/////////////////////////////////////////// // 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: 261, 29 LR fn, tp: 0, 7 LR f1 score: 0.326 LR cohens kappa score: 0.298 LR average precision score: 0.692 -> 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: 0.428 -> 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: 263, 27 LR fn, tp: 1, 6 LR f1 score: 0.300 LR cohens kappa score: 0.272 LR average precision score: 0.254 -> 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 '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: 242, 47 LR fn, tp: 0, 7 LR f1 score: 0.230 LR cohens kappa score: 0.196 LR average precision score: 0.566 -> 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: 256, 34 LR fn, tp: 0, 7 LR f1 score: 0.292 LR cohens kappa score: 0.262 LR average precision score: 0.682 -> 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: 276, 14 KNN fn, tp: 2, 5 KNN f1 score: 0.385 KNN cohens kappa score: 0.363 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 252, 38 LR fn, tp: 0, 7 LR f1 score: 0.269 LR cohens kappa score: 0.238 LR average precision score: 0.236 -> 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: 262, 28 LR fn, tp: 1, 6 LR f1 score: 0.293 LR cohens kappa score: 0.264 LR average precision score: 0.535 -> 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: 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: 258, 32 LR fn, tp: 2, 5 LR f1 score: 0.227 LR cohens kappa score: 0.195 LR average precision score: 0.524 -> 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: 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: 269, 20 LR fn, tp: 1, 6 LR f1 score: 0.364 LR cohens kappa score: 0.339 LR average precision score: 0.541 -> 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: 0.641 -> 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: 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: 246, 44 LR fn, tp: 0, 7 LR f1 score: 0.241 LR cohens kappa score: 0.209 LR average precision score: 0.734 -> 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: 267, 23 LR fn, tp: 2, 5 LR f1 score: 0.286 LR cohens kappa score: 0.258 LR average precision score: 0.408 -> 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: 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: 258, 32 LR fn, tp: 0, 7 LR f1 score: 0.304 LR cohens kappa score: 0.275 LR average precision score: 0.408 -> 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: 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: 2, 5 LR f1 score: 0.294 LR cohens kappa score: 0.267 LR average precision score: 0.424 -> 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: 284, 5 KNN fn, tp: 1, 6 KNN f1 score: 0.667 KNN cohens kappa score: 0.657 ====== 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 '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: 0.236 -> 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: 0.574 -> 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: 275, 15 KNN fn, tp: 2, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.348 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 263, 27 LR fn, tp: 1, 6 LR f1 score: 0.300 LR cohens kappa score: 0.272 LR average precision score: 0.647 -> 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: 0.679 -> 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: 3, 4 KNN f1 score: 0.421 KNN cohens kappa score: 0.403 ====== 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: 251, 39 LR fn, tp: 0, 7 LR f1 score: 0.264 LR cohens kappa score: 0.233 LR average precision score: 0.500 -> 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: 266, 24 LR fn, tp: 3, 4 LR f1 score: 0.229 LR cohens kappa score: 0.198 LR average precision score: 0.202 -> 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: 0.691 -> 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: 0.337 -> 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: 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: 0.439 -> 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, 47 LR fn, tp: 3, 7 LR f1 score: 0.387 LR cohens kappa score: 0.364 LR average precision score: 0.734 average: LR tn, fp: 260.76, 29.04 LR fn, tp: 1.0, 6.0 LR f1 score: 0.291 LR cohens kappa score: 0.262 LR average precision score: 0.506 minimum: LR tn, fp: 242, 17 LR fn, tp: 0, 4 LR f1 score: 0.226 LR cohens kappa score: 0.193 LR average precision score: 0.202 -----[ 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: 286.64, 3.16 GB fn, tp: 3.2, 3.8 GB f1 score: 0.544 GB cohens kappa score: 0.533 minimum: GB tn, fp: 283, 0 GB fn, tp: 0, 2 GB f1 score: 0.286 GB cohens kappa score: 0.268 -----[ KNN ]----- maximum: KNN tn, fp: 285, 16 KNN fn, tp: 3, 7 KNN f1 score: 0.667 KNN cohens kappa score: 0.657 average: KNN tn, fp: 279.32, 10.48 KNN fn, tp: 1.88, 5.12 KNN f1 score: 0.460 KNN cohens kappa score: 0.442 minimum: KNN tn, fp: 274, 5 KNN fn, tp: 0, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.296