/////////////////////////////////////////// // Running SimpleGAN 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 288, 0 LR fn, tp: 5, 4 LR f1 score: 0.615 LR cohens kappa score: 0.608 LR average precision score: 0.888 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 6, 3 GB f1 score: 0.462 GB cohens kappa score: 0.451 -> test with 'KNN' KNN tn, fp: 288, 0 KNN fn, tp: 4, 5 KNN f1 score: 0.714 KNN cohens kappa score: 0.708 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 282, 6 LR fn, tp: 4, 5 LR f1 score: 0.500 LR cohens kappa score: 0.483 LR average precision score: 0.723 -> test with 'GB' GB tn, fp: 283, 5 GB fn, tp: 0, 9 GB f1 score: 0.783 GB cohens kappa score: 0.774 -> test with 'KNN' KNN tn, fp: 286, 2 KNN fn, tp: 3, 6 KNN f1 score: 0.706 KNN cohens kappa score: 0.697 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 284, 4 LR fn, tp: 5, 4 LR f1 score: 0.471 LR cohens kappa score: 0.455 LR average precision score: 0.627 -> 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: 3, 6 KNN f1 score: 0.667 KNN cohens kappa score: 0.656 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 288, 0 LR fn, tp: 6, 3 LR f1 score: 0.500 LR cohens kappa score: 0.492 LR average precision score: 0.698 -> 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: 288, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.195 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 282, 6 LR fn, tp: 2, 6 LR f1 score: 0.600 LR cohens kappa score: 0.587 LR average precision score: 0.680 -> 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: 286, 2 KNN fn, tp: 3, 5 KNN f1 score: 0.667 KNN cohens kappa score: 0.658 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> 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: 0.707 -> 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: 287, 1 KNN fn, tp: 3, 6 KNN f1 score: 0.750 KNN cohens kappa score: 0.743 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 281, 7 LR fn, tp: 7, 2 LR f1 score: 0.222 LR cohens kappa score: 0.198 LR average precision score: 0.426 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 5, 4 GB f1 score: 0.471 GB cohens kappa score: 0.455 -> test with 'KNN' KNN tn, fp: 284, 4 KNN fn, tp: 7, 2 KNN f1 score: 0.267 KNN cohens kappa score: 0.248 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 286, 2 LR fn, tp: 6, 3 LR f1 score: 0.429 LR cohens kappa score: 0.416 LR average precision score: 0.762 -> 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: 288, 0 KNN fn, tp: 4, 5 KNN f1 score: 0.714 KNN cohens kappa score: 0.708 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> 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: 0.900 -> 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: 286, 2 KNN fn, tp: 3, 6 KNN f1 score: 0.706 KNN cohens kappa score: 0.697 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 286, 2 LR fn, tp: 4, 4 LR f1 score: 0.571 LR cohens kappa score: 0.561 LR average precision score: 0.640 -> 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: 288, 0 KNN fn, tp: 5, 3 KNN f1 score: 0.545 KNN cohens kappa score: 0.539 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> 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: 0.677 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 4, 5 GB f1 score: 0.667 GB cohens kappa score: 0.658 -> test with 'KNN' KNN tn, fp: 287, 1 KNN fn, tp: 5, 4 KNN f1 score: 0.571 KNN cohens kappa score: 0.562 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 283, 5 LR fn, tp: 3, 6 LR f1 score: 0.600 LR cohens kappa score: 0.586 LR average precision score: 0.650 -> 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: 287, 1 KNN fn, tp: 6, 3 KNN f1 score: 0.462 KNN cohens kappa score: 0.451 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 286, 2 LR fn, tp: 4, 5 LR f1 score: 0.625 LR cohens kappa score: 0.615 LR average precision score: 0.816 -> 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: 288, 0 KNN fn, tp: 7, 2 KNN f1 score: 0.364 KNN cohens kappa score: 0.357 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 286, 2 LR fn, tp: 6, 3 LR f1 score: 0.429 LR cohens kappa score: 0.416 LR average precision score: 0.738 -> 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: 288, 0 KNN fn, tp: 4, 5 KNN f1 score: 0.714 KNN cohens kappa score: 0.708 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> 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: 0.483 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 