/////////////////////////////////////////// // Running SpheredNoise 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 Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 597, 6 LR fn, tp: 20, 11 LR f1 score: 0.458 LR cohens kappa score: 0.439 LR average precision score: 0.527 -> test with 'GB' GB tn, fp: 601, 2 GB fn, tp: 7, 24 GB f1 score: 0.842 GB cohens kappa score: 0.835 -> test with 'KNN' KNN tn, fp: 600, 3 KNN fn, tp: 15, 16 KNN f1 score: 0.640 KNN cohens kappa score: 0.626 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:7.416198487095663 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 592, 11 LR fn, tp: 19, 12 LR f1 score: 0.444 LR cohens kappa score: 0.420 LR average precision score: 0.503 -> test with 'GB' GB tn, fp: 594, 9 GB fn, tp: 9, 22 GB f1 score: 0.710 GB cohens kappa score: 0.695 -> test with 'KNN' KNN tn, fp: 596, 7 KNN fn, tp: 15, 16 KNN f1 score: 0.593 KNN cohens kappa score: 0.575 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:91.97282207261013 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 594, 9 LR fn, tp: 24, 7 LR f1 score: 0.298 LR cohens kappa score: 0.274 LR average precision score: 0.420 -> 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: 599, 4 KNN fn, tp: 16, 15 KNN f1 score: 0.600 KNN cohens kappa score: 0.585 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 594, 9 LR fn, tp: 21, 10 LR f1 score: 0.400 LR cohens kappa score: 0.377 LR average precision score: 0.412 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 11, 20 GB f1 score: 0.741 GB cohens kappa score: 0.729 -> test with 'KNN' KNN tn, fp: 601, 2 KNN fn, tp: 16, 15 KNN f1 score: 0.625 KNN cohens kappa score: 0.612 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2412/124 points -> new disc -> calc distances -> statistics trained 124 points min:1.0 max:90.58145505565695 -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 596, 4 LR fn, tp: 17, 10 LR f1 score: 0.488 LR cohens kappa score: 0.472 LR average precision score: 0.566 -> test with 'GB' GB tn, fp: 598, 2 GB fn, tp: 5, 22 GB f1 score: 0.863 GB cohens kappa score: 0.857 -> test with 'KNN' KNN tn, fp: 597, 3 KNN fn, tp: 16, 11 KNN f1 score: 0.537 KNN cohens kappa score: 0.523 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:91.97282207261013 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 596, 7 LR fn, tp: 20, 11 LR f1 score: 0.449 LR cohens kappa score: 0.428 LR average precision score: 0.484 -> test with 'GB' GB tn, fp: 596, 7 GB fn, tp: 12, 19 GB f1 score: 0.667 GB cohens kappa score: 0.651 -> test with 'KNN' KNN tn, fp: 598, 5 KNN fn, tp: 22, 9 KNN f1 score: 0.400 KNN cohens kappa score: 0.381 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:7.416198487095663 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 593, 10 LR fn, tp: 24, 7 LR f1 score: 0.292 LR cohens kappa score: 0.266 LR average precision score: 0.492 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 9, 22 GB f1 score: 0.746 GB cohens kappa score: 0.733 -> test with 'KNN' KNN tn, fp: 600, 3 KNN fn, tp: 15, 16 KNN f1 score: 0.640 KNN cohens kappa score: 0.626 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 595, 8 LR fn, tp: 17, 14 LR f1 score: 0.528 LR cohens kappa score: 0.508 LR average precision score: 0.555 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 12, 19 GB f1 score: 0.717 GB cohens kappa score: 0.705 -> test with 'KNN' KNN tn, fp: 601, 2 KNN fn, tp: 16, 15 KNN f1 score: 0.625 KNN cohens kappa score: 0.612 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:9.219544457292887 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 596, 7 LR fn, tp: 26, 5 LR f1 score: 0.233 LR cohens kappa score: 0.211 LR average precision score: 0.409 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 6, 25 GB f1 score: 0.833 GB cohens kappa score: 0.825 -> test with 'KNN' KNN tn, fp: 596, 7 KNN fn, tp: 16, 15 KNN f1 score: 0.566 KNN cohens kappa score: 0.548 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2412/124 points -> new disc -> calc distances -> statistics trained 124 points