/////////////////////////////////////////// // Running SpheredNoise 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 Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 313, 19 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.049 LR average precision score: 0.038 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 7, 7 GB f1 score: 0.667 GB cohens kappa score: 0.657 -> test with 'KNN' KNN tn, fp: 330, 2 KNN fn, tp: 12, 2 KNN f1 score: 0.222 KNN cohens kappa score: 0.208 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 314, 18 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.048 LR average precision score: 0.033 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 4, 10 GB f1 score: 0.833 GB cohens kappa score: 0.828 -> test with 'KNN' KNN tn, fp: 331, 1 KNN fn, tp: 11, 3 KNN f1 score: 0.333 KNN cohens kappa score: 0.321 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 313, 19 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.049 LR average precision score: 0.043 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 5, 9 GB f1 score: 0.750 GB cohens kappa score: 0.741 -> test with 'KNN' KNN tn, fp: 329, 3 KNN fn, tp: 12, 2 KNN f1 score: 0.211 KNN cohens kappa score: 0.193 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 311, 21 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.051 LR average precision score: 0.038 -> 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: 329, 3 KNN fn, tp: 14, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.014 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1328/56 points -> new disc -> calc distances -> statistics trained 56 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 311, 20 LR fn, tp: 13, 0 LR f1 score: 0.000 LR cohens kappa score: -0.048 LR average precision score: 0.048 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 4, 9 GB f1 score: 0.720 GB cohens kappa score: 0.709 -> test with 'KNN' KNN tn, fp: 330, 1 KNN fn, tp: 11, 2 KNN f1 score: 0.250 KNN cohens kappa score: 0.239 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 312, 20 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.050 LR average precision score: 0.040 -> 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: 329, 3 KNN fn, tp: 10, 4 KNN f1 score: 0.381 KNN cohens kappa score: 0.364 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 317, 15 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.044 LR average precision score: 0.035 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 3, 11 GB f1 score: 0.880 GB cohens kappa score: 0.876 -> test with 'KNN' KNN tn, fp: 328, 4 KNN fn, tp: 8, 6 KNN f1 score: 0.500 KNN cohens kappa score: 0.483 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 317, 15 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.044 LR average precision score: 0.043 -> 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: 331, 1 KNN fn, tp: 13, 1 KNN f1 score: 0.125 KNN cohens kappa score: 0.116 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 314, 18 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.048 LR average precision score: 0.040 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 4, 10 GB f1 score: 0.833 GB cohens kappa score: 0.828 -> test with 'KNN' KNN tn, fp: 331, 1 KNN fn, tp: 12, 2 KNN f1 score: 0.235 KNN cohens kappa score: 0.224 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1328/56 points -> new disc -> calc distances -> statistics trained 56 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 307, 24 LR fn, tp: 13, 0 LR f1 score: 0.000 LR cohens kappa score: -0.052 LR average precision score: 0.040 -> test with 'GB' GB tn, fp: 330, 1 GB fn, tp: 3, 10 GB f1 score: 0.833 GB cohens kappa score: 0.827 -> test with 'KNN' KNN tn, fp: 327, 4 KNN fn, tp: 12, 1 KNN f1 score: 0.111 KNN cohens kappa score: 0.092 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.4142135623730951 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 312, 20 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.050 LR average precision score: 0.039 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 3, 11 GB f1 score: 0.880 GB cohens kappa score: 0.876 -> test with 'KNN' KNN tn, fp: 330, 2 KNN fn, tp: 11, 3 KNN f1 score: 0.316 KNN cohens kappa score: 0.301 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 315, 17 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.046 LR average precision score: 0.045 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 3, 11 GB f1 score: 0.815 GB cohens kappa score: 0.807 -> test with 'KNN' KNN tn, fp: 330, 2 KNN fn, tp: 12, 2 KNN f1 score: 0.222 KNN cohens kappa score: 0.208 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 317, 15 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.044 LR average precision score: 0.039 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 3, 11 GB f1 score: 0.815 GB cohens kappa score: 0.807 -> test with 'KNN' KNN tn, fp: 328, 4 KNN fn, tp: 12, 2 KNN f1 score: 0.200 KNN cohens kappa score: 0.180 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 318, 14 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.042 LR average precision score: 0.037 -> 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: 330, 2 KNN fn, tp: 13, 1 KNN f1 score: 0.118 KNN