/////////////////////////////////////////// // 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 'RF' RF tn, fp: 330, 3 RF fn, tp: 0, 13 RF f1 score: 0.897 RF cohens kappa score: 0.892 -> 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: 273, 60 KNN fn, tp: 0, 13 KNN f1 score: 0.302 KNN cohens kappa score: 0.255 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 269, 64 LR fn, tp: 0, 13 LR f1 score: 0.289 LR cohens kappa score: 0.240 LR average precision score: 0.276 -> test with 'RF' RF tn, fp: 326, 7 RF fn, tp: 0, 13 RF f1 score: 0.788 RF cohens kappa score: 0.778 -> 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: 268, 65 KNN fn, tp: 0, 13 KNN f1 score: 0.286 KNN cohens kappa score: 0.237 ------ Step 1/5: Slice 3/5 ------- -> 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: 0.389 -> test with 'RF' RF tn, fp: 325, 8 RF fn, tp: 0, 13 RF f1 score: 0.765 RF cohens kappa score: 0.753 -> 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: 264, 69 KNN fn, tp: 0, 13 KNN f1 score: 0.274 KNN cohens kappa score: 0.223 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 277, 56 LR fn, tp: 0, 13 LR f1 score: 0.317 LR cohens kappa score: 0.271 LR average precision score: 0.431 -> test with 'RF' RF tn, fp: 327, 6 RF fn, tp: 0, 13 RF f1 score: 0.813 RF cohens kappa score: 0.804 -> 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: 274, 59 KNN fn, tp: 0, 13 KNN f1 score: 0.306 KNN cohens kappa score: 0.259 ------ Step 1/5: 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: 0.414 -> test with 'RF' RF tn, fp: 324, 7 RF fn, tp: 0, 13 RF f1 score: 0.788 RF cohens kappa score: 0.778 -> 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: 281, 50 KNN fn, tp: 0, 13 KNN f1 score: 0.342 KNN cohens kappa score: 0.298 ====== 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: 286, 47 LR fn, tp: 0, 13 LR f1 score: 0.356 LR cohens kappa score: 0.314 LR average precision score: 0.274 -> test with 'RF' RF tn, fp: 325, 8 RF fn, tp: 0, 13 RF f1 score: 0.765 RF cohens kappa score: 0.753 -> 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: 276, 57 KNN fn, tp: 0, 13 KNN f1 score: 0.313 KNN cohens kappa score: 0.267 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 267, 66 LR fn, tp: 0, 13 LR f1 score: 0.283 LR cohens kappa score: 0.233 LR average precision score: 0.310 -> test with 'RF' RF tn, fp: 322, 11 RF fn, tp: 0, 13 RF f1 score: 0.703 RF cohens kappa score: 0.687 -> 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: 261, 72 KNN fn, tp: 0, 13 KNN f1 score: 0.265 KNN cohens kappa score: 0.214 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 287, 46 LR fn, tp: 0, 13 LR f1 score: 0.361 LR cohens kappa score: 0.319 LR average precision score: 0.334 -> test with 'RF' RF tn, fp: 328, 5 RF fn, tp: 0, 13 RF f1 score: 0.839 RF cohens kappa score: 0.831 -> 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: 269, 64 KNN fn, tp: 0, 13 KNN f1 score: 0.289 KNN cohens kappa score: 0.240 ------ 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: 0.347 -> test with 'RF' RF tn, fp: 328, 5 RF fn, tp: 0, 13 RF f1 score: 0.839 RF cohens kappa score: 0.831 -> 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 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 278, 53 LR fn, tp: 0, 13 LR f1 score: 0.329 LR cohens kappa score: 0.284 LR average precision score: 0.527 -> test with 'RF' RF tn, fp: 329, 