score: 0.276 -> test with 'GB' GB tn, fp: 327, 6 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 275, 58 KNN fn, tp: 0, 13 KNN f1 score: 0.310 KNN cohens kappa score: 0.263 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 274, 57 LR fn, tp: 0, 13 LR f1 score: 0.313 LR cohens kappa score: 0.266 LR average precision score: 0.348 -> test with 'GB' GB tn, fp: 323, 8 GB fn, tp: 0, 13 GB f1 score: 0.765 GB cohens kappa score: 0.753 -> test with 'KNN' KNN tn, fp: 265, 66 KNN fn, tp: 0, 13 KNN f1 score: 0.283 KNN cohens kappa score: 0.233 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 265, 68 LR fn, tp: 0, 13 LR f1 score: 0.277 LR cohens kappa score: 0.227 LR average precision score: 0.337 -> test with 'GB' GB tn, fp: 324, 9 GB fn, tp: 0, 13 GB f1 score: 0.743 GB cohens kappa score: 0.730 -> test with 'KNN' KNN tn, fp: 268, 65 KNN fn, tp: 0, 13 KNN f1 score: 0.286 KNN cohens kappa score: 0.237 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 284, 49 LR fn, tp: 0, 13 LR f1 score: 0.347 LR cohens kappa score: 0.303 LR average precision score: 0.364 -> test with 'GB' GB tn, fp: 330, 3 GB fn, tp: 0, 13 GB f1 score: 0.897 GB cohens kappa score: 0.892 -> test with 'KNN' KNN tn, fp: 283, 50 KNN fn, tp: 0, 13 KNN f1 score: 0.342 KNN cohens kappa score: 0.298 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 284, 49 LR fn, tp: 0, 13 LR f1 score: 0.347 LR cohens kappa score: 0.303 LR average precision score: 0.361 -> test with 'GB' GB tn, fp: 329, 4 GB fn, tp: 0, 13 GB f1 score: 0.867 GB cohens kappa score: 0.861 -> test with 'KNN' KNN tn, fp: 270, 63 KNN fn, tp: 0, 13 KNN f1 score: 0.292 KNN cohens kappa score: 0.244 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 275, 58 LR fn, tp: 0, 13 LR f1 score: 0.310 LR cohens kappa score: 0.263 LR average precision score: 0.308 -> test with 'GB' GB tn, fp: 324, 9 GB fn, tp: 0, 13 GB f1 score: 0.743 GB cohens kappa score: 0.730 -> test with 'KNN' KNN tn, fp: 275, 58 KNN fn, tp: 0, 13 KNN f1 score: 0.310 KNN cohens kappa score: 0.263 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 279, 52 LR fn, tp: 0, 13 LR f1 score: 0.333 LR cohens kappa score: 0.289 LR average precision score: 0.498 -> test with 'GB' GB tn, fp: 325, 6 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 277, 54 KNN fn, tp: 0, 13 KNN f1 score: 0.325 KNN cohens kappa score: 0.279 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 286, 70 LR fn, tp: 1, 13 LR f1 score: 0.356 LR cohens kappa score: 0.314 LR average precision score: 0.552 average: LR tn, fp: 277.4, 55.2 LR fn, tp: 0.04, 12.96 LR f1 score: 0.322 LR cohens kappa score: 0.276 LR average precision score: 0.368 minimum: LR tn, fp: 263, 47 LR fn, tp: 0, 12 LR f1 score: 0.271 LR cohens kappa score: 0.220 LR average precision score: 0.274 -----[ GB ]----- maximum: GB tn, fp: 330, 12 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 average: GB tn, fp: 326.4, 6.2 GB fn, tp: 0.0, 13.0 GB f1 score: 0.812 GB cohens kappa score: 0.803 minimum: GB tn, fp: 321, 2 GB fn, tp: 0, 13 GB f1 score: 0.684 GB cohens kappa score: 0.668 -----[ KNN ]----- maximum: KNN tn, fp: 283, 70 KNN fn, tp: 0, 13 KNN f1 score: 0.347 KNN cohens kappa score: 0.303 average: KNN tn, fp: 273.72, 58.88 KNN fn, tp: 0.0, 13.0 KNN f1 score: 0.308 KNN cohens kappa score: 0.260 minimum: KNN tn, fp: 263, 49 KNN fn, tp: 0, 13 KNN f1 score: 0.271 KNN cohens kappa score: 0.220