/////////////////////////////////////////// // Running SpheredNoise on folding_car-vgood /////////////////////////////////////////// Load 'data_input/folding_car-vgood' 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 Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 315, 18 LR fn, tp: 12, 1 LR f1 score: 0.062 LR cohens kappa score: 0.019 LR average precision score: 0.184 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 328, 5 KNN fn, tp: 10, 3 KNN f1 score: 0.286 KNN cohens kappa score: 0.265 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 314, 19 LR fn, tp: 9, 4 LR f1 score: 0.222 LR cohens kappa score: 0.183 LR average precision score: 0.182 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 332, 1 KNN fn, tp: 8, 5 KNN f1 score: 0.526 KNN cohens kappa score: 0.515 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 314, 19 LR fn, tp: 7, 6 LR f1 score: 0.316 LR cohens kappa score: 0.280 LR average precision score: 0.272 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 6, 7 KNN f1 score: 0.609 KNN cohens kappa score: 0.595 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 321, 12 LR fn, tp: 9, 4 LR f1 score: 0.276 LR cohens kappa score: 0.245 LR average precision score: 0.248 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 333, 0 KNN fn, tp: 8, 5 KNN f1 score: 0.556 KNN cohens kappa score: 0.546 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1332/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.4142135623730951 -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 320, 11 LR fn, tp: 8, 5 LR f1 score: 0.345 LR cohens kappa score: 0.316 LR average precision score: 0.222 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 329, 2 KNN fn, tp: 7, 6 KNN f1 score: 0.571 KNN cohens kappa score: 0.559 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 316, 17 LR fn, tp: 9, 4 LR f1 score: 0.235 LR cohens kappa score: 0.198 LR average precision score: 0.331 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 332, 1 KNN fn, tp: 8, 5 KNN f1 score: 0.526 KNN cohens kappa score: 0.515 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.4142135623730951 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 307, 26 LR fn, tp: 8, 5 LR f1 score: 0.227 LR cohens kappa score: 0.184 LR average precision score: 0.167 -> test with 'GB' GB tn, fp: 328, 5 GB fn, tp: 0, 13 GB f1 score: 0.839 GB cohens kappa score: 0.831 -> test with 'KNN' KNN tn, fp: 326, 7 KNN fn, tp: 6, 7 KNN f1 score: 0.519 KNN cohens kappa score: 0.499 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 320, 13 LR fn, tp: 10, 3 LR f1 score: 0.207 LR cohens kappa score: 0.173 LR average precision score: 0.208 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 8, 5 KNN f1 score: 0.476 KNN cohens kappa score: 0.461 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 322, 11 LR fn, tp: 9, 4 LR f1 score: 0.286 LR cohens kappa score: 0.256 LR average precision score: 0.307 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 3, 10 GB f1 score: 0.870 GB cohens kappa score: 0.865 -> test with 'KNN' KNN tn, fp: 333, 0 KNN fn, tp: 9, 4 KNN f1 score: 0.471 KNN cohens kappa score: 0.461 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1332/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 317, 14 LR fn, tp: 10, 3 LR f1 score: 0.200 LR cohens kappa score: 0.164 LR average precision score: 0.182 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 330, 1 KNN fn, tp: 8, 5 KNN f1 score: 0.526 KNN cohens kappa score: 0.515 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 318, 15 LR fn, tp: 11, 2 LR f1 score: 0.133 LR cohens kappa score: 0.095 LR average precision score: 0.192 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 3, 10 GB f1 score: 0.870 GB cohens kappa score: 0.865 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 7, 6 KNN f1 score: 0.545 KNN cohens kappa score: 0.531 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 314, 19 LR fn, tp: 9, 4 LR f1 score: 0.222 LR cohens kappa score: 0.183 LR average precision score: 0.184 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 8, 5 KNN f1 score: 0.476 KNN cohens kappa score: 0.461 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 309, 24 LR fn, tp: 6, 7 LR f1 score: 0.318 LR cohens kappa score: 0.280 LR average precision score: 0.246 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 7, 6 KNN f1 score: 0.545 KNN cohens kappa score: 0.531 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 320, 13 LR fn, tp: 9, 4 LR f1 score: 0.267 LR cohens kappa score: 0.234 LR average precision score: 0.234 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 329, 4 KNN fn, tp: 8, 5 KNN f1 score: 0.455 KNN cohens kappa score: 0.437 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1332/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 