min:1.0 max:75.78918128598566 -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 592, 8 LR fn, tp: 16, 11 LR f1 score: 0.478 LR cohens kappa score: 0.459 LR average precision score: 0.573 -> test with 'GB' GB tn, fp: 597, 3 GB fn, tp: 5, 22 GB f1 score: 0.846 GB cohens kappa score: 0.840 -> test with 'KNN' KNN tn, fp: 595, 5 KNN fn, tp: 12, 15 KNN f1 score: 0.638 KNN cohens kappa score: 0.625 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:7.416198487095663 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 599, 4 LR fn, tp: 22, 9 LR f1 score: 0.409 LR cohens kappa score: 0.392 LR average precision score: 0.559 -> test with 'GB' GB tn, fp: 603, 0 GB fn, tp: 12, 19 GB f1 score: 0.760 GB cohens kappa score: 0.751 -> test with 'KNN' KNN tn, fp: 602, 1 KNN fn, tp: 19, 12 KNN f1 score: 0.545 KNN cohens kappa score: 0.532 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:76.87652437513027 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 593, 10 LR fn, tp: 22, 9 LR f1 score: 0.360 LR cohens kappa score: 0.335 LR average precision score: 0.348 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 6, 25 GB f1 score: 0.781 GB cohens kappa score: 0.770 -> test with 'KNN' KNN tn, fp: 598, 5 KNN fn, tp: 16, 15 KNN f1 score: 0.588 KNN cohens kappa score: 0.572 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 594, 9 LR fn, tp: 16, 15 LR f1 score: 0.545 LR cohens kappa score: 0.525 LR average precision score: 0.665 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 5, 26 GB f1 score: 0.839 GB cohens kappa score: 0.830 -> test with 'KNN' KNN tn, fp: 596, 7 KNN fn, tp: 15, 16 KNN f1 score: 0.593 KNN cohens kappa score: 0.575 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:9.0 max:91.97282207261013 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 597, 6 LR fn, tp: 19, 12 LR f1 score: 0.490 LR cohens kappa score: 0.471 LR average precision score: 0.512 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 10, 21 GB f1 score: 0.724 GB cohens kappa score: 0.711 -> test with 'KNN' KNN tn, fp: 599, 4 KNN fn, tp: 17, 14 KNN f1 score: 0.571 KNN cohens kappa score: 0.555 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2412/124 points -> new disc -> calc distances -> statistics trained 124 points min:1.0 max:91.88035698668132 -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 595, 5 LR fn, tp: 22, 5 LR f1 score: 0.270 LR cohens kappa score: 0.253 LR average precision score: 0.356 -> test with 'GB' GB tn, fp: 597, 3 GB fn, tp: 8, 19 GB f1 score: 0.776 GB cohens kappa score: 0.766 -> test with 'KNN' KNN tn, fp: 599, 1 KNN fn, tp: 14, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.623 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:7.416198487095663 max:91.97282207261013 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 592, 11 LR fn, tp: 21, 10 LR f1 score: 0.385 LR cohens kappa score: 0.359 LR average precision score: 0.393 -> 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: 600, 3 KNN fn, tp: 21, 10 KNN f1 score: 0.455 KNN cohens kappa score: 0.438 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 595, 8 LR fn, tp: 21, 10 LR f1 score: 0.408 LR cohens kappa score: 0.386 LR average precision score: 0.495 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 8, 23 GB f1 score: 0.793 GB cohens kappa score: 0.783 -> test with 'KNN' KNN tn, fp: 599, 4 KNN fn, tp: 18, 13 KNN f1 score: 0.542 KNN cohens kappa score: 0.525 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 599, 4 LR fn, tp: 22, 9 LR f1 score: 0.409 LR cohens kappa score: 0.392 LR average precision score: 0.632 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 7, 24 GB f1 score: 0.814 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 600, 3 KNN fn, tp: 17, 14 KNN f1 score: 0.583 KNN cohens kappa score: 0.568 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:75.78918128598566 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 594, 9 LR fn, tp: 18, 13 LR f1 score: 0.491 LR cohens kappa score: 0.469 LR average precision score: 0.501 