3, 5 GB f1 score: 0.588 GB cohens kappa score: 0.576 -> test with 'KNN' KNN tn, fp: 285, 3 KNN fn, tp: 3, 5 KNN f1 score: 0.625 KNN cohens kappa score: 0.615 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> 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: 0.738 -> 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: 285, 3 KNN fn, tp: 5, 4 KNN f1 score: 0.500 KNN cohens kappa score: 0.486 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 284, 4 LR fn, tp: 5, 4 LR f1 score: 0.471 LR cohens kappa score: 0.455 LR average precision score: 0.615 -> 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: 6, 3 KNN f1 score: 0.462 KNN cohens kappa score: 0.451 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 282, 6 LR fn, tp: 4, 5 LR f1 score: 0.500 LR cohens kappa score: 0.483 LR average precision score: 0.703 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 3, 6 GB f1 score: 0.632 GB cohens kappa score: 0.619 -> test with 'KNN' KNN tn, fp: 283, 5 KNN fn, tp: 3, 6 KNN f1 score: 0.600 KNN cohens kappa score: 0.586 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 286, 2 LR fn, tp: 6, 3 LR f1 score: 0.429 LR cohens kappa score: 0.416 LR average precision score: 0.692 -> 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: 288, 0 KNN fn, tp: 6, 3 KNN f1 score: 0.500 KNN cohens kappa score: 0.492 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 284, 4 LR fn, tp: 3, 5 LR f1 score: 0.588 LR cohens kappa score: 0.576 LR average precision score: 0.607 -> test with 'GB' GB tn, fp: 288, 0 GB fn, tp: 3, 5 GB f1 score: 0.769 GB cohens kappa score: 0.764 -> test with 'KNN' KNN tn, fp: 288, 0 KNN fn, tp: 4, 4 KNN f1 score: 0.667 KNN cohens kappa score: 0.661 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> 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: 0.724 -> 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: 288, 0 KNN fn, tp: 2, 7 KNN f1 score: 0.875 KNN cohens kappa score: 0.872 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 288, 0 LR fn, tp: 6, 3 LR f1 score: 0.500 LR cohens kappa score: 0.492 LR average precision score: 0.769 -> 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: 288, 0 KNN fn, tp: 5, 4 KNN f1 score: 0.615 KNN cohens kappa score: 0.608 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> 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: 0.787 -> test with 'GB' GB tn, fp: 288, 0 GB fn, tp: 2, 7 GB f1 score: 0.875 GB cohens kappa score: 0.872 -> test with 'KNN' KNN tn, fp: 287, 1 KNN fn, tp: 5, 4 KNN f1 score: 0.571 KNN cohens kappa score: 0.562 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1117 synthetic samples -> test with 'LR' LR tn, fp: 286, 2 LR fn, tp: 6, 3 LR f1 score: 0.429 LR cohens kappa score: 0.416 LR average precision score: 0.649 -> test with 'GB' GB tn, fp: 287, 1 GB fn, tp: 4, 5 GB f1 score: 0.667 GB cohens kappa score: 0.658 -> test with 'KNN' KNN tn, fp: 287, 1 KNN fn, tp: 6, 3 KNN f1 score: 0.462 KNN cohens kappa score: 0.451 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1116 synthetic samples -> test with 'LR' LR tn, fp: 284, 4 LR fn, tp: 5, 3 LR f1 score: 0.400 LR cohens kappa score: 0.384 LR average precision score: 0.511 -> test with 'GB' GB tn, fp: 284, 4 GB fn, tp: 3, 5 GB f1 score: 0.588 GB cohens kappa score: 0.576 -> test with 'KNN' KNN tn, fp: 286, 2 KNN fn, tp: 4, 4 KNN f1 score: 0.571 KNN cohens kappa score: 0.561 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 288, 7 LR fn, tp: 7, 7 LR f1 score: 0.737 LR cohens kappa score: 0.728 LR average precision score: 0.900 average: LR tn, fp: 284.6, 3.4 LR fn, tp: 4.28, 4.52 LR f1 score: 0.533 LR cohens kappa score: 0.520 LR average precision score: 0.688 minimum: LR tn, fp: 281, 0 LR fn, tp: 2, 2 LR f1 score: 0.222 LR cohens kappa score: 0.198 LR average precision score: 0.426 -----[ GB ]----- maximum: GB tn, fp: 288, 5 GB fn, tp: 6, 9 GB f1 score: 0.875 GB cohens kappa score: 0.872 average: GB tn, fp: 286.32, 1.68 GB fn, tp: 3.16, 5.64 GB f1 score: 0.692 GB cohens kappa score: 0.684 minimum: GB tn, fp: 283, 0 GB fn, tp: 0, 2 GB f1 score: 0.333 GB cohens kappa score: 0.321 -----[ KNN ]----- maximum: KNN tn, fp: 288, 5 KNN fn, tp: 8, 7 KNN f1 score: 0.875 KNN cohens kappa score: 0.872 average: KNN tn, fp: 286.72, 1.28 KNN fn, tp: 4.56, 4.24 KNN f1 score: 0.580 KNN cohens kappa score: 0.571 minimum: KNN tn, fp: 283, 0 KNN fn, tp: 2, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.195