/////////////////////////////////////////// // Running convGAN-full 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: 119, 19 LR fn, tp: 0, 9 LR f1 score: 0.486 LR cohens kappa score: 0.434 LR average precision score: 0.879 -> 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: 120, 18 KNN fn, tp: 3, 6 KNN f1 score: 0.364 KNN cohens kappa score: 0.301 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 3, 6 LR f1 score: 0.522 LR cohens kappa score: 0.483 LR average precision score: 0.581 -> 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: 120, 18 KNN fn, tp: 1, 8 KNN f1 score: 0.457 KNN cohens kappa score: 0.403 ------ Step 1/5: Slice 3/5 ------- -> 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: 0.809 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 4, 5 GB f1 score: 0.500 GB cohens kappa score: 0.464 -> 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 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: 0.582 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 6, 3 GB f1 score: 0.429 GB cohens kappa score: 0.402 -> test with 'KNN' KNN tn, fp: 132, 6 KNN fn, tp: 3, 6 KNN f1 score: 0.571 KNN cohens kappa score: 0.539 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 127, 10 LR fn, tp: 2, 4 LR f1 score: 0.400 LR cohens kappa score: 0.363 LR average precision score: 0.452 -> test with 'GB' GB tn, fp: 134, 3 GB fn, tp: 4, 2 GB f1 score: 0.364 GB cohens kappa score: 0.338 -> 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 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: 122, 16 LR fn, tp: 1, 8 LR f1 score: 0.485 LR cohens kappa score: 0.434 LR average precision score: 0.651 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 5, 4 GB f1 score: 0.533 GB cohens kappa score: 0.509 -> 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: 1, 8 LR f1 score: 0.727 LR cohens kappa score: 0.706 LR average precision score: 0.783 -> 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: 127, 11 KNN fn, tp: 3, 6 KNN f1 score: 0.462 KNN cohens kappa score: 0.415 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 128, 10 LR fn, tp: 2, 7 LR f1 score: 0.538 LR cohens kappa score: 0.498 LR average precision score: 0.653 -> test with 'GB' GB tn, fp: 130, 8 GB fn, tp: 5, 4 GB f1 score: 0.381 GB cohens kappa score: 0.334 -> test with 'KNN' KNN tn, fp: 123, 15 KNN fn, tp: 2, 7 KNN f1 score: 0.452 KNN cohens kappa score: 0.399 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 128, 10 LR fn, tp: 0, 9 LR f1 score: 0.643 LR cohens kappa score: 0.610 LR average precision score: 0.719 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 6, 3 GB f1 score: 0.316 GB cohens kappa score: 0.269 -> 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: 0.660 -> test with 'GB' GB tn, fp: 128, 9 GB fn, tp: 3, 3 GB f1 score: 0.333 GB cohens kappa score: 0.294 -> test with 'KNN' KNN tn, fp: 120, 17 KNN fn, tp: 2, 4 KNN f1 score: 0.296 KNN cohens kappa score: 0.247 ====== 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: 128, 10 LR fn, tp: 4, 5 LR f1 score: 0.417 LR cohens kappa score: 0.368 LR average precision score: 0.556 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 7, 2 GB f1 score: 0.222 GB cohens kappa score: 0.171 -> test with 'KNN' KNN tn, fp: 121, 17 KNN fn, tp: 6, 3 KNN f1 score: 0.207 KNN cohens kappa score: 0.134 ------ 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 'GB' GB tn, fp: 133, 5 GB fn, tp: 2, 7 GB f1 score: 0.667 GB cohens kappa score: 0.642 -> test with 'KNN' KNN tn, fp: 124, 14 KNN fn, tp: 1, 8 KNN f1 score: 0.516 KNN cohens kappa score: 0.470 ------ Step 3/5: Slice 3/5 ------- -> 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: 0.693 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 7, 2 GB f1 score: 0.222 GB cohens kappa score: 0.171 -> test with 'KNN' KNN tn, fp: 127, 11 KNN fn, tp: 5, 4 KNN f1 score: 0.333 KNN cohens kappa score: 0.278 ------ 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: 2, 7 LR f1 score: 0.412 LR cohens kappa score: 0.354 LR average precision score: 0.630 -> test with 'GB' GB tn, fp: 130, 8 GB fn, tp: 6, 3 GB f1 score: 0.300 GB cohens kappa score: 0.249 -> test with 'KNN' KNN tn, fp: 123, 15 KNN fn, tp: 4, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.284 ------ Step 3/5: 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: 0.564 -> 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: 119, 18 KNN fn, tp: 2, 4 KNN f1 score: 0.286 KNN