/////////////////////////////////////////// // Running ProWRAS on folding_car_good /////////////////////////////////////////// Load 'data_input/folding_car_good' 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 1272 synthetic samples -> test with 'LR' LR tn, fp: 179, 153 LR fn, tp: 6, 8 LR f1 score: 0.091 LR cohens kappa score: 0.018 LR average precision score: 0.060 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 8, 6 RF f1 score: 0.600 RF cohens kappa score: 0.590 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 1, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 329, 3 KNN fn, tp: 1, 13 KNN f1 score: 0.867 KNN cohens kappa score: 0.861 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 181, 151 LR fn, tp: 4, 10 LR f1 score: 0.114 LR cohens kappa score: 0.043 LR average precision score: 0.082 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 6, 8 RF f1 score: 0.727 RF cohens kappa score: 0.719 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 0, 14 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 326, 6 KNN fn, tp: 1, 13 KNN f1 score: 0.788 KNN cohens kappa score: 0.778 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 179, 153 LR fn, tp: 8, 6 LR f1 score: 0.069 LR cohens kappa score: -0.005 LR average precision score: 0.058 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 8, 6 RF f1 score: 0.600 RF cohens kappa score: 0.590 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 2, 12 GB f1 score: 0.889 GB cohens kappa score: 0.884 -> test with 'KNN' KNN tn, fp: 326, 6 KNN fn, tp: 3, 11 KNN f1 score: 0.710 KNN cohens kappa score: 0.696 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 186, 146 LR fn, tp: 6, 8 LR f1 score: 0.095 LR cohens kappa score: 0.023 LR average precision score: 0.079 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 10, 4 RF f1 score: 0.444 RF cohens kappa score: 0.434 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 3, 11 GB f1 score: 0.846 GB cohens kappa score: 0.840 -> test with 'KNN' KNN tn, fp: 326, 6 KNN fn, tp: 4, 10 KNN f1 score: 0.667 KNN cohens kappa score: 0.652 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 181, 150 LR fn, tp: 4, 9 LR f1 score: 0.105 LR cohens kappa score: 0.037 LR average precision score: 0.057 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 11, 2 RF f1 score: 0.267 RF cohens kappa score: 0.259 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 2, 11 GB f1 score: 0.815 GB cohens kappa score: 0.807 -> test with 'KNN' KNN tn, fp: 326, 5 KNN fn, tp: 1, 12 KNN f1 score: 0.800 KNN cohens kappa score: 0.791 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 170, 162 LR fn, tp: 4, 10 LR f1 score: 0.108 LR cohens kappa score: 0.035 LR average precision score: 0.067 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 8, 6 RF f1 score: 0.600 RF cohens kappa score: 0.590 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 3, 11 GB f1 score: 0.846 GB cohens kappa score: 0.840 -> test with 'KNN' KNN tn, fp: 328, 4 KNN fn, tp: 2, 12 KNN f1 score: 0.800 KNN cohens kappa score: 0.791 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 178, 154 LR fn, tp: 4, 10 LR f1 score: 0.112 LR cohens kappa score: 0.041 LR average precision score: 0.070 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 8, 6 RF f1 score: 0.600 RF cohens kappa score: 0.590 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 1, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 326, 6 KNN fn, tp: 3, 11 KNN f1 score: 0.710 KNN cohens kappa score: 0.696 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 194, 138 LR fn, tp: 4, 10 LR f1 score: 0.123 LR cohens kappa score: 0.053 LR average precision score: 0.071 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 7, 7 RF f1 score: 0.667 RF cohens kappa score: 0.657 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 5, 9 GB f1 score: 0.783 GB cohens kappa score: 0.775 -> test with 'KNN' KNN tn, fp: 327, 5 KNN fn, tp: 3, 11 KNN f1 score: 0.733 KNN cohens kappa score: 0.721 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 192, 140 LR fn, tp: 9, 5 LR f1 score: 0.063 LR cohens kappa score: -0.012 LR average precision score: 0.050 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 10, 4 RF f1 score: 0.444 RF cohens kappa score: 0.434 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 3, 11 GB f1 score: 0.815 GB cohens kappa score: 0.807 -> test with 'KNN' KNN