/////////////////////////////////////////// // Running CTAB-GAN on imblearn_protein_homo /////////////////////////////////////////// Load 'data_input/imblearn_protein_homo' from imblearn 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 114528 synthetic samples -> test with 'LR' LR tn, fp: 28867, 24 LR fn, tp: 60, 200 LR f1 score: 0.826 LR cohens kappa score: 0.825 LR average precision score: 0.853 -> test with 'GB' GB tn, fp: 28861, 30 GB fn, tp: 58, 202 GB f1 score: 0.821 GB cohens kappa score: 0.820 -> test with 'KNN' KNN tn, fp: 28883, 8 KNN fn, tp: 161, 99 KNN f1 score: 0.540 KNN cohens kappa score: 0.537 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28879, 12 LR fn, tp: 57, 203 LR f1 score: 0.855 LR cohens kappa score: 0.854 LR average precision score: 0.888 -> test with 'GB' GB tn, fp: 28876, 15 GB fn, tp: 60, 200 GB f1 score: 0.842 GB cohens kappa score: 0.841 -> test with 'KNN' KNN tn, fp: 28874, 17 KNN fn, tp: 144, 116 KNN f1 score: 0.590 KNN cohens kappa score: 0.588 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28870, 21 LR fn, tp: 62, 198 LR f1 score: 0.827 LR cohens kappa score: 0.825 LR average precision score: 0.886 -> test with 'GB' GB tn, fp: 28867, 24 GB fn, tp: 73, 187 GB f1 score: 0.794 GB cohens kappa score: 0.792 -> test with 'KNN' KNN tn, fp: 28881, 10 KNN fn, tp: 170, 90 KNN f1 score: 0.500 KNN cohens kappa score: 0.498 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28877, 14 LR fn, tp: 68, 192 LR f1 score: 0.824 LR cohens kappa score: 0.823 LR average precision score: 0.852 -> test with 'GB' GB tn, fp: 28871, 20 GB fn, tp: 70, 190 GB f1 score: 0.809 GB cohens kappa score: 0.807 -> test with 'KNN' KNN tn, fp: 28884, 7 KNN fn, tp: 160, 100 KNN f1 score: 0.545 KNN cohens kappa score: 0.543 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114524 synthetic samples -> test with 'LR' LR tn, fp: 28880, 11 LR fn, tp: 84, 172 LR f1 score: 0.784 LR cohens kappa score: 0.782 LR average precision score: 0.827 -> test with 'GB' GB tn, fp: 28865, 26 GB fn, tp: 74, 182 GB f1 score: 0.784 GB cohens kappa score: 0.783 -> test with 'KNN' KNN tn, fp: 28886, 5 KNN fn, tp: 173, 83 KNN f1 score: 0.483 KNN cohens kappa score: 0.480 ====== 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 114528 synthetic samples -> test with 'LR' LR tn, fp: 28881, 10 LR fn, tp: 75, 185 LR f1 score: 0.813 LR cohens kappa score: 0.812 LR average precision score: 0.874 -> test with 'GB' GB tn, fp: 28868, 23 GB fn, tp: 74, 186 GB f1 score: 0.793 GB cohens kappa score: 0.792 -> test with 'KNN' KNN tn, fp: 28882, 9 KNN fn, tp: 163, 97 KNN f1 score: 0.530 KNN cohens kappa score: 0.528 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28876, 15 LR fn, tp: 59, 201 LR f1 score: 0.845 LR cohens kappa score: 0.843 LR average precision score: 0.892 -> test with 'GB' GB tn, fp: 28868, 23 GB fn, tp: 61, 199 GB f1 score: 0.826 GB cohens kappa score: 0.824 -> test with 'KNN' KNN tn, fp: 28890, 1 KNN fn, tp: 162, 98 KNN f1 score: 0.546 KNN cohens kappa score: 0.544 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28872, 19 LR fn, tp: 63, 197 LR f1 score: 0.828 LR cohens kappa score: 0.826 LR average precision score: 0.844 -> test with 'GB' GB tn, fp: 28862, 29 GB fn, tp: 67, 193 GB f1 score: 0.801 GB cohens kappa score: 0.799 -> test with 'KNN' KNN tn, fp: 28882, 9 KNN fn, tp: 152, 108 KNN f1 score: 0.573 KNN cohens kappa score: 0.571 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28867, 24 LR fn, tp: 65, 195 LR f1 score: 0.814 LR cohens kappa score: 0.813 LR average precision score: 0.860 -> test with 'GB' GB tn, fp: 28861, 30 GB fn, tp: 67, 193 GB f1 score: 0.799 GB cohens kappa score: 0.798 -> test with 'KNN' KNN tn, fp: 28883, 8 KNN fn, tp: 155, 105 KNN f1 score: 0.563 KNN cohens kappa score: 0.561 