/////////////////////////////////////////// // Running ctGAN on folding_kr-vs-k-zero-one_vs_draw /////////////////////////////////////////// Load 'data_input/folding_kr-vs-k-zero-one_vs_draw' 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 2152 synthetic samples -> test with 'LR' LR tn, fp: 496, 64 LR fn, tp: 0, 21 LR f1 score: 0.396 LR cohens kappa score: 0.359 LR average precision score: 0.881 -> test with 'GB' GB tn, fp: 533, 27 GB fn, tp: 0, 21 GB f1 score: 0.609 GB cohens kappa score: 0.588 -> test with 'KNN' KNN tn, fp: 547, 13 KNN fn, tp: 1, 20 KNN f1 score: 0.741 KNN cohens kappa score: 0.729 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 499, 61 LR fn, tp: 0, 21 LR f1 score: 0.408 LR cohens kappa score: 0.372 LR average precision score: 0.900 -> test with 'GB' GB tn, fp: 541, 19 GB fn, tp: 0, 21 GB f1 score: 0.689 GB cohens kappa score: 0.673 -> test with 'KNN' KNN tn, fp: 556, 4 KNN fn, tp: 0, 21 KNN f1 score: 0.913 KNN cohens kappa score: 0.909 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 487, 73 LR fn, tp: 0, 21 LR f1 score: 0.365 LR cohens kappa score: 0.325 LR average precision score: 0.832 -> test with 'GB' GB tn, fp: 534, 26 GB fn, tp: 0, 21 GB f1 score: 0.618 GB cohens kappa score: 0.598 -> test with 'KNN' KNN tn, fp: 547, 13 KNN fn, tp: 0, 21 KNN f1 score: 0.764 KNN cohens kappa score: 0.753 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 496, 64 LR fn, tp: 0, 21 LR f1 score: 0.396 LR cohens kappa score: 0.359 LR average precision score: 0.818 -> test with 'GB' GB tn, fp: 528, 32 GB fn, tp: 0, 21 GB f1 score: 0.568 GB cohens kappa score: 0.544 -> test with 'KNN' KNN tn, fp: 546, 14 KNN fn, tp: 1, 20 KNN f1 score: 0.727 KNN cohens kappa score: 0.715 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 502, 54 LR fn, tp: 0, 21 LR f1 score: 0.438 LR cohens kappa score: 0.404 LR average precision score: 0.927 -> test with 'GB' GB tn, fp: 538, 18 GB fn, tp: 0, 21 GB f1 score: 0.700 GB cohens kappa score: 0.685 -> test with 'KNN' KNN tn, fp: 546, 10 KNN fn, tp: 0, 21 KNN f1 score: 0.808 KNN cohens kappa score: 0.799 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 483, 77 LR fn, tp: 0, 21 LR f1 score: 0.353 LR cohens kappa score: 0.312 LR average precision score: 0.871 -> test with 'GB' GB tn, fp: 532, 28 GB fn, tp: 0, 21 GB f1 score: 0.600 GB cohens kappa score: 0.579 -> test with 'KNN' KNN tn, fp: 547, 13 KNN fn, tp: 0, 21 KNN f1 score: 0.764 KNN cohens kappa score: 0.753 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 504, 56 LR fn, tp: 0, 21 LR f1 score: 0.429 LR cohens kappa score: 0.394 LR average precision score: 0.852 -> test with 'GB' GB tn, fp: 533, 27 GB fn, tp: 0, 21 GB f1 score: 0.609 GB cohens kappa score: 0.588 -> test with 'KNN' KNN tn, fp: 552, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 503, 57 LR fn, tp: 0, 21 LR f1 score: 0.424 LR cohens kappa score: 0.389 LR average precision score: 0.889 -> test with 'GB' GB tn, fp: 539, 21 GB fn, tp: 0, 21 GB f1 score: 0.667 GB cohens kappa score: 0.650 -> test with 'KNN' KNN tn, fp: 551, 9 KNN fn, tp: 0, 21 KNN f1 score: 0.824 KNN cohens kappa score: 0.816 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 491, 69 LR fn, tp: 0, 21 LR f1 score: 0.378 LR cohens kappa score: 0.340 LR average precision score: 0.852 -> test with 'GB' GB tn, fp: 534, 26 GB fn, tp: 0, 21 GB f1 score: 0.618 GB cohens kappa score: 0.598 -> test with 'KNN' KNN tn, fp: 540, 20 KNN fn, tp: 0, 