/////////////////////////////////////////// // Running CTAB-GAN 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 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 283, 5 LR fn, tp: 1, 8 LR f1 score: 0.727 LR cohens kappa score: 0.717 LR average precision score: 0.925 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 3, 6 GB f1 score: 0.750 GB cohens kappa score: 0.743 -> test with 'KNN' KNN tn, fp: 285, 3 KNN fn, tp: 1, 8 KNN f1 score: 0.800 KNN cohens kappa score: 0.793 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 276, 12 LR fn, tp: 1, 8 LR f1 score: 0.552 LR cohens kappa score: 0.532 LR average precision score: 0.728 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 1, 8 GB f1 score: 0.762 GB cohens kappa score: 0.753 -> test with 'KNN' KNN tn, fp: 283, 5 KNN fn, tp: 0, 9 KNN f1 score: 0.783 KNN cohens kappa score: 0.774 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 280, 8 LR fn, tp: 1, 8 LR f1 score: 0.640 LR cohens kappa score: 0.625 LR average precision score: 0.595 -> test with 'GB' GB tn, fp: 285, 3 GB fn, tp: 2, 7 GB f1 score: 0.737 GB cohens kappa score: 0.728 -> 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 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 285, 3 LR fn, tp: 4, 5 LR f1 score: 0.588 LR cohens kappa score: 0.576 LR average precision score: 0.728 -> test with 'GB' GB tn, fp: 288, 0 GB fn, tp: 5, 4 GB f1 score: 0.615 GB cohens kappa score: 0.608 -> test with 'KNN' KNN tn, fp: 286, 2 KNN fn, tp: 4, 5 KNN f1 score: 0.625 KNN cohens kappa score: 0.615 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1116 synthetic samples -> test with 'LR' LR tn, fp: 279, 9 LR fn, tp: 2, 6 LR f1 score: 0.522 LR cohens kappa score: 0.504 LR average precision score: 0.679 -> test with 'GB' GB tn, fp: 285, 3 GB fn, tp: 2, 6 GB f1 score: 0.706 GB cohens kappa score: 0.697 -> test with 'KNN' KNN tn, fp: 282, 6 KNN fn, tp: 1, 7 KNN f1 score: 0.667 KNN cohens kappa score: 0.655 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 278, 10 LR fn, tp: 0, 9 LR f1 score: 0.643 LR cohens kappa score: 0.628 LR average precision score: 0.842 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 2, 7 GB f1 score: 0.778 GB cohens kappa score: 0.771 -> test with 'KNN' KNN tn, fp: 282, 6 KNN fn, tp: 1, 8 KNN f1 score: 0.696 KNN cohens kappa score: 0.684 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 280, 8 LR fn, tp: 5, 4 LR f1 score: 0.381 LR cohens kappa score: 0.359 LR average precision score: 0.415 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.334 -> test with 'KNN' KNN tn, fp: 282, 6 KNN fn, tp: 4, 5 KNN f1 score: 0.500 KNN cohens kappa score: 0.483 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 286, 2 LR fn, tp: 1, 8 LR f1 score: 0.842 LR cohens kappa score: 0.837 LR average precision score: 0.953 -> 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: 285, 3 KNN fn, tp: 1, 8 KNN f1 score: 0.800 KNN cohens kappa score: 0.793 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 283, 5 LR fn, tp: 1, 8 LR f1 score: 0.727 LR cohens kappa score: 0.717 LR average precision score: 0.874 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 3, 6 GB f1 score: 0.706 GB cohens kappa score: 0.697 -> test with 'KNN' KNN tn, fp: 284, 4 KNN fn, tp: 0, 9 KNN f1 score: 0.818 KNN cohens kappa score: 0.811 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1116 synthetic samples -> test with 'LR' LR tn, fp: 282, 6 LR fn, tp: 3, 5 LR f1 score: 0.526 LR cohens kappa score: 0.511 LR average precision score: 0.574 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 6, 2 GB f1 score: 0.333 GB cohens kappa score: 0.321 -> test with 'KNN' KNN tn, fp: 284, 4 KNN fn, tp: 3, 5 KNN f1 score: 0.588 KNN cohens kappa score: 0.576 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 284, 4 LR fn, tp: 3, 6 LR f1 score: 0.632 LR cohens kappa score: 0.619 LR average precision score: 0.705 -> test with 'GB' GB tn, fp: 285, 3 GB fn, tp: 3, 6 GB f1 score: 0.667 GB cohens kappa score: 0.656 -> test with 'KNN' KNN tn, fp: 287, 1 KNN fn, tp: 4, 5 KNN f1 score: 0.667 KNN cohens kappa score: 0.658 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.623 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 2, 7 GB f1 score: 0.700 GB cohens kappa score: 0.690 -> 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 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 283, 5 LR fn, tp: 2, 7 LR f1 score: 0.667 LR cohens kappa score: 0.655 LR average precision score: 0.806 -> test with 'GB' GB tn, fp: 288, 0 GB fn, tp: 4, 5 GB f1 score: 0.714 GB cohens kappa score: 0.708 -> test with 'KNN' KNN tn, fp: 286, 2 KNN fn, tp: 1, 8 KNN f1 score: 0.842 KNN cohens kappa score: 0.837 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 284, 4 LR fn, tp: 1, 8 LR f1 score: 0.762 LR cohens kappa score: 0.753 LR average precision score: 0.703 -> test with 