tn, fp: 322, 10 KNN fn, tp: 6, 8 KNN f1 score: 0.500 KNN cohens kappa score: 0.476 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 194, 137 LR fn, tp: 5, 8 LR f1 score: 0.101 LR cohens kappa score: 0.034 LR average precision score: 0.075 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 8, 5 RF f1 score: 0.556 RF cohens kappa score: 0.546 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 3, 10 GB f1 score: 0.769 GB cohens kappa score: 0.760 -> test with 'KNN' KNN tn, fp: 319, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 173, 159 LR fn, tp: 5, 9 LR f1 score: 0.099 LR cohens kappa score: 0.026 LR average precision score: 0.075 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 8, 6 RF f1 score: 0.600 RF cohens kappa score: 0.590 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 3, 11 GB f1 score: 0.880 GB cohens kappa score: 0.876 -> test with 'KNN' KNN tn, fp: 326, 6 KNN fn, tp: 3, 11 KNN f1 score: 0.710 KNN cohens kappa score: 0.696 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 196, 136 LR fn, tp: 5, 9 LR f1 score: 0.113 LR cohens kappa score: 0.043 LR average precision score: 0.067 -> test with 'RF' RF tn, fp: 331, 1 RF fn, tp: 10, 4 RF f1 score: 0.421 RF cohens kappa score: 0.408 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 1, 13 GB f1 score: 0.897 GB cohens kappa score: 0.892 -> test with 'KNN' KNN tn, fp: 324, 8 KNN fn, tp: 3, 11 KNN f1 score: 0.667 KNN cohens kappa score: 0.650 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 187, 145 LR fn, tp: 5, 9 LR f1 score: 0.107 LR cohens kappa score: 0.036 LR average precision score: 0.056 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 9, 5 RF f1 score: 0.526 RF cohens kappa score: 0.516 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 4, 10 GB f1 score: 0.769 GB cohens kappa score: 0.760 -> test with 'KNN' KNN tn, fp: 328, 4 KNN fn, tp: 3, 11 KNN f1 score: 0.759 KNN cohens kappa score: 0.748 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 174, 158 LR fn, tp: 4, 10 LR f1 score: 0.110 LR cohens kappa score: 0.038 LR average precision score: 0.082 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 8, 6 RF f1 score: 0.600 RF cohens kappa score: 0.590 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 2, 12 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> test with 'KNN' KNN tn, fp: 324, 8 KNN fn, tp: 1, 13 KNN f1 score: 0.743 KNN cohens kappa score: 0.730 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 174, 157 LR fn, tp: 6, 7 LR f1 score: 0.079 LR cohens kappa score: 0.010 LR average precision score: 0.054 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 4, 9 RF f1 score: 0.818 RF cohens kappa score: 0.812 -> 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: 318, 13 KNN fn, tp: 0, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 181, 151 LR fn, tp: 4, 10 LR f1 score: 0.114 LR cohens kappa score: 0.043 LR average precision score: 0.067 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 7, 7 RF f1 score: 0.667 RF cohens kappa score: 0.657 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 2, 12 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> test with 'KNN' KNN tn, fp: 330, 2 KNN fn, tp: 0, 14 KNN f1 score: 0.933 KNN cohens kappa score: 0.930 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 181, 151 LR fn, tp: 6, 8 LR f1 score: 0.092 LR cohens kappa score: 0.020 LR average precision score: 0.060 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 12, 2 RF f1 score: 0.250 RF cohens kappa score: 0.242 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 3, 11 GB f1 score: 0.880 GB cohens kappa score: 0.876 -> test with 'KNN' KNN tn, fp: 319, 13 KNN fn, tp: 7, 7 KNN f1 score: 0.412 KNN cohens kappa score: 0.382 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 176, 156 LR fn, tp: 5, 9 LR f1 score: 0.101 LR cohens kappa score: 0.028 LR average precision score: 0.068 -> test with 'RF' RF tn, fp: 331, 1 RF fn, tp: 8, 6 RF f1 score: 0.571 RF cohens kappa score: 0.560 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 4, 10 GB f1 score: 0.833 GB cohens kappa score: 0.828 -> test with 'KNN' KNN tn, fp: 318, 14 KNN fn, tp: 1, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 193, 139 LR fn, tp: 8, 6 LR f1 score: 0.075 LR cohens kappa score: 0.002 LR average precision score: 0.058 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 6, 8 RF f1 