/////////////////////////////////////////// // Running convGAN on folding_hypothyroid /////////////////////////////////////////// Load 'data_input/folding_hypothyroid' from pickle file non empty cut in data_input/folding_hypothyroid! (1 points) 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 2289 synthetic samples -> test with 'LR' LR tn, fp: 521, 82 LR fn, tp: 4, 27 LR f1 score: 0.386 LR cohens kappa score: 0.335 LR average precision score: 0.466 -> test with 'GB' GB tn, fp: 593, 10 GB fn, tp: 4, 27 GB f1 score: 0.794 GB cohens kappa score: 0.783 -> test with 'KNN' KNN tn, fp: 577, 26 KNN fn, tp: 5, 26 KNN f1 score: 0.627 KNN cohens kappa score: 0.602 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 507, 96 LR fn, tp: 2, 29 LR f1 score: 0.372 LR cohens kappa score: 0.318 LR average precision score: 0.459 -> test with 'GB' GB tn, fp: 583, 20 GB fn, tp: 2, 29 GB f1 score: 0.725 GB cohens kappa score: 0.707 -> test with 'KNN' KNN tn, fp: 566, 37 KNN fn, tp: 4, 27 KNN f1 score: 0.568 KNN cohens kappa score: 0.538 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 513, 90 LR fn, tp: 6, 25 LR f1 score: 0.342 LR cohens kappa score: 0.288 LR average precision score: 0.330 -> test with 'GB' GB tn, fp: 591, 12 GB fn, tp: 4, 27 GB f1 score: 0.771 GB cohens kappa score: 0.758 -> test with 'KNN' KNN tn, fp: 572, 31 KNN fn, tp: 6, 25 KNN f1 score: 0.575 KNN cohens kappa score: 0.546 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 503, 100 LR fn, tp: 4, 27 LR f1 score: 0.342 LR cohens kappa score: 0.286 LR average precision score: 0.405 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 6, 25 GB f1 score: 0.806 GB cohens kappa score: 0.797 -> test with 'KNN' KNN tn, fp: 578, 25 KNN fn, tp: 11, 20 KNN f1 score: 0.526 KNN cohens kappa score: 0.497 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 528, 72 LR fn, tp: 2, 25 LR f1 score: 0.403 LR cohens kappa score: 0.360 LR average precision score: 0.550 -> test with 'GB' GB tn, fp: 594, 6 GB fn, tp: 4, 23 GB f1 score: 0.821 GB cohens kappa score: 0.813 -> test with 'KNN' KNN tn, fp: 562, 38 KNN fn, tp: 5, 22 KNN f1 score: 0.506 KNN cohens kappa score: 0.475 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 529, 74 LR fn, tp: 6, 25 LR f1 score: 0.385 LR cohens kappa score: 0.335 LR average precision score: 0.488 -> test with 'GB' GB tn, fp: 589, 14 GB fn, tp: 6, 25 GB f1 score: 0.714 GB cohens kappa score: 0.698 -> test with 'KNN' KNN tn, fp: 581, 22 KNN fn, tp: 5, 26 KNN f1 score: 0.658 KNN cohens kappa score: 0.637 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 531, 72 LR fn, tp: 6, 25 LR f1 score: 0.391 LR cohens kappa score: 0.342 LR average precision score: 0.411 -> test with 'GB' GB tn, fp: 593, 10 GB fn, tp: 3, 28 GB f1 score: 0.812 GB cohens kappa score: 0.801 -> test with 'KNN' KNN tn, fp: 577, 26 KNN fn, tp: 6, 25 KNN f1 score: 0.610 KNN cohens kappa score: 0.584 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 506, 97 LR fn, tp: 2, 29 LR f1 score: 0.369 LR cohens kappa score: 0.316 LR average precision score: 0.571 -> test with 'GB' GB tn, fp: 594, 9 GB fn, tp: 3, 28 GB f1 score: 0.824 GB cohens kappa score: 0.814 -> test with 'KNN' KNN tn, fp: 570, 33 KNN fn, tp: 6, 25 KNN f1 score: 0.562 KNN cohens kappa score: 0.532 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 510, 93 LR fn, tp: 6, 25 LR f1 score: 0.336 LR cohens kappa score: 0.280 LR average precision score: 0.279 -> test with 'GB' GB tn, fp: 591, 12 GB fn, tp: 5, 26 GB f1 score: 0.754 GB cohens kappa score: 0.740 -> test with 