/////////////////////////////////////////// // 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: 0.889 -> test with 'RF' RF tn, fp: 541, 19 RF fn, tp: 0, 21 RF f1 score: 0.689 RF cohens kappa score: 0.673 -> 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: 537, 23 KNN fn, tp: 0, 21 KNN f1 score: 0.646 KNN cohens kappa score: 0.628 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 500, 60 LR fn, tp: 0, 21 LR f1 score: 0.412 LR cohens kappa score: 0.376 LR average precision score: 0.903 -> test with 'RF' RF tn, fp: 545, 15 RF fn, tp: 0, 21 RF f1 score: 0.737 RF cohens kappa score: 0.724 -> 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: 548, 12 KNN fn, tp: 0, 21 KNN f1 score: 0.778 KNN cohens kappa score: 0.768 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 494, 66 LR fn, tp: 0, 21 LR f1 score: 0.389 LR cohens kappa score: 0.351 LR average precision score: 0.833 -> test with 'RF' RF tn, fp: 535, 25 RF fn, tp: 0, 21 RF f1 score: 0.627 RF cohens kappa score: 0.607 -> test with 'GB' GB tn, fp: 531, 29 GB fn, tp: 0, 21 GB f1 score: 0.592 GB cohens kappa score: 0.570 -> test with 'KNN' KNN tn, fp: 538, 22 KNN fn, tp: 0, 21 KNN f1 score: 0.656 KNN cohens kappa score: 0.639 ------ Step 1/5: 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: 0.800 -> test with 'RF' RF tn, fp: 529, 31 RF fn, tp: 0, 21 RF f1 score: 0.575 RF cohens kappa score: 0.552 -> 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: 532, 28 KNN fn, tp: 0, 21 KNN f1 score: 0.600 KNN cohens kappa score: 0.579 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 495, 61 LR fn, tp: 0, 21 LR f1 score: 0.408 LR cohens kappa score: 0.371 LR average precision score: 0.910 -> test with 'RF' RF tn, fp: 539, 17 RF fn, tp: 0, 21 RF f1 score: 0.712 RF cohens kappa score: 0.698 -> test with 'GB' GB tn, fp: 537, 19 GB fn, tp: 0, 21 GB f1 score: 0.689 GB cohens kappa score: 0.673 -> 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 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: 487, 73 LR fn, tp: 0, 21 LR f1 score: 0.365 LR cohens kappa score: 0.325 LR average precision score: 0.883 -> test with 'RF' RF tn, fp: 540, 20 RF fn, tp: 0, 21 RF f1 score: 0.677 RF cohens kappa score: 0.661 -> test with 'GB' GB tn, fp: 531, 29 GB fn, tp: 0, 21 GB f1 score: 0.592 GB cohens kappa score: 0.570 -> test with 'KNN' KNN tn, fp: 544, 16 KNN fn, tp: 0, 21 KNN f1 score: 0.724 KNN cohens kappa score: 0.711 ------ 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: 0.819 -> test with 'RF' RF tn, fp: 542, 18 RF fn, tp: 0, 21 RF f1 score: 0.700 RF cohens kappa score: 0.685 -> 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: 546, 14 KNN fn, tp: 0, 21 KNN f1 score: 0.750 KNN cohens kappa score: 0.738 ------ 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: 0.906 -> test with 'RF' RF tn, fp: 546, 14 RF fn, tp: 0, 21 RF f1 score: 0.750 RF cohens kappa score: 0.738 -> 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: 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: 481, 79 LR fn, tp: 0, 21 LR f1 score: 0.347 LR cohens kappa score: 0.306 LR average precision score: 0.845 -> test with 'RF' RF tn, fp: 539, 21 RF fn, tp: 0, 21 RF f1 score: 0.667 RF cohens kappa score: 0.650 -> test with 'GB' GB tn, fp: 531, 29 GB fn, tp: 0, 21 GB f1 score: 0.592 GB cohens kappa score: 0.570 -> test with 'KNN' KNN tn, fp: 538, 22 KNN fn, tp: 0, 21 KNN f1 score: 0.656 KNN cohens kappa score: 0.639 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 487, 69 LR fn, tp: 0, 21 LR f1 score: 0.378 LR cohens kappa score: 0.339 LR average precision score: 0.841 -> test with 'RF' RF tn, fp: 537, 19 RF fn, tp: 0, 21 RF f1 score: 0.689 RF cohens kappa score: 0.673 -> 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: 538, 18 KNN fn, tp: 