/////////////////////////////////////////// // Running ProWRAS on kaggle_creditcard /////////////////////////////////////////// Load 'data_input/kaggle_creditcard' 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 227059 synthetic samples -> test with 'LR' LR tn, fp: 56773, 90 LR fn, tp: 25, 74 LR f1 score: 0.563 LR cohens kappa score: 0.562 LR average precision score: 0.550 -> test with 'GB' GB tn, fp: 56841, 22 GB fn, tp: 27, 72 GB f1 score: 0.746 GB cohens kappa score: 0.746 -> test with 'KNN' KNN tn, fp: 56730, 133 KNN fn, tp: 84, 15 KNN f1 score: 0.121 KNN cohens kappa score: 0.120 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56357, 506 LR fn, tp: 13, 86 LR f1 score: 0.249 LR cohens kappa score: 0.247 LR average precision score: 0.713 -> test with 'GB' GB tn, fp: 56852, 11 GB fn, tp: 19, 80 GB f1 score: 0.842 GB cohens kappa score: 0.842 -> test with 'KNN' KNN tn, fp: 56696, 167 KNN fn, tp: 88, 11 KNN f1 score: 0.079 KNN cohens kappa score: 0.077 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56625, 238 LR fn, tp: 14, 85 LR f1 score: 0.403 LR cohens kappa score: 0.401 LR average precision score: 0.721 -> test with 'GB' GB tn, fp: 56847, 16 GB fn, tp: 16, 83 GB f1 score: 0.838 GB cohens kappa score: 0.838 -> test with 'KNN' KNN tn, fp: 56725, 138 KNN fn, tp: 91, 8 KNN f1 score: 0.065 KNN cohens kappa score: 0.063 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56688, 175 LR fn, tp: 12, 87 LR f1 score: 0.482 LR cohens kappa score: 0.481 LR average precision score: 0.776 -> test with 'GB' GB tn, fp: 56848, 15 GB fn, tp: 17, 82 GB f1 score: 0.837 GB cohens kappa score: 0.836 -> test with 'KNN' KNN tn, fp: 56744, 119 KNN fn, tp: 85, 14 KNN f1 score: 0.121 KNN cohens kappa score: 0.119 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with 'LR' LR tn, fp: 56571, 292 LR fn, tp: 11, 85 LR f1 score: 0.359 LR cohens kappa score: 0.358 LR average precision score: 0.845 -> test with 'GB' GB tn, fp: 56854, 9 GB fn, tp: 16, 80 GB f1 score: 0.865 GB cohens kappa score: 0.865 -> test with 'KNN' KNN tn, fp: 56730, 133 KNN fn, tp: 86, 10 KNN f1 score: 0.084 KNN cohens kappa score: 0.082 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56593, 270 LR fn, tp: 13, 86 LR f1 score: 0.378 LR cohens kappa score: 0.376 LR average precision score: 0.746 -> test with 'GB' GB tn, fp: 56843, 20 GB fn, tp: 14, 85 GB f1 score: 0.833 GB cohens kappa score: 0.833 -> test with 'KNN' KNN tn, fp: 56675, 188 KNN fn, tp: 85, 14 KNN f1 score: 0.093 KNN cohens kappa score: 0.091 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56531, 332 LR fn, tp: 13, 86 LR f1 score: 0.333 LR cohens kappa score: 0.331 LR average precision score: 0.666 -> test with 'GB' GB tn, fp: 56840, 23 GB fn, tp: 17, 82 GB f1 score: 0.804 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 56724, 139 KNN fn, tp: 89, 10 KNN f1 score: 0.081 KNN cohens kappa score: 0.079 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56701, 162 LR fn, tp: 16, 83 LR f1 score: 0.483 LR cohens kappa score: 0.481 LR average precision score: 0.728 -> test with 'GB' GB tn, fp: 56851, 12 GB fn, tp: 21, 78 GB f1 score: 0.825 GB cohens kappa score: 0.825 -> test with 'KNN' KNN tn, fp: 56709, 154 KNN fn, tp: 86, 13 KNN f1 score: 0.098 KNN cohens kappa score: 0.096 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56521, 342 LR fn, tp: 18, 81 LR f1 score: 0.310 LR cohens kappa score: 0.308 LR average precision score: 0.702 -> test with 'GB' GB tn, fp: 56853, 10 GB fn, tp: 25, 74 GB f1 score: 0.809 GB cohens kappa score: 0.808 -> test with 'KNN' KNN tn, fp: 56742, 121 KNN fn, tp: 86, 13 KNN f1 score: 0.112 KNN cohens