'GB' GB tn, fp: 288, 0 GB fn, tp: 4, 5 GB f1 score: 0.714 GB cohens kappa score: 0.708 -> test with 'KNN' KNN tn, fp: 285, 3 KNN fn, tp: 1, 8 KNN f1 score: 0.800 KNN cohens kappa score: 0.793 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.453 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 1, 7 GB f1 score: 0.700 GB cohens kappa score: 0.690 -> test with 'KNN' KNN tn, fp: 282, 6 KNN fn, tp: 1, 7 KNN f1 score: 0.667 KNN cohens kappa score: 0.655 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.764 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 3, 6 GB f1 score: 0.706 GB cohens kappa score: 0.697 -> test with 'KNN' KNN tn, fp: 282, 6 KNN fn, tp: 1, 8 KNN f1 score: 0.696 KNN cohens kappa score: 0.684 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 279, 9 LR fn, tp: 4, 5 LR f1 score: 0.435 LR cohens kappa score: 0.413 LR average precision score: 0.544 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 3, 6 GB f1 score: 0.750 GB cohens kappa score: 0.743 -> test with 'KNN' KNN tn, fp: 286, 2 KNN fn, tp: 2, 7 KNN f1 score: 0.778 KNN cohens kappa score: 0.771 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.719 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 4, 5 GB f1 score: 0.526 GB cohens kappa score: 0.511 -> test with 'KNN' KNN tn, fp: 281, 7 KNN fn, tp: 1, 8 KNN f1 score: 0.667 KNN cohens kappa score: 0.654 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 286, 2 LR fn, tp: 5, 4 LR f1 score: 0.533 LR cohens kappa score: 0.522 LR average precision score: 0.638 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 4, 5 GB f1 score: 0.625 GB cohens kappa score: 0.615 -> test with 'KNN' KNN tn, fp: 288, 0 KNN fn, tp: 3, 6 KNN f1 score: 0.800 KNN cohens kappa score: 0.795 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1116 synthetic samples -> test with 'LR' LR tn, fp: 283, 5 LR fn, tp: 1, 7 LR f1 score: 0.700 LR cohens kappa score: 0.690 LR average precision score: 0.681 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 2, 6 GB f1 score: 0.750 GB cohens kappa score: 0.743 -> test with 'KNN' KNN tn, fp: 282, 6 KNN fn, tp: 1, 7 KNN f1 score: 0.667 KNN cohens kappa score: 0.655 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 280, 8 LR fn, tp: 0, 9 LR f1 score: 0.692 LR cohens kappa score: 0.680 LR average precision score: 0.703 -> 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: 285, 3 KNN fn, tp: 2, 7 KNN f1 score: 0.737 KNN cohens kappa score: 0.728 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.867 -> test with 'GB' GB tn, fp: 288, 0 GB fn, tp: 3, 6 GB f1 score: 0.800 GB cohens kappa score: 0.795 -> test with 'KNN' KNN tn, fp: 285, 3 KNN fn, tp: 1, 8 KNN f1 score: 0.800 KNN cohens kappa score: 0.793 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.787 -> test with 'GB' GB tn, fp: 286, 2 GB fn, tp: 2, 7 GB f1 score: 0.778 GB cohens kappa score: 0.771 -> test with 'KNN' KNN tn, fp: 284, 4 KNN fn, tp: 0, 9 KNN f1 score: 0.818 KNN cohens kappa score: 0.811 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1117 synthetic samples -> test with 'LR' LR tn, fp: 284, 4 LR fn, tp: 3, 6 LR f1 score: 0.632 LR cohens kappa score: 0.619 LR average precision score: 0.583 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 3, 6 GB f1 score: 0.750 GB cohens kappa score: 0.743 -> test with 'KNN' KNN tn, fp: 285, 3 KNN fn, tp: 2, 7 KNN f1 score: 0.737 KNN cohens kappa score: 0.728 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1116 synthetic samples -> test with 'LR' LR tn, fp: 283, 5 LR fn, tp: 5, 3 LR f1 score: 0.375 LR cohens kappa score: 0.358 LR average precision score: 0.480 -> test with 'GB' GB tn, fp: 282, 6 GB fn, tp: 3, 5 GB f1 score: 0.526 GB cohens kappa score: 0.511 -> test with 'KNN' KNN tn, fp: 282, 6 KNN fn, tp: 3, 5 KNN f1 score: 0.526 KNN cohens kappa score: 0.511 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 286, 12 LR fn, tp: 5, 9 LR f1 score: 0.842 LR cohens kappa score: 0.837 LR average precision score: 0.953 average: LR tn, fp: 281.84, 6.16 LR fn, tp: 2.16, 6.64 LR f1 score: 0.612 LR cohens kappa score: 0.598 LR average precision score: 0.695 minimum: LR tn, fp: 276, 2 LR fn, tp: 0, 3 LR f1 score: 0.375 LR cohens kappa score: 0.358 LR average precision score: 0.415 -----[ GB ]----- maximum: GB tn, fp: 288, 6 GB fn, tp: 6, 8 GB f1 score: 0.889 GB cohens kappa score: 0.885 average: GB tn, fp: 285.68, 2.32 GB fn, tp: 2.92, 5.88 GB f1 score: 0.687 GB cohens kappa score: 0.678 minimum: GB tn, fp: 282, 0 GB fn, tp: 1, 2 GB f1 score: 0.333 GB cohens kappa score: 0.321 -----[ KNN ]----- maximum: KNN tn, fp: 288, 8 KNN fn, tp: 4, 9 KNN f1 score: 0.842 KNN cohens kappa score: 0.837 average: KNN tn, fp: 283.72, 4.28 KNN fn, tp: 1.68, 7.12 KNN f1 score: 0.706 KNN cohens kappa score: 0.696 minimum: KNN tn, fp: 280, 0 KNN fn, tp: 0, 5 KNN f1 score: 0.500 KNN cohens kappa score: 0.483