0, 21 KNN f1 score: 0.700 KNN cohens kappa score: 0.685 ====== 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: 0.914 -> test with 'RF' RF tn, fp: 539, 21 RF fn, tp: 0, 21 RF f1 score: 0.667 RF cohens kappa score: 0.650 -> test with 'GB' GB tn, fp: 538, 22 GB fn, tp: 0, 21 GB f1 score: 0.656 GB cohens kappa score: 0.639 -> test with 'KNN' KNN tn, fp: 539, 21 KNN fn, tp: 0, 21 KNN f1 score: 0.667 KNN cohens kappa score: 0.650 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 511, 49 LR fn, tp: 0, 21 LR f1 score: 0.462 LR cohens kappa score: 0.430 LR average precision score: 0.876 -> test with 'RF' RF tn, fp: 547, 13 RF fn, tp: 0, 21 RF f1 score: 0.764 RF cohens kappa score: 0.753 -> 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: 548, 12 KNN fn, tp: 0, 21 KNN f1 score: 0.778 KNN cohens kappa score: 0.768 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 498, 62 LR fn, tp: 0, 21 LR f1 score: 0.404 LR cohens kappa score: 0.367 LR average precision score: 0.781 -> test with 'RF' RF tn, fp: 540, 20 RF fn, tp: 0, 21 RF f1 score: 0.677 RF cohens kappa score: 0.661 -> test with 'GB' GB tn, fp: 530, 30 GB fn, tp: 0, 21 GB f1 score: 0.583 GB cohens kappa score: 0.561 -> test with 'KNN' KNN tn, fp: 541, 19 KNN fn, tp: 0, 21 KNN f1 score: 0.689 KNN cohens kappa score: 0.673 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 489, 71 LR fn, tp: 0, 21 LR f1 score: 0.372 LR cohens kappa score: 0.332 LR average precision score: 0.890 -> test with 'RF' RF tn, fp: 546, 14 RF fn, tp: 0, 21 RF f1 score: 0.750 RF cohens kappa score: 0.738 -> 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: 547, 13 KNN fn, tp: 0, 21 KNN f1 score: 0.764 KNN cohens kappa score: 0.753 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 487, 69 LR fn, tp: 0, 21 LR f1 score: 0.378 LR cohens kappa score: 0.339 LR average precision score: 0.877 -> test with 'RF' RF tn, fp: 535, 21 RF fn, tp: 0, 21 RF f1 score: 0.667 RF cohens kappa score: 0.650 -> test with 'GB' GB tn, fp: 528, 28 GB fn, tp: 0, 21 GB f1 score: 0.600 GB cohens kappa score: 0.579 -> test with 'KNN' KNN tn, fp: 535, 21 KNN fn, tp: 0, 21 KNN f1 score: 0.667 KNN cohens kappa score: 0.650 ====== 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: 0.897 -> test with 'RF' RF tn, fp: 543, 17 RF fn, tp: 0, 21 RF f1 score: 0.712 RF cohens kappa score: 0.698 -> 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: 544, 16 KNN fn, tp: 0, 21 KNN f1 score: 0.724 KNN cohens kappa score: 0.711 ------ 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: 0.913 -> test with 'RF' RF tn, fp: 544, 16 RF fn, tp: 0, 21 RF f1 score: 0.724 RF cohens kappa score: 0.711 -> 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: 547, 13 KNN fn, tp: 0, 21 KNN f1 score: 0.764 KNN cohens kappa score: 0.753 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 497, 63 LR fn, tp: 0, 21 LR f1 score: 0.400 LR cohens kappa score: 0.363 LR average precision score: 0.748 -> test with 'RF' RF tn, fp: 547, 13 RF fn, tp: 0, 21 RF f1 score: 0.764 RF cohens kappa score: 0.753 -> test with 'GB' GB tn, fp: 538, 22 GB fn, tp: 0, 21 GB f1 score: 0.656 GB cohens kappa score: 0.639 -> 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 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 501, 59 LR fn, tp: 0, 21 LR f1 score: 0.416 LR cohens kappa score: 0.380 LR average precision score: 0.889 -> test with 'RF' RF tn, fp: 552, 8 RF fn, tp: 0, 21 RF f1 score: 0.840 RF cohens kappa score: 0.833 -> test with 'GB' GB tn, fp: 538, 22 GB fn, tp: 0, 21 GB f1 score: 0.656 GB cohens kappa score: 0.639 -> 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 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 497, 59 LR fn, tp: 0, 21 LR f1 score: 0.416 LR cohens kappa score: 0.380 LR average precision