/////////////////////////////////////////// // Running ctGAN on folding_yeast5 /////////////////////////////////////////// Load 'data_input/folding_yeast5' 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 1117 synthetic samples -> test with 'LR' LR tn, fp: 269, 19 LR fn, tp: 1, 8 LR f1 score: 0.444 LR cohens kappa score: 0.418 LR average precision score: 0.906 -> test with 'GB' GB tn, fp: 281, 7 GB fn, tp: 3, 6 GB f1 score: 0.545 GB cohens kappa score: 0.529 -> test with 'KNN' KNN tn, fp: 276, 12 KNN fn, tp: 1, 8 KNN f1 score: 0.552 KNN cohens kappa score: 0.532 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 281, 7 LR fn, tp: 2, 7 LR f1 score: 0.609 LR cohens kappa score: 0.594 LR average precision score: 0.709 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 1, 8 GB f1 score: 0.727 GB cohens kappa score: 0.717 -> test with 'KNN' KNN tn, fp: 283, 5 KNN fn, tp: 1, 8 KNN f1 score: 0.727 KNN cohens kappa score: 0.717 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 270, 18 LR fn, tp: 0, 9 LR f1 score: 0.500 LR cohens kappa score: 0.476 LR average precision score: 0.576 -> test with 'GB' GB tn, fp: 278, 10 GB fn, tp: 2, 7 GB f1 score: 0.538 GB cohens kappa score: 0.519 -> test with 'KNN' KNN tn, fp: 275, 13 KNN fn, tp: 0, 9 KNN f1 score: 0.581 KNN cohens kappa score: 0.562 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 268, 20 LR fn, tp: 0, 9 LR f1 score: 0.474 LR cohens kappa score: 0.448 LR average precision score: 0.604 -> test with 'GB' GB tn, fp: 280, 8 GB fn, tp: 3, 6 GB f1 score: 0.522 GB cohens kappa score: 0.503 -> test with 'KNN' KNN tn, fp: 275, 13 KNN fn, tp: 1, 8 KNN f1 score: 0.533 KNN cohens kappa score: 0.513 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 273, 15 LR fn, tp: 0, 8 LR f1 score: 0.516 LR cohens kappa score: 0.496 LR average precision score: 0.500 -> test with 'GB' GB tn, fp: 282, 6 GB fn, tp: 1, 7 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 277, 11 KNN fn, tp: 1, 7 KNN f1 score: 0.538 KNN cohens kappa score: 0.521 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 271, 17 LR fn, tp: 0, 9 LR f1 score: 0.514 LR cohens kappa score: 0.491 LR average precision score: 0.748 -> test with 'GB' GB tn, fp: 280, 8 GB fn, tp: 0, 9 GB f1 score: 0.692 GB cohens kappa score: 0.680 -> test with 'KNN' KNN tn, fp: 273, 15 KNN fn, tp: 0, 9 KNN f1 score: 0.545 KNN cohens kappa score: 0.524 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 275, 13 LR fn, tp: 4, 5 LR f1 score: 0.370 LR cohens kappa score: 0.344 LR average precision score: 0.409 -> test with 'GB' GB tn, fp: 276, 12 GB fn, tp: 3, 6 GB f1 score: 0.444 GB cohens kappa score: 0.421 -> test with 'KNN' KNN tn, fp: 276, 12 KNN fn, tp: 0, 9 KNN f1 score: 0.600 KNN cohens kappa score: 0.582 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 282, 6 LR fn, tp: 0, 9 LR f1 score: 0.750 LR cohens kappa score: 0.740 LR average precision score: 0.895 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 2, 7 GB f1 score: 0.824 GB cohens kappa score: 0.818 -> test with 'KNN' KNN tn, fp: 284, 4 KNN fn, tp: 1, 8 KNN f1 score: 0.762 KNN cohens kappa score: 0.753 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 275, 13 LR fn, tp: 0, 9 LR f1 score: 0.581 LR cohens kappa score: 0.562 LR average precision score: 0.647 -> test with 'GB' GB tn, fp: 280, 8 GB fn, tp: 1, 8 GB f1 score: 0.640 GB cohens kappa score: 0.625 -> test with 'KNN' KNN tn, fp: 278, 10 KNN fn, tp: 1, 8 KNN f1 score: 