/////////////////////////////////////////// // Running ctGAN 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: 163, 169 LR fn, tp: 5, 9 LR f1 score: 0.094 LR cohens kappa score: 0.020 LR average precision score: 0.059 -> test with 'GB' GB tn, fp: 304, 28 GB fn, tp: 0, 14 GB f1 score: 0.500 GB cohens kappa score: 0.468 -> test with 'KNN' KNN tn, fp: 244, 88 KNN fn, tp: 0, 14 KNN f1 score: 0.241 KNN cohens kappa score: 0.183 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 191, 141 LR fn, tp: 3, 11 LR f1 score: 0.133 LR cohens kappa score: 0.063 LR average precision score: 0.108 -> test with 'GB' GB tn, fp: 300, 32 GB fn, tp: 0, 14 GB f1 score: 0.467 GB cohens kappa score: 0.431 -> test with 'KNN' KNN tn, fp: 236, 96 KNN fn, tp: 0, 14 KNN f1 score: 0.226 KNN cohens kappa score: 0.166 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 182, 150 LR fn, tp: 7, 7 LR f1 score: 0.082 LR cohens kappa score: 0.008 LR average precision score: 0.061 -> test with 'GB' GB tn, fp: 311, 21 GB fn, tp: 0, 14 GB f1 score: 0.571 GB cohens kappa score: 0.545 -> test with 'KNN' KNN tn, fp: 259, 73 KNN fn, tp: 0, 14 KNN f1 score: 0.277 KNN cohens kappa score: 0.223 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 180, 152 LR fn, tp: 5, 9 LR f1 score: 0.103 LR cohens kappa score: 0.031 LR average precision score: 0.098 -> test with 'GB' GB tn, fp: 311, 21 GB fn, tp: 0, 14 GB f1 score: 0.571 GB cohens kappa score: 0.545 -> test with 'KNN' KNN tn, fp: 254, 78 KNN fn, tp: 0, 14 KNN f1 score: 0.264 KNN cohens kappa score: 0.209 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 177, 154 LR fn, tp: 4, 9 LR f1 score: 0.102 LR cohens kappa score: 0.035 LR average precision score: 0.046 -> test with 'GB' GB tn, fp: 310, 21 GB fn, tp: 0, 13 GB f1 score: 0.553 GB cohens kappa score: 0.527 -> test with 'KNN' KNN tn, fp: 263, 68 KNN fn, tp: 0, 13 KNN f1 score: 0.277 KNN cohens kappa score: 0.226 ====== 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: 162, 170 LR fn, tp: 5, 9 LR f1 score: 0.093 LR cohens kappa score: 0.020 LR average precision score: 0.076 -> test with 'GB' GB tn, fp: 312, 20 GB fn, tp: 0, 14 GB f1 score: 0.583 GB cohens kappa score: 0.558 -> test with 'KNN' KNN tn, fp: 247, 85 KNN fn, tp: 0, 14 KNN f1 score: 0.248 KNN cohens kappa score: 0.190 ------ Step 2/5: Slice 2/5 ------- -> 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: 0.064 -> test with 'GB' GB tn, fp: 316, 16 GB fn, tp: 0, 14 GB f1 score: 0.636 GB cohens kappa score: 0.615 -> test with 'KNN' KNN tn, fp: 261, 71 KNN fn, tp: 0, 14 KNN f1 score: 0.283 KNN cohens kappa score: 0.229 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 200, 132 LR fn, tp: 3, 11 LR f1 score: 0.140 LR cohens kappa score: 0.072 LR average precision score: 0.075 -> test with 'GB' GB tn, fp: 306, 26 GB fn, tp: 0, 14 GB f1 score: 0.519 GB cohens kappa score: 0.488 -> test with 'KNN' KNN tn, fp: 261, 71 KNN fn, tp: 0, 14 KNN f1 score: 0.283 KNN cohens kappa score: 0.229 ------ Step 2/5: 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: 6, 8 LR f1 score: 0.088 LR cohens kappa score: 0.015 LR average precision score: 0.046 -> test with 'GB' GB tn, fp: 296, 36 GB fn, tp: 0, 14 GB f1 score: 0.438 GB cohens kappa score: 0.400 -> test with 'KNN' KNN tn, fp: 217, 115 KNN fn, tp: 0, 14 KNN f1 score: 0.196 KNN cohens kappa score: 0.132 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 207, 124 LR fn, tp: 3, 