/////////////////////////////////////////// // Running CTAB-GAN on folding_abalone9-18 /////////////////////////////////////////// Load 'data_input/folding_abalone9-18' 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 518 synthetic samples -> test with 'LR' LR tn, fp: 124, 14 LR fn, tp: 2, 7 LR f1 score: 0.467 LR cohens kappa score: 0.417 LR average precision score: 0.695 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 4, 5 GB f1 score: 0.526 GB cohens kappa score: 0.494 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 8, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.140 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 4, 5 LR f1 score: 0.500 LR cohens kappa score: 0.464 LR average precision score: 0.521 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 7, 2 GB f1 score: 0.333 GB cohens kappa score: 0.312 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 8, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.140 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 127, 11 LR fn, tp: 2, 7 LR f1 score: 0.519 LR cohens kappa score: 0.476 LR average precision score: 0.704 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 6, 3 GB f1 score: 0.462 GB cohens kappa score: 0.440 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.023 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 134, 4 LR fn, tp: 3, 6 LR f1 score: 0.632 LR cohens kappa score: 0.606 LR average precision score: 0.653 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 6, 3 GB f1 score: 0.462 GB cohens kappa score: 0.440 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.023 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 516 synthetic samples -> test with 'LR' LR tn, fp: 121, 16 LR fn, tp: 2, 4 LR f1 score: 0.308 LR cohens kappa score: 0.260 LR average precision score: 0.514 -> test with 'GB' GB tn, fp: 133, 4 GB fn, tp: 5, 1 GB f1 score: 0.182 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 6, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ====== 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 518 synthetic samples -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 2, 7 LR f1 score: 0.667 LR cohens kappa score: 0.642 LR average precision score: 0.760 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 8, 1 GB f1 score: 0.143 GB cohens kappa score: 0.104 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 8, 1 KNN f1 score: 0.182 KNN cohens kappa score: 0.163 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 3, 6 LR f1 score: 0.522 LR cohens kappa score: 0.483 LR average precision score: 0.630 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.313 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 7, 2 KNN f1 score: 0.364 KNN cohens kappa score: 0.349 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 122, 16 LR fn, tp: 3, 6 LR f1 score: 0.387 LR cohens kappa score: 0.329 LR average precision score: 0.522 -> test with 'GB' GB tn, fp: 130, 8 GB fn, tp: 7, 2 GB f1 score: 0.211 GB cohens kappa score: 0.156 -> test with 'KNN' KNN tn, fp: 133, 5 KNN fn, tp: 7, 2 KNN f1 score: 0.250 KNN cohens kappa score: 0.208 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 118, 20 LR fn, tp: 1, 8 LR f1 score: 0.432 LR cohens kappa score: 0.374 LR average precision score: 0.730 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 5, 4 GB f1 score: 0.471 GB cohens kappa score: 0.438 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 516 synthetic samples -> test with 'LR' LR tn, fp: 128, 9 LR fn, tp: 2, 4 LR f1 score: 0.421 LR cohens kappa score: 0.386 LR average precision score: 0.717 -> test with 'GB' GB tn, fp: 135, 2 GB fn, tp: 3, 3 GB f1 score: 0.545 GB cohens kappa score: 0.527 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 6, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ====== 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 518 synthetic samples -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 4, 5 LR f1 score: 0.435 LR cohens kappa score: 0.389 LR average precision score: 0.492 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 8, 1 GB f1 score: 0.143 GB cohens kappa score: 0.104 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 119, 19 LR fn, tp: 1, 8 LR f1 score: 0.444 LR cohens kappa score: 0.388 LR average precision score: 0.734 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.163 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 6, 3 KNN f1 score: 0.429 KNN cohens kappa score: 0.402 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 4, 5 LR f1 score: 0.500 LR cohens kappa score: 0.464 LR average precision score: 0.619 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 7, 2 GB f1 score: 0.267 GB cohens kappa score: 0.229 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 7, 2 KNN f1 score: 0.364 KNN cohens kappa score: 0.349 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 123, 15 LR fn, tp: 2, 7 LR f1 score: 