/////////////////////////////////////////// // Running convGAN-proximary-5 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: 130, 8 GAN fn, tp: 1, 8 GAN f1 score: 0.640 GAN cohens kappa score: 0.609 -> 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: 0.918 -> 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: 117, 21 KNN fn, tp: 1, 8 KNN f1 score: 0.421 KNN cohens kappa score: 0.361 ------ 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: 123, 15 GAN fn, tp: 4, 5 GAN f1 score: 0.345 GAN cohens kappa score: 0.284 -> 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: 0.566 -> 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: 123, 15 KNN fn, tp: 3, 6 KNN f1 score: 0.400 KNN cohens kappa score: 0.344 ------ 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: 127, 11 GAN fn, tp: 2, 7 GAN f1 score: 0.519 GAN cohens kappa score: 0.476 -> 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: 0.818 -> 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: 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 GAN.predict GAN tn, fp: 118, 20 GAN fn, tp: 2, 7 GAN f1 score: 0.389 GAN cohens kappa score: 0.327 -> 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: 0.538 -> 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: 122, 16 KNN fn, tp: 3, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.329 ------ 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: 119, 18 GAN fn, tp: 2, 4 GAN f1 score: 0.286 GAN cohens kappa score: 0.235 -> 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: 0.470 -> 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: 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 GAN.predict GAN tn, fp: 109, 29 GAN fn, tp: 2, 7 GAN f1 score: 0.311 GAN cohens kappa score: 0.236 -> test with 'LR' LR tn, fp: 120, 18 LR fn, tp: 1, 8 LR f1 score: 0.457 LR cohens kappa score: 0.403 LR average precision score: 0.698 -> 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: 122, 16 KNN fn, tp: 5, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.209 ------ 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: 134, 4 GAN fn, tp: 3, 6 GAN f1 score: 0.632 GAN cohens kappa score: 0.606 -> 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: 0.767 -> 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: 125, 13 KNN fn, tp: 3, 6 KNN f1 score: 0.429 KNN cohens kappa score: 0.377 ------ 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: 129, 9 GAN fn, tp: 2, 7 GAN f1 score: 0.560 GAN cohens kappa score: 0.523 -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 2, 7 LR f1 score: 0.667 LR cohens kappa score: 0.642 LR average precision score: 0.686 -> 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: 123, 15 KNN fn, tp: 3, 6 KNN f1 score: 0.400 KNN cohens kappa score: 0.344 ------ 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: 126, 12 GAN fn, tp: 3, 6 GAN f1 score: 0.444 GAN cohens kappa score: 0.395 -> test with 'LR' LR tn, fp: 125, 13 LR fn, tp: 1, 8 LR f1 score: 0.533 LR cohens kappa score: 0.490 LR average precision score: 0.702 -> 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: 118, 20 KNN fn, tp: 3, 6 KNN f1 score: 0.343 KNN cohens kappa score: 0.277 ------ 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: 115, 22 GAN fn, tp: 1, 5 GAN f1 score: 0.303 GAN cohens kappa score: 0.252 -> 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: 0.590 -> test with 'GB' GB tn, fp: 128, 9 GB fn, tp: 2, 4 GB f1 score: 0.421 GB cohens kappa score: 0.386 -> test with 'KNN' KNN tn, fp: 121, 16 KNN fn, tp: 3, 3 KNN f1 score: 0.240 KNN cohens kappa score: 0.188 ====== 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: 124, 14 GAN fn, tp: 5, 4 GAN f1 score: 0.296 GAN cohens kappa score: 0.234 -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 4, 5 LR f1 score: 0.435 LR cohens kappa score: 0.389 LR average precision score: 0.552 -> 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: 4, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.284 ------ 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: 128, 10 GAN fn, tp: 0, 9 GAN f1 score: 0.643 GAN cohens kappa score: 0.610 -> 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: 0.878 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 3, 6 GB f1 score: 0.545 GB cohens kappa score: 0.510 -> 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 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 123, 15 GAN fn, tp: 4, 5 GAN f1 score: 0.345 GAN cohens kappa score: 0.284 -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 3, 6 LR f1 score: 0.571 LR cohens kappa score: 0.539 LR average precision score: 0.697 -> 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: 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 GAN.predict GAN tn, fp: 101, 37 GAN fn, tp: 1, 8 GAN f1 score: 0.296 GAN cohens kappa score: 0.216 -> 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: 0.627 -> 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: 121, 17 KNN fn, tp: 4, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.258 ------ 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: 133, 4 GAN fn, tp: 1, 5 GAN f1 score: 0.667 GAN cohens kappa score: 0.649 -> 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: 0.540 -> test with 'GB' GB tn, fp: 127, 10 GB fn, tp: 3, 3 GB f1 score: 0.316 GB cohens kappa score: 0.274 -> 