/////////////////////////////////////////// // Running SpheredNoise on folding_abalone9-18 /////////////////////////////////////////// Load 'data_input/folding_abalone9-18' 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.3975704340113837 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 137, 1 LR fn, tp: 3, 6 LR f1 score: 0.750 LR cohens kappa score: 0.736 LR average precision score: 0.829 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 4, 5 GB f1 score: 0.556 GB cohens kappa score: 0.527 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03311344137959691 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 135, 3 LR fn, tp: 5, 4 LR f1 score: 0.500 LR cohens kappa score: 0.472 LR average precision score: 0.499 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 6, 3 GB f1 score: 0.429 GB cohens kappa score: 0.402 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 136, 2 LR fn, tp: 5, 4 LR f1 score: 0.533 LR cohens kappa score: 0.509 LR average precision score: 0.711 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 6, 3 GB f1 score: 0.462 GB cohens kappa score: 0.440 -> test with 'KNN' KNN tn, fp: 138, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 137, 1 LR fn, tp: 6, 3 LR f1 score: 0.462 LR cohens kappa score: 0.440 LR average precision score: 0.619 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 5, 4 GB f1 score: 0.571 GB cohens kappa score: 0.552 -> test with 'KNN' KNN tn, fp: 138, 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 552/36 points -> new disc -> calc distances -> statistics trained 36 points min:0.03211308144666282 max:0.6863390197271315 -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 134, 3 LR fn, tp: 3, 3 LR f1 score: 0.500 LR cohens kappa score: 0.478 LR average precision score: 0.498 -> test with 'GB' GB tn, fp: 136, 1 GB fn, tp: 6, 0 GB f1 score: 0.000 GB cohens kappa score: -0.012 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 6, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03311344137959691 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 136, 2 LR fn, tp: 5, 4 LR f1 score: 0.533 LR cohens kappa score: 0.509 LR average precision score: 0.667 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 7, 2 GB f1 score: 0.286 GB cohens kappa score: 0.253 -> test with 'KNN' KNN tn, fp: 138, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03311344137959691 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 136, 2 LR fn, tp: 3, 6 LR f1 score: 0.706 LR cohens kappa score: 0.688 LR average precision score: 0.726 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 5, 4 GB f1 score: 0.533 GB cohens kappa score: 0.509 -> test with 'KNN' KNN tn, fp: 138, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.3752825602129679 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 136, 2 LR fn, tp: 6, 3 LR f1 score: 0.429 LR cohens kappa score: 0.402 LR average precision score: 0.537 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 6, 3 GB f1 score: 0.429 GB cohens kappa score: 0.402 -> test with 'KNN' KNN tn, fp: 138, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 136, 2 LR fn, tp: 6, 3 LR f1 score: 0.429 LR cohens kappa score: 0.402 LR average precision score: 0.657 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 5, 4 GB f1 score: 0.533 GB cohens kappa score: 0.509 -> test with 'KNN' KNN tn, fp: 138, 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 552/36 points -> new disc -> calc distances -> statistics trained 36 points min:0.03211308144666282 max:0.6176281243596343 -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 134, 3 LR fn, tp: 2, 4 LR f1 score: 0.615 LR cohens kappa score: 0.597 LR average precision score: 0.510 -> test with 'GB' GB tn, fp: 135, 2 GB fn, tp: 3, 3 GB f1 score: 0.545 GB cohens kappa score: 0.527 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 6, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 134, 4 LR fn, tp: 6, 3 LR f1 score: 0.375 LR cohens kappa score: 0.340 LR average precision score: 0.503 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 8, 1 GB f1 score: 0.154 GB cohens kappa score: 0.121 -> test with 'KNN' KNN tn, fp: 138, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 137, 1 LR fn, tp: 4, 5 LR f1 score: 0.667 LR cohens kappa score: 0.649 LR average precision score: 0.766 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 7, 2 GB f1 score: 0.333 GB cohens kappa score: 0.312 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03311344137959691 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 136, 2 LR fn, tp: 5, 4 LR f1 score: 0.533 LR cohens kappa score: 0.509 LR average precision score: 0.588 -> test with 'GB' GB tn, fp: 138, 0 GB fn, tp: 7, 2 GB f1 score: 0.364 GB cohens kappa score: 0.349 -> test with 'KNN' KNN tn, fp: 138, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.6610300673948196 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 135, 3 LR fn, tp: 3, 6 LR f1 score: 0.667 LR cohens kappa score: 0.645 LR average precision score: 0.630 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 4, 5 GB f1 score: 0.625 GB cohens kappa score: 0.604 -> test with 'KNN' KNN tn, fp: 138, 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 552/36 points -> new disc -> calc