/////////////////////////////////////////// // Running Repeater on folding_abalone9-18 /////////////////////////////////////////// Load 'folding_abalone9-18' from pickle file 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 518 synthetic samples -> test with 'LR' LR tn, fp: 115, 23 LR fn, tp: 0, 9 LR f1 score: 0.439 LR cohens kappa score: 0.380 LR average precision score: 0.927 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 7, 2 RF f1 score: 0.308 RF cohens kappa score: 0.281 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 3, 6 GB f1 score: 0.571 GB cohens kappa score: 0.539 -> test with 'KNN' KNN tn, fp: 124, 14 KNN fn, tp: 5, 4 KNN f1 score: 0.296 KNN cohens kappa score: 0.234 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 126, 12 LR fn, tp: 3, 6 LR f1 score: 0.444 LR cohens kappa score: 0.395 LR average precision score: 0.587 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 7, 2 RF f1 score: 0.308 RF cohens kappa score: 0.281 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 6, 3 GB f1 score: 0.316 GB cohens kappa score: 0.269 -> test with 'KNN' KNN tn, fp: 122, 16 KNN fn, tp: 5, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.209 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 126, 12 LR fn, tp: 1, 8 LR f1 score: 0.552 LR cohens kappa score: 0.510 LR average precision score: 0.805 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 6, 3 RF f1 score: 0.429 RF cohens kappa score: 0.402 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 7, 2 GB f1 score: 0.286 GB cohens kappa score: 0.253 -> test with 'KNN' KNN tn, fp: 126, 12 KNN fn, tp: 4, 5 KNN f1 score: 0.385 KNN cohens kappa score: 0.331 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 120, 18 LR fn, tp: 2, 7 LR f1 score: 0.412 LR cohens kappa score: 0.354 LR average precision score: 0.526 -> test with 'RF' RF tn, fp: 137, 1 RF fn, tp: 7, 2 RF f1 score: 0.333 RF cohens kappa score: 0.312 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 6, 3 GB f1 score: 0.462 GB cohens kappa score: 0.440 -> test with 'KNN' KNN tn, fp: 124, 14 KNN fn, tp: 2, 7 KNN f1 score: 0.467 KNN cohens kappa score: 0.417 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 124, 13 LR fn, tp: 1, 5 LR f1 score: 0.417 LR cohens kappa score: 0.377 LR average precision score: 0.482 -> test with 'RF' RF tn, fp: 136, 1 RF fn, tp: 5, 1 RF f1 score: 0.250 RF cohens kappa score: 0.234 -> test with 'GB' GB tn, fp: 132, 5 GB fn, tp: 5, 1 GB f1 score: 0.167 GB cohens kappa score: 0.130 -> test with 'KNN' KNN tn, fp: 127, 10 KNN fn, tp: 3, 3 KNN f1 score: 0.316 KNN cohens kappa score: 0.274 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 119, 19 LR fn, tp: 1, 8 LR f1 score: 0.444 LR cohens kappa score: 0.388 LR average precision score: 0.651 -> test with 'RF' RF tn, fp: 135, 3 RF fn, tp: 8, 1 RF f1 score: 0.154 RF cohens kappa score: 0.121 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.313 -> test with 'KNN' KNN tn, fp: 130, 8 KNN fn, tp: 5, 4 KNN f1 score: 0.381 KNN cohens kappa score: 0.334 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 127, 11 LR fn, tp: 1, 8 LR f1 score: 0.571 LR cohens kappa score: 0.533 LR average precision score: 0.787 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 7, 2 RF f1 score: 0.308 RF cohens kappa score: 0.281 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 5, 4 GB f1 