0.593 KNN cohens kappa score: 0.575 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 280, 8 LR fn, tp: 3, 5 LR f1 score: 0.476 LR cohens kappa score: 0.458 LR average precision score: 0.563 -> test with 'GB' GB tn, fp: 285, 3 GB fn, tp: 3, 5 GB f1 score: 0.625 GB cohens kappa score: 0.615 -> test with 'KNN' KNN tn, fp: 280, 8 KNN fn, tp: 2, 6 KNN f1 score: 0.545 KNN cohens kappa score: 0.529 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 272, 16 LR fn, tp: 1, 8 LR f1 score: 0.485 LR cohens kappa score: 0.461 LR average precision score: 0.721 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 3, 6 GB f1 score: 0.600 GB cohens kappa score: 0.586 -> test with 'KNN' KNN tn, fp: 276, 12 KNN fn, tp: 1, 8 KNN f1 score: 0.552 KNN cohens kappa score: 0.532 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 278, 10 LR fn, tp: 2, 7 LR f1 score: 0.538 LR cohens kappa score: 0.519 LR average precision score: 0.673 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 2, 7 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 280, 8 KNN fn, tp: 2, 7 KNN f1 score: 0.583 KNN cohens kappa score: 0.567 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 284, 4 LR fn, tp: 2, 7 LR f1 score: 0.700 LR cohens kappa score: 0.690 LR average precision score: 0.751 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 1, 8 GB f1 score: 0.889 GB cohens kappa score: 0.885 -> test with 'KNN' KNN tn, fp: 287, 1 KNN fn, tp: 2, 7 KNN f1 score: 0.824 KNN cohens kappa score: 0.818 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 281, 7 LR fn, tp: 0, 9 LR f1 score: 0.720 LR cohens kappa score: 0.709 LR average precision score: 0.717 -> test with 'GB' GB tn, fp: 281, 7 GB fn, tp: 3, 6 GB f1 score: 0.545 GB cohens kappa score: 0.529 -> test with 'KNN' KNN tn, fp: 280, 8 KNN fn, tp: 1, 8 KNN f1 score: 0.640 KNN cohens kappa score: 0.625 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 279, 9 LR fn, tp: 3, 5 LR f1 score: 0.455 LR cohens kappa score: 0.435 LR average precision score: 0.520 -> test with 'GB' GB tn, fp: 281, 7 GB fn, tp: 1, 7 GB f1 score: 0.636 GB cohens kappa score: 0.623 -> test with 'KNN' KNN tn, fp: 280, 8 KNN fn, tp: 1, 7 KNN f1 score: 0.609 KNN cohens kappa score: 0.594 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 270, 18 LR fn, tp: 0, 9 LR f1 score: 0.500 LR cohens kappa score: 0.476 LR average precision score: 0.619 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 2, 7 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 276, 12 KNN fn, tp: 0, 9 KNN f1 score: 0.600 KNN cohens kappa score: 0.582 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 275, 13 LR fn, tp: 1, 8 LR f1 score: 0.533 LR cohens kappa score: 0.513 LR average precision score: 0.556 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 2, 7 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 278, 10 KNN fn, tp: 0, 9 KNN f1 score: 0.643 KNN cohens kappa score: 0.628 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 269, 19 LR fn, tp: 0, 9 LR f1 score: 0.486 LR cohens kappa score: 0.462 LR average precision score: 0.816 -> test with 'GB' GB tn, fp: 280, 8 GB fn, tp: 3, 6 GB f1 score: 0.522 GB cohens kappa score: 0.503 -> test with 'KNN' KNN tn, fp: 271, 17 KNN fn, tp: 0, 9 KNN f1 score: 0.514 KNN cohens kappa score: 0.491 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 265, 23 LR fn, tp: 0, 9 LR f1 score: 0.439 LR cohens kappa score: 0.411 LR average precision score: 0.689 -> test