distances -> statistics trained 36 points min:0.0358887168898527 max:0.4738359420727812 -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 135, 2 LR fn, tp: 4, 2 LR f1 score: 0.400 LR cohens kappa score: 0.379 LR average precision score: 0.582 -> test with 'GB' GB tn, fp: 133, 4 GB fn, tp: 4, 2 GB f1 score: 0.333 GB cohens kappa score: 0.304 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 6, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03311344137959691 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 134, 4 LR fn, tp: 5, 4 LR f1 score: 0.471 LR cohens kappa score: 0.438 LR average precision score: 0.524 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 6, 3 GB f1 score: 0.375 GB cohens kappa score: 0.340 -> test with 'KNN' KNN tn, fp: 138, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03311344137959691 max:0.3975704340113837 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 137, 1 LR fn, tp: 5, 4 LR f1 score: 0.571 LR cohens kappa score: 0.552 LR average precision score: 0.600 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 5, 4 GB f1 score: 0.421 GB cohens kappa score: 0.381 -> test with 'KNN' KNN tn, fp: 138, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 137, 1 LR fn, tp: 4, 5 LR f1 score: 0.667 LR cohens kappa score: 0.649 LR average precision score: 0.610 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 6, 3 GB f1 score: 0.375 GB cohens kappa score: 0.340 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 137, 1 LR fn, tp: 5, 4 LR f1 score: 0.571 LR cohens kappa score: 0.552 LR average precision score: 0.805 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 7, 2 GB f1 score: 0.333 GB cohens kappa score: 0.312 -> test with 'KNN' KNN tn, fp: 138, 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 552/36 points -> new disc -> calc distances -> statistics trained 36 points min:0.03211308144666282 max:0.6610300673948196 -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 135, 2 LR fn, tp: 4, 2 LR f1 score: 0.400 LR cohens kappa score: 0.379 LR average precision score: 0.439 -> test with 'GB' GB tn, fp: 134, 3 GB fn, tp: 5, 1 GB f1 score: 0.200 GB cohens kappa score: 0.172 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 6, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.4217019089356842 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 3, 6 LR f1 score: 0.600 LR cohens kappa score: 0.571 LR average precision score: 0.707 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 6, 3 GB f1 score: 0.400 GB cohens kappa score: 0.369 -> test with 'KNN' KNN tn, fp: 138, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.6610300673948196 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 136, 2 LR fn, tp: 4, 5 LR f1 score: 0.625 LR cohens kappa score: 0.604 LR average precision score: 0.713 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.281 -> test with 'KNN' KNN tn, fp: 138, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.037393181196576475 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 135, 3 LR fn, tp: 6, 3 LR f1 score: 0.400 LR cohens kappa score: 0.369 LR average precision score: 0.479 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 6, 3 GB f1 score: 0.429 GB cohens kappa score: 0.402 -> test with 'KNN' KNN tn, fp: 138, 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 551/33 points -> new disc -> calc distances -> statistics trained 33 points min:0.03211308144666282 max:0.6176281243596343 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 137, 1 LR fn, tp: 5, 4 LR f1 score: 0.571 LR cohens kappa score: 0.552 LR average precision score: 0.711 -> test with 'GB' GB tn, fp: 138, 0 GB fn, tp: 6, 3 GB f1 score: 0.500 GB cohens kappa score: 0.484 -> test with 'KNN' KNN tn, fp: 138, 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 552/36 points -> new disc -> calc distances -> statistics trained 36 points min:0.03311344137959691 max:0.6176281243596343 -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 137, 0 LR fn, tp: 3, 3 LR f1 score: 0.667 LR cohens kappa score: 0.657 LR average precision score: 0.785 -> test with 'GB' GB tn, fp: 136, 1 GB fn, tp: 3, 3 GB f1 score: 0.600 GB cohens kappa score: 0.586 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 6, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 137, 5 LR fn, tp: 6, 6 LR f1 score: 0.750 LR cohens kappa score: 0.736 LR average precision score: 0.829 average: LR tn, fp: 135.68, 2.12 LR fn, tp: 4.4, 4.0 LR f1 score: 0.546 LR cohens kappa score: 0.523 LR average precision score: 0.628 minimum: LR tn, fp: 133, 0 LR fn, tp: 2, 2 LR f1 score: 0.375 LR cohens kappa score: 0.340 LR average precision score: 0.439 -----[ GB ]----- maximum: GB tn, fp: 138, 6 GB fn, tp: 8, 5 GB f1 score: 0.625 GB cohens kappa score: 0.604 average: GB tn, fp: 135.56, 2.24 GB fn, tp: 5.6, 2.8 GB f1 score: 0.404 GB cohens kappa score: 0.379 minimum: GB tn, fp: 132, 0 GB fn, tp: 3, 0 GB f1 score: 0.000 GB cohens kappa score: -0.012 -----[ KNN ]----- maximum: KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 average: KNN tn, fp: 137.8, 0.0 KNN fn, tp: 8.4, 0.0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 minimum: KNN tn, fp: 137, 0 KNN fn, tp: 6, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000