score: 0.500 GB cohens kappa score: 0.472 -> test with 'KNN' KNN tn, fp: 125, 13 KNN fn, tp: 4, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.314 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 127, 11 LR fn, tp: 2, 7 LR f1 score: 0.519 LR cohens kappa score: 0.476 LR average precision score: 0.726 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 8, 1 RF f1 score: 0.167 RF cohens kappa score: 0.140 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 6, 3 GB f1 score: 0.316 GB cohens kappa score: 0.269 -> test with 'KNN' KNN tn, fp: 119, 19 KNN fn, tp: 3, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.289 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 120, 18 LR fn, tp: 0, 9 LR f1 score: 0.500 LR cohens kappa score: 0.449 LR average precision score: 0.710 -> test with 'RF' RF tn, fp: 135, 3 RF fn, tp: 5, 4 RF f1 score: 0.500 RF cohens kappa score: 0.472 -> test with 'GB' GB tn, fp: 130, 8 GB fn, tp: 5, 4 GB f1 score: 0.381 GB cohens kappa score: 0.334 -> test with 'KNN' KNN tn, fp: 124, 14 KNN fn, tp: 4, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.299 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 124, 13 LR fn, tp: 1, 5 LR f1 score: 0.417 LR cohens kappa score: 0.377 LR average precision score: 0.661 -> test with 'RF' RF tn, fp: 137, 0 RF fn, tp: 3, 3 RF f1 score: 0.667 RF cohens kappa score: 0.657 -> test with 'GB' GB tn, fp: 129, 8 GB fn, tp: 2, 4 GB f1 score: 0.444 GB cohens kappa score: 0.412 -> test with 'KNN' KNN tn, fp: 124, 13 KNN fn, tp: 2, 4 KNN f1 score: 0.348 KNN cohens kappa score: 0.305 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 124, 14 LR fn, tp: 2, 7 LR f1 score: 0.467 LR cohens kappa score: 0.417 LR average precision score: 0.548 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 8, 1 RF f1 score: 0.167 RF cohens kappa score: 0.140 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 7, 2 GB f1 score: 0.250 GB cohens kappa score: 0.208 -> test with 'KNN' KNN tn, fp: 121, 17 KNN fn, tp: 3, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.315 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 127, 11 LR fn, tp: 0, 9 LR f1 score: 0.621 LR cohens kappa score: 0.586 LR average precision score: 0.906 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 8, 1 RF f1 score: 0.167 RF cohens kappa score: 0.140 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 6, 3 GB f1 score: 0.375 GB cohens kappa score: 0.340 -> test with 'KNN' KNN tn, fp: 119, 19 KNN fn, tp: 3, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.289 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 128, 10 LR fn, tp: 4, 5 LR f1 score: 0.417 LR cohens kappa score: 0.368 LR average precision score: 0.653 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 7, 2 RF f1 score: 0.308 RF cohens kappa score: 0.281 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 7, 2 GB f1 score: 0.250 GB cohens kappa score: 0.208 -> test with 'KNN' KNN tn, fp: 126, 12 KNN fn, tp: 6, 3 KNN f1 score: 0.250 KNN cohens kappa score: 0.188 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 114, 24 LR fn, tp: 2, 7 LR f1 score: 0.350 LR cohens kappa score: 0.282 LR average precision score: 0.620 -> test with 'RF' RF tn, fp: 138, 0 RF fn, tp: 7, 2 RF f1 score: 0.364 RF cohens kappa score: 0.349 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 4, 5 GB f1 score: 0.500 GB cohens