/////////////////////////////////////////// // Running convGAN on folding_abalone9-18 /////////////////////////////////////////// Load 'data_input/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: 124, 14 LR fn, tp: 0, 9 LR f1 score: 0.562 LR cohens kappa score: 0.520 LR average precision score: 0.892 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 5, 4 GB f1 score: 0.421 GB cohens kappa score: 0.381 -> test with 'KNN' KNN tn, fp: 127, 11 KNN fn, tp: 4, 5 KNN f1 score: 0.400 KNN cohens kappa score: 0.349 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 3, 6 LR f1 score: 0.500 LR cohens kappa score: 0.459 LR average precision score: 0.575 -> 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: 114, 24 KNN fn, tp: 3, 6 KNN f1 score: 0.308 KNN cohens kappa score: 0.236 ------ 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: 0, 9 LR f1 score: 0.600 LR cohens kappa score: 0.562 LR average precision score: 0.826 -> 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: 129, 9 KNN fn, tp: 4, 5 KNN f1 score: 0.435 KNN cohens kappa score: 0.389 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 126, 12 LR fn, tp: 2, 7 LR f1 score: 0.500 LR cohens kappa score: 0.455 LR average precision score: 0.542 -> 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: 124, 14 KNN fn, tp: 3, 6 KNN f1 score: 0.414 KNN cohens kappa score: 0.360 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 129, 8 LR fn, tp: 2, 4 LR f1 score: 0.444 LR cohens kappa score: 0.412 LR average precision score: 0.489 -> test with 'GB' GB tn, fp: 132, 5 GB fn, tp: 4, 2 GB f1 score: 0.308 GB cohens kappa score: 0.275 -> test with 'KNN' KNN tn, fp: 126, 11 KNN fn, tp: 2, 4 KNN f1 score: 0.381 KNN cohens kappa score: 0.341 ====== 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: 0.624 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 5, 4 GB f1 score: 0.533 GB cohens kappa score: 0.509 -> 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 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 1, 8 LR f1 score: 0.667 LR cohens kappa score: 0.639 LR average precision score: 0.782 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 5, 4 GB f1 score: 0.471 GB cohens kappa score: 0.438 -> test with 'KNN' KNN tn, fp: 131, 7 KNN fn, tp: 3, 6 KNN f1 score: 0.545 KNN cohens kappa score: 0.510 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 2, 7 LR f1 score: 0.583 LR cohens kappa score: 0.549 LR average precision score: 0.728 -> 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: 122, 16 KNN fn, tp: 2, 7 KNN f1 score: 0.438 KNN cohens kappa score: 0.383 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 125, 13 LR fn, tp: 0, 9 LR f1 score: 0.581 LR cohens kappa score: 0.541 LR average precision score: 0.720 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 5, 4 GB f1 score: 0.471 GB cohens kappa score: 0.438 -> test with 'KNN' KNN tn, fp: 120, 18 KNN fn, tp: 3, 6 KNN f1 score: 0.364 KNN cohens kappa score: 0.301 ------ Step 2/5: 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: 0.571 -> test with 'GB' GB tn, fp: 128, 9 GB fn, tp: 3, 3 GB f1 score: 0.333 GB cohens kappa score: 0.294 -> test with 'KNN' KNN tn, fp: 122, 15 KNN fn, tp: 3, 3 KNN f1 score: 0.250 KNN cohens kappa score: 0.200 ====== 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: 129, 9 LR fn, tp: 3, 6 LR f1 score: 0.500 LR cohens kappa score: 0.459 LR average precision score: 0.609 -> 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: 127, 11 KNN fn, tp: 5, 4 KNN f1 score: 0.333 KNN cohens kappa score: 0.278 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 0, 9 LR f1 score: 0.750 LR cohens kappa score: 0.729 LR average precision score: 0.897 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 2, 7 GB f1 score: 0.700 GB cohens kappa score: 0.678 -> test with 'KNN' KNN tn, fp: 115, 23 KNN fn, tp: 1, 8 KNN f1 score: 0.400 KNN cohens kappa score: 0.337 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 4, 5 LR f1 score: 0.455 LR cohens kappa score: 0.412 LR average precision score: 0.648 -> 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: 126, 12 KNN fn, tp: 5, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.262 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 117, 21 LR fn, tp: 2, 7 LR f1 score: 0.378 LR cohens kappa score: 0.315 LR average precision score: 0.656 -> test with 'GB' GB tn, fp: 129, 9 GB fn, tp: 6, 3 GB f1 score: 0.286 GB cohens kappa score: 0.232 -> test with 'KNN' KNN tn, fp: 117, 21 KNN fn, tp: 4, 5 KNN f1 score: 0.286 KNN cohens kappa score: 0.214 ------ Step 3/5: 