/////////////////////////////////////////// // Running convGAN-majority-full 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 GAN.predict GAN tn, fp: 597, 6 GAN fn, tp: 10, 21 GAN f1 score: 0.724 GAN cohens kappa score: 0.711 -> test with 'LR' LR tn, fp: 524, 79 LR fn, tp: 5, 26 LR f1 score: 0.382 LR cohens kappa score: 0.332 LR average precision score: 0.435 -> test with 'GB' GB tn, fp: 594, 9 GB fn, tp: 5, 26 GB f1 score: 0.788 GB cohens kappa score: 0.776 -> 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 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 584, 19 GAN fn, tp: 5, 26 GAN f1 score: 0.684 GAN cohens kappa score: 0.665 -> test with 'LR' LR tn, fp: 514, 89 LR fn, tp: 3, 28 LR f1 score: 0.378 LR cohens kappa score: 0.326 LR average precision score: 0.460 -> test with 'GB' GB tn, fp: 591, 12 GB fn, tp: 3, 28 GB f1 score: 0.789 GB cohens kappa score: 0.776 -> 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 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 584, 19 GAN fn, tp: 7, 24 GAN f1 score: 0.649 GAN cohens kappa score: 0.627 -> test with 'LR' LR tn, fp: 510, 93 LR fn, tp: 7, 24 LR f1 score: 0.324 LR cohens kappa score: 0.268 LR average precision score: 0.318 -> test with 'GB' GB tn, fp: 590, 13 GB fn, tp: 2, 29 GB f1 score: 0.795 GB cohens kappa score: 0.782 -> test with 'KNN' KNN tn, fp: 576, 27 KNN fn, tp: 6, 25 KNN f1 score: 0.602 KNN cohens kappa score: 0.576 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 591, 12 GAN fn, tp: 10, 21 GAN f1 score: 0.656 GAN cohens kappa score: 0.638 -> 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: 0.384 -> test with 'GB' GB tn, fp: 593, 10 GB fn, tp: 5, 26 GB f1 score: 0.776 GB cohens kappa score: 0.764 -> 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 GAN.predict GAN tn, fp: 592, 8 GAN fn, tp: 6, 21 GAN f1 score: 0.750 GAN cohens kappa score: 0.738 -> 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: 0.544 -> test with 'GB' GB tn, fp: 593, 7 GB fn, tp: 3, 24 GB f1 score: 0.828 GB cohens kappa score: 0.819 -> test with 'KNN' KNN tn, fp: 572, 28 KNN fn, tp: 4, 23 KNN f1 score: 0.590 KNN cohens kappa score: 0.565 ====== 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 GAN.predict GAN tn, fp: 596, 7 GAN fn, tp: 8, 23 GAN f1 score: 0.754 GAN cohens kappa score: 0.742 -> test with 'LR' LR tn, fp: 527, 76 LR fn, tp: 5, 26 LR f1 score: 0.391 LR cohens kappa score: 0.342 LR average precision score: 0.485 -> test with 'GB' GB tn, fp: 592, 11 GB fn, tp: 4, 27 GB f1 score: 0.783 GB cohens kappa score: 0.770 -> test with 'KNN' KNN tn, fp: 585, 18 KNN fn, tp: 6, 25 KNN f1 score: 0.676 KNN cohens kappa score: 0.656 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 585, 18 GAN fn, tp: 8, 23 GAN f1 score: 0.639 GAN cohens kappa score: 0.618 -> test with 'LR' LR tn, fp: 532, 71 LR fn, tp: 6, 25 LR f1 score: 0.394 LR cohens kappa score: 0.345 LR average precision score: 0.438 -> 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: 579, 24 KNN fn, tp: 6, 25 KNN f1 score: 0.625 KNN cohens kappa score: 0.601 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 580, 23 GAN fn, tp: 5, 26 GAN f1 score: 0.650 GAN cohens kappa score: 0.628 -> test with 'LR' LR