kappa score: 0.110 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with 'LR' LR tn, fp: 56663, 200 LR fn, tp: 19, 77 LR f1 score: 0.413 LR cohens kappa score: 0.411 LR average precision score: 0.751 -> test with 'GB' GB tn, fp: 56848, 15 GB fn, tp: 21, 75 GB f1 score: 0.806 GB cohens kappa score: 0.806 -> test with 'KNN' KNN tn, fp: 56742, 121 KNN fn, tp: 83, 13 KNN f1 score: 0.113 KNN cohens kappa score: 0.111 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56781, 82 LR fn, tp: 21, 78 LR f1 score: 0.602 LR cohens kappa score: 0.601 LR average precision score: 0.657 -> test with 'GB' GB tn, fp: 56835, 28 GB fn, tp: 24, 75 GB f1 score: 0.743 GB cohens kappa score: 0.742 -> test with 'KNN' KNN tn, fp: 56708, 155 KNN fn, tp: 88, 11 KNN f1 score: 0.083 KNN cohens kappa score: 0.081 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56753, 110 LR fn, tp: 17, 82 LR f1 score: 0.564 LR cohens kappa score: 0.563 LR average precision score: 0.660 -> test with 'GB' GB tn, fp: 56844, 19 GB fn, tp: 17, 82 GB f1 score: 0.820 GB cohens kappa score: 0.820 -> test with 'KNN' KNN tn, fp: 56710, 153 KNN fn, tp: 88, 11 KNN f1 score: 0.084 KNN cohens kappa score: 0.082 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 55881, 982 LR fn, tp: 15, 84 LR f1 score: 0.144 LR cohens kappa score: 0.141 LR average precision score: 0.702 -> test with 'GB' GB tn, fp: 56853, 10 GB fn, tp: 18, 81 GB f1 score: 0.853 GB cohens kappa score: 0.852 -> test with 'KNN' KNN tn, fp: 56714, 149 KNN fn, tp: 85, 14 KNN f1 score: 0.107 KNN cohens kappa score: 0.105 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56492, 371 LR fn, tp: 11, 88 LR f1 score: 0.315 LR cohens kappa score: 0.313 LR average precision score: 0.796 -> test with 'GB' GB tn, fp: 56850, 13 GB fn, tp: 17, 82 GB f1 score: 0.845 GB cohens kappa score: 0.845 -> test with 'KNN' KNN tn, fp: 56709, 154 KNN fn, tp: 87, 12 KNN f1 score: 0.091 KNN cohens kappa score: 0.089 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with 'LR' LR tn, fp: 56660, 203 LR fn, tp: 17, 79 LR f1 score: 0.418 LR cohens kappa score: 0.417 LR average precision score: 0.761 -> test with 'GB' GB tn, fp: 56851, 12 GB fn, tp: 21, 75 GB f1 score: 0.820 GB cohens kappa score: 0.819 -> test with 'KNN' KNN tn, fp: 56747, 116 KNN fn, tp: 85, 11 KNN f1 score: 0.099 KNN cohens kappa score: 0.097 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56418, 445 LR fn, tp: 11, 88 LR f1 score: 0.278 LR cohens kappa score: 0.276 LR average precision score: 0.717 -> test with 'GB' GB tn, fp: 56849, 14 GB fn, tp: 16, 83 GB f1 score: 0.847 GB cohens kappa score: 0.847 -> test with 'KNN' KNN tn, fp: 56722, 141 KNN fn, tp: 89, 10 KNN f1 score: 0.080 KNN cohens kappa score: 0.078 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 55857, 1006 LR fn, tp: 16, 83 LR f1 score: 0.140 LR cohens kappa score: 0.137 LR average precision score: 0.593 -> test with 'GB' GB tn, fp: 56845, 18 GB fn, tp: 18, 81 GB f1 score: 0.818 GB cohens kappa score: 0.818 -> test with 'KNN' KNN tn, fp: 56726, 137 KNN fn, tp: 91, 8 KNN f1 score: 0.066 KNN cohens kappa score: 0.064 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 55612, 1251 LR fn, tp: 17, 82 LR f1 score: 0.115 LR cohens kappa score: 0.112 LR average precision score: 0.675 -> test with 'GB' GB tn, fp: 56853, 10 GB fn, tp: 26, 73 GB f1 score: 0.802 GB cohens kappa score: 0.802 -> test with 'KNN' KNN tn, fp: 56710, 153 KNN fn, tp: 84, 15 KNN f1 score: 0.112 KNN cohens kappa score: 0.110 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56830, 33 LR fn, tp: 17, 82 LR f1 score: 0.766 LR cohens kappa score: 0.766 LR average precision