2 RF fn, tp: 0, 13 RF f1 score: 0.929 RF cohens kappa score: 0.926 -> 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: 274, 57 KNN fn, tp: 0, 13 KNN f1 score: 0.313 KNN cohens kappa score: 0.266 ====== 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: 287, 46 LR fn, tp: 0, 13 LR f1 score: 0.361 LR cohens kappa score: 0.319 LR average precision score: 0.294 -> test with 'RF' RF tn, fp: 328, 5 RF fn, tp: 0, 13 RF f1 score: 0.839 RF cohens kappa score: 0.831 -> 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: 268, 65 KNN fn, tp: 0, 13 KNN f1 score: 0.286 KNN cohens kappa score: 0.237 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 292, 41 LR fn, tp: 0, 13 LR f1 score: 0.388 LR cohens kappa score: 0.349 LR average precision score: 0.429 -> test with 'RF' RF tn, fp: 328, 5 RF fn, tp: 0, 13 RF f1 score: 0.839 RF cohens kappa score: 0.831 -> 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: 273, 60 KNN fn, tp: 0, 13 KNN f1 score: 0.302 KNN cohens kappa score: 0.255 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 266, 67 LR fn, tp: 0, 13 LR f1 score: 0.280 LR cohens kappa score: 0.230 LR average precision score: 0.312 -> test with 'RF' RF tn, fp: 321, 12 RF fn, tp: 0, 13 RF f1 score: 0.684 RF cohens kappa score: 0.668 -> 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: 266, 67 KNN fn, tp: 0, 13 KNN f1 score: 0.280 KNN cohens kappa score: 0.230 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 276, 57 LR fn, tp: 0, 13 LR f1 score: 0.313 LR cohens kappa score: 0.267 LR average precision score: 0.434 -> test with 'RF' RF tn, fp: 328, 5 RF fn, tp: 0, 13 RF f1 score: 0.839 RF cohens kappa score: 0.831 -> 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: 269, 64 KNN fn, tp: 0, 13 KNN f1 score: 0.289 KNN cohens kappa score: 0.240 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 276, 55 LR fn, tp: 1, 12 LR f1 score: 0.300 LR cohens kappa score: 0.253 LR average precision score: 0.365 -> test with 'RF' RF tn, fp: 327, 4 RF fn, tp: 0, 13 RF f1 score: 0.867 RF cohens kappa score: 0.861 -> 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: 277, 54 KNN fn, tp: 0, 13 KNN f1 score: 0.325 KNN cohens kappa score: 0.279 ====== 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: 279, 54 LR fn, tp: 0, 13 LR f1 score: 0.325 LR cohens kappa score: 0.280 LR average precision score: 0.402 -> test with 'RF' RF tn, fp: 327, 6 RF fn, tp: 0, 13 RF f1 score: 0.813 RF cohens kappa score: 0.804 -> 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 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: 0, 13 LR f1 score: 0.299 LR cohens kappa score: 0.251 LR average precision score: 0.433 -> test with 'RF' RF tn, fp: 328, 5 RF fn, tp: 0, 13 RF f1 score: 0.839 RF cohens kappa score: 0.831 -> 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: 268, 65 KNN fn, tp: 0, 13 KNN f1 score: 0.286 KNN cohens kappa score: 0.237 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 272, 61 LR fn, tp: 0, 13 LR f1 score: 0.299 LR cohens kappa score: 0.251 LR average precision score: 0.267 -> test with 'RF' RF tn, fp: 327, 6 RF fn, tp: 0, 13 RF f1 score: 0.813 RF cohens kappa score: 0.804 -> 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: 270, 63 KNN fn, tp: 0, 13 KNN f1 score: 0.292 KNN cohens kappa score: 0.244 ------ 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: 0.273 -> test with 'RF' RF