/////////////////////////////////////////// // Running convGAN-proximary-5 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: 499, 104 GAN fn, tp: 6, 25 GAN f1 score: 0.312 GAN cohens kappa score: 0.254 -> test with 'LR' LR tn, fp: 523, 80 LR fn, tp: 5, 26 LR f1 score: 0.380 LR cohens kappa score: 0.329 LR average precision score: 0.477 -> 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: 576, 27 KNN fn, tp: 3, 28 KNN f1 score: 0.651 KNN cohens kappa score: 0.628 ------ 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: 474, 129 GAN fn, tp: 8, 23 GAN f1 score: 0.251 GAN cohens kappa score: 0.185 -> test with 'LR' LR tn, fp: 519, 84 LR fn, tp: 3, 28 LR f1 score: 0.392 LR cohens kappa score: 0.341 LR average precision score: 0.473 -> test with 'GB' GB tn, fp: 592, 11 GB fn, tp: 3, 28 GB f1 score: 0.800 GB cohens kappa score: 0.788 -> test with 'KNN' KNN tn, fp: 569, 34 KNN fn, tp: 4, 27 KNN f1 score: 0.587 KNN cohens kappa score: 0.558 ------ 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: 520, 83 GAN fn, tp: 11, 20 GAN f1 score: 0.299 GAN cohens kappa score: 0.241 -> test with 'LR' LR tn, fp: 506, 97 LR fn, tp: 6, 25 LR f1 score: 0.327 LR cohens kappa score: 0.270 LR average precision score: 0.327 -> test with 'GB' GB tn, fp: 589, 14 GB fn, tp: 2, 29 GB f1 score: 0.784 GB cohens kappa score: 0.771 -> test with 'KNN' KNN tn, fp: 575, 28 KNN fn, tp: 7, 24 KNN f1 score: 0.578 KNN cohens kappa score: 0.551 ------ 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: 579, 24 GAN fn, tp: 12, 19 GAN f1 score: 0.514 GAN cohens kappa score: 0.484 -> 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: 0.429 -> 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: 573, 30 KNN fn, tp: 11, 20 KNN f1 score: 0.494 KNN cohens kappa score: 0.461 ------ 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: 585, 15 GAN fn, tp: 11, 16 GAN f1 score: 0.552 GAN cohens kappa score: 0.530 -> test with 'LR' LR tn, fp: 525, 75 LR fn, tp: 2, 25 LR f1 score: 0.394 LR cohens kappa score: 0.350 LR average precision score: 0.560 -> 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: 569, 31 KNN fn, tp: 2, 25 KNN f1 score: 0.602 KNN cohens kappa score: 0.578 ====== 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: 524, 79 GAN fn, tp: 8, 23 GAN f1 score: 0.346 GAN cohens kappa score: 0.293 -> 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: 0.482 -> 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: 581, 22 KNN fn, tp: 6, 25 KNN f1 score: 0.641 KNN cohens kappa score: 0.619 ------ 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: 454, 149 GAN fn, tp: 8, 23 GAN f1 score: 0.227 GAN cohens kappa score: 0.157 -> test with 'LR' LR tn, fp: 536, 67 LR fn, tp: 6, 25 LR f1 score: 0.407 LR cohens kappa score: 0.360 LR average precision score: 0.414 -> test with 'GB' GB tn, fp: 596, 7 GB fn, tp: 4, 27 GB f1 score: 0.831 GB cohens kappa score: 0.822 -> 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: 569, 34 GAN fn, tp: 10, 21 GAN f1 score: 0.488 GAN cohens kappa score: 0.454 -> test with 'LR' LR tn, fp: 507, 96 LR fn, tp: 4, 27 LR f1 score: 0.351 LR cohens kappa score: 0.296 LR average precision score: 0.582 -> 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: 575, 28 KNN fn, tp: 8, 23 KNN f1 score: 0.561 KNN cohens kappa score: 0.533 ------ 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: 511, 92 GAN fn, tp: 6, 25 GAN f1 score: 0.338 GAN cohens kappa score: 0.282 -> test with 'LR' LR tn, fp: 509, 94 LR fn, tp: 6, 25 LR f1 score: 0.333 LR cohens kappa score: 0.277 LR average precision score: 0.277 -> 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: 574, 29 KNN fn, tp: 7, 24 KNN f1 score: 0.571 KNN cohens kappa score: 0.543 ------ 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: 582, 18 GAN fn, tp: 8, 19 GAN f1 score: 0.594 GAN cohens kappa score: 0.572 -> test with 'LR' LR tn, fp: 500, 100 LR fn, tp: 1, 26 LR f1 score: 0.340 LR cohens kappa score: 0.289 LR average precision score: 0.507 -> test with 'GB' GB