/////////////////////////////////////////// // Running convGAN-proximary-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: 553, 50 GAN fn, tp: 4, 27 GAN f1 score: 0.500 GAN cohens kappa score: 0.463 -> test with 'LR' LR tn, fp: 540, 63 LR fn, tp: 5, 26 LR f1 score: 0.433 LR cohens kappa score: 0.389 LR average precision score: 0.475 -> 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: 583, 20 KNN fn, tp: 4, 27 KNN f1 score: 0.692 KNN cohens kappa score: 0.673 ------ 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: 529, 74 GAN fn, tp: 3, 28 GAN f1 score: 0.421 GAN cohens kappa score: 0.374 -> test with 'LR' LR tn, fp: 531, 72 LR fn, tp: 4, 27 LR f1 score: 0.415 LR cohens kappa score: 0.368 LR average precision score: 0.459 -> 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: 578, 25 KNN fn, tp: 7, 24 KNN f1 score: 0.600 KNN cohens kappa score: 0.575 ------ 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: 535, 68 GAN fn, tp: 4, 27 GAN f1 score: 0.429 GAN cohens kappa score: 0.383 -> test with 'LR' LR tn, fp: 517, 86 LR fn, tp: 7, 24 LR f1 score: 0.340 LR cohens kappa score: 0.286 LR average precision score: 0.330 -> test with 'GB' GB tn, fp: 592, 11 GB fn, tp: 2, 29 GB f1 score: 0.817 GB cohens kappa score: 0.806 -> 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 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 535, 68 GAN fn, tp: 3, 28 GAN f1 score: 0.441 GAN cohens kappa score: 0.396 -> test with 'LR' LR tn, fp: 515, 88 LR fn, tp: 4, 27 LR f1 score: 0.370 LR cohens kappa score: 0.317 LR average precision score: 0.389 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 8, 23 GB f1 score: 0.767 GB cohens kappa score: 0.755 -> test with 'KNN' KNN tn, fp: 582, 21 KNN fn, tp: 11, 20 KNN f1 score: 0.556 KNN cohens kappa score: 0.529 ------ 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: 540, 60 GAN fn, tp: 6, 21 GAN f1 score: 0.389 GAN cohens kappa score: 0.347 -> test with 'LR' LR tn, fp: 537, 63 LR fn, tp: 3, 24 LR f1 score: 0.421 LR cohens kappa score: 0.380 LR average precision score: 0.559 -> 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: 579, 21 KNN fn, tp: 5, 22 KNN f1 score: 0.629 KNN cohens kappa score: 0.608 ====== 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: 544, 59 GAN fn, tp: 3, 28 GAN f1 score: 0.475 GAN cohens kappa score: 0.434 -> test with 'LR' LR tn, fp: 535, 68 LR fn, tp: 6, 25 LR f1 score: 0.403 LR cohens kappa score: 0.356 LR average precision score: 0.487 -> 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: 586, 17 KNN fn, tp: 6, 25 KNN f1 score: 0.685 KNN cohens kappa score: 0.666 ------ 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: 547, 56 GAN fn, tp: 3, 28 GAN f1 score: 0.487 GAN cohens kappa score: 0.447 -> test with 'LR' LR tn, fp: 543, 60 LR fn, tp: 6, 25 LR f1 score: 0.431 LR cohens kappa score: 0.387 LR average precision score: 0.457 -> 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: 585, 18 KNN fn, tp: 6, 25 KNN f1 score: 0.676 KNN cohens kappa score: 0.656 ------ 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: 488, 115 GAN fn, tp: 2, 29 GAN f1 score: 0.331 GAN cohens kappa score: 0.273 -> test with 'LR' LR tn, fp: 538, 65 LR fn, tp: 5, 26 LR f1 score: 0.426 LR cohens kappa score: 0.381 LR average precision score: 0.553 -> 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: 583, 20 KNN fn, tp: 11, 20 KNN f1 score: 0.563 KNN cohens kappa score: 0.538 ------ 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: 516, 87 GAN fn, tp: 6, 25 GAN f1 score: 0.350 GAN cohens kappa score: 0.296 -> test with 'LR' LR tn, fp: 518, 85 LR fn, tp: 6, 25 LR f1 score: 0.355 LR cohens kappa score: 0.301 LR average precision score: 0.288 -> 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: 584, 19 KNN fn, tp: 7, 24 KNN f1 score: 0.649 KNN cohens kappa score: 0.627 ------ 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: 494, 106 GAN fn, tp: 2, 25 GAN f1 score: 0.316 GAN cohens kappa score: 