322, 9 LR fn, tp: 10, 3 LR f1 score: 0.240 LR cohens kappa score: 0.211 LR average precision score: 0.286 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 4, 9 GB f1 score: 0.818 GB cohens kappa score: 0.812 -> test with 'KNN' KNN tn, fp: 331, 0 KNN fn, tp: 8, 5 KNN f1 score: 0.556 KNN cohens kappa score: 0.546 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 319, 14 LR fn, tp: 10, 3 LR f1 score: 0.200 LR cohens kappa score: 0.164 LR average precision score: 0.215 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 333, 0 KNN fn, tp: 11, 2 KNN f1 score: 0.267 KNN cohens kappa score: 0.259 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 314, 19 LR fn, tp: 7, 6 LR f1 score: 0.316 LR cohens kappa score: 0.280 LR average precision score: 0.243 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 1, 12 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> test with 'KNN' KNN tn, fp: 328, 5 KNN fn, tp: 9, 4 KNN f1 score: 0.364 KNN cohens kappa score: 0.343 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 315, 18 LR fn, tp: 10, 3 LR f1 score: 0.176 LR cohens kappa score: 0.136 LR average precision score: 0.179 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 6, 7 KNN f1 score: 0.609 KNN cohens kappa score: 0.595 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 320, 13 LR fn, tp: 9, 4 LR f1 score: 0.267 LR cohens kappa score: 0.234 LR average precision score: 0.243 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 9, 4 KNN f1 score: 0.400 KNN cohens kappa score: 0.384 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1332/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 317, 14 LR fn, tp: 9, 4 LR f1 score: 0.258 LR cohens kappa score: 0.224 LR average precision score: 0.223 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.958 -> test with 'KNN' KNN tn, fp: 329, 2 KNN fn, tp: 7, 6 KNN f1 score: 0.571 KNN cohens kappa score: 0.559 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 310, 23 LR fn, tp: 7, 6 LR f1 score: 0.286 LR cohens kappa score: 0.247 LR average precision score: 0.230 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 325, 8 KNN fn, tp: 7, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.422 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 320, 13 LR fn, tp: 11, 2 LR f1 score: 0.143 LR cohens kappa score: 0.107 LR average precision score: 0.180 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 3, 10 GB f1 score: 0.870 GB cohens kappa score: 0.865 -> test with 'KNN' KNN tn, fp: 332, 1 KNN fn, tp: 9, 4 KNN f1 score: 0.444 KNN cohens kappa score: 0.433 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.4142135623730951 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 321, 12 LR fn, tp: 7, 6 LR f1 score: 0.387 LR cohens kappa score: 0.359 LR average precision score: 0.284 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 332, 1 KNN fn, tp: 8, 5 KNN f1 score: 0.526 KNN cohens kappa score: 0.515 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1330/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 314, 19 LR fn, tp: 9, 4 LR f1 score: 0.222 LR cohens kappa score: 0.183 LR average precision score: 0.197 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 330, 3 KNN fn, tp: 4, 9 KNN f1 score: 0.720 KNN cohens kappa score: 0.710 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1332/52 points -> new disc -> calc distances -> statistics trained 52 points min:1.0 max:1.0 -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 317, 14 LR fn, tp: 9, 4 LR f1 score: 0.258 LR cohens kappa score: 0.224 LR average precision score: 0.205 -> 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: 328, 3 KNN fn, tp: 6, 7 KNN f1 score: 0.609 KNN cohens kappa score: 0.595 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 322, 26 LR fn, tp: 12, 7 LR f1 score: 0.387 LR cohens kappa score: 0.359 LR average precision score: 0.331 average: LR tn, fp: 316.64, 15.96 LR fn, tp: 8.96, 4.04 LR f1 score: 0.243 LR cohens kappa score: 0.207 LR average precision score: 0.226 minimum: LR tn, fp: 307, 9 LR fn, tp: 6, 1 LR f1 score: 0.062 LR cohens kappa score: 0.019 LR average precision score: 0.167 -----[ GB ]----- maximum: GB tn, fp: 333, 5 GB fn, tp: 4, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 332.0, 0.6 GB fn, tp: 0.76, 12.24 GB f1 score: 0.946 GB cohens kappa score: 0.944 minimum: GB tn, fp: 328, 0 GB fn, tp: 0, 9 GB f1 score: 0.818 GB cohens kappa score: 0.812 -----[ KNN ]----- maximum: KNN tn, fp: 333, 8 KNN fn, tp: 11, 9 KNN f1 score: 0.720 KNN cohens kappa score: 0.710 average: KNN tn, fp: 330.0, 2.6 KNN fn, tp: 7.68, 5.32 KNN f1 score: 0.504 KNN cohens kappa score: 0.490 minimum: KNN tn, fp: 325, 0 KNN fn, tp: 4, 2 KNN f1 score: 0.267 KNN cohens kappa score: 0.259