10 LR f1 score: 0.136 LR cohens kappa score: 0.072 LR average precision score: 0.066 -> test with 'GB' GB tn, fp: 306, 25 GB fn, tp: 0, 13 GB f1 score: 0.510 GB cohens kappa score: 0.481 -> test with 'KNN' KNN tn, fp: 250, 81 KNN fn, tp: 0, 13 KNN f1 score: 0.243 KNN cohens kappa score: 0.189 ====== 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: 165, 167 LR fn, tp: 4, 10 LR f1 score: 0.105 LR cohens kappa score: 0.032 LR average precision score: 0.065 -> test with 'GB' GB tn, fp: 306, 26 GB fn, tp: 0, 14 GB f1 score: 0.519 GB cohens kappa score: 0.488 -> test with 'KNN' KNN tn, fp: 232, 100 KNN fn, tp: 0, 14 KNN f1 score: 0.219 KNN cohens kappa score: 0.158 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 170, 162 LR fn, tp: 5, 9 LR f1 score: 0.097 LR cohens kappa score: 0.024 LR average precision score: 0.057 -> test with 'GB' GB tn, fp: 312, 20 GB fn, tp: 0, 14 GB f1 score: 0.583 GB cohens kappa score: 0.558 -> test with 'KNN' KNN tn, fp: 261, 71 KNN fn, tp: 0, 14 KNN f1 score: 0.283 KNN cohens kappa score: 0.229 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 177, 155 LR fn, tp: 6, 8 LR f1 score: 0.090 LR cohens kappa score: 0.017 LR average precision score: 0.063 -> test with 'GB' GB tn, fp: 308, 24 GB fn, tp: 0, 14 GB f1 score: 0.538 GB cohens kappa score: 0.509 -> test with 'KNN' KNN tn, fp: 248, 84 KNN fn, tp: 0, 14 KNN f1 score: 0.250 KNN cohens kappa score: 0.193 ------ Step 3/5: 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: 3, 11 LR f1 score: 0.119 LR cohens kappa score: 0.048 LR average precision score: 0.073 -> test with 'GB' GB tn, fp: 305, 27 GB fn, tp: 0, 14 GB f1 score: 0.509 GB cohens kappa score: 0.478 -> test with 'KNN' KNN tn, fp: 251, 81 KNN fn, tp: 0, 14 KNN f1 score: 0.257 KNN cohens kappa score: 0.200 ------ 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: 0.057 -> test with 'GB' GB tn, fp: 305, 26 GB fn, tp: 0, 13 GB f1 score: 0.500 GB cohens kappa score: 0.470 -> test with 'KNN' KNN tn, fp: 230, 101 KNN fn, tp: 0, 13 KNN f1 score: 0.205 KNN cohens kappa score: 0.147 ====== 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: 178, 154 LR fn, tp: 3, 11 LR f1 score: 0.123 LR cohens kappa score: 0.052 LR average precision score: 0.084 -> test with 'GB' GB tn, fp: 315, 17 GB fn, tp: 0, 14 GB f1 score: 0.622 GB cohens kappa score: 0.600 -> test with 'KNN' KNN tn, fp: 256, 76 KNN fn, tp: 0, 14 KNN f1 score: 0.269 KNN cohens kappa score: 0.214 ------ Step 4/5: 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: 9, 5 LR f1 score: 0.061 LR cohens kappa score: -0.014 LR average precision score: 0.061 -> test with 'GB' GB tn, fp: 304, 28 GB fn, tp: 0, 14 GB f1 score: 0.500 GB cohens kappa score: 0.468 -> test with 'KNN' KNN tn, fp: 233, 99 KNN fn, tp: 0, 14 KNN f1 score: 0.220 KNN cohens kappa score: 0.160 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 164, 168 LR fn, tp: 1, 13 LR f1 score: 0.133 LR cohens kappa score: 0.063 LR average precision score: 0.068 -> test with 'GB' GB tn, fp: 304, 28 GB fn, tp: 0, 14 GB f1 score: 0.500 GB cohens kappa score: 0.468 -> test with 'KNN' KNN tn, fp: 242, 90 KNN fn, tp: 0, 14 KNN f1 score: 0.237 KNN cohens kappa score: 0.179 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 202, 130 LR fn, tp: 6, 8 LR f1 score: 0.105 LR cohens kappa score: 0.034 LR average precision score: 0.057 -> test with 'GB' GB tn, fp: 308, 24 GB fn, tp: 0, 14 GB f1 score: 0.538 