tn, fp: 327, 6 RF fn, tp: 0, 13 RF f1 score: 0.813 RF cohens kappa score: 0.804 -> 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 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 277, 54 LR fn, tp: 0, 13 LR f1 score: 0.325 LR cohens kappa score: 0.279 LR average precision score: 0.363 -> test with 'RF' RF tn, fp: 323, 8 RF fn, tp: 0, 13 RF f1 score: 0.765 RF cohens kappa score: 0.753 -> 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: 261, 70 KNN fn, tp: 0, 13 KNN f1 score: 0.271 KNN cohens kappa score: 0.220 ====== 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: 264, 69 LR fn, tp: 0, 13 LR f1 score: 0.274 LR cohens kappa score: 0.223 LR average precision score: 0.341 -> test with 'RF' RF tn, fp: 324, 9 RF fn, tp: 0, 13 RF f1 score: 0.743 RF cohens kappa score: 0.730 -> 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: 257, 76 KNN fn, tp: 0, 13 KNN f1 score: 0.255 KNN cohens kappa score: 0.203 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 283, 50 LR fn, tp: 0, 13 LR f1 score: 0.342 LR cohens kappa score: 0.298 LR average precision score: 0.366 -> test with 'RF' RF tn, fp: 330, 3 RF fn, tp: 0, 13 RF f1 score: 0.897 RF cohens kappa score: 0.892 -> 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: 282, 51 KNN fn, tp: 0, 13 KNN f1 score: 0.338 KNN cohens kappa score: 0.294 ------ Step 5/5: 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: 0.362 -> test with 'RF' RF tn, fp: 329, 4 RF fn, tp: 0, 13 RF f1 score: 0.867 RF cohens kappa score: 0.861 -> 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: 271, 62 LR fn, tp: 0, 13 LR f1 score: 0.295 LR cohens kappa score: 0.247 LR average precision score: 0.298 -> test with 'RF' RF tn, fp: 324, 9 RF fn, tp: 0, 13 RF f1 score: 0.743 RF cohens kappa score: 0.730 -> 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: 267, 66 KNN fn, tp: 0, 13 KNN f1 score: 0.283 KNN cohens kappa score: 0.233 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 284, 47 LR fn, tp: 0, 13 LR f1 score: 0.356 LR cohens kappa score: 0.314 LR average precision score: 0.463 -> test with 'RF' RF tn, fp: 325, 6 RF fn, tp: 0, 13 RF f1 score: 0.813 RF cohens kappa score: 0.804 -> 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: 274, 57 KNN fn, tp: 0, 13 KNN f1 score: 0.313 KNN cohens kappa score: 0.266 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 292, 69 LR fn, tp: 1, 13 LR f1 score: 0.388 LR cohens kappa score: 0.349 LR average precision score: 0.527 average: LR tn, fp: 278.0, 54.6 LR fn, tp: 0.04, 12.96 LR f1 score: 0.325 LR cohens kappa score: 0.279 LR average precision score: 0.362 minimum: LR tn, fp: 264, 41 LR fn, tp: 0, 12 LR f1 score: 0.274 LR cohens kappa score: 0.223 LR average precision score: 0.267 -----[ RF ]----- maximum: RF tn, fp: 330, 12 RF fn, tp: 0, 13 RF f1 score: 0.929 RF cohens kappa score: 0.926 average: RF tn, fp: 326.4, 6.2 RF fn, tp: 0.0, 13.0 RF f1 score: 0.812 RF cohens kappa score: 0.803 minimum: RF tn, fp: 321, 2 RF fn, tp: 0, 13 RF f1 score: 0.684 RF cohens kappa score: 0.668 -----[ 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: 282, 76 KNN fn, tp: 0, 13 KNN f1 score: 0.342 KNN cohens kappa score: 0.298 average: KNN tn, fp: 270.64, 61.96 KNN fn, tp: 0.0, 13.0 KNN f1 score: 0.297 KNN cohens kappa score: 0.249 minimum: KNN tn, fp: 257, 50 KNN fn, tp: 0, 13 KNN f1 score: 0.255 KNN cohens kappa score: 0.203