/////////////////////////////////////////// // Running SimpleGAN 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 310, 23 LR fn, tp: 13, 0 LR f1 score: 0.000 LR cohens kappa score: -0.050 LR average precision score: 0.056 -> 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: 333, 0 KNN fn, tp: 7, 6 KNN f1 score: 0.632 KNN cohens kappa score: 0.623 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 320, 13 LR fn, tp: 9, 4 LR f1 score: 0.267 LR cohens kappa score: 0.234 LR average precision score: 0.213 -> 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: 333, 0 KNN fn, tp: 5, 8 KNN f1 score: 0.762 KNN cohens kappa score: 0.755 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 323, 10 LR fn, tp: 6, 7 LR f1 score: 0.467 LR cohens kappa score: 0.443 LR average precision score: 0.315 -> 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: 333, 0 KNN fn, tp: 6, 7 KNN f1 score: 0.700 KNN cohens kappa score: 0.692 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 330, 3 LR fn, tp: 11, 2 LR f1 score: 0.222 LR cohens kappa score: 0.206 LR average precision score: 0.373 -> 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: 333, 0 KNN fn, tp: 6, 7 KNN f1 score: 0.700 KNN cohens kappa score: 0.692 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 327, 4 LR fn, tp: 10, 3 LR f1 score: 0.300 LR cohens kappa score: 0.281 LR average precision score: 0.430 -> 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: 331, 0 KNN fn, tp: 8, 5 KNN f1 score: 0.556 KNN cohens kappa score: 0.546 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 206, 127 LR fn, tp: 6, 7 LR f1 score: 0.095 LR cohens kappa score: 0.029 LR average precision score: 0.062 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 4, 9 GB f1 score: 0.750 GB cohens kappa score: 0.741 -> test with 'KNN' KNN tn, fp: 332, 1 KNN fn, tp: 7, 6 KNN f1 score: 0.600 KNN cohens kappa score: 0.589 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 313, 20 LR fn, tp: 9, 4 LR f1 score: 0.216 LR cohens kappa score: 0.176 LR average precision score: 0.157 -> 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: 333, 0 KNN fn, tp: 4, 9 KNN f1 score: 0.818 KNN cohens kappa score: 0.812 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 328, 5 LR fn, tp: 10, 3 LR f1 score: 0.286 LR cohens kappa score: 0.265 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: 333, 0 KNN fn, tp: 7, 6 KNN f1 score: 0.632 KNN cohens kappa score: 0.623 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 326, 7 LR fn, tp: 12, 1 LR f1 score: 0.095 LR cohens kappa score: 0.069 LR average precision score: 0.295 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 3, 10 GB f1 score: 0.870 GB cohens kappa score: 0.865 -> test with 'KNN' KNN tn, fp: 333, 0 KNN fn, tp: 9, 4 KNN f1 score: 0.471 KNN cohens kappa score: 0.461 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 329, 2 LR fn, tp: 9, 4 LR f1 score: 0.421 LR cohens kappa score: 0.407 LR average precision score: 0.566 -> 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: 331, 0 KNN fn, tp: 11, 2 KNN f1 score: 0.267 KNN cohens kappa score: 0.259 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 318, 15 LR fn, tp: 8, 5 LR f1 score: 0.303 LR cohens kappa score: 0.270 LR average precision score: 0.182 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 3, 10 GB f1 score: 0.870 GB cohens kappa score: 0.865 -> test with 'KNN' KNN tn, fp: 332, 1 KNN fn, tp: 7, 6 KNN f1 score: 0.600 KNN cohens kappa score: 0.589 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 210, 123 LR fn, tp: 7, 6 LR f1 score: 0.085 LR cohens kappa score: 0.017 LR average precision score: 0.054 -> 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: 332, 1 KNN fn, tp: 4, 9 KNN f1 score: 0.783 KNN cohens kappa score: 0.775 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 196, 137 LR fn, tp: 4, 9 LR f1 score: 0.113 LR cohens kappa score: 0.047 LR average precision score: 0.068 -> 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: 330, 3 KNN fn, tp: 5, 8 KNN f1 score: 0.667 KNN cohens kappa score: 0.655 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 328, 5 LR fn, tp: 11, 2 LR f1 score: 0.200 LR cohens kappa score: 0.178 LR average precision score: 0.390 -> 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: 333, 0 KNN fn, tp: 10, 3 KNN f1 score: 0.375 KNN cohens kappa score: 0.366 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 217, 114 LR fn, tp: 6, 7 LR f1 score: 0.104 LR cohens kappa score: 0.039 LR average precision score: 