/////////////////////////////////////////// // 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: 130, 8 LR fn, tp: 1, 8 LR f1 score: 0.640 LR cohens kappa score: 0.609 LR average precision score: 0.908 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 5, 4 RF f1 score: 0.533 RF cohens kappa score: 0.509 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 4, 5 GB f1 score: 0.476 GB cohens kappa score: 0.437 -> test with 'KNN' KNN tn, fp: 125, 13 KNN fn, tp: 4, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.314 ------ 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 'RF' RF tn, fp: 136, 2 RF fn, tp: 7, 2 RF f1 score: 0.308 RF cohens kappa score: 0.281 -> 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: 124, 14 KNN fn, tp: 4, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.299 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 1, 8 LR f1 score: 0.667 LR cohens kappa score: 0.639 LR average precision score: 0.797 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 6, 3 RF f1 score: 0.429 RF cohens kappa score: 0.402 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 6, 3 GB f1 score: 0.333 GB cohens kappa score: 0.290 -> 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: 0.598 -> test with 'RF' RF tn, fp: 137, 1 RF fn, tp: 7, 2 RF f1 score: 0.333 RF cohens kappa score: 0.312 -> 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: 128, 10 KNN fn, tp: 3, 6 KNN f1 score: 0.480 KNN cohens kappa score: 0.436 ------ Step 1/5: 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: 0.446 -> test with 'RF' RF tn, fp: 136, 1 RF fn, tp: 4, 2 RF f1 score: 0.444 RF cohens kappa score: 0.428 -> 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: 129, 8 KNN fn, tp: 2, 4 KNN f1 score: 0.444 KNN cohens kappa score: 0.412 ====== 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: 0.609 -> test with 'RF' RF tn, fp: 133, 5 RF fn, tp: 5, 4 RF f1 score: 0.444 RF cohens kappa score: 0.408 -> 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 'RF' RF tn, fp: 137, 1 RF fn, tp: 6, 3 RF f1 score: 0.462 RF cohens kappa score: 0.440 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 5, 4 GB f1 score: 0.500 GB cohens kappa score: 0.472 -> 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: 0.729 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 8, 1 RF f1 score: 0.167 RF cohens kappa score: 0.140 -> test with 'GB' GB tn, fp: 129, 9 GB fn, tp: 4, 5 GB f1 score: 0.435 GB cohens kappa score: 0.389 -> test with 'KNN' KNN tn, fp: 130, 8 KNN fn, tp: 2, 7 KNN f1 score: 0.583 KNN cohens kappa score: 0.549 ------ 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: 0.729 -> test with 'RF' RF tn, fp: 134, 4 RF fn, tp: 5, 4 RF f1 score: 0.471 RF cohens kappa score: 0.438 -> 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: 123, 15 KNN fn, tp: 5, 4 KNN f1 score: 0.286 KNN cohens kappa score: 0.221 ------ 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: 1, 5 LR f1 score: 0.500 LR cohens kappa score: 0.469 LR average precision score: 0.573 -> test with 'RF' RF tn, fp: 135, 2 RF fn, tp: 3, 3 RF f1 score: 0.545 RF cohens kappa score: 0.527 -> test with 'GB' GB tn, fp: 130, 7 GB fn, tp: 2, 4 GB f1 score: 0.471 GB cohens kappa score: 0.440 -> 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: 0.521 -> test with 'RF' RF tn, fp: 134, 4 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.039 -> 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: 130, 8 KNN fn, tp: 8, 1 KNN f1 score: 0.111 KNN cohens kappa score: 0.053 ------ 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 'RF' RF tn, fp: 135, 3 RF fn, tp: 5, 4 RF f1 score: 0.500 RF cohens kappa score: 0.472 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 2, 7 GB f1 score: 0.778 GB cohens kappa score: 0.763 -> 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: 5, 4 LR f1 score: 0.471 LR cohens kappa score: 0.438 LR average precision score: 0.647 -> test with 'RF' RF tn, fp: 137, 1 RF fn, tp: 7, 2 RF f1 score: 0.333 RF cohens kappa score: 0.312 -> 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: 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: 0, 9 LR f1 score: 0.500 LR cohens kappa score: 0.449 LR average precision score: 0.699 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 8, 1 RF f1 score: 0.167 RF cohens kappa score: 0.140 -> 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: 124, 14 KNN fn, tp: 4, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.299 ------ 