/////////////////////////////////////////// // Running convGAN 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: 291, 42 LR fn, tp: 0, 13 LR f1 score: 0.382 LR cohens kappa score: 0.342 LR average precision score: 0.363 -> 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 1/5: 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: 3, 10 LR f1 score: 0.323 LR cohens kappa score: 0.280 LR average precision score: 0.305 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 1, 12 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> test with 'KNN' KNN tn, fp: 321, 12 KNN fn, tp: 1, 12 KNN f1 score: 0.649 KNN cohens kappa score: 0.631 ------ Step 1/5: Slice 3/5 ------- -> 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: 0.404 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 1, 12 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> test with 'KNN' KNN tn, fp: 315, 18 KNN fn, tp: 0, 13 KNN f1 score: 0.591 KNN cohens kappa score: 0.568 ------ Step 1/5: Slice 4/5 ------- -> 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: 0.366 -> 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: 325, 8 KNN fn, tp: 0, 13 KNN f1 score: 0.765 KNN cohens kappa score: 0.753 ------ Step 1/5: 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: 2, 11 LR f1 score: 0.379 LR cohens kappa score: 0.341 LR average precision score: 0.449 -> test with 'GB' GB tn, fp: 326, 5 GB fn, tp: 1, 12 GB f1 score: 0.800 GB cohens kappa score: 0.791 -> test with 'KNN' KNN tn, fp: 317, 14 KNN fn, tp: 0, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ====== 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: 296, 37 LR fn, tp: 0, 13 LR f1 score: 0.413 LR cohens kappa score: 0.375 LR average precision score: 0.287 -> 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: 318, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ Step 2/5: Slice 2/5 ------- -> 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: 0.360 -> 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: 306, 27 KNN fn, tp: 0, 13 KNN f1 score: 0.491 KNN cohens kappa score: 0.460 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 293, 40 LR fn, tp: 2, 11 LR f1 score: 0.344 LR cohens kappa score: 0.302 LR average precision score: 0.335 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 312, 21 KNN fn, tp: 0, 13 KNN f1 score: 0.553 KNN cohens kappa score: 0.528 ------ 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: 0.283 -> 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: 2, 11 KNN f1 score: 0.688 KNN cohens kappa score: 0.673 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 291, 40 LR fn, tp: 0, 13 LR f1 score: 0.394 LR cohens kappa score: 0.355 LR average precision score: 0.546 -> 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: 328, 3 KNN fn, tp: 0, 13 KNN f1 score: 0.897 KNN cohens kappa score: 0.892 ====== 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: 295, 38 LR fn, tp: 1, 12 LR f1 score: 0.381 LR cohens kappa score: 0.342 LR average precision score: 0.295 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 3, 10 GB f1 score: 0.833 GB cohens kappa score: 0.827 -> test with 'KNN' KNN tn, fp: 321, 12 KNN fn, tp: 2, 11 KNN f1 score: 0.611 KNN cohens kappa score: 0.591 ------ Step 3/5: Slice 2/5 ------- -> 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: 0.446 -> 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 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 278, 55 LR fn, tp: 0, 13 LR f1 score: 0.321 LR cohens kappa score: 0.275 LR average precision score: 0.341 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 1, 12 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> test with 'KNN' KNN tn, fp: 312, 21 KNN fn, tp: 0, 13 KNN f1 score: 0.553 KNN cohens kappa score: 0.528 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 294, 39 LR fn, tp: 0, 13 LR f1 score: 0.400 LR cohens kappa score: 0.362 LR average precision score: 0.383 -> 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: 323, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 293, 38 LR fn, tp: 2, 11 LR f1 score: 0.355 LR cohens kappa score: 0.314 LR average precision score: 0.336 -> 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: 322, 9 KNN fn, tp: 0, 13 KNN f1 score: 0.743 KNN cohens kappa score: 0.730 ====== 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: 298, 35 LR fn, tp: 0, 13 LR f1 score: 0.426 LR cohens kappa score: 0.390 LR average precision score: 0.408 -> 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: 323, 10 KNN fn, tp: 1, 12 KNN f1 score: 0.686 KNN cohens kappa score: 0.670 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 289, 44 LR fn, tp: 1, 12 LR f1 score: 0.348 LR cohens kappa score: 0.305 LR average precision score: 0.508 -> 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: 316, 17 KNN fn, tp: 0, 13 KNN f1 score: 0.605 KNN cohens kappa score: 0.583 ------ Step 4/5: Slice 3/5 ------- -> 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: 0.313 -> 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: 323, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ------ Step 4/5: Slice 4/5 ------- -> 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: 0.301 -> 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: 317, 16 KNN fn, tp: 0, 13 KNN f1 score: 0.619 KNN cohens kappa score: 0.598 ------ Step 4/5: 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: 1, 12 LR f1 score: 0.414 LR cohens kappa score: 0.377 LR average precision score: 0.328 -> 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: 318, 13 KNN fn, tp: 0, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ====== 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: 270, 63 LR fn, tp: 0, 13 LR f1 score: 0.292 LR cohens kappa score: 0.244 LR average precision score: 0.342 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 2, 11 GB f1 score: 0.880 GB cohens kappa score: 0.876 -> test with 'KNN' KNN tn, fp: 320, 13 KNN fn, tp: 0, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ------ Step 5/5: Slice 2/5 ------- -> 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: 0.361 -> 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: 320, 13 KNN fn, tp: 0, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 306, 27 LR fn, tp: 1, 12 LR f1 score: 0.462 LR cohens kappa score: 0.429 LR average precision score: 0.336 -> 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: 1, 12 KNN f1 score: 0.800 KNN cohens kappa score: 0.791 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 288, 45 LR fn, tp: 0, 13 LR f1 score: 0.366 LR cohens kappa score: 0.325 LR average precision score: 0.295 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 1, 12 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> test with 'KNN' KNN tn, fp: 320, 13 KNN fn, tp: 0, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 289, 42 LR fn, tp: 0, 13 LR f1 score: 0.382 LR cohens kappa score: 0.342 LR average precision score: 0.556 -> test with 'GB' GB tn, fp: 330, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 318, 13 KNN fn, tp: 0, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 306, 63 LR fn, tp: 3, 13 LR f1 score: 0.462 LR cohens kappa score: 0.429 LR average precision score: 0.556 average: LR tn, fp: 291.24, 41.36 LR fn, tp: 0.6, 12.4 LR f1 score: 0.376 LR cohens kappa score: 0.336 LR average precision score: 0.370 minimum: LR tn, fp: 270, 27 LR fn, tp: 0, 10 LR f1 score: 0.292 LR cohens kappa score: 0.244 LR average precision score: 0.283 -----[ GB ]----- maximum: GB tn, fp: 333, 6 GB fn, tp: 3, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 331.56, 1.04 GB fn, tp: 0.52, 12.48 GB f1 score: 0.943 GB cohens kappa score: 0.941 minimum: GB tn, fp: 326, 0 GB fn, tp: 0, 10 GB f1 score: 0.800 GB cohens kappa score: 0.791 -----[ KNN ]----- maximum: KNN tn, fp: 328, 27 KNN fn, tp: 2, 13 KNN f1 score: 0.897 KNN cohens kappa score: 0.892 average: KNN tn, fp: 320.16, 12.44 KNN fn, tp: 0.28, 12.72 KNN f1 score: 0.680 KNN cohens kappa score: 0.663 minimum: KNN tn, fp: 306, 3 KNN fn, tp: 0, 11 KNN f1 score: 0.491 KNN cohens kappa score: 0.460