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: 2, 4 LR f1 score: 0.400 LR cohens kappa score: 0.363 LR average precision score: 0.530 -> test with 'GB' GB tn, fp: 129, 8 GB fn, tp: 4, 2 GB f1 score: 0.250 GB cohens kappa score: 0.208 -> test with 'KNN' KNN tn, fp: 118, 19 KNN fn, tp: 2, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.224 ====== 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: 129, 9 LR fn, tp: 3, 6 LR f1 score: 0.500 LR cohens kappa score: 0.459 LR average precision score: 0.567 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 6, 3 GB f1 score: 0.400 GB cohens kappa score: 0.369 -> test with 'KNN' KNN tn, fp: 121, 17 KNN fn, tp: 6, 3 KNN f1 score: 0.207 KNN cohens kappa score: 0.134 ------ Step 4/5: Slice 2/5 ------- -> 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: 0.706 -> 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: 121, 17 KNN fn, tp: 2, 7 KNN f1 score: 0.424 KNN cohens kappa score: 0.368 ------ Step 4/5: 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: 1, 8 LR f1 score: 0.593 LR cohens kappa score: 0.556 LR average precision score: 0.673 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 5, 4 GB f1 score: 0.533 GB cohens kappa score: 0.509 -> 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 4/5: 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: 0, 9 LR f1 score: 0.545 LR cohens kappa score: 0.501 LR average precision score: 0.967 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 5, 4 GB f1 score: 0.421 GB cohens kappa score: 0.381 -> test with 'KNN' KNN tn, fp: 119, 19 KNN fn, tp: 2, 7 KNN f1 score: 0.400 KNN cohens kappa score: 0.340 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 131, 6 LR fn, tp: 1, 5 LR f1 score: 0.588 LR cohens kappa score: 0.565 LR average precision score: 0.517 -> test with 'GB' GB tn, fp: 131, 6 GB fn, tp: 4, 2 GB f1 score: 0.286 GB cohens kappa score: 0.250 -> test with 'KNN' KNN tn, fp: 124, 13 KNN fn, tp: 1, 5 KNN f1 score: 0.417 KNN cohens kappa score: 0.377 ====== 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: 123, 15 LR fn, tp: 2, 7 LR f1 score: 0.452 LR cohens kappa score: 0.399 LR average precision score: 0.692 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 8, 1 GB f1 score: 0.125 GB cohens kappa score: 0.075 -> test with 'KNN' KNN tn, fp: 119, 19 KNN fn, tp: 5, 4 KNN f1 score: 0.250 KNN cohens kappa score: 0.178 ------ Step 5/5: 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: 1, 8 LR f1 score: 0.552 LR cohens kappa score: 0.510 LR average precision score: 0.707 -> 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: 118, 20 KNN fn, tp: 4, 5 KNN f1 score: 0.294 KNN cohens kappa score: 0.224 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 124, 14 LR fn, tp: 3, 6 LR f1 score: 0.414 LR cohens kappa score: 0.360 LR average precision score: 0.447 -> 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: 121, 17 KNN fn, tp: 6, 3 KNN f1 score: 0.207 KNN cohens kappa score: 0.134 ------ Step 5/5: Slice 4/5 ------- -> 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: 0.849 -> 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: 124, 14 KNN fn, tp: 4, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.299 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 130, 7 LR fn, tp: 0, 6 LR f1 score: 0.632 LR cohens kappa score: 0.609 LR average precision score: 0.850 -> test with 'GB' GB tn, fp: 135, 2 GB fn, tp: 3, 3 GB f1 score: 0.545 GB cohens kappa score: 0.527 -> test with 'KNN' KNN tn, fp: 126, 11 KNN fn, tp: 2, 4 KNN f1 score: 0.381 KNN cohens kappa score: 0.341 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 132, 21 LR fn, tp: 4, 9 LR f1 score: 0.750 LR cohens kappa score: 0.729 LR average precision score: 0.967 average: LR tn, fp: 126.44, 11.36 LR fn, tp: 1.48, 6.92 LR f1 score: 0.523 LR cohens kappa score: 0.482 LR average precision score: 0.683 minimum: LR tn, fp: 117, 6 LR fn, tp: 0, 4 LR f1 score: 0.378 LR cohens kappa score: 0.315 LR average precision score: 0.447 -----[ GB ]----- maximum: GB tn, fp: 136, 9 GB fn, tp: 8, 7 GB f1 score: 0.700 GB cohens kappa score: 0.678 average: GB tn, fp: 132.52, 5.28 GB fn, tp: 5.0, 3.4 GB f1 score: 0.397 GB cohens kappa score: 0.361 minimum: GB tn, fp: 128, 2 GB fn, tp: 2, 1 GB f1 score: 0.125 GB cohens kappa score: 0.075 -----[ KNN ]----- maximum: KNN tn, fp: 131, 24 KNN fn, tp: 6, 8 KNN f1 score: 0.545 KNN cohens kappa score: 0.510 average: KNN tn, fp: 122.2, 15.6 KNN fn, tp: 3.36, 5.04 KNN f1 score: 0.350 KNN cohens kappa score: 0.293 minimum: KNN tn, fp: 114, 7 KNN fn, tp: 1, 3 KNN f1 score: 0.207 KNN cohens kappa score: 0.134