tn, fp: 514, 89 LR fn, tp: 3, 28 LR f1 score: 0.378 LR cohens kappa score: 0.326 LR average precision score: 0.544 -> 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: 574, 29 KNN fn, tp: 9, 22 KNN f1 score: 0.537 KNN cohens kappa score: 0.507 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 590, 13 GAN fn, tp: 7, 24 GAN f1 score: 0.706 GAN cohens kappa score: 0.689 -> test with 'LR' LR tn, fp: 514, 89 LR fn, tp: 6, 25 LR f1 score: 0.345 LR cohens kappa score: 0.290 LR average precision score: 0.267 -> test with 'GB' GB tn, fp: 593, 10 GB fn, tp: 5, 26 GB f1 score: 0.776 GB cohens kappa score: 0.764 -> test with 'KNN' KNN tn, fp: 576, 27 KNN fn, tp: 7, 24 KNN f1 score: 0.585 KNN cohens kappa score: 0.559 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with GAN.predict GAN tn, fp: 583, 17 GAN fn, tp: 3, 24 GAN f1 score: 0.706 GAN cohens kappa score: 0.690 -> test with 'LR' LR tn, fp: 506, 94 LR fn, tp: 1, 26 LR f1 score: 0.354 LR cohens kappa score: 0.305 LR average precision score: 0.439 -> test with 'GB' GB tn, fp: 590, 10 GB fn, tp: 3, 24 GB f1 score: 0.787 GB cohens kappa score: 0.776 -> test with 'KNN' KNN tn, fp: 575, 25 KNN fn, tp: 5, 22 KNN f1 score: 0.595 KNN cohens kappa score: 0.571 ====== 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 GAN.predict GAN tn, fp: 597, 6 GAN fn, tp: 7, 24 GAN f1 score: 0.787 GAN cohens kappa score: 0.776 -> test with 'LR' LR tn, fp: 505, 98 LR fn, tp: 4, 27 LR f1 score: 0.346 LR cohens kappa score: 0.291 LR average precision score: 0.468 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 4, 27 GB f1 score: 0.857 GB cohens kappa score: 0.850 -> test with 'KNN' KNN tn, fp: 580, 23 KNN fn, tp: 7, 24 KNN f1 score: 0.615 KNN cohens kappa score: 0.591 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 588, 15 GAN fn, tp: 5, 26 GAN f1 score: 0.722 GAN cohens kappa score: 0.706 -> test with 'LR' LR tn, fp: 529, 74 LR fn, tp: 10, 21 LR f1 score: 0.333 LR cohens kappa score: 0.280 LR average precision score: 0.289 -> 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: 575, 28 KNN fn, tp: 8, 23 KNN f1 score: 0.561 KNN cohens kappa score: 0.533 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 588, 15 GAN fn, tp: 7, 24 GAN f1 score: 0.686 GAN cohens kappa score: 0.668 -> test with 'LR' LR tn, fp: 516, 87 LR fn, tp: 1, 30 LR f1 score: 0.405 LR cohens kappa score: 0.356 LR average precision score: 0.547 -> test with 'GB' GB tn, fp: 590, 13 GB fn, tp: 4, 27 GB f1 score: 0.761 GB cohens kappa score: 0.747 -> test with 'KNN' KNN tn, fp: 572, 31 KNN fn, tp: 9, 22 KNN f1 score: 0.524 KNN cohens kappa score: 0.492 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 590, 13 GAN fn, tp: 9, 22 GAN f1 score: 0.667 GAN cohens kappa score: 0.648 -> test with 'LR' LR tn, fp: 521, 82 LR fn, tp: 2, 29 LR f1 score: 0.408 LR cohens kappa score: 0.359 LR average precision score: 0.483 -> test with 'GB' GB tn, fp: 593, 10 GB fn, tp: 5, 26 GB f1 score: 0.776 GB cohens kappa score: 0.764 -> test with 'KNN' KNN tn, fp: 578, 25 KNN fn, tp: 7, 24 KNN f1 score: 0.600 KNN cohens kappa score: 0.575 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with GAN.predict GAN tn, fp: 594, 6 GAN fn, tp: 6, 21 GAN f1 