/////////////////////////////////////////// // Running SpheredNoise on folding_flare-F /////////////////////////////////////////// Load 'data_input/folding_flare-F' from pickle file non empty cut in data_input/folding_flare-F! (23 points) 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 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.4142135623730951 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 202, 3 LR fn, tp: 9, 0 LR f1 score: 0.000 LR cohens kappa score: -0.021 LR average precision score: 0.109 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.008 -> test with 'KNN' KNN tn, fp: 200, 5 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.031 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 202, 3 LR fn, tp: 6, 3 LR f1 score: 0.400 LR cohens kappa score: 0.379 LR average precision score: 0.373 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 204, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.008 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 204, 1 LR fn, tp: 8, 1 LR f1 score: 0.182 LR cohens kappa score: 0.169 LR average precision score: 0.309 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:2.0 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 203, 2 LR fn, tp: 5, 4 LR f1 score: 0.533 LR cohens kappa score: 0.517 LR average precision score: 0.726 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 820/36 points -> new disc -> calc distances -> statistics trained 36 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 196, 7 LR fn, tp: 5, 2 LR f1 score: 0.250 LR cohens kappa score: 0.221 LR average precision score: 0.199 -> test with 'GB' GB tn, fp: 200, 3 GB fn, tp: 6, 1 GB f1 score: 0.182 GB cohens kappa score: 0.161 -> test with 'KNN' KNN tn, fp: 203, 0 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.4142135623730951 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 202, 3 LR fn, tp: 6, 3 LR f1 score: 0.400 LR cohens kappa score: 0.379 LR average precision score: 0.399 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.289 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 203, 2 LR fn, tp: 7, 2 LR f1 score: 0.308 LR cohens kappa score: 0.289 LR average precision score: 0.316 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:2.0 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 202, 3 LR fn, tp: 7, 2 LR f1 score: 0.286 LR cohens kappa score: 0.264 LR average precision score: 0.281 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 8, 1 GB f1 score: 0.200 GB cohens kappa score: 0.193 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 202, 3 LR fn, tp: 8, 1 LR f1 score: 0.154 LR cohens kappa score: 0.131 LR average precision score: 0.278 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 820/36 points -> new disc -> calc distances -> statistics trained 36 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 201, 2 LR fn, tp: 4, 3 LR f1 score: 0.500 LR cohens kappa score: 0.486 LR average precision score: 0.473 -> test with 'GB' GB tn, fp: 201, 2 GB fn, tp: 6, 1 GB f1 score: 0.200 GB cohens kappa score: 0.184 -> test with 'KNN' KNN tn, fp: 203, 0 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 204, 1 LR fn, tp: 3, 6 LR f1 score: 0.750 LR cohens kappa score: 0.740 LR average precision score: 0.750 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:2.23606797749979 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 198, 7 LR fn, tp: 6, 3 LR f1 score: 0.316 LR cohens kappa score: 0.284 LR average precision score: 0.210 -> test with 'GB' GB tn, fp: 200, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.326 -> test with 'KNN' KNN tn, fp: 204, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.008 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 204, 1 LR fn, tp: 8, 1 LR f1 score: 0.182 LR cohens kappa score: 0.169 LR average precision score: 0.373 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.008 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 204, 1 LR fn, tp: 9, 0 LR f1 score: 0.000 LR cohens kappa score: -0.008 LR average precision score: 0.273 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.008 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 820/36 points -> new disc -> calc distances -> statistics trained 36 points min:1.0 max:2.0 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 