0.102 -> test with 'GB' GB tn, fp: 326, 5 GB fn, tp: 2, 11 GB f1 score: 0.759 GB cohens kappa score: 0.748 -> test with 'KNN' KNN tn, fp: 326, 5 KNN fn, tp: 9, 4 KNN f1 score: 0.364 KNN cohens kappa score: 0.343 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 200, 133 LR fn, tp: 6, 7 LR f1 score: 0.092 LR cohens kappa score: 0.024 LR average precision score: 0.061 -> test with 'GB' GB tn, fp: 330, 3 GB fn, tp: 5, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 13, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.014 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 199, 134 LR fn, tp: 3, 10 LR f1 score: 0.127 LR cohens kappa score: 0.063 LR average precision score: 0.097 -> test with 'GB' GB tn, fp: 327, 6 GB fn, tp: 3, 10 GB f1 score: 0.690 GB cohens kappa score: 0.676 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 5, 8 KNN f1 score: 0.667 KNN cohens kappa score: 0.655 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 190, 143 LR fn, tp: 9, 4 LR f1 score: 0.050 LR cohens kappa score: -0.020 LR average precision score: 0.049 -> test with 'GB' GB tn, fp: 328, 5 GB fn, tp: 1, 12 GB f1 score: 0.800 GB cohens kappa score: 0.791 -> test with 'KNN' KNN tn, fp: 329, 4 KNN fn, tp: 6, 7 KNN f1 score: 0.583 KNN cohens kappa score: 0.568 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 330, 3 LR fn, tp: 12, 1 LR f1 score: 0.118 LR cohens kappa score: 0.102 LR average precision score: 0.273 -> 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: 333, 0 KNN fn, tp: 9, 4 KNN f1 score: 0.471 KNN cohens kappa score: 0.461 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 305, 26 LR fn, tp: 10, 3 LR f1 score: 0.143 LR cohens kappa score: 0.096 LR average precision score: 0.127 -> 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: 330, 1 KNN fn, tp: 5, 8 KNN f1 score: 0.727 KNN cohens kappa score: 0.719 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 328, 5 LR fn, tp: 12, 1 LR f1 score: 0.105 LR cohens kappa score: 0.084 LR average precision score: 0.297 -> 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: 333, 0 KNN fn, tp: 5, 8 KNN f1 score: 0.762 KNN cohens kappa score: 0.755 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 322, 11 LR fn, tp: 10, 3 LR f1 score: 0.222 LR cohens kappa score: 0.191 LR average precision score: 0.213 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 3, 10 GB f1 score: 0.870 GB cohens kappa score: 0.865 -> test with 'KNN' KNN tn, fp: 333, 0 KNN fn, tp: 9, 4 KNN f1 score: 0.471 KNN cohens kappa score: 0.461 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 326, 7 LR fn, tp: 9, 4 LR f1 score: 0.333 LR cohens kappa score: 0.310 LR average precision score: 0.331 -> 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: 333, 0 KNN fn, tp: 6, 7 KNN f1 score: 0.700 KNN cohens kappa score: 0.692 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 211, 122 LR fn, tp: 5, 8 LR f1 score: 0.112 LR cohens kappa score: 0.047 LR average precision score: 0.065 -> test with 'GB' GB tn, fp: 326, 7 GB fn, tp: 1, 12 GB f1 score: 0.750 GB cohens kappa score: 0.738 -> test with 'KNN' KNN tn, fp: 329, 4 KNN fn, tp: 7, 6 KNN f1 score: 0.522 KNN cohens kappa score: 0.506 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 329, 2 LR fn, tp: 10, 3 LR f1 score: 0.333 LR cohens kappa score: 0.319 LR average precision score: 0.473 -> 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: 331, 0 KNN fn, tp: 9, 4 KNN f1 score: 0.471 KNN cohens kappa score: 0.461 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 330, 143 LR fn, tp: 13, 10 LR f1 score: 0.467 LR cohens kappa score: 0.443 LR average precision score: 0.566 average: LR tn, fp: 284.84, 47.76 LR fn, tp: 8.68, 4.32 LR f1 score: 0.192 LR cohens kappa score: 0.153 LR average precision score: 0.223 minimum: LR tn, fp: 190, 2 LR fn, tp: 3, 0 LR f1 score: 0.000 LR cohens kappa score: -0.050 LR average precision score: 0.049 -----[ GB ]----- maximum: GB tn, fp: 333, 7 GB fn, tp: 5, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 330.84, 1.76 GB fn, tp: 1.08, 11.92 GB f1 score: 0.896 GB cohens kappa score: 0.891 minimum: GB tn, fp: 326, 0 GB fn, tp: 0, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -----[ KNN ]----- maximum: KNN tn, fp: 333, 5 KNN fn, tp: 13, 9 KNN f1 score: 0.818 KNN cohens kappa score: 0.812 average: KNN tn, fp: 331.56, 1.04 KNN fn, tp: 7.16, 5.84 KNN f1 score: 0.572 KNN cohens kappa score: 0.562 minimum: KNN tn, fp: 326, 0 KNN fn, tp: 4, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.014