score: 0.900 -> test with 'RF' RF tn, fp: 535, 21 RF fn, tp: 0, 21 RF f1 score: 0.667 RF cohens kappa score: 0.650 -> test with 'GB' GB tn, fp: 530, 26 GB fn, tp: 0, 21 GB f1 score: 0.618 GB cohens kappa score: 0.597 -> test with 'KNN' KNN tn, fp: 536, 20 KNN fn, tp: 0, 21 KNN f1 score: 0.677 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 -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 495, 65 LR fn, tp: 0, 21 LR f1 score: 0.393 LR cohens kappa score: 0.355 LR average precision score: 0.927 -> test with 'RF' RF tn, fp: 535, 25 RF fn, tp: 0, 21 RF f1 score: 0.627 RF cohens kappa score: 0.607 -> 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: 539, 21 KNN fn, tp: 0, 21 KNN f1 score: 0.667 KNN cohens kappa score: 0.650 ------ Step 5/5: Slice 2/5 ------- -> 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: 0.844 -> test with 'RF' RF tn, fp: 542, 18 RF fn, tp: 0, 21 RF f1 score: 0.700 RF cohens kappa score: 0.685 -> 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: 545, 15 KNN fn, tp: 0, 21 KNN f1 score: 0.737 KNN cohens kappa score: 0.724 ------ Step 5/5: Slice 3/5 ------- -> 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: 0.838 -> test with 'RF' RF tn, fp: 543, 17 RF fn, tp: 0, 21 RF f1 score: 0.712 RF cohens kappa score: 0.698 -> 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: 545, 15 KNN fn, tp: 0, 21 KNN f1 score: 0.737 KNN cohens kappa score: 0.724 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2152 synthetic samples -> test with 'LR' LR tn, fp: 501, 59 LR fn, tp: 0, 21 LR f1 score: 0.416 LR cohens kappa score: 0.380 LR average precision score: 0.845 -> test with 'RF' RF tn, fp: 546, 14 RF fn, tp: 0, 21 RF f1 score: 0.750 RF cohens kappa score: 0.738 -> test with 'GB' GB tn, fp: 544, 16 GB fn, tp: 0, 21 GB f1 score: 0.724 GB cohens kappa score: 0.711 -> 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 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2156 synthetic samples -> test with 'LR' LR tn, fp: 489, 67 LR fn, tp: 0, 21 LR f1 score: 0.385 LR cohens kappa score: 0.347 LR average precision score: 0.884 -> test with 'RF' RF tn, fp: 541, 15 RF fn, tp: 0, 21 RF f1 score: 0.737 RF cohens kappa score: 0.724 -> test with 'GB' GB tn, fp: 532, 24 GB fn, tp: 0, 21 GB f1 score: 0.636 GB cohens kappa score: 0.617 -> test with 'KNN' KNN tn, fp: 544, 12 KNN fn, tp: 1, 20 KNN f1 score: 0.755 KNN cohens kappa score: 0.743 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 511, 79 LR fn, tp: 0, 21 LR f1 score: 0.462 LR cohens kappa score: 0.430 LR average precision score: 0.927 average: LR tn, fp: 495.24, 63.96 LR fn, tp: 0.0, 21.0 LR f1 score: 0.398 LR cohens kappa score: 0.361 LR average precision score: 0.866 minimum: LR tn, fp: 481, 49 LR fn, tp: 0, 21 LR f1 score: 0.347 LR cohens kappa score: 0.306 LR average precision score: 0.748 -----[ RF ]----- maximum: RF tn, fp: 552, 31 RF fn, tp: 0, 21 RF f1 score: 0.840 RF cohens kappa score: 0.833 average: RF tn, fp: 541.12, 18.08 RF fn, tp: 0.0, 21.0 RF f1 score: 0.703 RF cohens kappa score: 0.688 minimum: RF tn, fp: 529, 8 RF fn, tp: 0, 21 RF f1 score: 0.575 RF cohens kappa score: 0.552 -----[ GB ]----- maximum: GB tn, fp: 544, 32 GB fn, tp: 0, 21 GB f1 score: 0.724 GB cohens kappa score: 0.711 average: GB tn, fp: 535.08, 24.12 GB fn, tp: 0.0, 21.0 GB f1 score: 0.638 GB cohens kappa score: 0.619 minimum: GB tn, fp: 528, 16 GB fn, tp: 0, 21 GB f1 score: 0.568 GB cohens kappa score: 0.544 -----[ KNN ]----- maximum: KNN tn, fp: 552, 28 KNN fn, tp: 1, 21 KNN f1 score: 0.840 KNN cohens kappa score: 0.833 average: KNN tn, fp: 542.68, 16.52 KNN fn, tp: 0.04, 20.96 KNN f1 score: 0.721 KNN cohens kappa score: 0.708 minimum: KNN tn, fp: 532, 8 KNN fn, tp: 0, 20 KNN f1 score: 0.600 KNN cohens kappa score: 0.579