/////////////////////////////////////////// // Running Repeater 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: 170, 162 LR fn, tp: 4, 10 LR f1 score: 0.108 LR cohens kappa score: 0.035 LR average precision score: 0.061 -> test with 'GB' GB tn, fp: 325, 7 GB fn, tp: 0, 14 GB f1 score: 0.800 GB cohens kappa score: 0.790 -> test with 'KNN' KNN tn, fp: 274, 58 KNN fn, tp: 0, 14 KNN f1 score: 0.326 KNN cohens kappa score: 0.277 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 185, 147 LR fn, tp: 3, 11 LR f1 score: 0.128 LR cohens kappa score: 0.058 LR average precision score: 0.087 -> test with 'GB' GB tn, fp: 327, 5 GB fn, tp: 0, 14 GB f1 score: 0.848 GB cohens kappa score: 0.841 -> test with 'KNN' KNN tn, fp: 278, 54 KNN fn, tp: 0, 14 KNN f1 score: 0.341 KNN cohens kappa score: 0.294 ------ Step 1/5: Slice 3/5 ------- -> 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: 0.057 -> test with 'GB' GB tn, fp: 328, 4 GB fn, tp: 0, 14 GB f1 score: 0.875 GB cohens kappa score: 0.869 -> test with 'KNN' KNN tn, fp: 284, 48 KNN fn, tp: 0, 14 KNN f1 score: 0.368 KNN cohens kappa score: 0.324 ------ Step 1/5: Slice 4/5 ------- -> 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: 0.077 -> test with 'GB' GB tn, fp: 327, 5 GB fn, tp: 0, 14 GB f1 score: 0.848 GB cohens kappa score: 0.841 -> test with 'KNN' KNN tn, fp: 283, 49 KNN fn, tp: 0, 14 KNN f1 score: 0.364 KNN cohens kappa score: 0.319 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 175, 156 LR fn, tp: 4, 9 LR f1 score: 0.101 LR cohens kappa score: 0.033 LR average precision score: 0.056 -> test with 'GB' GB tn, fp: 325, 6 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 290, 41 KNN fn, tp: 0, 13 KNN f1 score: 0.388 KNN cohens kappa score: 0.348 ====== 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: 157, 175 LR fn, tp: 4, 10 LR f1 score: 0.101 LR cohens kappa score: 0.027 LR average precision score: 0.068 -> test with 'GB' GB tn, fp: 325, 7 GB fn, tp: 0, 14 GB f1 score: 0.800 GB cohens kappa score: 0.790 -> test with 'KNN' KNN tn, fp: 286, 46 KNN fn, tp: 1, 13 KNN f1 score: 0.356 KNN cohens kappa score: 0.311 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 169, 163 LR fn, tp: 3, 11 LR f1 score: 0.117 LR cohens kappa score: 0.046 LR average precision score: 0.070 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 0, 14 GB f1 score: 0.903 GB cohens kappa score: 0.899 -> test with 'KNN' KNN tn, fp: 285, 47 KNN fn, tp: 0, 14 KNN f1 score: 0.373 KNN cohens kappa score: 0.329 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 186, 146 LR fn, tp: 4, 10 LR f1 score: 0.118 LR cohens kappa score: 0.047 LR average precision score: 0.072 -> 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: 292, 40 KNN fn, tp: 0, 14 KNN f1 score: 0.412 KNN cohens kappa score: 0.371 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 183, 149 LR fn, tp: 7, 7 LR f1 score: 0.082 LR cohens kappa score: 0.009 LR average precision score: 0.050 -> test with 'GB' GB tn, fp: 323, 9 GB fn, tp: 0, 14 GB f1 score: 0.757 GB cohens kappa score: 0.744 -> test with 'KNN' KNN tn, fp: 269, 63 KNN fn, tp: 0, 14 KNN f1 score: 0.308 KNN cohens kappa score: 0.257 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 180, 151 LR fn, tp: 5, 8 LR f1 score: 0.093 LR cohens kappa score: 0.025 LR average