/////////////////////////////////////////// // Running ProWRAS 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: 296, 37 LR fn, tp: 0, 13 LR f1 score: 0.413 LR cohens kappa score: 0.375 LR average precision score: 0.362 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 0, 13 RF f1 score: 1.000 RF cohens kappa score: 1.000 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 0, 13 KNN f1 score: 0.897 KNN cohens kappa score: 0.892 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 299, 34 LR fn, tp: 3, 10 LR f1 score: 0.351 LR cohens kappa score: 0.311 LR average precision score: 0.301 -> test with 'RF' RF tn, fp: 332, 1 RF fn, tp: 3, 10 RF f1 score: 0.833 RF cohens kappa score: 0.827 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 326, 7 KNN fn, tp: 3, 10 KNN f1 score: 0.667 KNN cohens kappa score: 0.652 ------ Step 1/5: Slice 3/5 ------- -> 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: 0.386 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 2, 11 RF f1 score: 0.917 RF cohens kappa score: 0.914 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 325, 8 KNN fn, tp: 0, 13 KNN f1 score: 0.765 KNN cohens kappa score: 0.753 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 296, 37 LR fn, tp: 1, 12 LR f1 score: 0.387 LR cohens kappa score: 0.348 LR average precision score: 0.371 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 3, 10 RF f1 score: 0.870 RF cohens kappa score: 0.865 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 331, 2 KNN fn, tp: 1, 12 KNN f1 score: 0.889 KNN cohens kappa score: 0.884 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 304, 27 LR fn, tp: 2, 11 LR f1 score: 0.431 LR cohens kappa score: 0.397 LR average precision score: 0.441 -> test with 'RF' RF tn, fp: 330, 1 RF fn, tp: 2, 11 RF f1 score: 0.880 RF cohens kappa score: 0.875 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 1, 12 GB f1 score: 0.889 GB cohens kappa score: 0.884 -> test with 'KNN' KNN tn, fp: 329, 2 KNN fn, tp: 2, 11 KNN f1 score: 0.846 KNN cohens kappa score: 0.840 ====== 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: 305, 28 LR fn, tp: 4, 9 LR f1 score: 0.360 LR cohens kappa score: 0.322 LR average precision score: 0.288 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 4, 9 RF f1 score: 0.818 RF cohens kappa score: 0.812 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 326, 7 KNN fn, tp: 0, 13 KNN f1 score: 0.788 KNN cohens kappa score: 0.778 ------ Step 2/5: 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: 0.369 -> test with 'RF' RF tn, fp: 332, 1 RF fn, tp: 0, 13 RF f1 score: 0.963 RF cohens kappa score: 0.961 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 321, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 297, 36 LR fn, tp: 2, 11 LR f1 score: 0.367 LR cohens kappa score: 0.327 LR average precision score: 0.333 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 3, 10 RF f1 score: 0.870 RF cohens kappa score: 0.865 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 328, 5 KNN fn, tp: 3, 10 KNN f1 score: 0.714 KNN cohens kappa score: 0.702 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 298, 35 LR fn, tp: 0, 13 LR f1 score: 0.426 LR cohens kappa score: 0.390 LR average precision score: 0.286 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 0, 13 RF f1 score: 1.000 RF cohens kappa score: 1.000 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 327, 6 KNN fn, tp: 2, 11 KNN f1 score: 0.733 KNN cohens kappa score: 0.721 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 295, 36 LR fn, tp: 1, 12 LR f1 score: 0.393 LR cohens kappa score: 0.355 LR average precision score: 0.554 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 0, 13 RF f1 score: 1.000 RF cohens kappa score: 1.000 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 329, 2 KNN fn, tp: 0, 13 KNN f1 score: 0.929 KNN cohens kappa score: 0.926 ====== 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: 298, 35 LR fn, tp: 1, 12 LR f1 score: 0.400 LR cohens kappa score: 0.362 LR average precision score: 0.311 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 4, 9 RF f1 score: 0.818 RF cohens kappa score: 0.812 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 4, 9 KNN f1 score: 0.720 KNN cohens kappa score: 0.710 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 303, 30 LR fn, tp: 0, 13 LR f1 score: 0.464 LR cohens kappa score: 0.431 LR average precision score: 0.431 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 0, 13 RF f1 score: 1.000 RF cohens kappa score: 1.000 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 331, 2 KNN fn, tp: 1, 12 KNN f1 score: 0.889 KNN cohens kappa score: 0.884 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 289, 44 LR fn, tp: 0, 13 LR f1 score: 0.371 LR cohens kappa score: 0.330 LR average precision score: 0.315 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 1, 12 RF f1 score: 0.960 RF cohens kappa score: 0.959 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 327, 6 KNN fn, tp: 1, 12 KNN f1 score: 0.774 KNN cohens kappa score: 0.764 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 297, 36 LR fn, tp: 0, 13 LR f1 score: 0.419 LR cohens kappa score: 0.383 LR average precision score: 0.388 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 0, 13 RF f1 score: 1.000 RF cohens kappa score: 1.000 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 329, 4 KNN fn, tp: 0, 13 KNN f1 score: 0.867 KNN cohens kappa score: 0.861 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 298, 33 LR fn, tp: 3, 10 LR f1 score: 0.357 LR cohens kappa score: 