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: 0.529 -> test with 'RF' RF tn, fp: 135, 2 RF fn, tp: 4, 2 RF f1 score: 0.400 RF cohens kappa score: 0.379 -> test with 'GB' GB tn, fp: 132, 5 GB fn, tp: 3, 3 GB f1 score: 0.429 GB cohens kappa score: 0.400 -> test with 'KNN' KNN tn, fp: 126, 11 KNN fn, tp: 2, 4 KNN f1 score: 0.381 KNN cohens kappa score: 0.341 ====== 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: 0.514 -> test with 'RF' RF tn, fp: 134, 4 RF fn, tp: 6, 3 RF f1 score: 0.375 RF cohens kappa score: 0.340 -> 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: 126, 12 KNN fn, tp: 5, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.262 ------ 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: 0.669 -> test with 'RF' RF tn, fp: 133, 5 RF fn, tp: 6, 3 RF f1 score: 0.353 RF cohens kappa score: 0.313 -> test with 'GB' GB tn, fp: 126, 12 GB fn, tp: 4, 5 GB f1 score: 0.385 GB cohens kappa score: 0.331 -> 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: 131, 7 LR fn, tp: 2, 7 LR f1 score: 0.609 LR cohens kappa score: 0.577 LR average precision score: 0.654 -> test with 'RF' RF tn, fp: 134, 4 RF fn, tp: 7, 2 RF f1 score: 0.267 RF cohens kappa score: 0.229 -> 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: 5, 4 KNN f1 score: 0.381 KNN cohens kappa score: 0.334 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 0, 9 LR f1 score: 0.692 LR cohens kappa score: 0.666 LR average precision score: 0.912 -> test with 'RF' RF tn, fp: 134, 4 RF fn, tp: 7, 2 RF f1 score: 0.267 RF cohens kappa score: 0.229 -> test with 'GB' GB tn, fp: 128, 10 GB fn, tp: 6, 3 GB f1 score: 0.273 GB cohens kappa score: 0.216 -> test with 'KNN' KNN tn, fp: 126, 12 KNN fn, tp: 2, 7 KNN f1 score: 0.500 KNN cohens kappa score: 0.455 ------ 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: 0.603 -> test with 'RF' RF tn, fp: 135, 2 RF fn, tp: 4, 2 RF f1 score: 0.400 RF cohens kappa score: 0.379 -> 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: 0.677 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 8, 1 RF f1 score: 0.167 RF cohens kappa score: 0.140 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 8, 1 GB f1 score: 0.125 GB cohens kappa score: 0.075 -> 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: 0, 9 LR f1 score: 0.621 LR cohens kappa score: 0.586 LR average precision score: 0.709 -> test with 'RF' RF tn, fp: 135, 3 RF fn, tp: 6, 3 RF f1 score: 0.400 RF cohens kappa score: 0.369 -> 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: 128, 10 KNN fn, tp: 4, 5 KNN f1 score: 0.417 KNN cohens kappa score: 0.368 ------ 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: 0.529 -> test with 'RF' RF tn, fp: 137, 1 RF fn, tp: 7, 2 RF f1 score: 0.333 RF cohens kappa score: 0.312 -> 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: 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: 1, 8 LR f1 score: 0.800 LR cohens kappa score: 0.786 LR average precision score: 0.921 -> test with 'RF' RF tn, fp: 134, 4 RF fn, tp: 8, 1 RF f1 score: 0.143 RF cohens kappa score: 0.104 -> 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: 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 'RF' RF tn, fp: 135, 2 RF fn, tp: 5, 1 RF f1 score: 0.222 RF cohens kappa score: 0.200 -> 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: 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: 0.800 LR cohens kappa score: 0.786 LR average precision score: 0.921 average: LR tn, fp: 130.48, 7.32 LR fn, tp: 2.12, 6.28 LR f1 score: 0.566 LR cohens kappa score: 0.534 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: 0.446 -----[ RF ]----- maximum: RF tn, fp: 137, 5 RF fn, tp: 9, 4 RF f1 score: 0.545 RF cohens kappa score: 0.527 average: RF tn, fp: 135.2, 2.6 RF fn, tp: 6.12, 2.28 RF f1 score: 0.338 RF cohens kappa score: 0.311 minimum: RF tn, fp: 133, 1 RF fn, tp: 3, 0 RF f1 score: 0.000 RF cohens kappa score: -0.039 -----[ GB ]----- maximum: GB tn, fp: 136, 12 GB fn, tp: 8, 7 GB f1 score: 0.778 GB cohens kappa score: 0.763 average: GB tn, fp: 131.6, 6.2 GB fn, tp: 4.92, 3.48 GB f1 score: 0.384 GB cohens kappa score: 0.344 minimum: GB tn, fp: 126, 2 GB fn, tp: 2, 1 GB f1 score: 0.125 GB cohens kappa score: 0.075 -----[ KNN ]----- maximum: KNN tn, fp: 133, 16 KNN fn, tp: 8, 7 KNN f1 score: 0.600 KNN cohens kappa score: 0.571 average: KNN tn, fp: 127.04, 10.76 KNN fn, tp: 3.88, 4.52 KNN f1 score: 0.383 KNN cohens kappa score: 0.334 minimum: KNN tn, fp: 122, 5 KNN fn, tp: 2, 1 KNN f1 score: 0.111 KNN cohens kappa score: 0.053