precision score: 0.077 -> test with 'GB' GB tn, fp: 325, 6 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 271, 60 KNN fn, tp: 0, 13 KNN f1 score: 0.302 KNN cohens kappa score: 0.254 ====== 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: 168, 164 LR fn, tp: 3, 11 LR f1 score: 0.116 LR cohens kappa score: 0.045 LR average precision score: 0.077 -> test with 'GB' GB tn, fp: 328, 4 GB fn, tp: 0, 14 GB f1 score: 0.875 GB cohens kappa score: 0.869 -> test with 'KNN' KNN tn, fp: 285, 47 KNN fn, tp: 0, 14 KNN f1 score: 0.373 KNN cohens kappa score: 0.329 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 190, 142 LR fn, tp: 3, 11 LR f1 score: 0.132 LR cohens kappa score: 0.062 LR average precision score: 0.067 -> test with 'GB' GB tn, fp: 324, 8 GB fn, tp: 0, 14 GB f1 score: 0.778 GB cohens kappa score: 0.766 -> test with 'KNN' KNN tn, fp: 284, 48 KNN fn, tp: 0, 14 KNN f1 score: 0.368 KNN cohens kappa score: 0.324 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 180, 152 LR fn, tp: 6, 8 LR f1 score: 0.092 LR cohens kappa score: 0.019 LR average precision score: 0.056 -> test with 'GB' GB tn, fp: 326, 6 GB fn, tp: 0, 14 GB f1 score: 0.824 GB cohens kappa score: 0.815 -> test with 'KNN' KNN tn, fp: 285, 47 KNN fn, tp: 1, 13 KNN f1 score: 0.351 KNN cohens kappa score: 0.306 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 170, 162 LR fn, tp: 3, 11 LR f1 score: 0.118 LR cohens kappa score: 0.046 LR average precision score: 0.083 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 0, 14 GB f1 score: 0.903 GB cohens kappa score: 0.899 -> test with 'KNN' KNN tn, fp: 291, 41 KNN fn, tp: 0, 14 KNN f1 score: 0.406 KNN cohens kappa score: 0.365 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 168, 163 LR fn, tp: 4, 9 LR f1 score: 0.097 LR cohens kappa score: 0.029 LR average precision score: 0.055 -> test with 'GB' GB tn, fp: 325, 6 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 268, 63 KNN fn, tp: 0, 13 KNN f1 score: 0.292 KNN cohens kappa score: 0.243 ====== 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: 177, 155 LR fn, tp: 3, 11 LR f1 score: 0.122 LR cohens kappa score: 0.051 LR average precision score: 0.067 -> test with 'GB' GB tn, fp: 327, 5 GB fn, tp: 0, 14 GB f1 score: 0.848 GB cohens kappa score: 0.841 -> test with 'KNN' KNN tn, fp: 286, 46 KNN fn, tp: 0, 14 KNN f1 score: 0.378 KNN cohens kappa score: 0.335 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 173, 159 LR fn, tp: 6, 8 LR f1 score: 0.088 LR cohens kappa score: 0.015 LR average precision score: 0.063 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 0, 14 GB f1 score: 0.903 GB cohens kappa score: 0.899 -> test with 'KNN' KNN tn, fp: 284, 48 KNN fn, tp: 0, 14 KNN f1 score: 0.368 KNN cohens kappa score: 0.324 ------ Step 4/5: Slice 3/5 ------- -> 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: 0.066 -> test with 'GB' GB tn, fp: 325, 7 GB fn, tp: 0, 14 GB f1 score: 0.800 GB cohens kappa score: 0.790 -> test with 'KNN' KNN tn, fp: 284, 48 KNN fn, tp: 0, 14 KNN f1 score: 0.368 KNN cohens kappa score: 0.324 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 189, 143 LR fn, tp: 6, 8 LR f1 score: 0.097 LR cohens kappa score: 0.025 LR average precision score: 0.056 -> test with 'GB' GB tn, fp: 326, 6 GB fn, tp: 0, 14 GB f1 score: 0.824 