/////////////////////////////////////////// // Running ProWRAS 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 -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 262, 28 LR fn, tp: 0, 7 LR f1 score: 0.333 LR cohens kappa score: 0.306 LR average precision score: 0.688 -> test with 'RF' RF tn, fp: 288, 2 RF fn, tp: 4, 3 RF f1 score: 0.500 RF cohens kappa score: 0.490 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 3, 4 GB f1 score: 0.571 GB cohens kappa score: 0.561 -> test with 'KNN' KNN tn, fp: 281, 9 KNN fn, tp: 2, 5 KNN f1 score: 0.476 KNN cohens kappa score: 0.459 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 267, 23 LR fn, tp: 2, 5 LR f1 score: 0.286 LR cohens kappa score: 0.258 LR average precision score: 0.432 -> test with 'RF' RF tn, fp: 286, 4 RF fn, tp: 4, 3 RF f1 score: 0.429 RF cohens kappa score: 0.415 -> 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: 279, 11 KNN fn, tp: 3, 4 KNN f1 score: 0.364 KNN cohens kappa score: 0.343 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 258, 32 LR fn, tp: 1, 6 LR f1 score: 0.267 LR cohens kappa score: 0.236 LR average precision score: 0.268 -> test with 'RF' RF tn, fp: 290, 0 RF fn, tp: 3, 4 RF f1 score: 0.727 RF cohens kappa score: 0.723 -> test with 'GB' GB tn, fp: 290, 0 GB fn, tp: 3, 4 GB f1 score: 0.727 GB cohens kappa score: 0.723 -> test with 'KNN' KNN tn, fp: 277, 13 KNN fn, tp: 1, 6 KNN f1 score: 0.462 KNN cohens kappa score: 0.442 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 270, 20 LR fn, tp: 2, 5 LR f1 score: 0.312 LR cohens kappa score: 0.286 LR average precision score: 0.554 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 4, 3 RF f1 score: 0.545 RF cohens kappa score: 0.538 -> 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: 281, 9 KNN fn, tp: 2, 5 KNN f1 score: 0.476 KNN cohens kappa score: 0.459 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 240, 49 LR fn, tp: 0, 7 LR f1 score: 0.222 LR cohens kappa score: 0.188 LR average precision score: 0.536 -> test with 'RF' RF tn, fp: 288, 1 RF fn, tp: 1, 6 RF f1 score: 0.857 RF cohens kappa score: 0.854 -> test with 'GB' GB tn, fp: 283, 6 GB fn, tp: 2, 5 GB f1 score: 0.556 GB cohens kappa score: 0.542 -> test with 'KNN' KNN tn, fp: 278, 11 KNN fn, tp: 1, 6 KNN f1 score: 0.500 KNN cohens kappa score: 0.483 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 262, 28 LR fn, tp: 0, 7 LR f1 score: 0.333 LR cohens kappa score: 0.306 LR average precision score: 0.682 -> test with 'RF' RF tn, fp: 287, 3 RF fn, tp: 3, 4 RF f1 score: 0.571 RF cohens kappa score: 0.561 -> 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: 277, 13 KNN fn, tp: 2, 5 KNN f1 score: 0.400 KNN cohens kappa score: 0.379 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 251, 39 LR fn, tp: 0, 7 LR f1 score: 0.264 LR cohens kappa score: 0.233 LR average precision score: 0.242 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 3, 4 RF f1 score: 0.667 RF cohens kappa score: 0.660 -> test with 'GB' GB tn, fp: 285, 5 GB fn, tp: 1, 6 GB f1 score: 0.667 GB cohens kappa score: 0.657 -> test with 'KNN' KNN tn, fp: 275, 15 KNN fn, tp: 0, 7 KNN f1 score: 0.483 KNN cohens kappa score: 0.464 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 261, 29 LR fn, tp: 1, 6 LR f1 score: 0.286 LR cohens kappa score: 0.257 LR average precision score: 0.536 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 4, 3 RF f1 score: 0.545 RF cohens kappa score: 0.538 -> test with 'GB' GB tn, fp: 290, 0 GB fn, tp: 4, 3 GB f1 score: 0.600 GB cohens kappa score: 0.594 -> test with 'KNN' KNN tn, fp: 281, 9 KNN fn, tp: 2, 5 KNN f1 score: 0.476 KNN cohens kappa score: 0.459 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 257, 33 LR fn, tp: 2, 5 LR f1 score: 0.222 LR cohens kappa score: 0.190 LR average precision score: 0.524 -> test with 'RF' RF tn, fp: 287, 3 RF fn, tp: 4, 3 RF f1 score: 0.462 RF cohens kappa score: 0.450 -> 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: 276, 14 KNN fn, tp: 3, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.296 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 270, 19 LR fn, tp: 1, 6 LR f1 score: 0.375 LR