score: 0.727 RF cohens kappa score: 0.719 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 2, 12 GB f1 score: 0.889 GB cohens kappa score: 0.884 -> test with 'KNN' KNN tn, fp: 330, 2 KNN fn, tp: 1, 13 KNN f1 score: 0.897 KNN cohens kappa score: 0.892 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 178, 153 LR fn, tp: 2, 11 LR f1 score: 0.124 LR cohens kappa score: 0.058 LR average precision score: 0.079 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 9, 4 RF f1 score: 0.471 RF cohens kappa score: 0.461 -> test with 'GB' GB tn, fp: 327, 4 GB fn, tp: 4, 9 GB f1 score: 0.692 GB cohens kappa score: 0.680 -> test with 'KNN' KNN tn, fp: 322, 9 KNN fn, tp: 2, 11 KNN f1 score: 0.667 KNN cohens kappa score: 0.651 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 190, 142 LR fn, tp: 8, 6 LR f1 score: 0.074 LR cohens kappa score: 0.000 LR average precision score: 0.053 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 10, 4 RF f1 score: 0.444 RF cohens kappa score: 0.434 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 6, 8 GB f1 score: 0.696 GB cohens kappa score: 0.686 -> test with 'KNN' KNN tn, fp: 322, 10 KNN fn, tp: 7, 7 KNN f1 score: 0.452 KNN cohens kappa score: 0.426 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 193, 139 LR fn, tp: 6, 8 LR f1 score: 0.099 LR cohens kappa score: 0.028 LR average precision score: 0.072 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 5, 9 RF f1 score: 0.783 RF cohens kappa score: 0.775 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 3, 11 GB f1 score: 0.846 GB cohens kappa score: 0.840 -> test with 'KNN' KNN tn, fp: 329, 3 KNN fn, tp: 0, 14 KNN f1 score: 0.903 KNN cohens kappa score: 0.899 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 163, 169 LR fn, tp: 3, 11 LR f1 score: 0.113 LR cohens kappa score: 0.041 LR average precision score: 0.078 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 6, 8 RF f1 score: 0.727 RF cohens kappa score: 0.719 -> test with 'GB' GB tn, fp: 328, 4 GB fn, tp: 1, 13 GB f1 score: 0.839 GB cohens kappa score: 0.831 -> test with 'KNN' KNN tn, fp: 325, 7 KNN fn, tp: 1, 13 KNN f1 score: 0.765 KNN cohens kappa score: 0.753 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 181, 151 LR fn, tp: 5, 9 LR f1 score: 0.103 LR cohens kappa score: 0.031 LR average precision score: 0.075 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 10, 4 RF f1 score: 0.444 RF cohens kappa score: 0.434 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 4, 10 GB f1 score: 0.800 GB cohens kappa score: 0.793 -> test with 'KNN' KNN tn, fp: 328, 4 KNN fn, tp: 3, 11 KNN f1 score: 0.759 KNN cohens kappa score: 0.748 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 185, 146 LR fn, tp: 4, 9 LR f1 score: 0.107 LR cohens kappa score: 0.040 LR average precision score: 0.065 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 10, 3 RF f1 score: 0.375 RF cohens kappa score: 0.366 -> 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: 324, 7 KNN fn, tp: 0, 13 KNN f1 score: 0.788 KNN cohens kappa score: 0.778 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 196, 169 LR fn, tp: 9, 11 LR f1 score: 0.124 LR cohens kappa score: 0.058 LR average precision score: 0.082 average: LR tn, fp: 182.36, 149.44 LR fn, tp: 5.2, 8.6 LR f1 score: 0.100 LR cohens kappa score: 0.028 LR average precision score: 0.067 minimum: LR tn, fp: 163, 136 LR fn, tp: 2, 5 LR f1 score: 0.063 LR cohens kappa score: -0.012 LR average precision score: 0.050 -----[ RF ]----- maximum: RF tn, fp: 332, 1 RF fn, tp: 12, 9 RF f1 score: 0.818 RF cohens kappa score: 0.812 average: RF tn, fp: 331.72, 0.08 RF fn, tp: 8.24, 5.56 RF f1 score: 0.557 RF cohens kappa score: 0.548 minimum: RF tn, fp: 331, 0 RF fn, tp: 4, 2 RF f1 score: 0.250 RF cohens kappa score: 0.242 -----[ GB ]----- maximum: GB tn, fp: 332, 4 GB fn, tp: 6, 14 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 330.6, 1.2 GB fn, tp: 2.6, 11.2 GB f1 score: 0.853 GB cohens kappa score: 0.847 minimum: GB tn, fp: 327, 0 GB fn, tp: 0, 8 GB f1 score: 0.692 GB cohens kappa score: 0.680 -----[ KNN ]----- maximum: KNN tn, fp: 330, 14 KNN fn, tp: 7, 14 KNN f1 score: 0.933 KNN cohens kappa score: 0.930 average: KNN tn, fp: 324.88, 6.92 KNN fn, tp: 2.24, 11.56 KNN f1 score: 0.720 KNN cohens kappa score: 0.707 minimum: KNN tn, fp: 318, 2 KNN fn, tp: 0, 7 KNN f1 score: 0.412 KNN cohens kappa score: 0.382