GB cohens kappa score: 0.815 -> test with 'KNN' KNN tn, fp: 288, 44 KNN fn, tp: 0, 14 KNN f1 score: 0.389 KNN cohens kappa score: 0.346 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 170, 161 LR fn, tp: 1, 12 LR f1 score: 0.129 LR cohens kappa score: 0.063 LR average precision score: 0.081 -> test with 'GB' GB tn, fp: 325, 6 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 279, 52 KNN fn, tp: 0, 13 KNN f1 score: 0.333 KNN cohens kappa score: 0.289 ====== 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: 175, 157 LR fn, tp: 8, 6 LR f1 score: 0.068 LR cohens kappa score: -0.007 LR average precision score: 0.054 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 0, 14 GB f1 score: 0.903 GB cohens kappa score: 0.899 -> test with 'KNN' KNN tn, fp: 288, 44 KNN fn, tp: 0, 14 KNN f1 score: 0.389 KNN cohens kappa score: 0.346 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 187, 145 LR fn, tp: 6, 8 LR f1 score: 0.096 LR cohens kappa score: 0.023 LR average precision score: 0.070 -> test with 'GB' GB tn, fp: 327, 5 GB fn, tp: 0, 14 GB f1 score: 0.848 GB cohens kappa score: 0.841 -> test with 'KNN' KNN tn, fp: 279, 53 KNN fn, tp: 0, 14 KNN f1 score: 0.346 KNN cohens kappa score: 0.299 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 162, 170 LR fn, tp: 3, 11 LR f1 score: 0.113 LR cohens kappa score: 0.041 LR average precision score: 0.079 -> test with 'GB' GB tn, fp: 323, 9 GB fn, tp: 0, 14 GB f1 score: 0.757 GB cohens kappa score: 0.744 -> test with 'KNN' KNN tn, fp: 276, 56 KNN fn, tp: 0, 14 KNN f1 score: 0.333 KNN cohens kappa score: 0.285 ------ Step 5/5: Slice 4/5 ------- -> 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: 0.078 -> test with 'GB' GB tn, fp: 326, 6 GB fn, tp: 0, 14 GB f1 score: 0.824 GB cohens kappa score: 0.815 -> test with 'KNN' KNN tn, fp: 282, 50 KNN fn, tp: 0, 14 KNN f1 score: 0.359 KNN cohens kappa score: 0.313 ------ 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: 4, 9 LR f1 score: 0.102 LR cohens kappa score: 0.034 LR average precision score: 0.065 -> test with 'GB' GB tn, fp: 327, 4 GB fn, tp: 0, 13 GB f1 score: 0.867 GB cohens kappa score: 0.861 -> test with 'KNN' KNN tn, fp: 279, 52 KNN fn, tp: 0, 13 KNN f1 score: 0.333 KNN cohens kappa score: 0.289 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 190, 175 LR fn, tp: 8, 12 LR f1 score: 0.132 LR cohens kappa score: 0.063 LR average precision score: 0.087 average: LR tn, fp: 175.32, 156.48 LR fn, tp: 4.24, 9.56 LR f1 score: 0.106 LR cohens kappa score: 0.035 LR average precision score: 0.068 minimum: LR tn, fp: 157, 142 LR fn, tp: 1, 6 LR f1 score: 0.068 LR cohens kappa score: -0.007 LR average precision score: 0.050 -----[ GB ]----- maximum: GB tn, fp: 330, 9 GB fn, tp: 0, 14 GB f1 score: 0.933 GB cohens kappa score: 0.930 average: GB tn, fp: 326.4, 5.4 GB fn, tp: 0.0, 13.8 GB f1 score: 0.839 GB cohens kappa score: 0.831 minimum: GB tn, fp: 323, 2 GB fn, tp: 0, 13 GB f1 score: 0.757 GB cohens kappa score: 0.744 -----[ KNN ]----- maximum: KNN tn, fp: 292, 63 KNN fn, tp: 1, 14 KNN f1 score: 0.412 KNN cohens kappa score: 0.371 average: KNN tn, fp: 282.0, 49.8 KNN fn, tp: 0.08, 13.72 KNN f1 score: 0.357 KNN cohens kappa score: 0.312 minimum: KNN tn, fp: 268, 40 KNN fn, tp: 0, 13 KNN f1 score: 0.292 KNN cohens kappa score: 0.243