21 KNN f1 score: 0.677 KNN cohens kappa score: 0.661 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 500, 56 LR fn, tp: 0, 21 LR f1 score: 0.429 LR cohens kappa score: 0.394 LR average precision score: 0.866 -> test with 'GB' GB tn, fp: 534, 22 GB fn, tp: 0, 21 GB f1 score: 0.656 GB cohens kappa score: 0.639 -> test with 'KNN' KNN tn, fp: 548, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 506, 54 LR fn, tp: 0, 21 LR f1 score: 0.438 LR cohens kappa score: 0.404 LR average precision score: 0.920 -> test with 'GB' GB tn, fp: 535, 25 GB fn, tp: 0, 21 GB f1 score: 0.627 GB cohens kappa score: 0.607 -> test with 'KNN' KNN tn, fp: 542, 18 KNN fn, tp: 0, 21 KNN f1 score: 0.700 KNN cohens kappa score: 0.685 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 512, 48 LR fn, tp: 0, 21 LR f1 score: 0.467 LR cohens kappa score: 0.435 LR average precision score: 0.875 -> test with 'GB' GB tn, fp: 539, 21 GB fn, tp: 0, 21 GB f1 score: 0.667 GB cohens kappa score: 0.650 -> test with 'KNN' KNN tn, fp: 552, 8 KNN fn, tp: 0, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 491, 69 LR fn, tp: 0, 21 LR f1 score: 0.378 LR cohens kappa score: 0.340 LR average precision score: 0.757 -> test with 'GB' GB tn, fp: 533, 27 GB fn, tp: 0, 21 GB f1 score: 0.609 GB cohens kappa score: 0.588 -> test with 'KNN' KNN tn, fp: 547, 13 KNN fn, tp: 0, 21 KNN f1 score: 0.764 KNN cohens kappa score: 0.753 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 496, 64 LR fn, tp: 0, 21 LR f1 score: 0.396 LR cohens kappa score: 0.359 LR average precision score: 0.902 -> test with 'GB' GB tn, fp: 543, 17 GB fn, tp: 0, 21 GB f1 score: 0.712 GB cohens kappa score: 0.698 -> test with 'KNN' KNN tn, fp: 556, 4 KNN fn, tp: 1, 20 KNN f1 score: 0.889 KNN cohens kappa score: 0.884 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 481, 75 LR fn, tp: 0, 21 LR f1 score: 0.359 LR cohens kappa score: 0.318 LR average precision score: 0.844 -> test with 'GB' GB tn, fp: 527, 29 GB fn, tp: 0, 21 GB f1 score: 0.592 GB cohens kappa score: 0.569 -> test with 'KNN' KNN tn, fp: 542, 14 KNN fn, tp: 0, 21 KNN f1 score: 0.750 KNN cohens kappa score: 0.738 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 499, 61 LR fn, tp: 0, 21 LR f1 score: 0.408 LR cohens kappa score: 0.372 LR average precision score: 0.894 -> test with 'GB' GB tn, fp: 533, 27 GB fn, tp: 0, 21 GB f1 score: 0.609 GB cohens kappa score: 0.588 -> test with 'KNN' KNN tn, fp: 547, 13 KNN fn, tp: 0, 21 KNN f1 score: 0.764 KNN cohens kappa score: 0.753 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 486, 74 LR fn, tp: 0, 21 LR f1 score: 0.362 LR cohens kappa score: 0.322 LR average precision score: 0.910 -> test with 'GB' GB tn, fp: 536, 24 GB fn, tp: 0, 21 GB f1 score: 0.636 GB cohens kappa score: 0.618 -> test with 'KNN' KNN tn, fp: 546, 14 KNN fn, tp: 0, 21 KNN f1 score: 0.750 KNN cohens kappa score: 0.738 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 493, 67 LR fn, tp: 0, 21 LR f1 score: 0.385 LR cohens kappa score: 0.347 LR average precision score: 0.724 -> test with 'GB' GB tn, fp: 536, 24 GB fn, tp: 0, 21 GB f1 score: 0.636 GB cohens kappa score: 0.618 -> test with 'KNN' KNN tn, fp: 547, 13 KNN fn, tp: 0, 21 KNN f1 score: 0.764 KNN cohens kappa score: 0.753 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 504, 56 LR fn, tp: 0, 21 LR f1 score: 0.429 LR cohens kappa score: 0.394 LR average precision score: 0.891 -> test with 'GB' GB tn, fp: 