score: 0.763 -> test with 'GB' GB tn, fp: 56855, 8 GB fn, tp: 21, 78 GB f1 score: 0.843 GB cohens kappa score: 0.843 -> test with 'KNN' KNN tn, fp: 56729, 134 KNN fn, tp: 80, 19 KNN f1 score: 0.151 KNN cohens kappa score: 0.149 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with 'LR' LR tn, fp: 56653, 210 LR fn, tp: 21, 75 LR f1 score: 0.394 LR cohens kappa score: 0.392 LR average precision score: 0.710 -> test with 'GB' GB tn, fp: 56850, 13 GB fn, tp: 23, 73 GB f1 score: 0.802 GB cohens kappa score: 0.802 -> test with 'KNN' KNN tn, fp: 56733, 130 KNN fn, tp: 85, 11 KNN f1 score: 0.093 KNN cohens kappa score: 0.091 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56750, 113 LR fn, tp: 24, 75 LR f1 score: 0.523 LR cohens kappa score: 0.522 LR average precision score: 0.644 -> test with 'GB' GB tn, fp: 56848, 15 GB fn, tp: 26, 73 GB f1 score: 0.781 GB cohens kappa score: 0.780 -> test with 'KNN' KNN tn, fp: 56731, 132 KNN fn, tp: 90, 9 KNN f1 score: 0.075 KNN cohens kappa score: 0.073 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56593, 270 LR fn, tp: 12, 87 LR f1 score: 0.382 LR cohens kappa score: 0.380 LR average precision score: 0.759 -> test with 'GB' GB tn, fp: 56845, 18 GB fn, tp: 17, 82 GB f1 score: 0.824 GB cohens kappa score: 0.824 -> test with 'KNN' KNN tn, fp: 56716, 147 KNN fn, tp: 83, 16 KNN f1 score: 0.122 KNN cohens kappa score: 0.120 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56695, 168 LR fn, tp: 20, 79 LR f1 score: 0.457 LR cohens kappa score: 0.455 LR average precision score: 0.689 -> test with 'GB' GB tn, fp: 56845, 18 GB fn, tp: 21, 78 GB f1 score: 0.800 GB cohens kappa score: 0.800 -> test with 'KNN' KNN tn, fp: 56735, 128 KNN fn, tp: 90, 9 KNN f1 score: 0.076 KNN cohens kappa score: 0.074 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 56407, 456 LR fn, tp: 13, 86 LR f1 score: 0.268 LR cohens kappa score: 0.266 LR average precision score: 0.764 -> test with 'GB' GB tn, fp: 56856, 7 GB fn, tp: 19, 80 GB f1 score: 0.860 GB cohens kappa score: 0.860 -> test with 'KNN' KNN tn, fp: 56736, 127 KNN fn, tp: 83, 16 KNN f1 score: 0.132 KNN cohens kappa score: 0.130 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with 'LR' LR tn, fp: 56670, 193 LR fn, tp: 15, 81 LR f1 score: 0.438 LR cohens kappa score: 0.436 LR average precision score: 0.712 -> test with 'GB' GB tn, fp: 56847, 16 GB fn, tp: 21, 75 GB f1 score: 0.802 GB cohens kappa score: 0.802 -> test with 'KNN' KNN tn, fp: 56730, 133 KNN fn, tp: 84, 12 KNN f1 score: 0.100 KNN cohens kappa score: 0.098 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 56830, 1251 LR fn, tp: 25, 88 LR f1 score: 0.766 LR cohens kappa score: 0.766 LR average precision score: 0.845 average: LR tn, fp: 56523.0, 340.0 LR fn, tp: 16.04, 82.36 LR f1 score: 0.391 LR cohens kappa score: 0.389 LR average precision score: 0.712 minimum: LR tn, fp: 55612, 33 LR fn, tp: 11, 74 LR f1 score: 0.115 LR cohens kappa score: 0.112 LR average precision score: 0.550 -----[ GB ]----- maximum: GB tn, fp: 56856, 28 GB fn, tp: 27, 85 GB f1 score: 0.865 GB cohens kappa score: 0.865 average: GB tn, fp: 56848.12, 14.88 GB fn, tp: 19.92, 78.48 GB f1 score: 0.819 GB cohens kappa score: 0.818 minimum: GB tn, fp: 56835, 7 GB fn, tp: 14, 72 GB f1 score: 0.743 GB cohens kappa score: 0.742 -----[ KNN ]----- maximum: KNN tn, fp: 56747, 188 KNN fn, tp: 91, 19 KNN f1 score: 0.151 KNN cohens kappa score: 0.149 average: KNN tn, fp: 56722.92, 140.08 KNN fn, tp: 86.2, 12.2 KNN f1 score: 0.097 KNN cohens kappa score: 0.096 minimum: KNN tn, fp: 56675, 116 KNN fn, tp: 80, 8 KNN f1 score: 0.065 KNN cohens kappa score: 0.063