0.264 -> test with 'LR' LR tn, fp: 518, 82 LR fn, tp: 1, 26 LR f1 score: 0.385 LR cohens kappa score: 0.340 LR average precision score: 0.490 -> 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: 578, 22 KNN fn, tp: 6, 21 KNN f1 score: 0.600 KNN cohens kappa score: 0.578 ====== 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: 501, 102 GAN fn, tp: 3, 28 GAN f1 score: 0.348 GAN cohens kappa score: 0.292 -> test with 'LR' LR tn, fp: 517, 86 LR fn, tp: 4, 27 LR f1 score: 0.375 LR cohens kappa score: 0.323 LR average precision score: 0.488 -> test with 'GB' GB tn, fp: 599, 4 GB fn, tp: 5, 26 GB f1 score: 0.852 GB cohens kappa score: 0.845 -> test with 'KNN' KNN tn, fp: 586, 17 KNN fn, tp: 7, 24 KNN f1 score: 0.667 KNN cohens kappa score: 0.647 ------ 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: 520, 83 GAN fn, tp: 4, 27 GAN f1 score: 0.383 GAN cohens kappa score: 0.332 -> test with 'LR' LR tn, fp: 539, 64 LR fn, tp: 11, 20 LR f1 score: 0.348 LR cohens kappa score: 0.298 LR average precision score: 0.299 -> 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: 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: 544, 59 GAN fn, tp: 4, 27 GAN f1 score: 0.462 GAN cohens kappa score: 0.420 -> test with 'LR' LR tn, fp: 528, 75 LR fn, tp: 1, 30 LR f1 score: 0.441 LR cohens kappa score: 0.396 LR average precision score: 0.568 -> 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: 569, 34 KNN fn, tp: 8, 23 KNN f1 score: 0.523 KNN cohens kappa score: 0.490 ------ 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: 528, 75 GAN fn, tp: 4, 27 GAN f1 score: 0.406 GAN cohens kappa score: 0.358 -> test with 'LR' LR tn, fp: 525, 78 LR fn, tp: 2, 29 LR f1 score: 0.420 LR cohens kappa score: 0.373 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: 581, 22 KNN fn, tp: 8, 23 KNN f1 score: 0.605 KNN cohens kappa score: 0.581 ------ 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: 549, 51 GAN fn, tp: 2, 25 GAN f1 score: 0.485 GAN cohens kappa score: 0.451 -> test with 'LR' LR tn, fp: 536, 64 LR fn, tp: 5, 22 LR f1 score: 0.389 LR cohens kappa score: 0.347 LR average precision score: 0.381 -> test with 'GB' GB tn, fp: 596, 4 GB fn, tp: 1, 26 GB f1 score: 0.912 GB cohens kappa score: 0.908 -> test with 'KNN' KNN tn, fp: 592, 8 KNN fn, tp: 4, 23 KNN f1 score: 0.793 KNN cohens kappa score: 0.783 ====== 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: 545, 58 GAN fn, tp: 8, 23 GAN f1 score: 0.411 GAN cohens kappa score: 0.366 -> 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: 0.359 -> 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: 578, 25 KNN fn, tp: 5, 26 KNN f1 score: 0.634 KNN cohens kappa score: 0.610 ------ 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: 509, 94 GAN fn, tp: 2, 29 GAN f1 score: 0.377 GAN cohens kappa score: 0.324 -> test with 'LR' LR tn, fp: 540, 63 LR fn, tp: 6, 25 LR f1 score: 0.420 LR cohens kappa score: 0.375 LR average precision score: 0.457 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 4, 27 GB f1 score: 0.844 GB cohens kappa score: 0.835 -> 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 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2289 synthetic samples -> test with GAN.predict GAN tn, fp: 507, 96 GAN fn, tp: 3, 28 GAN f1 score: 0.361 GAN cohens kappa score: 0.307 -> test with 'LR' LR tn, fp: 538, 65 LR fn, tp: 3, 28 LR f1 score: 0.452 LR cohens kappa score: 0.408 LR average precision score: 0.591 -> test with 'GB' GB tn, fp: 598, 5 GB fn, tp: 6, 25 GB f1 score: 0.820 GB cohens kappa score: 0.811 -> test with 'KNN' KNN tn, fp: 587, 16 KNN fn, tp: 7, 24 KNN f1 score: 0.676 KNN cohens kappa score: 0.657 ------ 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: 541, 62 GAN fn, tp: 3, 28 GAN f1 score: 0.463 GAN cohens kappa score: 0.421 -> test with 'LR' LR tn, fp: 519, 84 LR fn, tp: 2, 29 LR f1 score: 0.403 LR cohens kappa score: 0.353 LR average precision score: 0.504 -> 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: 