/////////////////////////////////////////// // Running convGAN-full 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: 0.057 -> 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: 308, 24 KNN fn, tp: 0, 14 KNN f1 score: 0.538 KNN cohens kappa score: 0.509 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 179, 153 LR fn, tp: 4, 10 LR f1 score: 0.113 LR cohens kappa score: 0.042 LR average precision score: 0.064 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 6, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 321, 11 KNN fn, tp: 0, 14 KNN f1 score: 0.718 KNN cohens kappa score: 0.703 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 178, 154 LR fn, tp: 6, 8 LR f1 score: 0.091 LR cohens kappa score: 0.018 LR average precision score: 0.056 -> 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: 320, 12 KNN fn, tp: 0, 14 KNN f1 score: 0.700 KNN cohens kappa score: 0.683 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 182, 150 LR fn, tp: 3, 11 LR f1 score: 0.126 LR cohens kappa score: 0.055 LR average precision score: 0.078 -> 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: 308, 24 KNN fn, tp: 1, 13 KNN f1 score: 0.510 KNN cohens kappa score: 0.479 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 184, 147 LR fn, tp: 2, 11 LR f1 score: 0.129 LR cohens kappa score: 0.063 LR average precision score: 0.058 -> test with 'GB' GB tn, fp: 330, 1 GB fn, tp: 4, 9 GB f1 score: 0.783 GB cohens kappa score: 0.775 -> 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 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: 164, 168 LR fn, tp: 5, 9 LR f1 score: 0.094 LR cohens kappa score: 0.021 LR average precision score: 0.066 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 6, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 323, 9 KNN fn, tp: 1, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 174, 158 LR fn, tp: 3, 11 LR f1 score: 0.120 LR cohens kappa score: 0.049 LR average precision score: 0.070 -> 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: 323, 9 KNN fn, tp: 1, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ------ 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 'GB' GB tn, fp: 331, 1 GB fn, tp: 7, 7 GB f1 score: 0.636 GB cohens kappa score: 0.625 -> test with 'KNN' KNN tn, fp: 327, 5 KNN fn, tp: 1, 13 KNN f1 score: 0.813 KNN cohens kappa score: 0.804 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 190, 142 LR fn, tp: 9, 5 LR f1 score: 0.062 LR cohens kappa score: -0.013 LR average precision score: 0.051 -> 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: 322, 10 KNN fn, tp: 3, 11 KNN f1 score: 0.629 KNN cohens kappa score: 0.610 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 187, 144 LR fn, tp: 5, 8 LR f1 score: 0.097 LR cohens kappa score: 0.029 LR average precision score: 0.073 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 1, 12 GB f1 score: 0.857 GB cohens kappa score: 0.851 -> test with 'KNN' KNN tn, fp: 314, 17 KNN fn, tp: 0, 13 KNN f1 score: 0.605 KNN cohens kappa score: 0.583 ====== 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: 177, 155 LR fn, tp: 3, 11 LR f1 score: 0.122 LR cohens kappa score: 0.051 LR average precision score: 0.068 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 5, 9 GB f1 score: 0.720 GB cohens kappa score: 0.710 -> test with 'KNN' KNN tn, fp: 310, 22 KNN fn, tp: 1, 13 KNN f1 score: 0.531 KNN cohens kappa score: 0.502 ------ Step 3/5: 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: 5, 9 LR f1 score: 0.111 LR cohens kappa score: 0.040 LR average precision score: 0.067 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 0, 14 GB f1 score: 0.933 GB cohens kappa score: 0.930 -> test with 'KNN' KNN tn, fp: 311, 21 KNN fn, tp: 0, 14 KNN f1 score: 0.571 KNN cohens kappa score: 0.545 ------ Step 3/5: Slice 3/5 ------- -> 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: 0.056 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 8, 6 GB f1 score: 0.545 GB cohens kappa score: 0.532 -> test with 'KNN' KNN tn, fp: 319, 13 KNN fn, tp: 2, 12 KNN f1 score: 0.615 KNN cohens kappa score: 0.594 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 179, 153 LR fn, tp: 3, 11 LR f1 score: 0.124 LR cohens kappa score: 0.053 LR average precision score: 0.083 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 8, 6 GB f1 score: 0.600 GB cohens kappa score: 0.590 -> test with 'KNN' KNN tn, fp: 314, 18 KNN fn, tp: 0, 14 KNN f1 score: 0.609 KNN cohens kappa score: 0.585 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 173, 158 LR fn, tp: 5, 8 LR f1 score: 0.089 LR cohens kappa score: 0.021 LR average precision score: 0.052 -> 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: 