/////////////////////////////////////////// // Running SpheredNoise on folding_yeast6 /////////////////////////////////////////// Load 'data_input/folding_yeast6' 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 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.04795831523312718 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 289, 1 LR fn, tp: 4, 3 LR f1 score: 0.545 LR cohens kappa score: 0.538 LR average precision score: 0.635 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> test with 'KNN' KNN tn, fp: 289, 1 KNN fn, tp: 5, 2 KNN f1 score: 0.400 KNN cohens kappa score: 0.391 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.22248595461286985 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 285, 5 LR fn, tp: 5, 2 LR f1 score: 0.286 LR cohens kappa score: 0.268 LR average precision score: 0.431 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 5, 2 GB f1 score: 0.333 GB cohens kappa score: 0.320 -> test with 'KNN' KNN tn, fp: 287, 3 KNN fn, tp: 4, 3 KNN f1 score: 0.462 KNN cohens kappa score: 0.450 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.042426406871192875 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 289, 1 LR fn, tp: 6, 1 LR f1 score: 0.222 LR cohens kappa score: 0.214 LR average precision score: 0.455 -> test with 'GB' GB tn, fp: 290, 0 GB fn, tp: 5, 2 GB f1 score: 0.444 GB cohens kappa score: 0.439 -> test with 'KNN' KNN tn, fp: 288, 2 KNN fn, tp: 6, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.189 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 289, 1 LR fn, tp: 5, 2 LR f1 score: 0.400 LR cohens kappa score: 0.391 LR average precision score: 0.618 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> test with 'KNN' KNN tn, fp: 290, 0 KNN fn, tp: 5, 2 KNN f1 score: 0.444 KNN cohens kappa score: 0.439 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1160/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.19924858845171275 -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 287, 2 LR fn, tp: 3, 4 LR f1 score: 0.615 LR cohens kappa score: 0.607 LR average precision score: 0.662 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 3, 4 GB f1 score: 0.667 GB cohens kappa score: 0.660 -> test with 'KNN' KNN tn, fp: 289, 0 KNN fn, tp: 1, 6 KNN f1 score: 0.923 KNN cohens kappa score: 0.921 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.045825756949558344 max:0.15874507866387544 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 289, 1 LR fn, tp: 3, 4 LR f1 score: 0.667 LR cohens kappa score: 0.660 LR average precision score: 0.677 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 4, 3 GB f1 score: 0.500 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 288, 2 KNN fn, tp: 4, 3 KNN f1 score: 0.500 KNN cohens kappa score: 0.490 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 286, 4 LR fn, tp: 5, 2 LR f1 score: 0.308 LR cohens kappa score: 0.292 LR average precision score: 0.403 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 5, 2 GB f1 score: 0.333 GB cohens kappa score: 0.320 -> test with 'KNN' KNN tn, fp: 290, 0 KNN fn, tp: 5, 2 KNN f1 score: 0.444 KNN cohens kappa score: 0.439 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 289, 1 LR fn, tp: 5, 2 LR f1 score: 0.400 LR cohens kappa score: 0.391 LR average precision score: 0.511 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 6, 1 GB f1 score: 0.200 GB cohens kappa score: 0.189 -> test with 'KNN' KNN tn, fp: 290, 0 KNN fn, tp: 4, 3 KNN f1 score: 0.600 KNN cohens kappa score: 0.594 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.2054263858417414 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 288, 2 LR fn, tp: 4, 3 LR f1 score: 0.500 LR cohens kappa score: 0.490 LR average precision score: 0.560 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 5, 2 GB f1 score: 0.308 GB cohens kappa score: 0.292 -> test with 'KNN' KNN tn, fp: 288, 2 KNN fn, tp: 5, 2 KNN f1 score: 0.364 KNN cohens kappa score: 0.353 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1160/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.19924858845171275 -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 288, 1 LR fn, tp: 5, 2 LR f1 score: 0.400 LR cohens kappa score: 0.391 LR average precision score: 0.507 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 289, 0 KNN fn, tp: 4, 3 KNN f1 score: 0.600 KNN cohens kappa score: 0.594 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.042426406871192875 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 289, 1 LR fn, tp: 4, 3 LR f1 score: 0.545 LR cohens kappa score: 0.538 LR average precision score: 0.646 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 5, 2 GB f1 score: 0.364 GB cohens kappa score: 0.353 -> test with 'KNN' KNN tn, fp: 289, 1 KNN fn, tp: 3, 4 KNN f1 score: 0.667 KNN cohens kappa score: 0.660 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.15132745950421558 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 290, 0 LR fn, tp: 3, 4 LR f1 score: 0.727 LR cohens kappa score: 0.723 LR average precision score: 0.791 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.538 -> test with 'KNN' KNN tn, fp: 290, 0 KNN fn, tp: 4, 3 KNN f1 score: 0.600 KNN cohens kappa score: 0.594 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.22248595461286985 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 288, 2 LR fn, tp: 5, 2 LR f1 score: 0.364 LR cohens kappa score: 0.353 LR average precision score: 0.451 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 290, 0 KNN fn, tp: 4, 3 KNN f1 score: 0.600 KNN cohens kappa score: 0.594 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.045825756949558344 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 285, 5 LR fn, tp: 3, 4 LR f1 score: 0.500 LR cohens kappa score: 0.486 LR average precision score: 