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 8, 23 GB f1 score: 0.807 GB cohens kappa score: 0.798 -> test with 'KNN' KNN tn, fp: 597, 6 KNN fn, tp: 17, 14 KNN f1 score: 0.549 KNN cohens kappa score: 0.531 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2412/124 points -> new disc -> calc distances -> statistics trained 124 points min:9.0 max:91.88035698668132 -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 593, 7 LR fn, tp: 19, 8 LR f1 score: 0.381 LR cohens kappa score: 0.361 LR average precision score: 0.436 -> test with 'GB' GB tn, fp: 595, 5 GB fn, tp: 9, 18 GB f1 score: 0.720 GB cohens kappa score: 0.708 -> test with 'KNN' KNN tn, fp: 593, 7 KNN fn, tp: 12, 15 KNN f1 score: 0.612 KNN cohens kappa score: 0.597 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 589, 14 LR fn, tp: 19, 12 LR f1 score: 0.421 LR cohens kappa score: 0.394 LR average precision score: 0.480 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 9, 22 GB f1 score: 0.772 GB cohens kappa score: 0.761 -> test with 'KNN' KNN tn, fp: 599, 4 KNN fn, tp: 17, 14 KNN f1 score: 0.571 KNN cohens kappa score: 0.555 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 598, 5 LR fn, tp: 20, 11 LR f1 score: 0.468 LR cohens kappa score: 0.450 LR average precision score: 0.480 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 8, 23 GB f1 score: 0.807 GB cohens kappa score: 0.798 -> test with 'KNN' KNN tn, fp: 599, 4 KNN fn, tp: 18, 13 KNN f1 score: 0.542 KNN cohens kappa score: 0.525 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:7.416198487095663 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 593, 10 LR fn, tp: 18, 13 LR f1 score: 0.481 LR cohens kappa score: 0.459 LR average precision score: 0.553 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 14, 17 GB f1 score: 0.642 GB cohens kappa score: 0.626 -> test with 'KNN' KNN tn, fp: 598, 5 KNN fn, tp: 18, 13 KNN f1 score: 0.531 KNN cohens kappa score: 0.513 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2409/120 points -> new disc -> calc distances -> statistics trained 120 points min:1.0 max:91.88035698668132 -> create 2289 synthetic samples -> test with 'LR' LR tn, fp: 601, 2 LR fn, tp: 22, 9 LR f1 score: 0.429 LR cohens kappa score: 0.414 LR average precision score: 0.552 -> test with 'GB' GB tn, fp: 600, 3 GB fn, tp: 6, 25 GB f1 score: 0.847 GB cohens kappa score: 0.840 -> test with 'KNN' KNN tn, fp: 601, 2 KNN fn, tp: 14, 17 KNN f1 score: 0.680 KNN cohens kappa score: 0.668 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 2412/124 points -> new disc -> calc distances -> statistics trained 124 points min:1.0 max:75.78918128598566 -> create 2288 synthetic samples -> test with 'LR' LR tn, fp: 590, 10 LR fn, tp: 21, 6 LR f1 score: 0.279 LR cohens kappa score: 0.255 LR average precision score: 0.399 -> test with 'GB' GB tn, fp: 596, 4 GB fn, tp: 8, 19 GB f1 score: 0.760 GB cohens kappa score: 0.750 -> test with 'KNN' KNN tn, fp: 597, 3 KNN fn, tp: 17, 10 KNN f1 score: 0.500 KNN cohens kappa score: 0.486 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 601, 14 LR fn, tp: 26, 15 LR f1 score: 0.545 LR cohens kappa score: 0.525 LR average precision score: 0.665 average: LR tn, fp: 594.68, 7.72 LR fn, tp: 20.24, 9.96 LR f1 score: 0.412 LR cohens kappa score: 0.391 LR average precision score: 0.492 minimum: LR tn, fp: 589, 2 LR fn, tp: 16, 5 LR f1 score: 0.233 LR cohens kappa score: 0.211 LR average precision score: 0.348 -----[ GB ]----- maximum: GB tn, fp: 603, 9 GB fn, tp: 14, 27 GB f1 score: 0.863 GB cohens kappa score: 0.857 average: GB tn, fp: 598.24, 4.16 GB fn, tp: 8.2, 22.0 GB f1 score: 0.780 GB cohens kappa score: 0.769 minimum: GB tn, fp: 594, 0 GB fn, tp: 4, 17 GB f1 score: 0.642 GB cohens kappa score: 0.626 -----[ KNN ]----- maximum: KNN tn, fp: 602, 7 KNN fn, tp: 22, 17 KNN f1 score: 0.680 KNN cohens kappa score: 0.668 average: KNN tn, fp: 598.4, 4.0 KNN fn, tp: 16.36, 13.84 KNN f1 score: 0.574 KNN cohens kappa score: 0.559 minimum: KNN tn, fp: 593, 1 KNN fn, tp: 12, 9 KNN f1 score: 0.400 KNN cohens kappa score: 0.381