tn, fp: 589, 11 GB fn, tp: 1, 26 GB f1 score: 0.812 GB cohens kappa score: 0.803 -> test with 'KNN' KNN tn, fp: 575, 25 KNN fn, tp: 4, 23 KNN f1 score: 0.613 KNN cohens kappa score: 0.591 ====== 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: 564, 39 GAN fn, tp: 13, 18 GAN f1 score: 0.409 GAN cohens kappa score: 0.369 -> test with 'LR' LR tn, fp: 501, 102 LR fn, tp: 5, 26 LR f1 score: 0.327 LR cohens kappa score: 0.270 LR average precision score: 0.474 -> 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: 577, 26 KNN fn, tp: 10, 21 KNN f1 score: 0.538 KNN cohens kappa score: 0.510 ------ 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: 558, 45 GAN fn, tp: 10, 21 GAN f1 score: 0.433 GAN cohens kappa score: 0.393 -> test with 'LR' LR tn, fp: 527, 76 LR fn, tp: 10, 21 LR f1 score: 0.328 LR cohens kappa score: 0.274 LR average precision score: 0.288 -> test with 'GB' GB tn, fp: 587, 16 GB fn, tp: 3, 28 GB f1 score: 0.747 GB cohens kappa score: 0.731 -> 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 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 586, 17 GAN fn, tp: 7, 24 GAN f1 score: 0.667 GAN cohens kappa score: 0.647 -> test with 'LR' LR tn, fp: 512, 91 LR fn, tp: 1, 30 LR f1 score: 0.395 LR cohens kappa score: 0.344 LR average precision score: 0.515 -> test with 'GB' GB tn, fp: 589, 14 GB fn, tp: 3, 28 GB f1 score: 0.767 GB cohens kappa score: 0.753 -> test with 'KNN' KNN tn, fp: 566, 37 KNN fn, tp: 7, 24 KNN f1 score: 0.522 KNN cohens kappa score: 0.489 ------ 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: 552, 51 GAN fn, tp: 8, 23 GAN f1 score: 0.438 GAN cohens kappa score: 0.397 -> test with 'LR' LR tn, fp: 514, 89 LR fn, tp: 2, 29 LR f1 score: 0.389 LR cohens kappa score: 0.338 LR average precision score: 0.487 -> test with 'GB' GB tn, fp: 586, 17 GB fn, tp: 5, 26 GB f1 score: 0.703 GB cohens kappa score: 0.685 -> test with 'KNN' KNN tn, fp: 569, 34 KNN fn, tp: 7, 24 KNN f1 score: 0.539 KNN cohens kappa score: 0.508 ------ 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: 516, 84 GAN fn, tp: 7, 20 GAN f1 score: 0.305 GAN cohens kappa score: 0.254 -> test with 'LR' LR tn, fp: 508, 92 LR fn, tp: 4, 23 LR f1 score: 0.324 LR cohens kappa score: 0.273 LR average precision score: 0.301 -> test with 'GB' GB tn, fp: 591, 9 GB fn, tp: 1, 26 GB f1 score: 0.839 GB cohens kappa score: 0.830 -> test with 'KNN' KNN tn, fp: 575, 25 KNN fn, tp: 2, 25 KNN f1 score: 0.649 KNN cohens kappa score: 0.629 ====== 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: 503, 100 GAN fn, tp: 5, 26 GAN f1 score: 0.331 GAN cohens kappa score: 0.274 -> 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: 0.397 -> 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: 572, 31 KNN fn, tp: 7, 24 KNN f1 score: 0.558 KNN cohens kappa score: 0.529 ------ 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: 472, 131 GAN fn, tp: 9, 22 GAN f1 score: 0.239 GAN cohens kappa score: 0.172 -> 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: 0.429 -> 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: 572, 31 KNN fn, tp: 4, 27 KNN f1 score: 0.607 KNN cohens kappa score: 0.580 ------ 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: 570, 33 GAN fn, tp: 13, 18 GAN f1 score: 0.439 GAN cohens kappa score: 0.403 -> 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: 0.585 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 5, 26 GB f1 score: 0.825 GB cohens kappa score: 0.816 -> test with 'KNN' KNN tn, fp: 576, 27 KNN fn, tp: 4, 27 KNN f1 score: 0.635 KNN cohens kappa score: 0.611 ------ 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: 483, 120 GAN fn, tp: 8, 23 GAN f1 score: 0.264 GAN cohens kappa score: 0.200 -> test with 'LR' LR tn, fp: 500, 103 LR fn, tp: 3, 28 LR f1 score: 0.346 LR cohens kappa score: 0.289 LR average precision score: 0.428 -> test with 'GB' GB tn, fp: 590, 13 GB fn, tp: 0, 31 GB f1 score: 0.827 GB cohens kappa score: 0.816 -> 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 