score: 0.778 GAN cohens kappa score: 0.768 -> test with 'LR' LR tn, fp: 515, 85 LR fn, tp: 5, 22 LR f1 score: 0.328 LR cohens kappa score: 0.279 LR average precision score: 0.345 -> test with 'GB' GB tn, fp: 594, 6 GB fn, tp: 1, 26 GB f1 score: 0.881 GB cohens kappa score: 0.876 -> test with 'KNN' KNN tn, fp: 583, 17 KNN fn, tp: 3, 24 KNN f1 score: 0.706 KNN cohens kappa score: 0.690 ====== 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 GAN.predict GAN tn, fp: 590, 13 GAN fn, tp: 7, 24 GAN f1 score: 0.706 GAN cohens kappa score: 0.689 -> test with 'LR' LR tn, fp: 522, 81 LR fn, tp: 4, 27 LR f1 score: 0.388 LR cohens kappa score: 0.338 LR average precision score: 0.382 -> test with 'GB' GB tn, fp: 589, 14 GB fn, tp: 4, 27 GB f1 score: 0.750 GB cohens kappa score: 0.735 -> test with 'KNN' KNN tn, fp: 579, 24 KNN fn, tp: 5, 26 KNN f1 score: 0.642 KNN cohens kappa score: 0.619 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 593, 10 GAN fn, tp: 8, 23 GAN f1 score: 0.719 GAN cohens kappa score: 0.704 -> test with 'LR' LR tn, fp: 532, 71 LR fn, tp: 5, 26 LR f1 score: 0.406 LR cohens kappa score: 0.359 LR average precision score: 0.431 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 3, 28 GB f1 score: 0.836 GB cohens kappa score: 0.827 -> test with 'KNN' KNN tn, fp: 579, 24 KNN fn, tp: 6, 25 KNN f1 score: 0.625 KNN cohens kappa score: 0.601 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 594, 9 GAN fn, tp: 5, 26 GAN f1 score: 0.788 GAN cohens kappa score: 0.776 -> test with 'LR' LR tn, fp: 511, 92 LR fn, tp: 3, 28 LR f1 score: 0.371 LR cohens kappa score: 0.318 LR average precision score: 0.565 -> test with 'GB' GB tn, fp: 596, 7 GB fn, tp: 5, 26 GB f1 score: 0.812 GB cohens kappa score: 0.803 -> test with 'KNN' KNN tn, fp: 579, 24 KNN fn, tp: 6, 25 KNN f1 score: 0.625 KNN cohens kappa score: 0.601 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 591, 12 GAN fn, tp: 7, 24 GAN f1 score: 0.716 GAN cohens kappa score: 0.701 -> test with 'LR' LR tn, fp: 501, 102 LR fn, tp: 3, 28 LR f1 score: 0.348 LR cohens kappa score: 0.292 LR average precision score: 0.437 -> 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: 578, 25 KNN fn, tp: 9, 22 KNN f1 score: 0.564 KNN cohens kappa score: 0.537 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with GAN.predict GAN tn, fp: 592, 8 GAN fn, tp: 8, 19 GAN f1 score: 0.704 GAN cohens kappa score: 0.690 -> test with 'LR' LR tn, fp: 509, 91 LR fn, tp: 5, 22 LR f1 score: 0.314 LR cohens kappa score: 0.263 LR average precision score: 0.393 -> test with 'GB' GB tn, fp: 589, 11 GB fn, tp: 4, 23 GB f1 score: 0.754 GB cohens kappa score: 0.742 -> test with 'KNN' KNN tn, fp: 568, 32 KNN fn, tp: 6, 21 KNN f1 score: 0.525 KNN cohens kappa score: 0.496 ====== 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 GAN.predict GAN tn, fp: 591, 12 GAN fn, tp: 9, 22 GAN f1 score: 0.677 GAN cohens kappa score: 0.660 -> test with 'LR' LR tn, fp: 518, 85 LR fn, tp: 4, 27 LR f1 score: 0.378 LR cohens kappa score: 0.326 LR average precision score: 0.375 -> test with 'GB' GB tn, fp: 592, 11 GB fn, tp: 4, 27 GB f1 score: 0.783 GB cohens kappa score: 0.770 -> test with 'KNN' KNN tn, fp: 581, 22 KNN fn, tp: 6, 25 