/////////////////////////////////////////// // Running convGAN-proximary-full 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 GAN.predict GAN tn, fp: 135, 3 GAN fn, tp: 4, 5 GAN f1 score: 0.588 GAN cohens kappa score: 0.563 -> 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: 0.910 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.163 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 8, 1 KNN f1 score: 0.182 KNN cohens kappa score: 0.163 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 136, 2 GAN fn, tp: 5, 4 GAN f1 score: 0.533 GAN cohens kappa score: 0.509 -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 3, 6 LR f1 score: 0.545 LR cohens kappa score: 0.510 LR average precision score: 0.573 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 8, 1 GB f1 score: 0.154 GB cohens kappa score: 0.121 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 8, 1 KNN f1 score: 0.154 KNN cohens kappa score: 0.121 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 136, 2 GAN fn, tp: 4, 5 GAN f1 score: 0.625 GAN cohens kappa score: 0.604 -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 1, 8 LR f1 score: 0.640 LR cohens kappa score: 0.609 LR average precision score: 0.795 -> 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: 137, 1 KNN fn, tp: 7, 2 KNN f1 score: 0.333 KNN cohens kappa score: 0.312 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 138, 0 GAN fn, tp: 6, 3 GAN f1 score: 0.500 GAN cohens kappa score: 0.484 -> test with 'LR' LR tn, fp: 134, 4 LR fn, tp: 4, 5 LR f1 score: 0.556 LR cohens kappa score: 0.527 LR average precision score: 0.619 -> 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: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with GAN.predict GAN tn, fp: 137, 0 GAN fn, tp: 5, 1 GAN f1 score: 0.286 GAN cohens kappa score: 0.277 -> test with 'LR' LR tn, fp: 134, 3 LR fn, tp: 2, 4 LR f1 score: 0.615 LR cohens kappa score: 0.597 LR average precision score: 0.525 -> test with 'GB' GB tn, fp: 136, 1 GB fn, tp: 5, 1 GB f1 score: 0.250 GB cohens kappa score: 0.234 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 5, 1 KNN f1 score: 0.286 KNN cohens kappa score: 0.277 ====== 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 GAN.predict GAN tn, fp: 135, 3 GAN fn, tp: 8, 1 GAN f1 score: 0.154 GAN cohens kappa score: 0.121 -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 2, 7 LR f1 score: 0.609 LR cohens kappa score: 0.577 LR average precision score: 0.612 -> 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: 136, 2 KNN fn, tp: 8, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.140 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 133, 5 GAN fn, tp: 3, 6 GAN f1 score: 0.600 GAN cohens kappa score: 0.571 -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 2, 7 LR f1 score: 0.636 LR cohens kappa score: 0.608 LR average precision score: 0.796 -> 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: 137, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.012 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 132, 6 GAN fn, tp: 4, 5 GAN f1 score: 0.500 GAN cohens kappa score: 0.464 -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 2, 7 LR f1 score: 0.636 LR cohens kappa score: 0.608 LR average precision score: 0.733 -> 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: 137, 1 KNN fn, tp: 7, 2 KNN f1 score: 0.333 KNN cohens kappa score: 0.312 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 135, 3 GAN fn, tp: 6, 3 GAN f1 score: 0.400 GAN cohens kappa score: 0.369 -> 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: 0.736 -> 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: 137, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.012 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with GAN.predict GAN tn, fp: 135, 2 GAN fn, tp: 3, 3 GAN f1 score: 0.545 GAN cohens kappa score: 0.527 -> test with 'LR' LR tn, fp: 130, 7 LR fn, tp: 1, 5 LR f1 score: 0.556 LR cohens kappa score: 0.529 LR average precision score: 0.640 -> test with 'GB' GB tn, fp: 136, 1 GB fn, tp: 5, 1 GB f1 score: 0.250 GB cohens kappa score: 0.234 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 5, 1 KNN f1 score: 0.286 KNN cohens kappa score: 0.277 ====== 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 GAN.predict GAN tn, fp: 135, 3 GAN fn, tp: 4, 5 GAN f1 score: 0.588 GAN cohens kappa score: 0.563 -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 5, 4 LR f1 score: 0.381 LR cohens kappa score: 0.334 LR average precision score: 0.526 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.140 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 8, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.140 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 129, 9 GAN fn, tp: 5, 4 GAN f1 score: 0.364 GAN cohens kappa score: 0.314 -> test with 'LR' LR tn, fp: 134, 4 LR fn, tp: 0, 9 LR f1 score: 0.818 LR cohens kappa score: 0.804 LR average precision score: 0.832 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.140 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.023 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 131, 7 GAN fn, tp: 6, 3 GAN f1 score: 0.316 GAN cohens kappa score: 0.269 -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 4, 5 LR f1 score: 0.526 LR cohens kappa score: 0.494 LR average precision score: 0.693 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.281 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.012 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 138, 0 GAN fn, tp: 5, 4 GAN f1 score: 0.615 GAN cohens kappa score: 0.600 -> 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: 0.650 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 5, 4 GB f1 score: 0.571 GB cohens kappa score: 0.552 