/////////////////////////////////////////// // Running ProWRAS 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 -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 128, 10 LR fn, tp: 1, 8 LR f1 score: 0.593 LR cohens kappa score: 0.556 LR average precision score: 0.908 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 4, 5 GB f1 score: 0.500 GB cohens kappa score: 0.464 -> test with 'KNN' KNN tn, fp: 124, 14 KNN fn, tp: 4, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.299 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 4, 5 LR f1 score: 0.500 LR cohens kappa score: 0.464 LR average precision score: 0.569 -> 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: 123, 15 KNN fn, tp: 4, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.284 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 1, 8 LR f1 score: 0.696 LR cohens kappa score: 0.671 LR average precision score: 0.815 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 5, 4 GB f1 score: 0.471 GB cohens kappa score: 0.438 -> test with 'KNN' KNN tn, fp: 128, 10 KNN fn, tp: 4, 5 KNN f1 score: 0.417 KNN cohens kappa score: 0.368 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 4, 5 LR f1 score: 0.500 LR cohens kappa score: 0.464 LR average precision score: 0.599 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.313 -> test with 'KNN' KNN tn, fp: 129, 9 KNN fn, tp: 3, 6 KNN f1 score: 0.500 KNN cohens kappa score: 0.459 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 128, 9 LR fn, tp: 2, 4 LR f1 score: 0.421 LR cohens kappa score: 0.386 LR average precision score: 0.442 -> test with 'GB' GB tn, fp: 131, 6 GB fn, tp: 4, 2 GB f1 score: 0.286 GB cohens kappa score: 0.250 -> test with 'KNN' KNN tn, fp: 130, 7 KNN fn, tp: 2, 4 KNN f1 score: 0.471 KNN cohens kappa score: 0.440 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 1, 8 LR f1 score: 0.615 LR cohens kappa score: 0.582 LR average precision score: 0.610 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 5, 4 GB f1 score: 0.400 GB cohens kappa score: 0.357 -> test with 'KNN' KNN tn, fp: 129, 9 KNN fn, tp: 5, 4 KNN f1 score: 0.364 KNN cohens kappa score: 0.314 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> 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: 0.796 -> 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: 133, 5 KNN fn, tp: 3, 6 KNN f1 score: 0.600 KNN cohens kappa score: 0.571 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 134, 4 LR fn, tp: 2, 7 LR f1 score: 0.700 LR cohens kappa score: 0.678 LR average precision score: 0.728 -> test with 'GB' GB tn, fp: 130, 8 GB fn, tp: 7, 2 GB f1 score: 0.211 GB cohens kappa score: 0.156 -> test with 'KNN' KNN tn, fp: 129, 9 KNN fn, tp: 1, 8 KNN f1 score: 0.615 KNN cohens kappa score: 0.582 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 2, 7 LR f1 score: 0.609 LR cohens kappa score: 0.577 LR average precision score: 0.726 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 5, 4 GB f1 score: 0.444 GB cohens kappa score: 0.408 -> test with 'KNN' KNN tn, fp: 124, 14 KNN fn, tp: 5, 4 KNN f1 score: 0.296 KNN cohens kappa score: 0.234 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 128, 9 LR fn, tp: 0, 6 LR f1 score: 0.571 LR cohens kappa score: 0.544 LR average precision score: 0.573 -> test with 'GB' GB tn, fp: 130, 7 GB fn, tp: 3, 3 GB f1 score: 0.375 GB cohens kappa score: 0.340 -> test with 'KNN' KNN tn, fp: 123, 14 KNN fn, tp: 3, 3 KNN f1 score: 0.261 KNN cohens kappa score: 0.212 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 5, 4 LR f1 score: 0.421 LR cohens kappa score: 0.381 LR average precision score: 0.515 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 6, 3 GB f1 score: 0.316 GB cohens kappa score: 0.269 -> test with 'KNN' KNN tn, fp: 130, 8 KNN fn, tp: 7, 2 KNN f1 score: 0.211 KNN cohens kappa score: 0.156 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 0, 9 LR f1 score: 0.783 LR cohens kappa score: 0.765 LR average precision score: 0.906 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 3, 6 GB f1 score: 0.667 GB cohens kappa score: 0.645 -> test with 'KNN' KNN tn, fp: 122, 16 KNN fn, tp: 4, 5 KNN f1 score: 0.333 KNN cohens kappa score: 0.271 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 134, 4 LR fn, tp: 4, 5 LR f1 score: 0.556 LR cohens kappa score: 0.527 LR average precision score: 0.649 -> 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: 130, 8 KNN fn, tp: 5, 4 KNN f1 score: 0.381 KNN cohens kappa score: 0.334 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 120, 18 LR fn, tp: 1, 8 LR f1 score: 0.457 LR cohens kappa score: 0.403 LR average precision score: 0.699 -> test with 'GB' GB tn, fp: 129, 9 GB fn, tp: 5, 4 GB f1 score: 0.364 GB cohens kappa score: 0.314 -> test with 'KNN' KNN tn, fp: 123, 15 KNN fn, tp: 4, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.284 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 