537, 23 GB fn, tp: 0, 21 GB f1 score: 0.646 GB cohens kappa score: 0.628 -> test with 'KNN' KNN tn, fp: 550, 10 KNN fn, tp: 0, 21 KNN f1 score: 0.808 KNN cohens kappa score: 0.799 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 499, 57 LR fn, tp: 0, 21 LR f1 score: 0.424 LR cohens kappa score: 0.389 LR average precision score: 0.906 -> test with 'GB' GB tn, fp: 535, 21 GB fn, tp: 0, 21 GB f1 score: 0.667 GB cohens kappa score: 0.650 -> test with 'KNN' KNN tn, fp: 545, 11 KNN fn, tp: 0, 21 KNN f1 score: 0.792 KNN cohens kappa score: 0.783 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 493, 67 LR fn, tp: 0, 21 LR f1 score: 0.385 LR cohens kappa score: 0.347 LR average precision score: 0.926 -> test with 'GB' GB tn, fp: 529, 31 GB fn, tp: 0, 21 GB f1 score: 0.575 GB cohens kappa score: 0.552 -> test with 'KNN' KNN tn, fp: 545, 15 KNN fn, tp: 0, 21 KNN f1 score: 0.737 KNN cohens kappa score: 0.724 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 504, 56 LR fn, tp: 0, 21 LR f1 score: 0.429 LR cohens kappa score: 0.394 LR average precision score: 0.844 -> test with 'GB' GB tn, fp: 535, 25 GB fn, tp: 0, 21 GB f1 score: 0.627 GB cohens kappa score: 0.607 -> test with 'KNN' KNN tn, fp: 548, 12 KNN fn, tp: 0, 21 KNN f1 score: 0.778 KNN cohens kappa score: 0.768 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 487, 73 LR fn, tp: 0, 21 LR f1 score: 0.365 LR cohens kappa score: 0.325 LR average precision score: 0.810 -> test with 'GB' GB tn, fp: 540, 20 GB fn, tp: 0, 21 GB f1 score: 0.677 GB cohens kappa score: 0.661 -> test with 'KNN' KNN tn, fp: 547, 13 KNN fn, tp: 0, 21 KNN f1 score: 0.764 KNN cohens kappa score: 0.753 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 484, 76 LR fn, tp: 0, 21 LR f1 score: 0.356 LR cohens kappa score: 0.315 LR average precision score: 0.817 -> test with 'GB' GB tn, fp: 536, 24 GB fn, tp: 0, 21 GB f1 score: 0.636 GB cohens kappa score: 0.618 -> test with 'KNN' KNN tn, fp: 551, 9 KNN fn, tp: 0, 21 KNN f1 score: 0.824 KNN cohens kappa score: 0.816 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 498, 58 LR fn, tp: 0, 21 LR f1 score: 0.420 LR cohens kappa score: 0.385 LR average precision score: 0.898 -> test with 'GB' GB tn, fp: 533, 23 GB fn, tp: 0, 21 GB f1 score: 0.646 GB cohens kappa score: 0.628 -> test with 'KNN' KNN tn, fp: 545, 11 KNN fn, tp: 0, 21 KNN f1 score: 0.792 KNN cohens kappa score: 0.783 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 512, 77 LR fn, tp: 0, 21 LR f1 score: 0.467 LR cohens kappa score: 0.435 LR average precision score: 0.927 average: LR tn, fp: 495.76, 63.44 LR fn, tp: 0.0, 21.0 LR f1 score: 0.401 LR cohens kappa score: 0.364 LR average precision score: 0.864 minimum: LR tn, fp: 481, 48 LR fn, tp: 0, 21 LR f1 score: 0.353 LR cohens kappa score: 0.312 LR average precision score: 0.724 -----[ GB ]----- maximum: GB tn, fp: 543, 32 GB fn, tp: 0, 21 GB f1 score: 0.712 GB cohens kappa score: 0.698 average: GB tn, fp: 534.92, 24.28 GB fn, tp: 0.0, 21.0 GB f1 score: 0.636 GB cohens kappa score: 0.617 minimum: GB tn, fp: 527, 17 GB fn, tp: 0, 21 GB f1 score: 0.568 GB cohens kappa score: 0.544 -----[ KNN ]----- maximum: KNN tn, fp: 556, 20 KNN fn, tp: 1, 21 KNN f1 score: 0.913 KNN cohens kappa score: 0.909 average: KNN tn, fp: 547.6, 11.6 KNN fn, tp: 0.12, 20.88 KNN f1 score: 0.784 KNN cohens kappa score: 0.774 minimum: KNN tn, fp: 540, 4 KNN fn, tp: 0, 20 KNN f1 score: 0.677 KNN cohens kappa score: 0.661