kappa score: 0.464 -> test with 'KNN' KNN tn, fp: 122, 16 KNN fn, tp: 4, 5 KNN f1 score: 0.333 KNN cohens kappa score: 0.271 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 122, 15 LR fn, tp: 1, 5 LR f1 score: 0.385 LR cohens kappa score: 0.342 LR average precision score: 0.528 -> test with 'RF' RF tn, fp: 134, 3 RF fn, tp: 4, 2 RF f1 score: 0.364 RF cohens kappa score: 0.338 -> test with 'GB' GB tn, fp: 132, 5 GB fn, tp: 3, 3 GB f1 score: 0.429 GB cohens kappa score: 0.400 -> test with 'KNN' KNN tn, fp: 126, 11 KNN fn, tp: 3, 3 KNN f1 score: 0.300 KNN cohens kappa score: 0.256 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 125, 13 LR fn, tp: 4, 5 LR f1 score: 0.370 LR cohens kappa score: 0.314 LR average precision score: 0.529 -> test with 'RF' RF tn, fp: 135, 3 RF fn, tp: 6, 3 RF f1 score: 0.400 RF cohens kappa score: 0.369 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 6, 3 GB f1 score: 0.333 GB cohens kappa score: 0.290 -> test with 'KNN' KNN tn, fp: 124, 14 KNN fn, tp: 5, 4 KNN f1 score: 0.296 KNN cohens kappa score: 0.234 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 120, 18 LR fn, tp: 2, 7 LR f1 score: 0.412 LR cohens kappa score: 0.354 LR average precision score: 0.650 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 5, 4 RF f1 score: 0.533 RF cohens kappa score: 0.509 -> test with 'GB' GB tn, fp: 126, 12 GB fn, tp: 3, 6 GB f1 score: 0.444 GB cohens kappa score: 0.395 -> test with 'KNN' KNN tn, fp: 124, 14 KNN fn, tp: 2, 7 KNN f1 score: 0.467 KNN cohens kappa score: 0.417 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 123, 15 LR fn, tp: 1, 8 LR f1 score: 0.500 LR cohens kappa score: 0.452 LR average precision score: 0.743 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 8, 1 RF f1 score: 0.167 RF cohens kappa score: 0.140 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 5, 4 GB f1 score: 0.400 GB cohens kappa score: 0.357 -> test with 'KNN' KNN tn, fp: 128, 10 KNN fn, tp: 5, 4 KNN f1 score: 0.348 KNN cohens kappa score: 0.295 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 120, 18 LR fn, tp: 0, 9 LR f1 score: 0.500 LR cohens kappa score: 0.449 LR average precision score: 0.947 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 7, 2 RF f1 score: 0.308 RF cohens kappa score: 0.281 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 4, 5 GB f1 score: 0.500 GB cohens kappa score: 0.464 -> test with 'KNN' KNN tn, fp: 122, 16 KNN fn, tp: 3, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.329 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 124, 13 LR fn, tp: 1, 5 LR f1 score: 0.417 LR cohens kappa score: 0.377 LR average precision score: 0.589 -> test with 'RF' RF tn, fp: 134, 3 RF fn, tp: 4, 2 RF f1 score: 0.364 RF cohens kappa score: 0.338 -> test with 'GB' GB tn, fp: 133, 4 GB fn, tp: 4, 2 GB f1 score: 0.333 GB cohens kappa score: 0.304 -> test with 'KNN' KNN tn, fp: 121, 16 KNN fn, tp: 2, 4 KNN f1 score: 0.308 KNN cohens kappa score: 0.260 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 116, 22 LR fn, tp: 2, 7 LR f1 score: 0.368 LR cohens kappa score: 0.303 LR average precision score: 0.690 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 8, 1 RF f1 score: 0.167 RF cohens kappa score: 0.140 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 7, 2 GB f1 score: 0.250 GB cohens kappa score: 0.208 -> test with 'KNN' KNN