cohens kappa score: 0.235 ====== 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: 129, 9 LR fn, tp: 3, 6 LR f1 score: 0.500 LR cohens kappa score: 0.459 LR average precision score: 0.547 -> 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: 128, 10 KNN fn, tp: 6, 3 KNN f1 score: 0.273 KNN cohens kappa score: 0.216 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 126, 12 LR fn, tp: 2, 7 LR f1 score: 0.500 LR cohens kappa score: 0.455 LR average precision score: 0.732 -> test with 'GB' GB tn, fp: 128, 10 GB fn, tp: 4, 5 GB f1 score: 0.417 GB cohens kappa score: 0.368 -> test with 'KNN' KNN tn, fp: 120, 18 KNN fn, tp: 3, 6 KNN f1 score: 0.364 KNN cohens kappa score: 0.301 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 125, 13 LR fn, tp: 1, 8 LR f1 score: 0.533 LR cohens kappa score: 0.490 LR average precision score: 0.664 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 4, 5 GB f1 score: 0.500 GB cohens kappa score: 0.464 -> test with 'KNN' KNN tn, fp: 121, 17 KNN fn, tp: 3, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.315 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 123, 15 LR fn, tp: 0, 9 LR f1 score: 0.545 LR cohens kappa score: 0.501 LR average precision score: 0.906 -> test with 'GB' GB tn, fp: 130, 8 GB fn, tp: 6, 3 GB f1 score: 0.300 GB cohens kappa score: 0.249 -> test with 'KNN' KNN tn, fp: 116, 22 KNN fn, tp: 2, 7 KNN f1 score: 0.368 KNN cohens kappa score: 0.303 ------ 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: 0.510 -> test with 'GB' GB tn, fp: 130, 7 GB fn, tp: 4, 2 GB f1 score: 0.267 GB cohens kappa score: 0.228 -> test with 'KNN' KNN tn, fp: 125, 12 KNN fn, tp: 1, 5 KNN f1 score: 0.435 KNN cohens kappa score: 0.397 ====== 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: 125, 13 LR fn, tp: 2, 7 LR f1 score: 0.483 LR cohens kappa score: 0.435 LR average precision score: 0.693 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 7, 2 GB f1 score: 0.222 GB cohens kappa score: 0.171 -> test with 'KNN' KNN tn, fp: 125, 13 KNN fn, tp: 7, 2 KNN f1 score: 0.167 KNN cohens kappa score: 0.098 ------ 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: 1, 8 LR f1 score: 0.571 LR cohens kappa score: 0.533 LR average precision score: 0.677 -> 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: 114, 24 KNN fn, tp: 5, 4 KNN f1 score: 0.216 KNN cohens kappa score: 0.136 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 4, 5 LR f1 score: 0.435 LR cohens kappa score: 0.389 LR average precision score: 0.532 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 7, 2 GB f1 score: 0.222 GB cohens kappa score: 0.171 -> test with 'KNN' KNN tn, fp: 122, 16 KNN fn, tp: 5, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.209 ------ Step 5/5: 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: 1, 8 LR f1 score: 0.667 LR cohens kappa score: 0.639 LR average precision score: 0.841 -> 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: 128, 10 KNN fn, tp: 2, 7 KNN f1 score: 0.538 KNN cohens kappa score: 0.498 ------ Step 5/5: 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: 1, 5 LR f1 score: 0.526 LR cohens kappa score: 0.497 LR average precision score: 0.809 -> test with 'GB' GB tn, fp: 133, 4 GB fn, tp: 3, 3 GB f1 score: 0.462 GB cohens kappa score: 0.436 -> test with 'KNN' KNN tn, fp: 128, 9 KNN fn, tp: 3, 3 KNN f1 score: 0.333 KNN cohens kappa score: 0.294 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 133, 19 LR fn, tp: 4, 9 LR f1 score: 0.783 LR cohens kappa score: 0.765 LR average precision score: 0.906 average: LR tn, fp: 127.72, 10.08 LR fn, tp: 1.64, 6.76 LR f1 score: 0.539 LR cohens kappa score: 0.501 LR average precision score: 0.681 minimum: LR tn, fp: 119, 5 LR fn, tp: 0, 4 LR f1 score: 0.400 LR cohens kappa score: 0.354 LR average precision score: 0.452 -----[ GB ]----- maximum: GB tn, fp: 136, 10 GB fn, tp: 7, 7 GB f1 score: 0.667 GB cohens kappa score: 0.642 average: GB tn, fp: 131.64, 6.16 GB fn, tp: 5.04, 3.36 GB f1 score: 0.374 GB cohens kappa score: 0.334 minimum: GB tn, fp: 128, 2 GB fn, tp: 2, 2 GB f1 score: 0.222 GB cohens kappa score: 0.171 -----[ KNN ]----- maximum: KNN tn, fp: 132, 24 KNN fn, tp: 7, 8 KNN f1 score: 0.571 KNN cohens kappa score: 0.539 average: KNN tn, fp: 123.6, 14.2 KNN fn, tp: 3.36, 5.04 KNN f1 score: 0.367 KNN cohens kappa score: 0.312 minimum: KNN tn, fp: 114, 6 KNN fn, tp: 1, 2 KNN f1 score: 0.167 KNN cohens kappa score: 0.098