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2288 synthetic samples -> test with GAN.predict GAN tn, fp: 514, 86 GAN fn, tp: 7, 20 GAN f1 score: 0.301 GAN cohens kappa score: 0.249 -> test with 'LR' LR tn, fp: 510, 90 LR fn, tp: 6, 21 LR f1 score: 0.304 LR cohens kappa score: 0.253 LR average precision score: 0.415 -> test with 'GB' GB tn, fp: 590, 10 GB fn, tp: 5, 22 GB f1 score: 0.746 GB cohens kappa score: 0.733 -> test with 'KNN' KNN tn, fp: 564, 36 KNN fn, tp: 6, 21 KNN f1 score: 0.500 KNN cohens kappa score: 0.469 ====== 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: 524, 79 GAN fn, tp: 8, 23 GAN f1 score: 0.346 GAN cohens kappa score: 0.293 -> test with 'LR' LR tn, fp: 523, 80 LR fn, tp: 5, 26 LR f1 score: 0.380 LR cohens kappa score: 0.329 LR average precision score: 0.408 -> 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: 581, 22 KNN fn, tp: 7, 24 KNN f1 score: 0.623 KNN cohens kappa score: 0.600 ------ 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: 415, 188 GAN fn, tp: 14, 17 GAN f1 score: 0.144 GAN cohens kappa score: 0.065 -> 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: 0.501 -> 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: 575, 28 KNN fn, tp: 7, 24 KNN f1 score: 0.578 KNN cohens kappa score: 0.551 ------ 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: 564, 39 GAN fn, tp: 9, 22 GAN f1 score: 0.478 GAN cohens kappa score: 0.442 -> test with 'LR' LR tn, fp: 502, 101 LR fn, tp: 2, 29 LR f1 score: 0.360 LR cohens kappa score: 0.305 LR average precision score: 0.502 -> test with 'GB' GB tn, fp: 593, 10 GB fn, tp: 8, 23 GB f1 score: 0.719 GB cohens kappa score: 0.704 -> test with 'KNN' KNN tn, fp: 572, 31 KNN fn, tp: 8, 23 KNN f1 score: 0.541 KNN cohens kappa score: 0.511 ------ 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: 509, 94 GAN fn, tp: 4, 27 GAN f1 score: 0.355 GAN cohens kappa score: 0.301 -> test with 'LR' LR tn, fp: 509, 94 LR fn, tp: 3, 28 LR f1 score: 0.366 LR cohens kappa score: 0.312 LR average precision score: 0.501 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 1, 30 GB f1 score: 0.870 GB cohens kappa score: 0.862 -> test with 'KNN' KNN tn, fp: 573, 30 KNN fn, tp: 6, 25 KNN f1 score: 0.581 KNN cohens kappa score: 0.553 ------ 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: 553, 47 GAN fn, tp: 6, 21 GAN f1 score: 0.442 GAN cohens kappa score: 0.405 -> test with 'LR' LR tn, fp: 520, 80 LR fn, tp: 3, 24 LR f1 score: 0.366 LR cohens kappa score: 0.320 LR average precision score: 0.301 -> 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: 572, 28 KNN fn, tp: 4, 23 KNN f1 score: 0.590 KNN cohens kappa score: 0.565 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 536, 103 LR fn, tp: 10, 30 LR f1 score: 0.407 LR cohens kappa score: 0.360 LR average precision score: 0.585 average: LR tn, fp: 514.52, 87.88 LR fn, tp: 4.08, 26.12 LR f1 score: 0.363 LR cohens kappa score: 0.311 LR average precision score: 0.442 minimum: LR tn, fp: 500, 67 LR fn, tp: 1, 21 LR f1 score: 0.304 LR cohens kappa score: 0.253 LR average precision score: 0.277 -----[ GB ]----- maximum: GB tn, fp: 599, 17 GB fn, tp: 8, 31 GB f1 score: 0.870 GB cohens kappa score: 0.862 average: GB tn, fp: 591.88, 10.52 GB fn, tp: 3.52, 26.68 GB f1 score: 0.793 GB cohens kappa score: 0.781 minimum: GB tn, fp: 586, 4 GB fn, tp: 0, 22 GB f1 score: 0.703 GB cohens kappa score: 0.685 -----[ KNN ]----- maximum: KNN tn, fp: 581, 37 KNN fn, tp: 11, 28 KNN f1 score: 0.651 KNN cohens kappa score: 0.629 average: KNN tn, fp: 573.52, 28.88 KNN fn, tp: 6.0, 24.2 KNN f1 score: 0.582 KNN cohens kappa score: 0.555 minimum: KNN tn, fp: 564, 22 KNN fn, tp: 2, 20 KNN f1 score: 0.494 KNN cohens kappa score: 0.461 -----[ GAN ]----- maximum: GAN tn, fp: 586, 188 GAN fn, tp: 14, 27 GAN f1 score: 0.667 GAN cohens kappa score: 0.647 average: GAN tn, fp: 527.2, 75.2 GAN fn, tp: 8.64, 21.56 GAN f1 score: 0.380 GAN cohens kappa score: 0.333 minimum: GAN tn, fp: 415, 15 GAN fn, tp: 4, 16 GAN f1 score: 0.144 GAN cohens kappa score: 0.065