tn, fp: 123, 15 KNN fn, tp: 5, 4 KNN f1 score: 0.286 KNN cohens kappa score: 0.221 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 122, 16 LR fn, tp: 1, 8 LR f1 score: 0.485 LR cohens kappa score: 0.434 LR average precision score: 0.680 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 7, 2 RF f1 score: 0.308 RF cohens kappa score: 0.281 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 4, 5 GB f1 score: 0.526 GB cohens kappa score: 0.494 -> test with 'KNN' KNN tn, fp: 122, 16 KNN fn, tp: 4, 5 KNN f1 score: 0.333 KNN cohens kappa score: 0.271 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 123, 15 LR fn, tp: 3, 6 LR f1 score: 0.400 LR cohens kappa score: 0.344 LR average precision score: 0.521 -> test with 'RF' RF tn, fp: 137, 1 RF fn, tp: 7, 2 RF f1 score: 0.333 RF cohens kappa score: 0.312 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 7, 2 GB f1 score: 0.235 GB cohens kappa score: 0.189 -> test with 'KNN' KNN tn, fp: 124, 14 KNN fn, tp: 5, 4 KNN f1 score: 0.296 KNN cohens kappa score: 0.234 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 123, 15 LR fn, tp: 1, 8 LR f1 score: 0.500 LR cohens kappa score: 0.452 LR average precision score: 0.878 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 6, 3 RF f1 score: 0.429 RF cohens kappa score: 0.402 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 3, 6 GB f1 score: 0.600 GB cohens kappa score: 0.571 -> test with 'KNN' KNN tn, fp: 126, 12 KNN fn, tp: 4, 5 KNN f1 score: 0.385 KNN cohens kappa score: 0.331 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 127, 10 LR fn, tp: 0, 6 LR f1 score: 0.545 LR cohens kappa score: 0.516 LR average precision score: 0.819 -> test with 'RF' RF tn, fp: 136, 1 RF fn, tp: 5, 1 RF f1 score: 0.250 RF cohens kappa score: 0.234 -> test with 'GB' GB tn, fp: 134, 3 GB fn, tp: 4, 2 GB f1 score: 0.364 GB cohens kappa score: 0.338 -> test with 'KNN' KNN tn, fp: 124, 13 KNN fn, tp: 3, 3 KNN f1 score: 0.273 KNN cohens kappa score: 0.225 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 128, 24 LR fn, tp: 4, 9 LR f1 score: 0.621 LR cohens kappa score: 0.586 LR average precision score: 0.947 average: LR tn, fp: 122.64, 15.16 LR fn, tp: 1.44, 6.96 LR f1 score: 0.458 LR cohens kappa score: 0.409 LR average precision score: 0.687 minimum: LR tn, fp: 114, 10 LR fn, tp: 0, 5 LR f1 score: 0.350 LR cohens kappa score: 0.282 LR average precision score: 0.482 -----[ RF ]----- maximum: RF tn, fp: 138, 3 RF fn, tp: 8, 4 RF f1 score: 0.667 RF cohens kappa score: 0.657 average: RF tn, fp: 135.92, 1.88 RF fn, tp: 6.4, 2.0 RF f1 score: 0.322 RF cohens kappa score: 0.297 minimum: RF tn, fp: 134, 0 RF fn, tp: 3, 1 RF f1 score: 0.154 RF cohens kappa score: 0.121 -----[ GB ]----- maximum: GB tn, fp: 137, 12 GB fn, tp: 7, 6 GB f1 score: 0.600 GB cohens kappa score: 0.571 average: GB tn, fp: 132.32, 5.48 GB fn, tp: 5.0, 3.4 GB f1 score: 0.383 GB cohens kappa score: 0.346 minimum: GB tn, fp: 126, 1 GB fn, tp: 2, 1 GB f1 score: 0.167 GB cohens kappa score: 0.130 -----[ KNN ]----- maximum: KNN tn, fp: 130, 19 KNN fn, tp: 6, 7 KNN f1 score: 0.467 KNN cohens kappa score: 0.417 average: KNN tn, fp: 123.88, 13.92 KNN fn, tp: 3.76, 4.64 KNN f1 score: 0.342 KNN cohens kappa score: 0.286 minimum: KNN tn, fp: 119, 8 KNN fn, tp: 2, 3 KNN f1 score: 0.250 KNN cohens kappa score: 0.188 wall time: 00:00:13s, process time: 00:00:42s