/////////////////////////////////////////// // Running convGAN-full 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: 270, 20 LR fn, tp: 1, 6 LR f1 score: 0.364 LR cohens kappa score: 0.339 LR average precision score: 0.664 -> 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: 278, 12 KNN fn, tp: 1, 6 KNN f1 score: 0.480 KNN cohens kappa score: 0.462 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 268, 22 LR fn, tp: 2, 5 LR f1 score: 0.294 LR cohens kappa score: 0.267 LR average precision score: 0.425 -> 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: 271, 19 KNN fn, tp: 2, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.297 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 260, 30 LR fn, tp: 1, 6 LR f1 score: 0.279 LR cohens kappa score: 0.249 LR average precision score: 0.319 -> test with 'GB' GB tn, fp: 290, 0 GB fn, tp: 5, 2 GB f1 score: 0.444 GB cohens kappa score: 0.439 -> test with 'KNN' KNN tn, fp: 272, 18 KNN fn, tp: 1, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.364 ------ 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: 0.615 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 5, 2 GB f1 score: 0.333 GB cohens kappa score: 0.320 -> test with 'KNN' KNN tn, fp: 275, 15 KNN fn, tp: 1, 6 KNN f1 score: 0.429 KNN cohens kappa score: 0.408 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 250, 39 LR fn, tp: 0, 7 LR f1 score: 0.264 LR cohens kappa score: 0.233 LR average precision score: 0.655 -> test with 'GB' GB tn, fp: 289, 0 GB fn, tp: 3, 4 GB f1 score: 0.727 GB cohens kappa score: 0.722 -> test with 'KNN' KNN tn, fp: 268, 21 KNN fn, tp: 1, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.328 ====== 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: 266, 24 LR fn, tp: 1, 6 LR f1 score: 0.324 LR cohens kappa score: 0.297 LR average precision score: 0.677 -> 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: 271, 19 KNN fn, tp: 1, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.351 ------ 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: 0.261 -> 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: 260, 30 KNN fn, tp: 0, 7 KNN f1 score: 0.318 KNN cohens kappa score: 0.290 ------ Step 2/5: 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: 0.513 -> 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: 273, 17 KNN fn, tp: 1, 6 KNN f1 score: 0.400 KNN cohens kappa score: 0.378 ------ Step 2/5: Slice 4/5 ------- -> 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: 0.625 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 5, 2 GB f1 score: 0.333 GB cohens kappa score: 0.320 -> 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 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: 0.524 -> test with 'GB' GB tn, fp: 289, 0 GB fn, tp: 6, 1 GB f1 score: 0.250 GB cohens kappa score: 0.246 -> test with 'KNN' KNN tn, fp: 279, 10 KNN fn, tp: 2, 5 KNN f1 score: 0.455 KNN cohens kappa score: 0.436 ====== 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: 268, 22 LR fn, tp: 1, 6 LR f1 score: 0.343 LR cohens kappa score: 0.317 LR average precision score: 0.655 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 3, 4 GB f1 score: 0.615 GB cohens kappa score: 0.607 -> test with 'KNN' KNN tn, fp: 276, 14 KNN fn, tp: 1, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.424 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 257, 33 LR fn, tp: 0, 7 LR f1 score: 0.298 LR cohens kappa score: 0.269 LR average precision score: 0.827 -> 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: 264, 26 KNN fn, tp: 0, 7 KNN f1 score: 0.350 KNN cohens kappa score: 0.324 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 273, 17 LR fn, tp: 2, 5 LR f1 score: 0.345 LR cohens kappa score: 0.321 LR average precision score: 0.418 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 5, 2 GB f1 score: 0.364 GB cohens kappa score: 0.353 -> 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 3/5: Slice 4/5 ------- -> 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: 0.394 -> 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: 269, 21 KNN fn, tp: 1, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.328 ------ Step 3/5: 