199, 4 LR fn, tp: 6, 1 LR f1 score: 0.167 LR cohens kappa score: 0.143 LR average precision score: 0.326 -> test with 'GB' GB tn, fp: 198, 5 GB fn, tp: 5, 2 GB f1 score: 0.286 GB cohens kappa score: 0.261 -> test with 'KNN' KNN tn, fp: 200, 3 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.020 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 200, 5 LR fn, tp: 9, 0 LR f1 score: 0.000 LR cohens kappa score: -0.031 LR average precision score: 0.170 -> test with 'GB' GB tn, fp: 199, 6 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.035 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 205, 0 LR fn, tp: 6, 3 LR f1 score: 0.500 LR cohens kappa score: 0.489 LR average precision score: 0.558 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:2.0 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 200, 5 LR fn, tp: 7, 2 LR f1 score: 0.250 LR cohens kappa score: 0.221 LR average precision score: 0.209 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 8, 1 GB f1 score: 0.200 GB cohens kappa score: 0.193 -> test with 'KNN' KNN tn, fp: 204, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.008 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.4142135623730951 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 201, 4 LR fn, tp: 6, 3 LR f1 score: 0.375 LR cohens kappa score: 0.351 LR average precision score: 0.385 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.289 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 820/36 points -> new disc -> calc distances -> statistics trained 36 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 203, 0 LR fn, tp: 4, 3 LR f1 score: 0.600 LR cohens kappa score: 0.592 LR average precision score: 0.607 -> test with 'GB' GB tn, fp: 201, 2 GB fn, tp: 6, 1 GB f1 score: 0.200 GB cohens kappa score: 0.184 -> test with 'KNN' KNN tn, fp: 203, 0 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 204, 1 LR fn, tp: 8, 1 LR f1 score: 0.182 LR cohens kappa score: 0.169 LR average precision score: 0.217 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 203, 2 LR fn, tp: 6, 3 LR f1 score: 0.429 LR cohens kappa score: 0.411 LR average precision score: 0.418 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.4142135623730951 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 199, 6 LR fn, tp: 4, 5 LR f1 score: 0.500 LR cohens kappa score: 0.476 LR average precision score: 0.411 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 8, 1 GB f1 score: 0.200 GB cohens kappa score: 0.193 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 818/34 points -> new disc -> calc distances -> statistics trained 34 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 203, 2 LR fn, tp: 9, 0 LR f1 score: 0.000 LR cohens kappa score: -0.016 LR average precision score: 0.186 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 820/36 points -> new disc -> calc distances -> statistics trained 36 points min:1.0 max:1.7320508075688772 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 203, 0 LR fn, tp: 5, 2 LR f1 score: 0.444 LR cohens kappa score: 0.436 LR average precision score: 0.438 -> test with 'GB' GB tn, fp: 199, 4 GB fn, tp: 5, 2 GB f1 score: 0.308 GB cohens kappa score: 0.286 -> test with 'KNN' KNN tn, fp: 202, 1 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.008 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 205, 7 LR fn, tp: 9, 6 LR f1 score: 0.750 LR cohens kappa score: 0.740 LR average precision score: 0.750 average: LR tn, fp: 201.88, 2.72 LR fn, tp: 6.44, 2.16 LR f1 score: 0.308 LR cohens kappa score: 0.290 LR average precision score: 0.360 minimum: LR tn, fp: 196, 0 LR fn, tp: 3, 0 LR f1 score: 0.000 LR cohens kappa score: -0.031 LR average precision score: 0.109 -----[ GB ]----- maximum: GB tn, fp: 205, 6 GB fn, tp: 9, 3 GB f1 score: 0.353 GB cohens kappa score: 0.326 average: GB tn, fp: 202.8, 1.8 GB fn, tp: 7.72, 0.88 GB f1 score: 0.145 GB cohens kappa score: 0.131 minimum: GB tn, fp: 198, 0 GB fn, tp: 5, 0 GB f1 score: 0.000 GB cohens kappa score: -0.035 -----[ KNN ]----- maximum: KNN tn, fp: 205, 5 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 average: KNN tn, fp: 204.12, 0.48 KNN fn, tp: 8.6, 0.0 KNN f1 score: 0.000 KNN cohens kappa score: -0.003 minimum: KNN tn, fp: 200, 0 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.031