KNN f1 score: 0.641 KNN cohens kappa score: 0.619 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 598, 5 GAN fn, tp: 9, 22 GAN f1 score: 0.759 GAN cohens kappa score: 0.747 -> test with 'LR' LR tn, fp: 526, 77 LR fn, tp: 5, 26 LR f1 score: 0.388 LR cohens kappa score: 0.338 LR average precision score: 0.503 -> 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: 9, 22 KNN f1 score: 0.530 KNN cohens kappa score: 0.499 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 594, 9 GAN fn, tp: 9, 22 GAN f1 score: 0.710 GAN cohens kappa score: 0.695 -> test with 'LR' LR tn, fp: 507, 96 LR fn, tp: 1, 30 LR f1 score: 0.382 LR cohens kappa score: 0.330 LR average precision score: 0.504 -> test with 'GB' GB tn, fp: 593, 10 GB fn, tp: 7, 24 GB f1 score: 0.738 GB cohens kappa score: 0.724 -> test with 'KNN' KNN tn, fp: 578, 25 KNN fn, tp: 7, 24 KNN f1 score: 0.600 KNN cohens kappa score: 0.575 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 586, 17 GAN fn, tp: 5, 26 GAN f1 score: 0.703 GAN cohens kappa score: 0.685 -> test with 'LR' LR tn, fp: 513, 90 LR fn, tp: 3, 28 LR f1 score: 0.376 LR cohens kappa score: 0.323 LR average precision score: 0.580 -> test with 'GB' GB tn, fp: 591, 12 GB fn, tp: 1, 30 GB f1 score: 0.822 GB cohens kappa score: 0.811 -> 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 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with GAN.predict GAN tn, fp: 591, 9 GAN fn, tp: 7, 20 GAN f1 score: 0.714 GAN cohens kappa score: 0.701 -> test with 'LR' LR tn, fp: 525, 75 LR fn, tp: 3, 24 LR f1 score: 0.381 LR cohens kappa score: 0.336 LR average precision score: 0.297 -> test with 'GB' GB tn, fp: 591, 9 GB fn, tp: 4, 23 GB f1 score: 0.780 GB cohens kappa score: 0.769 -> test with 'KNN' KNN tn, fp: 574, 26 KNN fn, tp: 5, 22 KNN f1 score: 0.587 KNN cohens kappa score: 0.563 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 532, 102 LR fn, tp: 10, 30 LR f1 score: 0.408 LR cohens kappa score: 0.359 LR average precision score: 0.580 average: LR tn, fp: 516.72, 85.68 LR fn, tp: 4.04, 26.16 LR f1 score: 0.369 LR cohens kappa score: 0.318 LR average precision score: 0.437 minimum: LR tn, fp: 501, 71 LR fn, tp: 1, 21 LR f1 score: 0.314 LR cohens kappa score: 0.263 LR average precision score: 0.267 -----[ GB ]----- maximum: GB tn, fp: 598, 14 GB fn, tp: 7, 30 GB f1 score: 0.881 GB cohens kappa score: 0.876 average: GB tn, fp: 592.56, 9.84 GB fn, tp: 3.68, 26.52 GB f1 score: 0.797 GB cohens kappa score: 0.786 minimum: GB tn, fp: 589, 5 GB fn, tp: 1, 23 GB f1 score: 0.738 GB cohens kappa score: 0.724 -----[ KNN ]----- maximum: KNN tn, fp: 585, 32 KNN fn, tp: 11, 26 KNN f1 score: 0.706 KNN cohens kappa score: 0.690 average: KNN tn, fp: 576.72, 25.68 KNN fn, tp: 6.56, 23.64 KNN f1 score: 0.596 KNN cohens kappa score: 0.570 minimum: KNN tn, fp: 568, 17 KNN fn, tp: 3, 20 KNN f1 score: 0.524 KNN cohens kappa score: 0.492 -----[ GAN ]----- maximum: GAN tn, fp: 598, 23 GAN fn, tp: 10, 26 GAN f1 score: 0.788 GAN cohens kappa score: 0.776 average: GAN tn, fp: 590.36, 12.04 GAN fn, tp: 7.08, 23.12 GAN f1 score: 0.710 GAN cohens kappa score: 0.694 minimum: GAN tn, fp: 580, 5 GAN fn, tp: 3, 19 GAN f1 score: 0.639 GAN cohens kappa score: 0.618