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: 0.381 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -> test with 'KNN' KNN tn, fp: 282, 7 KNN fn, tp: 1, 6 KNN f1 score: 0.600 KNN cohens kappa score: 0.587 ====== 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: 276, 14 LR fn, tp: 1, 6 LR f1 score: 0.444 LR cohens kappa score: 0.424 LR average precision score: 0.741 -> 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: 278, 12 KNN fn, tp: 1, 6 KNN f1 score: 0.480 KNN cohens kappa score: 0.462 ------ Step 4/5: 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: 1, 6 LR f1 score: 0.324 LR cohens kappa score: 0.297 LR average precision score: 0.256 -> 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: 0, 7 KNN f1 score: 0.500 KNN cohens kappa score: 0.482 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 256, 34 LR fn, tp: 1, 6 LR f1 score: 0.255 LR cohens kappa score: 0.224 LR average precision score: 0.630 -> 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: 263, 27 KNN fn, tp: 0, 7 KNN f1 score: 0.341 KNN cohens kappa score: 0.315 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 268, 22 LR fn, tp: 1, 6 LR f1 score: 0.343 LR cohens kappa score: 0.317 LR average precision score: 0.649 -> 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: 279, 11 KNN fn, tp: 2, 5 KNN f1 score: 0.435 KNN cohens kappa score: 0.416 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 273, 16 LR fn, tp: 2, 5 LR f1 score: 0.357 LR cohens kappa score: 0.334 LR average precision score: 0.648 -> 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: 278, 11 KNN fn, tp: 2, 5 KNN f1 score: 0.435 KNN cohens kappa score: 0.416 ====== 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: 261, 29 LR fn, tp: 0, 7 LR f1 score: 0.326 LR cohens kappa score: 0.298 LR average precision score: 0.510 -> 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: 264, 26 KNN fn, tp: 1, 6 KNN f1 score: 0.308 KNN cohens kappa score: 0.280 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 270, 20 LR fn, tp: 3, 4 LR f1 score: 0.258 LR cohens kappa score: 0.230 LR average precision score: 0.214 -> 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: 277, 13 KNN fn, tp: 3, 4 KNN f1 score: 0.333 KNN cohens kappa score: 0.310 ------ Step 5/5: 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: 0, 7 LR f1 score: 0.368 LR cohens kappa score: 0.343 LR average precision score: 0.759 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 1, 6 GB f1 score: 0.857 GB cohens kappa score: 0.854 -> test with 'KNN' KNN tn, fp: 270, 20 KNN fn, tp: 0, 7 KNN f1 score: 0.412 KNN cohens kappa score: 0.389 ------ Step 5/5: 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: 0.522 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> test with 'KNN' KNN tn, fp: 273, 17 KNN fn, tp: 2, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.321 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 274, 15 LR fn, tp: 2, 5 LR f1 score: 0.370 LR cohens kappa score: 0.348 LR average precision score: 0.440 -> 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: 278, 11 KNN fn, tp: 2, 5 KNN f1 score: 0.435 KNN cohens kappa score: 0.416 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 276, 39 LR fn, tp: 3, 7 LR f1 score: 0.444 LR cohens kappa score: 0.424 LR average precision score: 0.827 average: LR tn, fp: 265.56, 24.24 LR fn, tp: 1.16, 5.84 LR f1 score: 0.321 LR cohens kappa score: 0.294 LR average precision score: 0.533 minimum: LR tn, fp: 250, 14 LR fn, tp: 0, 4 LR f1 score: 0.255 LR cohens kappa score: 0.224 LR average precision score: 0.214 -----[ GB ]----- maximum: GB tn, fp: 290, 6 GB fn, tp: 7, 6 GB f1 score: 0.857 GB cohens kappa score: 0.854 average: GB tn, fp: 287.84, 1.96 GB fn, tp: 4.0, 3.0 GB f1 score: 0.487 GB cohens kappa score: 0.478 minimum: GB tn, fp: 284, 0 GB fn, tp: 1, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -----[ KNN ]----- maximum: KNN tn, fp: 282, 30 KNN fn, tp: 3, 7 KNN f1 score: 0.600 KNN cohens kappa score: 0.587 average: KNN tn, fp: 273.28, 16.52 KNN fn, tp: 1.24, 5.76 KNN f1 score: 0.404 KNN cohens kappa score: 0.382 minimum: KNN tn, fp: 260, 7 KNN fn, tp: 0, 4 KNN f1 score: 0.308 KNN cohens kappa score: 0.280