'KNN' KNN tn, fp: 572, 31 KNN fn, tp: 7, 24 KNN f1 score: 0.558 KNN cohens kappa score: 0.529 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 495, 105 LR fn, tp: 1, 26 LR f1 score: 0.329 LR cohens kappa score: 0.278 LR average precision score: 0.426 -> test with 'GB' GB tn, fp: 591, 9 GB fn, tp: 2, 25 GB f1 score: 0.820 GB cohens kappa score: 0.811 -> test with 'KNN' KNN tn, fp: 571, 29 KNN fn, tp: 4, 23 KNN f1 score: 0.582 KNN cohens kappa score: 0.557 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 513, 90 LR fn, tp: 6, 25 LR f1 score: 0.342 LR cohens kappa score: 0.288 LR average precision score: 0.488 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 7, 24 GB f1 score: 0.828 GB cohens kappa score: 0.819 -> test with 'KNN' KNN tn, fp: 580, 23 KNN fn, tp: 10, 21 KNN f1 score: 0.560 KNN cohens kappa score: 0.533 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 534, 69 LR fn, tp: 11, 20 LR f1 score: 0.333 LR cohens kappa score: 0.281 LR average precision score: 0.295 -> test with 'GB' GB tn, fp: 590, 13 GB fn, tp: 3, 28 GB f1 score: 0.778 GB cohens kappa score: 0.765 -> test with 'KNN' KNN tn, fp: 569, 34 KNN fn, tp: 5, 26 KNN f1 score: 0.571 KNN cohens kappa score: 0.542 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 511, 92 LR fn, tp: 1, 30 LR f1 score: 0.392 LR cohens kappa score: 0.341 LR average precision score: 0.513 -> test with 'GB' GB tn, fp: 588, 15 GB fn, tp: 3, 28 GB f1 score: 0.757 GB cohens kappa score: 0.742 -> test with 'KNN' KNN tn, fp: 571, 32 KNN fn, tp: 8, 23 KNN f1 score: 0.535 KNN cohens kappa score: 0.504 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 506, 97 LR fn, tp: 2, 29 LR f1 score: 0.369 LR cohens kappa score: 0.316 LR average precision score: 0.484 -> test with 'GB' GB tn, fp: 592, 11 GB fn, tp: 5, 26 GB f1 score: 0.765 GB cohens kappa score: 0.751 -> test with 'KNN' KNN tn, fp: 570, 33 KNN fn, tp: 8, 23 KNN f1 score: 0.529 KNN cohens kappa score: 0.497 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 512, 88 LR fn, tp: 4, 23 LR f1 score: 0.333 LR cohens kappa score: 0.284 LR average precision score: 0.311 -> test with 'GB' GB tn, fp: 593, 7 GB fn, tp: 1, 26 GB f1 score: 0.867 GB cohens kappa score: 0.860 -> test with 'KNN' KNN tn, fp: 578, 22 KNN fn, tp: 4, 23 KNN f1 score: 0.639 KNN cohens kappa score: 0.618 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 524, 79 LR fn, tp: 4, 27 LR f1 score: 0.394 LR cohens kappa score: 0.345 LR average precision score: 0.357 -> test with 'GB' GB tn, fp: 593, 10 GB fn, tp: 4, 27 GB f1 score: 0.794 GB cohens kappa score: 0.783 -> test with 'KNN' KNN tn, fp: 573, 30 KNN fn, tp: 7, 24 KNN f1 score: 0.565 KNN cohens kappa score: 0.536 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 528, 75 LR fn, tp: 6, 25 LR f1 score: 0.382 LR cohens kappa score: 0.332 LR average precision score: 0.440 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 4, 27 GB f1 score: 0.818 GB cohens kappa score: 0.808 -> test with 'KNN' KNN tn, fp: 578, 25 KNN fn, tp: 6, 25 KNN f1 score: 0.617 KNN cohens kappa score: 0.593 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 517, 86 LR fn, tp: 3, 28 LR f1 score: 0.386 LR cohens kappa score: 0.335 LR average precision score: 0.584 -> test with 'GB' GB tn, fp: 596, 7 GB fn, tp: 6, 25 GB f1 score: 0.794 GB cohens kappa score: 0.783 -> test with 'KNN' KNN tn, fp: 582, 21 KNN fn, tp: 5, 26 KNN f1 score: 0.667 KNN cohens kappa score: 0.646 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 496, 107 LR fn, tp: 2, 29 LR f1 score: 0.347 LR