/////////////////////////////////////////// // Running convGAN-proxymary-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: 123, 15 GAN fn, tp: 0, 9 GAN f1 score: 0.545 GAN cohens kappa score: 0.501 -> 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: 0.886 -> test with 'GB' GB tn, fp: 129, 9 GB fn, tp: 7, 2 GB f1 score: 0.200 GB cohens kappa score: 0.142 -> test with 'KNN' KNN tn, fp: 115, 23 KNN fn, tp: 3, 6 KNN f1 score: 0.316 KNN cohens kappa score: 0.245 ------ 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: 129, 9 GAN fn, tp: 4, 5 GAN f1 score: 0.435 GAN cohens kappa score: 0.389 -> 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: 0.580 -> 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: 120, 18 KNN fn, tp: 4, 5 KNN f1 score: 0.312 KNN cohens kappa score: 0.246 ------ 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: 135, 3 GAN fn, tp: 3, 6 GAN f1 score: 0.667 GAN cohens kappa score: 0.645 -> 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: 0.782 -> 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: 126, 12 KNN fn, tp: 3, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.395 ------ 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: 129, 9 GAN fn, tp: 3, 6 GAN f1 score: 0.500 GAN cohens kappa score: 0.459 -> 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: 0.638 -> 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: 125, 13 KNN fn, tp: 2, 7 KNN f1 score: 0.483 KNN cohens kappa score: 0.435 ------ 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: 127, 10 GAN fn, tp: 3, 3 GAN f1 score: 0.316 GAN cohens kappa score: 0.274 -> 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: 0.488 -> test with 'GB' GB tn, fp: 130, 7 GB fn, tp: 4, 2 GB f1 score: 0.267 GB cohens kappa score: 0.228 -> test with 'KNN' KNN tn, fp: 127, 10 KNN fn, tp: 2, 4 KNN f1 score: 0.400 KNN cohens kappa score: 0.363 ====== 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: 134, 4 GAN fn, tp: 5, 4 GAN f1 score: 0.471 GAN cohens kappa score: 0.438 -> 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: 0.628 -> 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: 122, 16 KNN fn, tp: 3, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.329 ------ 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: 128, 10 GAN fn, tp: 2, 7 GAN f1 score: 0.538 GAN cohens kappa score: 0.498 -> 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: 0.771 -> 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: 128, 10 KNN fn, tp: 3, 6 KNN f1 score: 0.480 KNN cohens kappa score: 0.436 ------ 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: 2, 7 GAN f1 score: 0.636 GAN cohens kappa score: 0.608 -> 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: 0.731 -> 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: 1, 8 KNN f1 score: 0.444 KNN cohens kappa score: 0.388 ------ 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: 130, 8 GAN fn, tp: 3, 6 GAN f1 score: 0.522 GAN cohens kappa score: 0.483 -> 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: 0.711 -> 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: 125, 13 KNN fn, tp: 4, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.314 ------ 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: 123, 14 GAN fn, tp: 1, 5 GAN f1 score: 0.400 GAN cohens kappa score: 0.359 -> test with 'LR' LR tn, fp: 128, 9 LR fn, tp: 1, 5 LR f1 score: 0.500 LR cohens kappa score: 0.469 LR average precision score: 0.587 -> 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: 118, 19 KNN fn, tp: 2, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.224 ====== 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: 130, 8 GAN fn, tp: 5, 4 GAN f1 score: 0.381 GAN cohens kappa score: 0.334 -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 4, 5 LR f1 score: 0.500 LR cohens kappa score: 0.464 LR average precision score: 0.538 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 8, 1 GB f1 score: 0.118 GB cohens kappa score: 0.064 -> test with 'KNN' KNN tn, fp: 125, 13 KNN fn, tp: 5, 4 KNN f1 score: 0.308 KNN cohens kappa score: 0.247 ------ 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: 1, 