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114524 synthetic samples -> test with 'LR' LR tn, fp: 28876, 15 LR fn, tp: 67, 189 LR f1 score: 0.822 LR cohens kappa score: 0.820 LR average precision score: 0.854 -> test with 'GB' GB tn, fp: 28860, 31 GB fn, tp: 74, 182 GB f1 score: 0.776 GB cohens kappa score: 0.774 -> test with 'KNN' KNN tn, fp: 28884, 7 KNN fn, tp: 166, 90 KNN f1 score: 0.510 KNN cohens kappa score: 0.508 ====== 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 114528 synthetic samples -> test with 'LR' LR tn, fp: 28875, 16 LR fn, tp: 59, 201 LR f1 score: 0.843 LR cohens kappa score: 0.841 LR average precision score: 0.875 -> test with 'GB' GB tn, fp: 28869, 22 GB fn, tp: 66, 194 GB f1 score: 0.815 GB cohens kappa score: 0.814 -> test with 'KNN' KNN tn, fp: 28886, 5 KNN fn, tp: 168, 92 KNN f1 score: 0.515 KNN cohens kappa score: 0.513 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28874, 17 LR fn, tp: 67, 193 LR f1 score: 0.821 LR cohens kappa score: 0.820 LR average precision score: 0.858 -> test with 'GB' GB tn, fp: 28874, 17 GB fn, tp: 70, 190 GB f1 score: 0.814 GB cohens kappa score: 0.812 -> test with 'KNN' KNN tn, fp: 28886, 5 KNN fn, tp: 172, 88 KNN f1 score: 0.499 KNN cohens kappa score: 0.496 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28868, 23 LR fn, tp: 72, 188 LR f1 score: 0.798 LR cohens kappa score: 0.797 LR average precision score: 0.838 -> test with 'GB' GB tn, fp: 28865, 26 GB fn, tp: 70, 190 GB f1 score: 0.798 GB cohens kappa score: 0.797 -> test with 'KNN' KNN tn, fp: 28878, 13 KNN fn, tp: 158, 102 KNN f1 score: 0.544 KNN cohens kappa score: 0.541 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28873, 18 LR fn, tp: 73, 187 LR f1 score: 0.804 LR cohens kappa score: 0.803 LR average precision score: 0.857 -> test with 'GB' GB tn, fp: 28864, 27 GB fn, tp: 68, 192 GB f1 score: 0.802 GB cohens kappa score: 0.800 -> test with 'KNN' KNN tn, fp: 28886, 5 KNN fn, tp: 165, 95 KNN f1 score: 0.528 KNN cohens kappa score: 0.525 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114524 synthetic samples -> test with 'LR' LR tn, fp: 28880, 11 LR fn, tp: 62, 194 LR f1 score: 0.842 LR cohens kappa score: 0.840 LR average precision score: 0.882 -> test with 'GB' GB tn, fp: 28872, 19 GB fn, tp: 62, 194 GB f1 score: 0.827 GB cohens kappa score: 0.826 -> test with 'KNN' KNN tn, fp: 28880, 11 KNN fn, tp: 146, 110 KNN f1 score: 0.584 KNN cohens kappa score: 0.581 ====== 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 114528 synthetic samples -> test with 'LR' LR tn, fp: 28875, 16 LR fn, tp: 62, 198 LR f1 score: 0.835 LR cohens kappa score: 0.834 LR average precision score: 0.884 -> test with 'GB' GB tn, fp: 28859, 32 GB fn, tp: 61, 199 GB f1 score: 0.811 GB cohens kappa score: 0.809 -> test with 'KNN' KNN tn, fp: 28888, 3 KNN fn, tp: 160, 100 KNN f1 score: 0.551 KNN cohens kappa score: 0.549 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28870, 21 LR fn, tp: 75, 185 LR f1 score: 0.794 LR cohens kappa score: 0.792 LR average precision score: 0.841 -> test with 'GB' GB tn, fp: 28856, 35 GB fn, tp: 72, 188 GB f1 score: 0.778 GB cohens kappa score: 0.777 -> test with 'KNN' KNN tn, fp: 28880, 11 KNN fn, tp: 162, 98 KNN f1 score: 0.531 KNN cohens kappa score: 0.529 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28876, 15 LR fn, tp: 60, 200 LR f1 score: 0.842 LR cohens kappa score: 0.841 LR average precision score: 0.855 -> test with 'GB' GB tn, fp: 28866, 25 GB fn, tp: 61, 199 GB f1 score: 0.822 GB cohens kappa score: 0.821 -> test with 'KNN' KNN tn, fp: 28883, 8 KNN fn, tp: 168, 92 KNN f1 score: 0.511 KNN cohens kappa score: 0.509 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28875, 16 LR fn, tp: 67, 193 LR f1 score: 0.823 LR cohens kappa score: 0.822 LR average precision score: 0.877 -> test