with 'GB' GB tn, fp: 282, 6 GB fn, tp: 5, 4 GB f1 score: 0.421 GB cohens kappa score: 0.402 -> test with 'KNN' KNN tn, fp: 269, 19 KNN fn, tp: 0, 9 KNN f1 score: 0.486 KNN cohens kappa score: 0.462 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 268, 20 LR fn, tp: 1, 7 LR f1 score: 0.400 LR cohens kappa score: 0.374 LR average precision score: 0.312 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 2, 6 GB f1 score: 0.667 GB cohens kappa score: 0.656 -> test with 'KNN' KNN tn, fp: 279, 9 KNN fn, tp: 2, 6 KNN f1 score: 0.522 KNN cohens kappa score: 0.504 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 271, 17 LR fn, tp: 0, 9 LR f1 score: 0.514 LR cohens kappa score: 0.491 LR average precision score: 0.459 -> test with 'GB' GB tn, fp: 278, 10 GB fn, tp: 2, 7 GB f1 score: 0.538 GB cohens kappa score: 0.519 -> test with 'KNN' KNN tn, fp: 271, 17 KNN fn, tp: 0, 9 KNN f1 score: 0.514 KNN cohens kappa score: 0.491 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 285, 3 LR fn, tp: 2, 7 LR f1 score: 0.737 LR cohens kappa score: 0.728 LR average precision score: 0.828 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 2, 7 GB f1 score: 0.824 GB cohens kappa score: 0.818 -> test with 'KNN' KNN tn, fp: 287, 1 KNN fn, tp: 2, 7 KNN f1 score: 0.824 KNN cohens kappa score: 0.818 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 276, 12 LR fn, tp: 0, 9 LR f1 score: 0.600 LR cohens kappa score: 0.582 LR average precision score: 0.700 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 1, 8 GB f1 score: 0.842 GB cohens kappa score: 0.837 -> test with 'KNN' KNN tn, fp: 277, 11 KNN fn, tp: 0, 9 KNN f1 score: 0.621 KNN cohens kappa score: 0.604 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 280, 8 LR fn, tp: 3, 6 LR f1 score: 0.522 LR cohens kappa score: 0.503 LR average precision score: 0.517 -> test with 'GB' GB tn, fp: 281, 7 GB fn, tp: 3, 6 GB f1 score: 0.545 GB cohens kappa score: 0.529 -> test with 'KNN' KNN tn, fp: 283, 5 KNN fn, tp: 2, 7 KNN f1 score: 0.667 KNN cohens kappa score: 0.655 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 267, 21 LR fn, tp: 1, 7 LR f1 score: 0.389 LR cohens kappa score: 0.362 LR average precision score: 0.288 -> test with 'GB' GB tn, fp: 278, 10 GB fn, tp: 2, 6 GB f1 score: 0.500 GB cohens kappa score: 0.481 -> test with 'KNN' KNN tn, fp: 268, 20 KNN fn, tp: 1, 7 KNN f1 score: 0.400 KNN cohens kappa score: 0.374 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 285, 23 LR fn, tp: 4, 9 LR f1 score: 0.750 LR cohens kappa score: 0.740 LR average precision score: 0.906 average: LR tn, fp: 274.56, 13.44 LR fn, tp: 1.04, 7.76 LR f1 score: 0.530 LR cohens kappa score: 0.510 LR average precision score: 0.629 minimum: LR tn, fp: 265, 3 LR fn, tp: 0, 5 LR f1 score: 0.370 LR cohens kappa score: 0.344 LR average precision score: 0.288 -----[ GB ]----- maximum: GB tn, fp: 287, 12 GB fn, tp: 5, 9 GB f1 score: 0.889 GB cohens kappa score: 0.885 average: GB tn, fp: 281.96, 6.04 GB fn, tp: 2.12, 6.68 GB f1 score: 0.630 GB cohens kappa score: 0.617 minimum: GB tn, fp: 276, 1 GB fn, tp: 0, 4 GB f1 score: 0.421 GB cohens kappa score: 0.402 -----[ KNN ]----- maximum: KNN tn, fp: 287, 20 KNN fn, tp: 2, 9 KNN f1 score: 0.824 KNN cohens kappa score: 0.818 average: KNN tn, fp: 277.56, 10.44 KNN fn, tp: 0.88, 7.92 KNN f1 score: 0.599 KNN cohens kappa score: 0.582 minimum: KNN tn, fp: 268, 1 KNN fn, tp: 0, 6 KNN f1 score: 0.400 KNN cohens kappa score: 0.374