305, 26 KNN fn, tp: 1, 12 KNN f1 score: 0.471 KNN cohens kappa score: 0.439 ====== 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: 166, 166 LR fn, tp: 4, 10 LR f1 score: 0.105 LR cohens kappa score: 0.033 LR average precision score: 0.061 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 6, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 321, 11 KNN fn, tp: 0, 14 KNN f1 score: 0.718 KNN cohens kappa score: 0.703 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 184, 148 LR fn, tp: 6, 8 LR f1 score: 0.094 LR cohens kappa score: 0.021 LR average precision score: 0.055 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 7, 7 GB f1 score: 0.609 GB cohens kappa score: 0.596 -> test with 'KNN' KNN tn, fp: 300, 32 KNN fn, tp: 0, 14 KNN f1 score: 0.467 KNN cohens kappa score: 0.431 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 173, 159 LR fn, tp: 4, 10 LR f1 score: 0.109 LR cohens kappa score: 0.037 LR average precision score: 0.069 -> 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: 313, 19 KNN fn, tp: 1, 13 KNN f1 score: 0.565 KNN cohens kappa score: 0.539 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 185, 147 LR fn, tp: 6, 8 LR f1 score: 0.095 LR cohens kappa score: 0.022 LR average precision score: 0.053 -> test with 'GB' GB tn, fp: 327, 5 GB fn, tp: 6, 8 GB f1 score: 0.593 GB cohens kappa score: 0.576 -> test with 'KNN' KNN tn, fp: 321, 11 KNN fn, tp: 0, 14 KNN f1 score: 0.718 KNN cohens kappa score: 0.703 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 172, 159 LR fn, tp: 2, 11 LR f1 score: 0.120 LR cohens kappa score: 0.054 LR average precision score: 0.082 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 8, 5 GB f1 score: 0.476 GB cohens kappa score: 0.461 -> test with 'KNN' KNN tn, fp: 313, 18 KNN fn, tp: 1, 12 KNN f1 score: 0.558 KNN cohens kappa score: 0.534 ====== 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: 187, 145 LR fn, tp: 8, 6 LR f1 score: 0.073 LR cohens kappa score: -0.001 LR average precision score: 0.051 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 6, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 313, 19 KNN fn, tp: 3, 11 KNN f1 score: 0.500 KNN cohens kappa score: 0.471 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 183, 149 LR fn, tp: 4, 10 LR f1 score: 0.116 LR cohens kappa score: 0.045 LR average precision score: 0.069 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 5, 9 GB f1 score: 0.720 GB cohens kappa score: 0.710 -> test with 'KNN' KNN tn, fp: 313, 19 KNN fn, tp: 0, 14 KNN f1 score: 0.596 KNN cohens kappa score: 0.571 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 166, 166 LR fn, tp: 4, 10 LR f1 score: 0.105 LR cohens kappa score: 0.033 LR average precision score: 0.075 -> test with 'GB' GB tn, fp: 327, 5 GB fn, tp: 2, 12 GB f1 score: 0.774 GB cohens kappa score: 0.764 -> test with 'KNN' KNN tn, fp: 319, 13 KNN fn, tp: 0, 14 KNN f1 score: 0.683 KNN cohens kappa score: 0.665 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 172, 160 LR fn, tp: 4, 10 LR f1 score: 0.109 LR cohens kappa score: 0.037 LR average precision score: 0.072 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 6, 8 GB f1 score: 0.727 GB cohens kappa score: 0.719 -> test with 'KNN' KNN tn, fp: 322, 10 KNN fn, tp: 0, 14 KNN f1 score: 0.737 KNN cohens kappa score: 0.723 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 176, 155 LR fn, tp: 3, 10 LR f1 score: 0.112 LR cohens kappa score: 0.045 LR average precision score: 0.057 -> 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: 315, 16 KNN fn, tp: 0, 13 KNN f1 score: 0.619 KNN cohens kappa score: 0.598 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 194, 168 LR fn, tp: 9, 11 LR f1 score: 0.129 LR cohens kappa score: 0.063 LR average precision score: 0.083 average: LR tn, fp: 179.12, 152.68 LR fn, tp: 4.56, 9.24 LR f1 score: 0.105 LR cohens kappa score: 0.034 LR average precision score: 0.064 minimum: LR tn, fp: 164, 138 LR fn, tp: 2, 5 LR f1 score: 0.062 LR cohens kappa score: -0.013 LR average precision score: 0.051 -----[ GB ]----- maximum: GB tn, fp: 332, 5 GB fn, tp: 8, 14 GB f1 score: 0.963 GB cohens kappa score: 0.961 average: GB tn, fp: 330.0, 1.8 GB fn, tp: 4.68, 9.12 GB f1 score: 0.730 GB cohens kappa score: 0.721 minimum: GB tn, fp: 327, 0 GB fn, tp: 0, 5 GB f1 score: 0.476 GB cohens kappa score: 0.461 -----[ KNN ]----- maximum: KNN tn, fp: 327, 32 KNN fn, tp: 3, 14 KNN f1 score: 0.813 KNN cohens kappa score: 0.804 average: KNN tn, fp: 315.72, 16.08 KNN fn, tp: 0.64, 13.16 KNN f1 score: 0.623 KNN cohens kappa score: 0.602 minimum: KNN tn, fp: 300, 5 KNN fn, tp: 0, 11 KNN f1 score: 0.467 KNN cohens kappa score: 0.431