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with GAN.predict GAN tn, fp: 134, 3 GAN fn, tp: 5, 1 GAN f1 score: 0.200 GAN cohens kappa score: 0.172 -> 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: 0.534 -> test with 'GB' GB tn, fp: 134, 3 GB fn, tp: 5, 1 GB f1 score: 0.200 GB cohens kappa score: 0.172 -> test with 'KNN' KNN tn, fp: 136, 1 KNN fn, tp: 6, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.012 ====== 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 GAN.predict GAN tn, fp: 136, 2 GAN fn, tp: 4, 5 GAN f1 score: 0.625 GAN cohens kappa score: 0.604 -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 5, 4 LR f1 score: 0.381 LR cohens kappa score: 0.334 LR average precision score: 0.531 -> 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: 137, 1 KNN fn, tp: 7, 2 KNN f1 score: 0.333 KNN cohens kappa score: 0.312 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 132, 6 GAN fn, tp: 4, 5 GAN f1 score: 0.500 GAN cohens kappa score: 0.464 -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 3, 6 LR f1 score: 0.522 LR cohens kappa score: 0.483 LR average precision score: 0.725 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 4, 5 GB f1 score: 0.588 GB cohens kappa score: 0.563 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 130, 8 GAN fn, tp: 5, 4 GAN f1 score: 0.381 GAN cohens kappa score: 0.334 -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 1, 8 LR f1 score: 0.615 LR cohens kappa score: 0.582 LR average precision score: 0.717 -> 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: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 132, 6 GAN fn, tp: 3, 6 GAN f1 score: 0.571 GAN cohens kappa score: 0.539 -> 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: 0.928 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.140 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.032 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with GAN.predict GAN tn, fp: 134, 3 GAN fn, tp: 3, 3 GAN f1 score: 0.500 GAN cohens kappa score: 0.478 -> test with 'LR' LR tn, fp: 132, 5 LR fn, tp: 1, 5 LR f1 score: 0.625 LR cohens kappa score: 0.604 LR average precision score: 0.611 -> test with 'GB' GB tn, fp: 137, 0 GB fn, tp: 5, 1 GB f1 score: 0.286 GB cohens kappa score: 0.277 -> test with 'KNN' KNN tn, fp: 136, 1 KNN fn, tp: 5, 1 KNN f1 score: 0.250 KNN cohens kappa score: 0.234 ====== 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 GAN.predict GAN tn, fp: 127, 11 GAN fn, tp: 2, 7 GAN f1 score: 0.519 GAN cohens kappa score: 0.476 -> 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: 0.674 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.163 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.032 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 136, 2 GAN fn, tp: 5, 4 GAN f1 score: 0.533 GAN cohens kappa score: 0.509 -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 2, 7 LR f1 score: 0.560 LR cohens kappa score: 0.523 LR average precision score: 0.674 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 7, 2 GB f1 score: 0.267 GB cohens kappa score: 0.229 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 7, 2 KNN f1 score: 0.308 KNN cohens kappa score: 0.281 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 132, 6 GAN fn, tp: 6, 3 GAN f1 score: 0.333 GAN cohens kappa score: 0.290 -> 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: 0.557 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.281 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 7, 2 KNN f1 score: 0.286 KNN cohens kappa score: 0.253 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 136, 2 GAN fn, tp: 4, 5 GAN f1 score: 0.625 GAN cohens kappa score: 0.604 -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 1, 8 LR f1 score: 0.727 LR cohens kappa score: 0.706 LR average precision score: 0.886 -> 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: 136, 2 KNN fn, tp: 7, 2 KNN f1 score: 0.308 KNN cohens kappa score: 0.281 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with GAN.predict GAN tn, fp: 131, 6 GAN fn, tp: 1, 5 GAN f1 score: 0.588 GAN cohens kappa score: 0.565 -> 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: 0.827 -> test with 'GB' GB tn, fp: 137, 0 GB fn, tp: 5, 1 GB f1 score: 0.286 GB cohens kappa score: 0.277 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 3, 3 KNN f1 score: 0.667 KNN cohens kappa score: 0.657 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 134, 13 LR fn, tp: 5, 9 LR f1 score: 0.818 LR cohens kappa score: 0.804 LR average precision score: 0.928 average: LR tn, fp: 130.68, 7.12 LR fn, tp: 2.04, 6.36 LR f1 score: 0.580 LR cohens kappa score: 0.548 LR average precision score: 0.692 minimum: LR tn, fp: 125, 3 LR fn, tp: 0, 4 LR f1 score: 0.381 LR cohens kappa score: 0.334 LR average precision score: 0.525 -----[ GB ]----- maximum: GB tn, fp: 137, 4 GB fn, tp: 8, 5 GB f1 score: 0.588 GB cohens kappa score: 0.563 average: GB tn, fp: 135.96, 1.84 GB fn, tp: 6.28, 2.12 GB f1 score: 0.328 GB cohens kappa score: 0.304 minimum: GB tn, fp: 134, 0 GB fn, tp: 4, 1 GB f1 score: 0.154 GB cohens kappa score: 0.121 -----[ KNN ]----- maximum: KNN tn, fp: 138, 3 KNN fn, tp: 9, 3 KNN f1 score: 0.667 KNN cohens kappa score: 0.657 average: KNN tn, fp: 136.56, 1.24 KNN fn, tp: 7.36, 1.04 KNN f1 score: 0.194 KNN cohens kappa score: 0.175 minimum: KNN tn, fp: 135, 0 KNN fn, tp: 3, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.032 -----[ GAN ]----- maximum: GAN tn, fp: 138, 11 GAN fn, tp: 8, 7 GAN f1 score: 0.625 GAN cohens kappa score: 0.604 average: GAN tn, fp: 133.8, 4.0 GAN fn, tp: 4.4, 4.0 GAN f1 score: 0.480 GAN cohens kappa score: 0.451 minimum: GAN tn, fp: 127, 0 GAN fn, tp: 1, 1 GAN f1 score: 0.154 GAN cohens kappa score: 0.121