cohens kappa score: 0.351 LR average precision score: 0.541 -> test with 'RF' RF tn, fp: 289, 0 RF fn, tp: 4, 3 RF f1 score: 0.600 RF cohens kappa score: 0.594 -> test with 'GB' GB tn, fp: 289, 0 GB fn, tp: 4, 3 GB f1 score: 0.600 GB cohens kappa score: 0.594 -> test with 'KNN' KNN tn, fp: 284, 5 KNN fn, tp: 3, 4 KNN f1 score: 0.500 KNN cohens kappa score: 0.486 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 266, 24 LR fn, tp: 1, 6 LR f1 score: 0.324 LR cohens kappa score: 0.297 LR average precision score: 0.640 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 3, 4 RF f1 score: 0.667 RF cohens kappa score: 0.660 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 3, 4 GB f1 score: 0.667 GB cohens kappa score: 0.660 -> test with 'KNN' KNN tn, fp: 283, 7 KNN fn, tp: 2, 5 KNN f1 score: 0.526 KNN cohens kappa score: 0.512 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 248, 42 LR fn, tp: 0, 7 LR f1 score: 0.250 LR cohens kappa score: 0.218 LR average precision score: 0.753 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 3, 4 RF f1 score: 0.667 RF cohens kappa score: 0.660 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 3, 4 GB f1 score: 0.571 GB cohens kappa score: 0.561 -> test with 'KNN' KNN tn, fp: 274, 16 KNN fn, tp: 1, 6 KNN f1 score: 0.414 KNN cohens kappa score: 0.392 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 266, 24 LR fn, tp: 2, 5 LR f1 score: 0.278 LR cohens kappa score: 0.249 LR average precision score: 0.410 -> test with 'RF' RF tn, fp: 288, 2 RF fn, tp: 5, 2 RF f1 score: 0.364 RF cohens kappa score: 0.353 -> 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: 282, 8 KNN fn, tp: 3, 4 KNN f1 score: 0.421 KNN cohens kappa score: 0.403 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 262, 28 LR fn, tp: 1, 6 LR f1 score: 0.293 LR cohens kappa score: 0.264 LR average precision score: 0.409 -> test with 'RF' RF tn, fp: 288, 2 RF fn, tp: 3, 4 RF f1 score: 0.615 RF cohens kappa score: 0.607 -> test with 'GB' GB tn, fp: 285, 5 GB fn, tp: 3, 4 GB f1 score: 0.500 GB cohens kappa score: 0.486 -> test with 'KNN' KNN tn, fp: 276, 14 KNN fn, tp: 2, 5 KNN f1 score: 0.385 KNN cohens kappa score: 0.363 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 267, 22 LR fn, tp: 1, 6 LR f1 score: 0.343 LR cohens kappa score: 0.317 LR average precision score: 0.424 -> test with 'RF' RF tn, fp: 289, 0 RF fn, tp: 7, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> test with 'KNN' KNN tn, fp: 285, 4 KNN fn, tp: 1, 6 KNN f1 score: 0.706 KNN cohens kappa score: 0.697 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 272, 18 LR fn, tp: 1, 6 LR f1 score: 0.387 LR cohens kappa score: 0.364 LR average precision score: 0.731 -> test with 'RF' RF tn, fp: 290, 0 RF fn, tp: 2, 5 RF f1 score: 0.833 RF cohens kappa score: 0.830 -> test with 'GB' GB tn, fp: 290, 0 GB fn, tp: 2, 5 GB f1 score: 0.833 GB cohens kappa score: 0.830 -> test with 'KNN' KNN tn, fp: 277, 13 KNN fn, tp: 1, 6 KNN f1 score: 0.462 KNN cohens kappa score: 0.442 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 259, 31 LR fn, tp: 1, 6 LR f1 score: 0.273 LR cohens kappa score: 0.243 LR average precision score: 0.233 -> test with 'RF' RF tn, fp: 287, 3 RF fn, tp: 5, 2 RF f1 score: 0.333 RF cohens kappa score: 0.320 -> 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: 280, 10 KNN fn, tp: 2, 5 KNN f1 score: 0.455 KNN cohens kappa score: 0.436 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 250, 40 LR fn, tp: 1, 6 LR f1 score: 0.226 LR cohens kappa score: 0.193 LR average precision score: 0.482 -> test with 'RF' RF tn, fp: 286, 4 RF fn, tp: 2, 5 RF f1 score: 0.625 RF cohens kappa score: 0.615 -> test with 'GB' GB tn, fp: 284, 6 GB fn, tp: 2, 5 GB f1 score: 0.556 GB cohens kappa score: 0.542 -> test with 'KNN' KNN tn, fp: 274, 16 KNN fn, tp: 2, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.334 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 262, 28 LR fn, tp: 1, 6 LR f1 score: 0.293 LR cohens kappa score: 0.264 LR average precision score: 0.647 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 5, 2 RF f1 score: 0.400 RF cohens kappa score: 0.391 -> 