cohens kappa score: 0.105 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1328/56 points -> new disc -> calc distances -> statistics trained 56 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 304, 27 LR fn, tp: 13, 0 LR f1 score: 0.000 LR cohens kappa score: -0.054 LR average precision score: 0.037 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 5, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 329, 2 KNN fn, tp: 9, 4 KNN f1 score: 0.421 KNN cohens kappa score: 0.407 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 309, 23 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.053 LR average precision score: 0.040 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 0, 14 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 329, 3 KNN fn, tp: 9, 5 KNN f1 score: 0.455 KNN cohens kappa score: 0.438 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 319, 13 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.041 LR average precision score: 0.037 -> 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: 330, 2 KNN fn, tp: 12, 2 KNN f1 score: 0.222 KNN cohens kappa score: 0.208 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 317, 15 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.044 LR average precision score: 0.037 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 8, 6 GB f1 score: 0.571 GB cohens kappa score: 0.560 -> test with 'KNN' KNN tn, fp: 329, 3 KNN fn, tp: 13, 1 KNN f1 score: 0.111 KNN cohens kappa score: 0.095 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 307, 25 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.055 LR average precision score: 0.037 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 2, 12 GB f1 score: 0.889 GB cohens kappa score: 0.884 -> test with 'KNN' KNN tn, fp: 327, 5 KNN fn, tp: 11, 3 KNN f1 score: 0.273 KNN cohens kappa score: 0.251 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1328/56 points -> new disc -> calc distances -> statistics trained 56 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 313, 18 LR fn, tp: 13, 0 LR f1 score: 0.000 LR cohens kappa score: -0.046 LR average precision score: 0.046 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 4, 9 GB f1 score: 0.750 GB cohens kappa score: 0.741 -> test with 'KNN' KNN tn, fp: 328, 3 KNN fn, tp: 10, 3 KNN f1 score: 0.316 KNN cohens kappa score: 0.299 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 309, 23 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.053 LR average precision score: 0.034 -> 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: 329, 3 KNN fn, tp: 12, 2 KNN f1 score: 0.211 KNN cohens kappa score: 0.193 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 316, 16 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.045 LR average precision score: 0.041 -> 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: 331, 1 KNN fn, tp: 13, 1 KNN f1 score: 0.125 KNN cohens kappa score: 0.116 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 316, 16 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.045 LR average precision score: 0.043 -> 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: 329, 3 KNN fn, tp: 11, 3 KNN f1 score: 0.300 KNN cohens kappa score: 0.283 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1327/55 points -> new disc -> calc distances -> statistics trained 55 points min:1.0 max:1.0 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 311, 21 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.051 LR average precision score: 0.046 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 7, 7 GB f1 score: 0.667 GB cohens kappa score: 0.657 -> test with 'KNN' KNN tn, fp: 329, 3 KNN fn, tp: 13, 1 KNN f1 score: 0.111 KNN cohens kappa score: 0.095 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1328/56 points -> new disc -> calc distances -> statistics trained 56 points min:1.0 max:1.4142135623730951 -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 311, 20 LR fn, tp: 13, 0 LR f1 score: 0.000 LR cohens kappa score: -0.048 LR average precision score: 0.034 -> test with 'GB' GB tn, fp: 330, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 328, 3 KNN fn, tp: 11, 2 KNN f1 score: 0.222 KNN cohens kappa score: 0.206 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 319, 27 LR fn, tp: 14, 0 LR f1 score: 0.000 LR cohens kappa score: -0.041 LR average precision score: 0.048 average: LR tn, fp: 312.92, 18.88 LR fn, tp: 13.8, 0.0 LR f1 score: 0.000 LR cohens kappa score: -0.048 LR average precision score: 0.040 minimum: LR tn, fp: 304, 13 LR fn, tp: 13, 0 LR f1 score: 0.000 LR cohens kappa score: -0.055 LR average precision score: 0.033 -----[ GB ]----- maximum: GB tn, fp: 332, 3 GB fn, tp: 8, 14 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 330.92, 0.88 GB fn, tp: 4.2, 9.6 GB f1 score: 0.782 GB cohens kappa score: 0.775 minimum: GB tn, fp: 328, 0 GB fn, tp: 0, 6 GB f1 score: 0.571 GB cohens kappa score: 0.560 -----[ KNN ]----- maximum: KNN tn, fp: 331, 5 KNN fn, tp: 14, 6 KNN f1 score: 0.500 KNN cohens kappa score: 0.483 average: KNN tn, fp: 329.24, 2.56 KNN fn, tp: 11.48, 2.32 KNN f1 score: 0.240 KNN cohens kappa score: 0.224 minimum: KNN tn, fp: 327, 1 KNN fn, tp: 8, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.014