GB cohens kappa score: 0.509 -> test with 'KNN' KNN tn, fp: 257, 75 KNN fn, tp: 0, 14 KNN f1 score: 0.272 KNN cohens kappa score: 0.217 ------ Step 4/5: 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: 3, 10 LR f1 score: 0.108 LR cohens kappa score: 0.040 LR average precision score: 0.081 -> test with 'GB' GB tn, fp: 305, 26 GB fn, tp: 0, 13 GB f1 score: 0.500 GB cohens kappa score: 0.470 -> test with 'KNN' KNN tn, fp: 237, 94 KNN fn, tp: 0, 13 KNN f1 score: 0.217 KNN cohens kappa score: 0.160 ====== 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: 164, 168 LR fn, tp: 3, 11 LR f1 score: 0.114 LR cohens kappa score: 0.042 LR average precision score: 0.058 -> test with 'GB' GB tn, fp: 303, 29 GB fn, tp: 0, 14 GB f1 score: 0.491 GB cohens kappa score: 0.458 -> test with 'KNN' KNN tn, fp: 246, 86 KNN fn, tp: 0, 14 KNN f1 score: 0.246 KNN cohens kappa score: 0.188 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 170, 162 LR fn, tp: 2, 12 LR f1 score: 0.128 LR cohens kappa score: 0.057 LR average precision score: 0.062 -> test with 'GB' GB tn, fp: 314, 18 GB fn, tp: 0, 14 GB f1 score: 0.609 GB cohens kappa score: 0.585 -> test with 'KNN' KNN tn, fp: 241, 91 KNN fn, tp: 0, 14 KNN f1 score: 0.235 KNN cohens kappa score: 0.176 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 148, 184 LR fn, tp: 3, 11 LR f1 score: 0.105 LR cohens kappa score: 0.032 LR average precision score: 0.147 -> test with 'GB' GB tn, fp: 301, 31 GB fn, tp: 0, 14 GB f1 score: 0.475 GB cohens kappa score: 0.440 -> test with 'KNN' KNN tn, fp: 244, 88 KNN fn, tp: 0, 14 KNN f1 score: 0.241 KNN cohens kappa score: 0.183 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 180, 152 LR fn, tp: 3, 11 LR f1 score: 0.124 LR cohens kappa score: 0.054 LR average precision score: 0.102 -> test with 'GB' GB tn, fp: 309, 23 GB fn, tp: 0, 14 GB f1 score: 0.549 GB cohens kappa score: 0.521 -> test with 'KNN' KNN tn, fp: 245, 87 KNN fn, tp: 0, 14 KNN f1 score: 0.243 KNN cohens kappa score: 0.186 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 191, 140 LR fn, tp: 4, 9 LR f1 score: 0.111 LR cohens kappa score: 0.045 LR average precision score: 0.064 -> test with 'GB' GB tn, fp: 309, 22 GB fn, tp: 0, 13 GB f1 score: 0.542 GB cohens kappa score: 0.515 -> test with 'KNN' KNN tn, fp: 250, 81 KNN fn, tp: 0, 13 KNN f1 score: 0.243 KNN cohens kappa score: 0.189 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 207, 184 LR fn, tp: 9, 13 LR f1 score: 0.140 LR cohens kappa score: 0.072 LR average precision score: 0.147 average: LR tn, fp: 176.36, 155.44 LR fn, tp: 4.28, 9.52 LR f1 score: 0.107 LR cohens kappa score: 0.036 LR average precision score: 0.072 minimum: LR tn, fp: 148, 124 LR fn, tp: 1, 5 LR f1 score: 0.061 LR cohens kappa score: -0.014 LR average precision score: 0.046 -----[ GB ]----- maximum: GB tn, fp: 316, 36 GB fn, tp: 0, 14 GB f1 score: 0.636 GB cohens kappa score: 0.615 average: GB tn, fp: 307.2, 24.6 GB fn, tp: 0.0, 13.8 GB f1 score: 0.533 GB cohens kappa score: 0.504 minimum: GB tn, fp: 296, 16 GB fn, tp: 0, 13 GB f1 score: 0.438 GB cohens kappa score: 0.400 -----[ KNN ]----- maximum: KNN tn, fp: 263, 115 KNN fn, tp: 0, 14 KNN f1 score: 0.283 KNN cohens kappa score: 0.229 average: KNN tn, fp: 246.6, 85.2 KNN fn, tp: 0.0, 13.8 KNN f1 score: 0.247 KNN cohens kappa score: 0.190 minimum: KNN tn, fp: 217, 68 KNN fn, tp: 0, 13 KNN f1 score: 0.196 KNN cohens kappa score: 0.132