0.452 LR cohens kappa score: 0.399 LR average precision score: 0.696 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 5, 4 GB f1 score: 0.500 GB cohens kappa score: 0.472 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 8, 1 KNN f1 score: 0.154 KNN cohens kappa score: 0.121 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 516 synthetic samples -> test with 'LR' LR tn, fp: 119, 18 LR fn, tp: 1, 5 LR f1 score: 0.345 LR cohens kappa score: 0.298 LR average precision score: 0.730 -> test with 'GB' GB tn, fp: 133, 4 GB fn, tp: 3, 3 GB f1 score: 0.462 GB cohens kappa score: 0.436 -> test with 'KNN' KNN tn, fp: 136, 1 KNN fn, tp: 6, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.012 ====== 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 518 synthetic samples -> test with 'LR' LR tn, fp: 125, 13 LR fn, tp: 3, 6 LR f1 score: 0.429 LR cohens kappa score: 0.377 LR average precision score: 0.617 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 6, 3 GB f1 score: 0.400 GB cohens kappa score: 0.369 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 114, 24 LR fn, tp: 3, 6 LR f1 score: 0.308 LR cohens kappa score: 0.236 LR average precision score: 0.647 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 4, 5 GB f1 score: 0.500 GB cohens kappa score: 0.464 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 3, 6 LR f1 score: 0.600 LR cohens kappa score: 0.571 LR average precision score: 0.741 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.313 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 3, 6 LR f1 score: 0.500 LR cohens kappa score: 0.459 LR average precision score: 0.620 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.313 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 8, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.140 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 516 synthetic samples -> test with 'LR' LR tn, fp: 131, 6 LR fn, tp: 1, 5 LR f1 score: 0.588 LR cohens kappa score: 0.565 LR average precision score: 0.595 -> test with 'GB' GB tn, fp: 134, 3 GB fn, tp: 5, 1 GB f1 score: 0.200 GB cohens kappa score: 0.172 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 5, 1 KNN f1 score: 0.286 KNN cohens kappa score: 0.277 ====== 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 518 synthetic samples -> test with 'LR' LR tn, fp: 114, 24 LR fn, tp: 2, 7 LR f1 score: 0.350 LR cohens kappa score: 0.282 LR average precision score: 0.516 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 7, 2 GB f1 score: 0.235 GB cohens kappa score: 0.189 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 127, 11 LR fn, tp: 2, 7 LR f1 score: 0.519 LR cohens kappa score: 0.476 LR average precision score: 0.727 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 6, 3 GB f1 score: 0.375 GB cohens kappa score: 0.340 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 5, 4 LR f1 score: 0.421 LR cohens kappa score: 0.381 LR average precision score: 0.557 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 6, 3 GB f1 score: 0.429 GB cohens kappa score: 0.402 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 518 synthetic samples -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 2, 7 LR f1 score: 0.560 LR cohens kappa score: 0.523 LR average precision score: 0.750 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 4, 5 GB f1 score: 0.588 GB cohens kappa score: 0.563 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 7, 2 KNN f1 score: 0.364 KNN cohens kappa score: 0.349 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 516 synthetic samples -> test with 'LR' LR tn, fp: 130, 7 LR fn, tp: 2, 4 LR f1 score: 0.471 LR cohens kappa score: 0.440 LR average precision score: 0.722 -> test with 'GB' GB tn, fp: 136, 1 GB fn, tp: 3, 3 GB f1 score: 0.600 GB cohens kappa score: 0.586 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 6, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 134, 24 LR fn, tp: 5, 8 LR f1 score: 0.667 LR cohens kappa score: 0.642 LR average precision score: 0.760 average: LR tn, fp: 126.2, 11.6 LR fn, tp: 2.48, 5.92 LR f1 score: 0.471 LR cohens kappa score: 0.427 LR average precision score: 0.648 minimum: LR tn, fp: 114, 4 LR fn, tp: 1, 4 LR f1 score: 0.308 LR cohens kappa score: 0.236 LR average precision score: 0.492 -----[ GB ]----- maximum: GB tn, fp: 137, 8 GB fn, tp: 8, 5 GB f1 score: 0.600 GB cohens kappa score: 0.586 average: GB tn, fp: 134.24, 3.56 GB fn, tp: 5.64, 2.76 GB f1 score: 0.371 GB cohens kappa score: 0.340 minimum: GB tn, fp: 130, 1 GB fn, tp: 3, 1 GB f1 score: 0.143 GB cohens kappa score: 0.104 -----[ KNN ]----- maximum: KNN tn, fp: 138, 5 KNN fn, tp: 9, 3 KNN f1 score: 0.429 KNN cohens kappa score: 0.402 average: KNN tn, fp: 136.92, 0.88 KNN fn, tp: 7.64, 0.76 KNN f1 score: 0.132 KNN cohens kappa score: 0.118 minimum: KNN tn, fp: 133, 0 KNN fn, tp: 5, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.023