129, 8 LR fn, tp: 2, 4 LR f1 score: 0.444 LR cohens kappa score: 0.412 LR average precision score: 0.532 -> test with 'GB' GB tn, fp: 131, 6 GB fn, tp: 3, 3 GB f1 score: 0.400 GB cohens kappa score: 0.368 -> test with 'KNN' KNN tn, fp: 127, 10 KNN fn, tp: 2, 4 KNN f1 score: 0.400 KNN cohens kappa score: 0.363 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 5, 4 LR f1 score: 0.381 LR cohens kappa score: 0.334 LR average precision score: 0.500 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 5, 4 GB f1 score: 0.471 GB cohens kappa score: 0.438 -> test with 'KNN' KNN tn, fp: 125, 13 KNN fn, tp: 5, 4 KNN f1 score: 0.308 KNN cohens kappa score: 0.247 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 3, 6 LR f1 score: 0.500 LR cohens kappa score: 0.459 LR average precision score: 0.668 -> test with 'GB' GB tn, fp: 127, 11 GB fn, tp: 4, 5 GB f1 score: 0.400 GB cohens kappa score: 0.349 -> test with 'KNN' KNN tn, fp: 124, 14 KNN fn, tp: 3, 6 KNN f1 score: 0.414 KNN cohens kappa score: 0.360 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 2, 7 LR f1 score: 0.667 LR cohens kappa score: 0.642 LR average precision score: 0.654 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 4, 5 GB f1 score: 0.526 GB cohens kappa score: 0.494 -> test with 'KNN' KNN tn, fp: 129, 9 KNN fn, tp: 4, 5 KNN f1 score: 0.435 KNN cohens kappa score: 0.389 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 0, 9 LR f1 score: 0.720 LR cohens kappa score: 0.696 LR average precision score: 0.912 -> test with 'GB' GB tn, fp: 129, 9 GB fn, tp: 6, 3 GB f1 score: 0.286 GB cohens kappa score: 0.232 -> test with 'KNN' KNN tn, fp: 128, 10 KNN fn, tp: 2, 7 KNN f1 score: 0.538 KNN cohens kappa score: 0.498 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 131, 6 LR fn, tp: 1, 5 LR f1 score: 0.588 LR cohens kappa score: 0.565 LR average precision score: 0.602 -> test with 'GB' GB tn, fp: 131, 6 GB fn, tp: 3, 3 GB f1 score: 0.400 GB cohens kappa score: 0.368 -> test with 'KNN' KNN tn, fp: 127, 10 KNN fn, tp: 2, 4 KNN f1 score: 0.400 KNN cohens kappa score: 0.363 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 2, 7 LR f1 score: 0.560 LR cohens kappa score: 0.523 LR average precision score: 0.676 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 7, 2 GB f1 score: 0.250 GB cohens kappa score: 0.208 -> test with 'KNN' KNN tn, fp: 125, 13 KNN fn, tp: 6, 3 KNN f1 score: 0.240 KNN cohens kappa score: 0.175 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 127, 11 LR fn, tp: 1, 8 LR f1 score: 0.571 LR cohens kappa score: 0.533 LR average precision score: 0.710 -> 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: 130, 8 KNN fn, tp: 4, 5 KNN f1 score: 0.455 KNN cohens kappa score: 0.412 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 4, 5 LR f1 score: 0.500 LR cohens kappa score: 0.464 LR average precision score: 0.528 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 5, 4 GB f1 score: 0.471 GB cohens kappa score: 0.438 -> test with 'KNN' KNN tn, fp: 126, 12 KNN fn, tp: 6, 3 KNN f1 score: 0.250 KNN cohens kappa score: 0.188 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 135, 3 LR fn, tp: 2, 7 LR f1 score: 0.737 LR cohens kappa score: 0.719 LR average precision score: 0.925 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 5, 4 GB f1 score: 0.471 GB cohens kappa score: 0.438 -> test with 'KNN' KNN tn, fp: 129, 9 KNN fn, tp: 4, 5 KNN f1 score: 0.435 KNN cohens kappa score: 0.389 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 131, 6 LR fn, tp: 2, 4 LR f1 score: 0.500 LR cohens kappa score: 0.472 LR average precision score: 0.802 -> test with 'GB' GB tn, fp: 134, 3 GB fn, tp: 4, 2 GB f1 score: 0.364 GB cohens kappa score: 0.338 -> test with 'KNN' KNN tn, fp: 131, 6 KNN fn, tp: 2, 4 KNN f1 score: 0.500 KNN cohens kappa score: 0.472 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 135, 18 LR fn, tp: 5, 9 LR f1 score: 0.783 LR cohens kappa score: 0.765 LR average precision score: 0.925 average: LR tn, fp: 130.52, 7.28 LR fn, tp: 2.16, 6.24 LR f1 score: 0.568 LR cohens kappa score: 0.535 LR average precision score: 0.682 minimum: LR tn, fp: 120, 3 LR fn, tp: 0, 4 LR f1 score: 0.381 LR cohens kappa score: 0.334 LR average precision score: 0.442 -----[ GB ]----- maximum: GB tn, fp: 135, 11 GB fn, tp: 7, 6 GB f1 score: 0.667 GB cohens kappa score: 0.645 average: GB tn, fp: 132.12, 5.68 GB fn, tp: 4.88, 3.52 GB f1 score: 0.399 GB cohens kappa score: 0.361 minimum: GB tn, fp: 127, 3 GB fn, tp: 3, 2 GB f1 score: 0.211 GB cohens kappa score: 0.156 -----[ KNN ]----- maximum: KNN tn, fp: 133, 16 KNN fn, tp: 7, 8 KNN f1 score: 0.615 KNN cohens kappa score: 0.582 average: KNN tn, fp: 127.12, 10.68 KNN fn, tp: 3.76, 4.64 KNN f1 score: 0.395 KNN cohens kappa score: 0.347 minimum: KNN tn, fp: 122, 5 KNN fn, tp: 1, 2 KNN f1 score: 0.211 KNN cohens kappa score: 0.156