cohens kappa score: 0.291 LR average precision score: 0.441 -> test with 'GB' GB tn, fp: 594, 9 GB fn, tp: 2, 29 GB f1 score: 0.841 GB cohens kappa score: 0.832 -> test with 'KNN' KNN tn, fp: 573, 30 KNN fn, tp: 8, 23 KNN f1 score: 0.548 KNN cohens kappa score: 0.518 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 520, 80 LR fn, tp: 6, 21 LR f1 score: 0.328 LR cohens kappa score: 0.279 LR average precision score: 0.400 -> test with 'GB' GB tn, fp: 589, 11 GB fn, tp: 5, 22 GB f1 score: 0.733 GB cohens kappa score: 0.720 -> test with 'KNN' KNN tn, fp: 557, 43 KNN fn, tp: 6, 21 KNN f1 score: 0.462 KNN cohens kappa score: 0.427 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 520, 83 LR fn, tp: 4, 27 LR f1 score: 0.383 LR cohens kappa score: 0.332 LR average precision score: 0.440 -> test with 'GB' GB tn, fp: 591, 12 GB fn, tp: 5, 26 GB f1 score: 0.754 GB cohens kappa score: 0.740 -> test with 'KNN' KNN tn, fp: 577, 26 KNN fn, tp: 6, 25 KNN f1 score: 0.610 KNN cohens kappa score: 0.584 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 518, 85 LR fn, tp: 5, 26 LR f1 score: 0.366 LR cohens kappa score: 0.314 LR average precision score: 0.465 -> test with 'GB' GB tn, fp: 592, 11 GB fn, tp: 1, 30 GB f1 score: 0.833 GB cohens kappa score: 0.824 -> test with 'KNN' KNN tn, fp: 571, 32 KNN fn, tp: 9, 22 KNN f1 score: 0.518 KNN cohens kappa score: 0.486 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 504, 99 LR fn, tp: 2, 29 LR f1 score: 0.365 LR cohens kappa score: 0.310 LR average precision score: 0.501 -> test with 'GB' GB tn, fp: 593, 10 GB fn, tp: 6, 25 GB f1 score: 0.758 GB cohens kappa score: 0.744 -> test with 'KNN' KNN tn, fp: 578, 25 KNN fn, tp: 8, 23 KNN f1 score: 0.582 KNN cohens kappa score: 0.556 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 511, 92 LR fn, tp: 4, 27 LR f1 score: 0.360 LR cohens kappa score: 0.306 LR average precision score: 0.579 -> test with 'GB' GB tn, fp: 594, 9 GB fn, tp: 2, 29 GB f1 score: 0.841 GB cohens kappa score: 0.832 -> test with 'KNN' KNN tn, fp: 569, 34 KNN fn, tp: 5, 26 KNN f1 score: 0.571 KNN cohens kappa score: 0.542 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 524, 76 LR fn, tp: 3, 24 LR f1 score: 0.378 LR cohens kappa score: 0.333 LR average precision score: 0.318 -> test with 'GB' GB tn, fp: 590, 10 GB fn, tp: 4, 23 GB f1 score: 0.767 GB cohens kappa score: 0.755 -> test with 'KNN' KNN tn, fp: 568, 32 KNN fn, tp: 5, 22 KNN f1 score: 0.543 KNN cohens kappa score: 0.515 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 534, 107 LR fn, tp: 11, 30 LR f1 score: 0.403 LR cohens kappa score: 0.360 LR average precision score: 0.584 average: LR tn, fp: 515.24, 87.16 LR fn, tp: 4.08, 26.12 LR f1 score: 0.365 LR cohens kappa score: 0.313 LR average precision score: 0.440 minimum: LR tn, fp: 495, 69 LR fn, tp: 1, 20 LR f1 score: 0.328 LR cohens kappa score: 0.278 LR average precision score: 0.279 -----[ GB ]----- maximum: GB tn, fp: 600, 20 GB fn, tp: 7, 30 GB f1 score: 0.867 GB cohens kappa score: 0.860 average: GB tn, fp: 592.24, 10.16 GB fn, tp: 3.88, 26.32 GB f1 score: 0.791 GB cohens kappa score: 0.779 minimum: GB tn, fp: 583, 3 GB fn, tp: 1, 22 GB f1 score: 0.714 GB cohens kappa score: 0.698 -----[ KNN ]----- maximum: KNN tn, fp: 582, 43 KNN fn, tp: 11, 27 KNN f1 score: 0.667 KNN cohens kappa score: 0.646 average: KNN tn, fp: 572.8, 29.6 KNN fn, tp: 6.36, 23.84 KNN f1 score: 0.572 KNN cohens kappa score: 0.544 minimum: KNN tn, fp: 557, 21 KNN fn, tp: 4, 20 KNN f1 score: 0.462 KNN cohens kappa score: 0.427