0.453 -> test with 'GB' GB tn, fp: 285, 5 GB fn, tp: 5, 2 GB f1 score: 0.286 GB cohens kappa score: 0.268 -> test with 'KNN' KNN tn, fp: 285, 5 KNN fn, tp: 3, 4 KNN f1 score: 0.500 KNN cohens kappa score: 0.486 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1160/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.19924858845171275 -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 289, 0 LR fn, tp: 7, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.358 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -> test with 'KNN' KNN tn, fp: 288, 1 KNN fn, tp: 6, 1 KNN f1 score: 0.222 KNN cohens kappa score: 0.214 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 290, 0 LR fn, tp: 5, 2 LR f1 score: 0.444 LR cohens kappa score: 0.439 LR average precision score: 0.767 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.538 -> test with 'KNN' KNN tn, fp: 288, 2 KNN fn, tp: 4, 3 KNN f1 score: 0.500 KNN cohens kappa score: 0.490 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.2054263858417414 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 286, 4 LR fn, tp: 6, 1 LR f1 score: 0.167 LR cohens kappa score: 0.150 LR average precision score: 0.263 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 6, 1 GB f1 score: 0.167 GB cohens kappa score: 0.150 -> test with 'KNN' KNN tn, fp: 286, 4 KNN fn, tp: 5, 2 KNN f1 score: 0.308 KNN cohens kappa score: 0.292 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 285, 5 LR fn, tp: 3, 4 LR f1 score: 0.500 LR cohens kappa score: 0.486 LR average precision score: 0.550 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 3, 4 GB f1 score: 0.533 GB cohens kappa score: 0.521 -> test with 'KNN' KNN tn, fp: 289, 1 KNN fn, tp: 3, 4 KNN f1 score: 0.667 KNN cohens kappa score: 0.660 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 290, 0 LR fn, tp: 4, 3 LR f1 score: 0.600 LR cohens kappa score: 0.594 LR average precision score: 0.670 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.538 -> test with 'KNN' KNN tn, fp: 290, 0 KNN fn, tp: 4, 3 KNN f1 score: 0.600 KNN cohens kappa score: 0.594 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1160/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.042426406871192875 max:0.1923538406167134 -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 289, 0 LR fn, tp: 5, 2 LR f1 score: 0.444 LR cohens kappa score: 0.439 LR average precision score: 0.587 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.537 -> test with 'KNN' KNN tn, fp: 289, 0 KNN fn, tp: 5, 2 KNN f1 score: 0.444 KNN cohens kappa score: 0.439 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 288, 2 LR fn, tp: 3, 4 LR f1 score: 0.615 LR cohens kappa score: 0.607 LR average precision score: 0.501 -> test with 'GB' GB tn, fp: 285, 5 GB fn, tp: 4, 3 GB f1 score: 0.400 GB cohens kappa score: 0.385 -> test with 'KNN' KNN tn, fp: 287, 3 KNN fn, tp: 3, 4 KNN f1 score: 0.571 KNN cohens kappa score: 0.561 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 289, 1 LR fn, tp: 6, 1 LR f1 score: 0.222 LR cohens kappa score: 0.214 LR average precision score: 0.236 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 5, 2 GB f1 score: 0.364 GB cohens kappa score: 0.353 -> test with 'KNN' KNN tn, fp: 290, 0 KNN fn, tp: 6, 1 KNN f1 score: 0.250 KNN cohens kappa score: 0.246 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.042426406871192875 max:0.19924858845171275 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 290, 0 LR fn, tp: 4, 3 LR f1 score: 0.600 LR cohens kappa score: 0.594 LR average precision score: 0.744 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 2, 5 GB f1 score: 0.769 GB cohens kappa score: 0.764 -> test with 'KNN' KNN tn, fp: 288, 2 KNN fn, tp: 4, 3 KNN f1 score: 0.500 KNN cohens kappa score: 0.490 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1159/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.045825756949558344 max:0.1923538406167134 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 288, 2 LR fn, tp: 5, 2 LR f1 score: 0.364 LR cohens kappa score: 0.353 LR average precision score: 0.536 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> test with 'KNN' KNN tn, fp: 290, 0 KNN fn, tp: 5, 2 KNN f1 score: 0.444 KNN cohens kappa score: 0.439 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1160/28 points -> new disc -> calc distances -> statistics trained 28 points min:0.03464101615137758 max:0.2054263858417414 -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 285, 4 LR fn, tp: 4, 3 LR f1 score: 0.429 LR cohens kappa score: 0.415 LR average precision score: 0.443 -> test with 'GB' GB tn, fp: 287, 2 GB fn, tp: 5, 2 GB f1 score: 0.364 GB cohens kappa score: 0.353 -> test with 'KNN' KNN tn, fp: 289, 0 KNN fn, tp: 4, 3 KNN f1 score: 0.600 KNN cohens kappa score: 0.594 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 290, 5 LR fn, tp: 7, 4 LR f1 score: 0.727 LR cohens kappa score: 0.723 LR average precision score: 0.791 average: LR tn, fp: 288.0, 1.8 LR fn, tp: 4.48, 2.52 LR f1 score: 0.435 LR cohens kappa score: 0.425 LR average precision score: 0.538 minimum: LR tn, fp: 285, 0 LR fn, tp: 3, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.236 -----[ GB ]----- maximum: GB tn, fp: 290, 5 GB fn, tp: 7, 5 GB f1 score: 0.769 GB cohens kappa score: 0.764 average: GB tn, fp: 287.64, 2.16 GB fn, tp: 4.64, 2.36 GB f1 score: 0.399 GB cohens kappa score: 0.389 minimum: GB tn, fp: 285, 0 GB fn, tp: 2, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -----[ KNN ]----- maximum: KNN tn, fp: 290, 5 KNN fn, tp: 6, 6 KNN f1 score: 0.923 KNN cohens kappa score: 0.921 average: KNN tn, fp: 288.64, 1.16 KNN fn, tp: 4.24, 2.76 KNN f1 score: 0.496 KNN cohens kappa score: 0.488 minimum: KNN tn, fp: 285, 0 KNN fn, tp: 1, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.189