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 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: 126, 12 GAN fn, tp: 5, 4 GAN f1 score: 0.320 GAN cohens kappa score: 0.262 -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 4, 5 LR f1 score: 0.435 LR cohens kappa score: 0.389 LR average precision score: 0.521 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 5, 4 GB f1 score: 0.444 GB cohens kappa score: 0.408 -> 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 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 118, 20 GAN fn, tp: 2, 7 GAN f1 score: 0.389 GAN cohens kappa score: 0.327 -> test with 'LR' LR tn, fp: 122, 16 LR fn, tp: 2, 7 LR f1 score: 0.438 LR cohens kappa score: 0.383 LR average precision score: 0.719 -> test with 'GB' GB tn, fp: 126, 12 GB fn, tp: 4, 5 GB f1 score: 0.385 GB cohens kappa score: 0.331 -> 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 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 128, 10 GAN fn, tp: 4, 5 GAN f1 score: 0.417 GAN cohens kappa score: 0.368 -> 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: 0.730 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 7, 2 GB f1 score: 0.222 GB cohens kappa score: 0.171 -> 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 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 125, 13 GAN fn, tp: 3, 6 GAN f1 score: 0.429 GAN cohens kappa score: 0.377 -> test with 'LR' LR tn, fp: 121, 17 LR fn, tp: 0, 9 LR f1 score: 0.514 LR cohens kappa score: 0.466 LR average precision score: 0.967 -> 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: 119, 19 KNN fn, tp: 3, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.289 ------ 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: 131, 6 GAN fn, tp: 2, 4 GAN f1 score: 0.500 GAN cohens kappa score: 0.472 -> test with 'LR' LR tn, fp: 129, 8 LR fn, tp: 1, 5 LR f1 score: 0.526 LR cohens kappa score: 0.497 LR average precision score: 0.543 -> test with 'GB' GB tn, fp: 128, 9 GB fn, tp: 4, 2 GB f1 score: 0.235 GB cohens kappa score: 0.191 -> 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 GAN.predict GAN tn, fp: 128, 10 GAN fn, tp: 4, 5 GAN f1 score: 0.417 GAN cohens kappa score: 0.368 -> test with 'LR' LR tn, fp: 122, 16 LR fn, tp: 2, 7 LR f1 score: 0.438 LR cohens kappa score: 0.383 LR average precision score: 0.696 -> test with 'GB' GB tn, fp: 130, 8 GB fn, tp: 8, 1 GB f1 score: 0.111 GB cohens kappa score: 0.053 -> test with 'KNN' KNN tn, fp: 119, 19 KNN fn, tp: 6, 3 KNN f1 score: 0.194 KNN cohens kappa score: 0.117 ------ 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: 89, 49 GAN fn, tp: 1, 8 GAN f1 score: 0.242 GAN cohens kappa score: 0.153 -> 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: 0.721 -> 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: 115, 23 KNN fn, tp: 5, 4 KNN f1 score: 0.222 KNN cohens kappa score: 0.144 ------ 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: 133, 5 GAN fn, tp: 4, 5 GAN f1 score: 0.526 GAN cohens kappa score: 0.494 -> test with 'LR' LR tn, fp: 128, 10 LR fn, tp: 3, 6 LR f1 score: 0.480 LR cohens kappa score: 0.436 LR average precision score: 0.543 -> 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: 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 GAN.predict GAN tn, fp: 132, 6 GAN fn, tp: 2, 7 GAN f1 score: 0.636 GAN cohens kappa score: 0.608 -> 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: 0.910 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 5, 4 GB f1 score: 0.444 GB cohens kappa score: 0.408 -> 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 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with GAN.predict GAN tn, fp: 132, 5 GAN fn, tp: 2, 4 GAN f1 score: 0.533 GAN cohens kappa score: 0.509 -> test with 'LR' LR tn, fp: 131, 6 LR fn, tp: 0, 6 LR f1 score: 0.667 LR cohens kappa score: 0.647 LR average precision score: 0.859 -> test with 'GB' GB tn, fp: 134, 3 GB fn, tp: 3, 3 GB f1 score: 0.500 GB cohens kappa score: 0.478 -> test with 'KNN' KNN tn, fp: 128, 9 KNN fn, tp: 2, 4 KNN f1 score: 0.421 KNN cohens kappa score: 0.386 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 133, 24 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: 126.28, 11.52 LR fn, tp: 1.56, 6.84 LR f1 score: 0.518 LR cohens kappa score: 0.478 LR average precision score: 0.690 minimum: LR tn, fp: 114, 5 LR fn, tp: 0, 4 LR f1 score: 0.350 LR cohens kappa score: 0.282 LR average precision score: 0.470 -----[ GB ]----- maximum: GB tn, fp: 136, 12 GB fn, tp: 8, 6 GB f1 score: 0.545 GB cohens kappa score: 0.510 average: GB tn, fp: 132.08, 5.72 GB fn, tp: 5.16, 3.24 GB f1 score: 0.374 GB cohens kappa score: 0.335 minimum: GB tn, fp: 126, 2 GB fn, tp: 2, 1 GB f1 score: 0.111 GB cohens kappa score: 0.053 -----[ KNN ]----- maximum: KNN tn, fp: 128, 23 KNN fn, tp: 6, 8 KNN f1 score: 0.429 KNN cohens kappa score: 0.386 average: KNN tn, fp: 121.8, 16.0 KNN fn, tp: 3.36, 5.04 KNN f1 score: 0.342 KNN cohens kappa score: 0.283 minimum: KNN tn, fp: 115, 9 KNN fn, tp: 1, 3 KNN f1 score: 0.194 KNN cohens kappa score: 0.117 -----[ GAN ]----- maximum: GAN tn, fp: 134, 49 GAN fn, tp: 5, 9 GAN f1 score: 0.667 GAN cohens kappa score: 0.649 average: GAN tn, fp: 123.24, 14.56 GAN fn, tp: 2.48, 5.92 GAN f1 score: 0.443 GAN cohens kappa score: 0.395 minimum: GAN tn, fp: 89, 4 GAN fn, tp: 0, 4 GAN f1 score: 0.242 GAN cohens kappa score: 0.153