9, 22 KNN f1 score: 0.571 KNN cohens kappa score: 0.545 ------ 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: 492, 108 GAN fn, tp: 5, 22 GAN f1 score: 0.280 GAN cohens kappa score: 0.225 -> test with 'LR' LR tn, fp: 521, 79 LR fn, tp: 6, 21 LR f1 score: 0.331 LR cohens kappa score: 0.282 LR average precision score: 0.405 -> 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: 575, 25 KNN fn, tp: 6, 21 KNN f1 score: 0.575 KNN cohens kappa score: 0.551 ====== 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: 465, 138 GAN fn, tp: 4, 27 GAN f1 score: 0.276 GAN cohens kappa score: 0.211 -> 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: 0.383 -> 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: 582, 21 KNN fn, tp: 6, 25 KNN f1 score: 0.649 KNN cohens kappa score: 0.628 ------ 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: 541, 62 GAN fn, tp: 6, 25 GAN f1 score: 0.424 GAN cohens kappa score: 0.379 -> 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: 0.513 -> test with 'GB' GB tn, fp: 597, 6 GB fn, tp: 3, 28 GB f1 score: 0.862 GB cohens kappa score: 0.854 -> test with 'KNN' KNN tn, fp: 583, 20 KNN fn, tp: 9, 22 KNN f1 score: 0.603 KNN cohens kappa score: 0.579 ------ 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: 477, 126 GAN fn, tp: 6, 25 GAN f1 score: 0.275 GAN cohens kappa score: 0.211 -> test with 'LR' LR tn, fp: 523, 80 LR fn, tp: 3, 28 LR f1 score: 0.403 LR cohens kappa score: 0.354 LR average precision score: 0.513 -> test with 'GB' GB tn, fp: 595, 8 GB fn, tp: 12, 19 GB f1 score: 0.655 GB cohens kappa score: 0.639 -> test with 'KNN' KNN tn, fp: 586, 17 KNN fn, tp: 9, 22 KNN f1 score: 0.629 KNN cohens kappa score: 0.607 ------ 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: 503, 100 GAN fn, tp: 4, 27 GAN f1 score: 0.342 GAN cohens kappa score: 0.286 -> test with 'LR' LR tn, fp: 526, 77 LR fn, tp: 4, 27 LR f1 score: 0.400 LR cohens kappa score: 0.351 LR average precision score: 0.556 -> 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: 577, 26 KNN fn, tp: 7, 24 KNN f1 score: 0.593 KNN cohens kappa score: 0.566 ------ 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: 482, 118 GAN fn, tp: 3, 24 GAN f1 score: 0.284 GAN cohens kappa score: 0.228 -> test with 'LR' LR tn, fp: 537, 63 LR fn, tp: 4, 23 LR f1 score: 0.407 LR cohens kappa score: 0.365 LR average precision score: 0.336 -> test with 'GB' GB tn, fp: 592, 8 GB fn, tp: 6, 21 GB f1 score: 0.750 GB cohens kappa score: 0.738 -> test with 'KNN' KNN tn, fp: 587, 13 KNN fn, tp: 7, 20 KNN f1 score: 0.667 KNN cohens kappa score: 0.650 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 543, 88 LR fn, tp: 11, 30 LR f1 score: 0.452 LR cohens kappa score: 0.408 LR average precision score: 0.591 average: LR tn, fp: 529.16, 73.24 LR fn, tp: 4.52, 25.68 LR f1 score: 0.399 LR cohens kappa score: 0.351 LR average precision score: 0.453 minimum: LR tn, fp: 515, 60 LR fn, tp: 1, 20 LR f1 score: 0.331 LR cohens kappa score: 0.282 LR average precision score: 0.288 -----[ GB ]----- maximum: GB tn, fp: 599, 13 GB fn, tp: 12, 29 GB f1 score: 0.912 GB cohens kappa score: 0.908 average: GB tn, fp: 594.52, 7.88 GB fn, tp: 4.36, 25.84 GB f1 score: 0.809 GB cohens kappa score: 0.798 minimum: GB tn, fp: 590, 4 GB fn, tp: 1, 19 GB f1 score: 0.655 GB cohens kappa score: 0.639 -----[ KNN ]----- maximum: KNN tn, fp: 592, 34 KNN fn, tp: 11, 27 KNN f1 score: 0.793 KNN cohens kappa score: 0.783 average: KNN tn, fp: 581.32, 21.08 KNN fn, tp: 7.0, 23.2 KNN f1 score: 0.626 KNN cohens kappa score: 0.603 minimum: KNN tn, fp: 569, 8 KNN fn, tp: 4, 20 KNN f1 score: 0.523 KNN cohens kappa score: 0.490 -----[ GAN ]----- maximum: GAN tn, fp: 553, 138 GAN fn, tp: 8, 29 GAN f1 score: 0.500 GAN cohens kappa score: 0.463 average: GAN tn, fp: 519.4, 83.0 GAN fn, tp: 3.88, 26.32 GAN f1 score: 0.389 GAN cohens kappa score: 0.339 minimum: GAN tn, fp: 465, 50 GAN fn, tp: 2, 21 GAN f1 score: 0.275 GAN cohens kappa score: 0.211