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: 285, 5 KNN fn, tp: 2, 5 KNN f1 score: 0.588 KNN cohens kappa score: 0.576 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 267, 22 LR fn, tp: 2, 5 LR f1 score: 0.294 LR cohens kappa score: 0.267 LR average precision score: 0.634 -> test with 'RF' RF tn, fp: 286, 3 RF fn, tp: 4, 3 RF f1 score: 0.462 RF cohens kappa score: 0.450 -> test with 'GB' GB tn, fp: 285, 4 GB fn, tp: 4, 3 GB f1 score: 0.429 GB cohens kappa score: 0.415 -> test with 'KNN' KNN tn, fp: 281, 8 KNN fn, tp: 2, 5 KNN f1 score: 0.500 KNN cohens kappa score: 0.484 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 250, 40 LR fn, tp: 0, 7 LR f1 score: 0.259 LR cohens kappa score: 0.228 LR average precision score: 0.454 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 3, 4 RF f1 score: 0.667 RF cohens kappa score: 0.660 -> test with 'GB' GB tn, fp: 284, 6 GB fn, tp: 3, 4 GB f1 score: 0.471 GB cohens kappa score: 0.455 -> test with 'KNN' KNN tn, fp: 274, 16 KNN fn, tp: 2, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.334 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 267, 23 LR fn, tp: 3, 4 LR f1 score: 0.235 LR cohens kappa score: 0.206 LR average precision score: 0.227 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 4, 3 RF f1 score: 0.545 RF cohens kappa score: 0.538 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 3, 4 GB f1 score: 0.571 GB cohens kappa score: 0.561 -> test with 'KNN' KNN tn, fp: 281, 9 KNN fn, tp: 3, 4 KNN f1 score: 0.400 KNN cohens kappa score: 0.381 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 259, 31 LR fn, tp: 0, 7 LR f1 score: 0.311 LR cohens kappa score: 0.283 LR average precision score: 0.688 -> test with 'RF' RF tn, fp: 288, 2 RF fn, tp: 2, 5 RF f1 score: 0.714 RF cohens kappa score: 0.707 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 0, 7 GB f1 score: 0.824 GB cohens kappa score: 0.818 -> test with 'KNN' KNN tn, fp: 281, 9 KNN fn, tp: 0, 7 KNN f1 score: 0.609 KNN cohens kappa score: 0.595 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 256, 34 LR fn, tp: 0, 7 LR f1 score: 0.292 LR cohens kappa score: 0.262 LR average precision score: 0.339 -> test with 'RF' RF tn, fp: 290, 0 RF fn, tp: 5, 2 RF f1 score: 0.444 RF cohens kappa score: 0.439 -> 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: 281, 9 KNN fn, tp: 2, 5 KNN f1 score: 0.476 KNN cohens kappa score: 0.459 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 272, 17 LR fn, tp: 2, 5 LR f1 score: 0.345 LR cohens kappa score: 0.320 LR average precision score: 0.418 -> test with 'RF' RF tn, fp: 288, 1 RF fn, tp: 4, 3 RF f1 score: 0.545 RF cohens kappa score: 0.537 -> 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: 281, 8 KNN fn, tp: 2, 5 KNN f1 score: 0.500 KNN cohens kappa score: 0.484 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 272, 49 LR fn, tp: 3, 7 LR f1 score: 0.387 LR cohens kappa score: 0.364 LR average precision score: 0.753 average: LR tn, fp: 260.84, 28.96 LR fn, tp: 1.0, 6.0 LR f1 score: 0.292 LR cohens kappa score: 0.263 LR average precision score: 0.500 minimum: LR tn, fp: 240, 17 LR fn, tp: 0, 4 LR f1 score: 0.222 LR cohens kappa score: 0.188 LR average precision score: 0.227 -----[ RF ]----- maximum: RF tn, fp: 290, 4 RF fn, tp: 7, 6 RF f1 score: 0.857 RF cohens kappa score: 0.854 average: RF tn, fp: 288.28, 1.52 RF fn, tp: 3.64, 3.36 RF f1 score: 0.551 RF cohens kappa score: 0.543 minimum: RF tn, fp: 286, 0 RF fn, tp: 1, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -----[ GB ]----- maximum: GB tn, fp: 290, 6 GB fn, tp: 5, 7 GB f1 score: 0.833 GB cohens kappa score: 0.830 average: GB tn, fp: 286.76, 3.04 GB fn, tp: 3.28, 3.72 GB f1 score: 0.539 GB cohens kappa score: 0.529 minimum: GB tn, fp: 283, 0 GB fn, tp: 0, 2 GB f1 score: 0.308 GB cohens kappa score: 0.292 -----[ KNN ]----- maximum: KNN tn, fp: 285, 16 KNN fn, tp: 3, 7 KNN f1 score: 0.706 KNN cohens kappa score: 0.697 average: KNN tn, fp: 279.36, 10.44 KNN fn, tp: 1.84, 5.16 KNN f1 score: 0.464 KNN cohens kappa score: 0.447 minimum: KNN tn, fp: 274, 4 KNN fn, tp: 0, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.296