/////////////////////////////////////////// // Running Repeater 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: 252, 38 LR fn, tp: 0, 7 LR f1 score: 0.269 LR cohens kappa score: 0.238 LR average precision score: 0.706 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 3, 4 GB f1 score: 0.444 GB cohens kappa score: 0.428 -> 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 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 255, 35 LR fn, tp: 2, 5 LR f1 score: 0.213 LR cohens kappa score: 0.180 LR average precision score: 0.426 -> 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: 270, 20 KNN fn, tp: 2, 5 KNN f1 score: 0.312 KNN cohens kappa score: 0.286 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 245, 45 LR fn, tp: 1, 6 LR f1 score: 0.207 LR cohens kappa score: 0.173 LR average precision score: 0.272 -> test with 'GB' GB tn, fp: 284, 6 GB fn, tp: 1, 6 GB f1 score: 0.632 GB cohens kappa score: 0.620 -> test with 'KNN' KNN tn, fp: 279, 11 KNN fn, tp: 1, 6 KNN f1 score: 0.500 KNN cohens kappa score: 0.483 ------ Step 1/5: 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: 0.623 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 4, 3 GB f1 score: 0.353 GB cohens kappa score: 0.334 -> 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 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 235, 54 LR fn, tp: 0, 7 LR f1 score: 0.206 LR cohens kappa score: 0.171 LR average precision score: 0.491 -> test with 'GB' GB tn, fp: 281, 8 GB fn, tp: 1, 6 GB f1 score: 0.571 GB cohens kappa score: 0.557 -> test with 'KNN' KNN tn, fp: 268, 21 KNN fn, tp: 0, 7 KNN f1 score: 0.400 KNN cohens kappa score: 0.376 ====== 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: 244, 46 LR fn, tp: 0, 7 LR f1 score: 0.233 LR cohens kappa score: 0.200 LR average precision score: 0.657 -> 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: 275, 15 KNN fn, tp: 1, 6 KNN f1 score: 0.429 KNN cohens kappa score: 0.408 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 236, 54 LR fn, tp: 0, 7 LR f1 score: 0.206 LR cohens kappa score: 0.171 LR average precision score: 0.197 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 3, 4 GB f1 score: 0.444 GB cohens kappa score: 0.428 -> test with 'KNN' KNN tn, fp: 274, 16 KNN fn, tp: 0, 7 KNN f1 score: 0.467 KNN cohens kappa score: 0.447 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 251, 39 LR fn, tp: 1, 6 LR f1 score: 0.231 LR cohens kappa score: 0.198 LR average precision score: 0.525 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 3, 4 GB f1 score: 0.444 GB cohens kappa score: 0.428 -> 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 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 248, 42 LR fn, tp: 2, 5 LR f1 score: 0.185 LR cohens kappa score: 0.150 LR average precision score: 0.485 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 4, 3 GB f1 score: 0.353 GB cohens kappa score: 0.334 -> 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 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 259, 30 LR fn, tp: 1, 6 LR f1 score: 0.279 LR cohens kappa score: 0.249 LR average precision score: 0.503 -> 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: 275, 14 KNN fn, tp: 3, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.296 ====== 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: 251, 39 LR fn, tp: 1, 6 LR f1 score: 0.231 LR cohens kappa score: 0.198 LR average precision score: 0.603 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 2, 5 GB f1 score: 0.526 GB cohens kappa score: 0.512 -> 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 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 238, 52 LR fn, tp: 0, 7 LR f1 score: 0.212 LR cohens kappa score: 0.177 LR average precision score: 0.656 -> 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: 269, 21 KNN fn, tp: 0, 7 KNN f1 score: 0.400 KNN cohens kappa score: 0.376 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 255, 35 LR fn, tp: 2, 5 LR f1 score: 0.213 LR cohens kappa score: 0.180 LR average precision score: 0.373 -> 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: 279, 11 KNN fn, tp: 2, 5 KNN f1 score: 0.435 KNN cohens kappa score: 0.416 ------ Step 3/5: Slice 4/5 ------- -> 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: 0.378 -> test with 'GB' GB tn, fp: 277, 13 GB fn, tp: 3, 4 GB f1 score: 0.333 GB cohens kappa score: 0.310 -> test with 'KNN' KNN tn, fp: 266, 24 KNN fn, tp: 1, 6 KNN f1 score: 0.324 KNN cohens kappa score: 0.297 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 266, 23 LR fn, tp: 1, 6 LR f1 score: 0.333 LR cohens kappa score: 0.307 LR average precision score: 0.355 -> 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: 1, 6 KNN f1 score: 0.571 KNN cohens kappa score: 0.557 ====== 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: 262, 28 LR fn, tp: 1, 6 LR f1 score: 0.293 LR cohens kappa score: 0.264 LR average precision score: 0.552 -> 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: 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: 249, 41 LR fn, tp: 0, 7 LR f1 score: 0.255 LR cohens kappa score: 0.223 LR average precision score: 0.272 -> 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: 275, 15 KNN fn, tp: 1, 6 KNN f1 score: 0.429 KNN cohens kappa score: 0.408 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 240, 50 LR fn, tp: 1, 6 LR f1 score: 0.190 LR cohens kappa score: 0.155 LR average precision score: 0.428 -> test with 'GB' GB tn, fp: 284, 6 GB fn, tp: 1, 6 GB f1 score: 0.632 GB cohens kappa score: 0.620 -> test with 'KNN' KNN tn, fp: 265, 25 KNN fn, tp: 0, 7 KNN f1 score: 0.359 KNN cohens kappa score: 0.333 ------ Step 4/5: 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: 1, 6 LR f1 score: 0.267 LR cohens kappa score: 0.236 LR average precision score: 0.594 -> 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: 276, 14 KNN fn, tp: 1, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.424 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 258, 31 LR fn, tp: 1, 6 LR f1 score: 0.273 LR cohens kappa score: 0.243 LR average precision score: 0.625 -> test with 'GB' GB tn, fp: 284, 5 GB fn, tp: 4, 3 GB f1 score: 0.400 GB cohens kappa score: 0.384 -> 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: 241, 49 LR fn, tp: 0, 7 LR f1 score: 0.222 LR cohens kappa score: 0.188 LR average precision score: 0.516 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 2, 5 GB f1 score: 0.526 GB cohens kappa score: 0.512 -> 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 5/5: 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: 2, 5 LR f1 score: 0.222 LR cohens kappa score: 0.190 LR average precision score: 0.225 -> 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: 275, 15 KNN fn, tp: 3, 4 KNN f1 score: 0.308 KNN cohens kappa score: 0.283 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 249, 41 LR fn, tp: 0, 7 LR f1 score: 0.255 LR cohens kappa score: 0.223 LR average precision score: 0.711 -> test with 'GB' GB tn, fp: 285, 5 GB fn, tp: 0, 7 GB f1 score: 0.737 GB cohens kappa score: 0.729 -> test with 'KNN' KNN tn, fp: 273, 17 KNN fn, tp: 0, 7 KNN f1 score: 0.452 KNN cohens kappa score: 0.431 ------ Step 5/5: Slice 4/5 ------- -> 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: 0.267 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 5, 2 GB f1 score: 0.250 GB cohens kappa score: 0.230 -> 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 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 260, 29 LR fn, tp: 2, 5 LR f1 score: 0.244 LR cohens kappa score: 0.213 LR average precision score: 0.403 -> 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: 273, 16 KNN fn, tp: 2, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.334 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 266, 54 LR fn, tp: 2, 7 LR f1 score: 0.333 LR cohens kappa score: 0.307 LR average precision score: 0.711 average: LR tn, fp: 250.8, 39.0 LR fn, tp: 0.8, 6.2 LR f1 score: 0.242 LR cohens kappa score: 0.210 LR average precision score: 0.474 minimum: LR tn, fp: 235, 23 LR fn, tp: 0, 5 LR f1 score: 0.185 LR cohens kappa score: 0.150 LR average precision score: 0.197 -----[ GB ]----- maximum: GB tn, fp: 287, 13 GB fn, tp: 5, 7 GB f1 score: 0.737 GB cohens kappa score: 0.729 average: GB tn, fp: 284.0, 5.8 GB fn, tp: 2.92, 4.08 GB f1 score: 0.478 GB cohens kappa score: 0.464 minimum: GB tn, fp: 277, 2 GB fn, tp: 0, 2 GB f1 score: 0.250 GB cohens kappa score: 0.230 -----[ KNN ]----- maximum: KNN tn, fp: 281, 25 KNN fn, tp: 3, 7 KNN f1 score: 0.571 KNN cohens kappa score: 0.557 average: KNN tn, fp: 273.84, 15.96 KNN fn, tp: 1.2, 5.8 KNN f1 score: 0.409 KNN cohens kappa score: 0.387 minimum: KNN tn, fp: 265, 8 KNN fn, tp: 0, 4 KNN f1 score: 0.308 KNN cohens kappa score: 0.283