0.318 LR average precision score: 0.362 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 5, 8 RF f1 score: 0.762 RF cohens kappa score: 0.755 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 329, 2 KNN fn, tp: 4, 9 KNN f1 score: 0.750 KNN cohens kappa score: 0.741 ====== 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: 301, 32 LR fn, tp: 1, 12 LR f1 score: 0.421 LR cohens kappa score: 0.385 LR average precision score: 0.416 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 1, 12 RF f1 score: 0.960 RF cohens kappa score: 0.959 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 328, 5 KNN fn, tp: 2, 11 KNN f1 score: 0.759 KNN cohens kappa score: 0.748 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 294, 39 LR fn, tp: 1, 12 LR f1 score: 0.375 LR cohens kappa score: 0.335 LR average precision score: 0.518 -> test with 'RF' RF tn, fp: 332, 1 RF fn, tp: 2, 11 RF f1 score: 0.880 RF cohens kappa score: 0.876 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 329, 4 KNN fn, tp: 1, 12 KNN f1 score: 0.828 KNN cohens kappa score: 0.820 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 291, 42 LR fn, tp: 0, 13 LR f1 score: 0.382 LR cohens kappa score: 0.342 LR average precision score: 0.319 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 0, 13 RF f1 score: 1.000 RF cohens kappa score: 1.000 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 329, 4 KNN fn, tp: 1, 12 KNN f1 score: 0.828 KNN cohens kappa score: 0.820 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 299, 34 LR fn, tp: 2, 11 LR f1 score: 0.379 LR cohens kappa score: 0.341 LR average precision score: 0.268 -> test with 'RF' RF tn, fp: 332, 1 RF fn, tp: 6, 7 RF f1 score: 0.667 RF cohens kappa score: 0.657 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 328, 5 KNN fn, tp: 1, 12 KNN f1 score: 0.800 KNN cohens kappa score: 0.791 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 300, 31 LR fn, tp: 1, 12 LR f1 score: 0.429 LR cohens kappa score: 0.393 LR average precision score: 0.324 -> test with 'RF' RF tn, fp: 330, 1 RF fn, tp: 2, 11 RF f1 score: 0.880 RF cohens kappa score: 0.875 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 324, 7 KNN fn, tp: 0, 13 KNN f1 score: 0.788 KNN cohens kappa score: 0.778 ====== 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: 286, 47 LR fn, tp: 0, 13 LR f1 score: 0.356 LR cohens kappa score: 0.314 LR average precision score: 0.291 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 2, 11 RF f1 score: 0.917 RF cohens kappa score: 0.914 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 328, 5 KNN fn, tp: 0, 13 KNN f1 score: 0.839 KNN cohens kappa score: 0.831 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 305, 28 LR fn, tp: 3, 10 LR f1 score: 0.392 LR cohens kappa score: 0.356 LR average precision score: 0.357 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 4, 9 RF f1 score: 0.818 RF cohens kappa score: 0.812 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 329, 4 KNN fn, tp: 1, 12 KNN f1 score: 0.828 KNN cohens kappa score: 0.820 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 308, 25 LR fn, tp: 2, 11 LR f1 score: 0.449 LR cohens kappa score: 0.417 LR average precision score: 0.335 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 1, 12 RF f1 score: 0.960 RF cohens kappa score: 0.959 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 2, 11 KNN f1 score: 0.815 KNN cohens kappa score: 0.807 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 289, 44 LR fn, tp: 0, 13 LR f1 score: 0.371 LR cohens kappa score: 0.330 LR average precision score: 0.291 -> test with 'RF' RF tn, fp: 332, 1 RF fn, tp: 1, 12 RF f1 score: 0.923 RF cohens kappa score: 0.920 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 327, 6 KNN fn, tp: 0, 13 KNN f1 score: 0.813 KNN cohens kappa score: 0.804 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 297, 34 LR fn, tp: 0, 13 LR f1 score: 0.433 LR cohens kappa score: 0.398 LR average precision score: 0.536 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 2, 11 RF f1 score: 0.917 RF cohens kappa score: 0.914 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.958 -> test with 'KNN' KNN tn, fp: 330, 1 KNN fn, tp: 1, 12 KNN f1 score: 0.923 KNN cohens kappa score: 0.920 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 308, 50 LR fn, tp: 4, 13 LR f1 score: 0.464 LR cohens kappa score: 0.431 LR average precision score: 0.554 average: LR tn, fp: 296.8, 35.8 LR fn, tp: 1.08, 11.92 LR f1 score: 0.394 LR cohens kappa score: 0.356 LR average precision score: 0.366 minimum: LR tn, fp: 283, 25 LR fn, tp: 0, 9 LR f1 score: 0.342 LR cohens kappa score: 0.298 LR average precision score: 0.268 -----[ RF ]----- maximum: RF tn, fp: 333, 1 RF fn, tp: 6, 13 RF f1 score: 1.000 RF cohens kappa score: 1.000 average: RF tn, fp: 332.32, 0.28 RF fn, tp: 1.92, 11.08 RF f1 score: 0.904 RF cohens kappa score: 0.901 minimum: RF tn, fp: 330, 0 RF fn, tp: 0, 7 RF f1 score: 0.667 RF cohens kappa score: 0.657 -----[ GB ]----- maximum: GB tn, fp: 333, 2 GB fn, tp: 2, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 332.44, 0.16 GB fn, tp: 0.52, 12.48 GB f1 score: 0.973 GB cohens kappa score: 0.972 minimum: GB tn, fp: 329, 0 GB fn, tp: 0, 11 GB f1 score: 0.889 GB cohens kappa score: 0.884 -----[ KNN ]----- maximum: KNN tn, fp: 331, 12 KNN fn, tp: 4, 13 KNN f1 score: 0.929 KNN cohens kappa score: 0.926 average: KNN tn, fp: 328.0, 4.6 KNN fn, tp: 1.2, 11.8 KNN f1 score: 0.805 KNN cohens kappa score: 0.797 minimum: KNN tn, fp: 321, 1 KNN fn, tp: 0, 9 KNN f1 score: 0.667 KNN cohens kappa score: 0.652