with 'GB' GB tn, fp: 28868, 23 GB fn, tp: 75, 185 GB f1 score: 0.791 GB cohens kappa score: 0.789 -> test with 'KNN' KNN tn, fp: 28886, 5 KNN fn, tp: 159, 101 KNN f1 score: 0.552 KNN cohens kappa score: 0.550 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114524 synthetic samples -> test with 'LR' LR tn, fp: 28875, 16 LR fn, tp: 67, 189 LR f1 score: 0.820 LR cohens kappa score: 0.819 LR average precision score: 0.854 -> test with 'GB' GB tn, fp: 28861, 30 GB fn, tp: 68, 188 GB f1 score: 0.793 GB cohens kappa score: 0.792 -> test with 'KNN' KNN tn, fp: 28882, 9 KNN fn, tp: 156, 100 KNN f1 score: 0.548 KNN cohens kappa score: 0.546 ====== 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 114528 synthetic samples -> test with 'LR' LR tn, fp: 28868, 23 LR fn, tp: 61, 199 LR f1 score: 0.826 LR cohens kappa score: 0.824 LR average precision score: 0.867 -> test with 'GB' GB tn, fp: 28864, 27 GB fn, tp: 62, 198 GB f1 score: 0.816 GB cohens kappa score: 0.815 -> test with 'KNN' KNN tn, fp: 28883, 8 KNN fn, tp: 164, 96 KNN f1 score: 0.527 KNN cohens kappa score: 0.525 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28880, 11 LR fn, tp: 66, 194 LR f1 score: 0.834 LR cohens kappa score: 0.833 LR average precision score: 0.866 -> test with 'GB' GB tn, fp: 28861, 30 GB fn, tp: 71, 189 GB f1 score: 0.789 GB cohens kappa score: 0.787 -> test with 'KNN' KNN tn, fp: 28888, 3 KNN fn, tp: 160, 100 KNN f1 score: 0.551 KNN cohens kappa score: 0.549 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28874, 17 LR fn, tp: 74, 186 LR f1 score: 0.803 LR cohens kappa score: 0.802 LR average precision score: 0.860 -> test with 'GB' GB tn, fp: 28871, 20 GB fn, tp: 76, 184 GB f1 score: 0.793 GB cohens kappa score: 0.791 -> test with 'KNN' KNN tn, fp: 28883, 8 KNN fn, tp: 165, 95 KNN f1 score: 0.523 KNN cohens kappa score: 0.521 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114528 synthetic samples -> test with 'LR' LR tn, fp: 28877, 14 LR fn, tp: 65, 195 LR f1 score: 0.832 LR cohens kappa score: 0.830 LR average precision score: 0.857 -> test with 'GB' GB tn, fp: 28863, 28 GB fn, tp: 67, 193 GB f1 score: 0.802 GB cohens kappa score: 0.801 -> test with 'KNN' KNN tn, fp: 28884, 7 KNN fn, tp: 165, 95 KNN f1 score: 0.525 KNN cohens kappa score: 0.522 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 114524 synthetic samples -> test with 'LR' LR tn, fp: 28872, 19 LR fn, tp: 63, 193 LR f1 score: 0.825 LR cohens kappa score: 0.823 LR average precision score: 0.861 -> test with 'GB' GB tn, fp: 28866, 25 GB fn, tp: 66, 190 GB f1 score: 0.807 GB cohens kappa score: 0.805 -> test with 'KNN' KNN tn, fp: 28880, 11 KNN fn, tp: 147, 109 KNN f1 score: 0.580 KNN cohens kappa score: 0.577 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 28881, 24 LR fn, tp: 84, 203 LR f1 score: 0.855 LR cohens kappa score: 0.854 LR average precision score: 0.892 average: LR tn, fp: 28874.28, 16.72 LR fn, tp: 66.12, 193.08 LR f1 score: 0.823 LR cohens kappa score: 0.822 LR average precision score: 0.862 minimum: LR tn, fp: 28867, 10 LR fn, tp: 57, 172 LR f1 score: 0.784 LR cohens kappa score: 0.782 LR average precision score: 0.827 -----[ GB ]----- maximum: GB tn, fp: 28876, 35 GB fn, tp: 76, 202 GB f1 score: 0.842 GB cohens kappa score: 0.841 average: GB tn, fp: 28865.52, 25.48 GB fn, tp: 67.72, 191.48 GB f1 score: 0.804 GB cohens kappa score: 0.803 minimum: GB tn, fp: 28856, 15 GB fn, tp: 58, 182 GB f1 score: 0.776 GB cohens kappa score: 0.774 -----[ KNN ]----- maximum: KNN tn, fp: 28890, 17 KNN fn, tp: 173, 116 KNN f1 score: 0.590 KNN cohens kappa score: 0.588 average: KNN tn, fp: 28883.28, 7.72 KNN fn, tp: 160.84, 98.36 KNN f1 score: 0.538 KNN cohens kappa score: 0.536 minimum: KNN tn, fp: 28874, 1 KNN fn, tp: 144, 83 KNN f1 score: 0.483 KNN cohens kappa score: 0.480