8 GAN f1 score: 0.593 GAN cohens kappa score: 0.556 -> 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: 0.906 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 2, 7 GB f1 score: 0.737 GB cohens kappa score: 0.719 -> test with 'KNN' KNN tn, fp: 117, 21 KNN fn, tp: 0, 9 KNN f1 score: 0.462 KNN cohens kappa score: 0.406 ------ 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: 134, 4 GAN fn, tp: 6, 3 GAN f1 score: 0.375 GAN cohens kappa score: 0.340 -> 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: 0.660 -> 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: 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: 130, 8 GAN fn, tp: 3, 6 GAN f1 score: 0.522 GAN cohens kappa score: 0.483 -> 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: 0.668 -> 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: 118, 20 KNN fn, tp: 4, 5 KNN f1 score: 0.294 KNN cohens kappa score: 0.224 ------ 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: 126, 11 GAN fn, tp: 0, 6 GAN f1 score: 0.522 GAN cohens kappa score: 0.490 -> test with 'LR' LR tn, fp: 125, 12 LR fn, tp: 1, 5 LR f1 score: 0.435 LR cohens kappa score: 0.397 LR average precision score: 0.599 -> test with 'GB' GB tn, fp: 130, 7 GB fn, tp: 4, 2 GB f1 score: 0.267 GB cohens kappa score: 0.228 -> test with 'KNN' KNN tn, fp: 116, 21 KNN fn, tp: 3, 3 KNN f1 score: 0.200 KNN cohens kappa score: 0.142 ====== 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: 129, 9 GAN fn, tp: 4, 5 GAN f1 score: 0.435 GAN cohens kappa score: 0.389 -> 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: 0.529 -> 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: 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 GAN.predict GAN tn, fp: 124, 14 GAN fn, tp: 2, 7 GAN f1 score: 0.467 GAN cohens kappa score: 0.417 -> 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: 0.721 -> 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: 118, 20 KNN fn, tp: 2, 7 KNN f1 score: 0.389 KNN cohens kappa score: 0.327 ------ 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: 127, 11 GAN fn, tp: 4, 5 GAN f1 score: 0.400 GAN cohens kappa score: 0.349 -> 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: 0.684 -> 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: 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: 130, 8 GAN fn, tp: 3, 6 GAN f1 score: 0.522 GAN cohens kappa score: 0.483 -> 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: 0.928 -> test with 'GB' GB tn, fp: 130, 8 GB fn, tp: 4, 5 GB f1 score: 0.455 GB cohens kappa score: 0.412 -> test with 'KNN' KNN tn, fp: 122, 16 KNN fn, tp: 1, 8 KNN f1 score: 0.485 KNN cohens kappa score: 0.434 ------ 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: 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: 1, 5 LR f1 score: 0.588 LR cohens kappa score: 0.565 LR average precision score: 0.609 -> test with 'GB' GB tn, fp: 131, 6 GB fn, tp: 3, 3 GB f1 score: 0.400 GB cohens kappa score: 0.368 -> 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 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: 126, 12 GAN fn, tp: 4, 5 GAN f1 score: 0.385 GAN cohens kappa score: 0.331 -> 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: 0.768 -> 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: 122, 16 KNN fn, tp: 8, 1 KNN f1 score: 0.077 KNN cohens kappa score: -0.003 ------ 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: 126, 12 GAN fn, tp: 2, 7 GAN f1 score: 0.500 GAN cohens kappa score: 0.455 -> test with 'LR' LR tn, fp: 128, 10 LR fn, tp: 0, 9 LR f1 score: 0.643 LR cohens kappa score: 0.610 LR average precision score: 0.731 -> 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: 129, 9 GAN fn, tp: 4, 5 GAN f1 score: 0.435 GAN cohens kappa score: 0.389 -> test with 'LR' LR tn, fp: 127, 11 LR fn, tp: 3, 6 LR f1 score: 0.462 LR cohens kappa score: 0.415 LR average precision score: 0.548 -> 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: 123, 15 KNN fn, tp: 5, 4 KNN f1 score: 0.286 KNN cohens kappa score: 0.221 ------ 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: 127, 11 GAN fn, tp: 1, 8 GAN f1 score: 0.571 GAN cohens kappa score: 0.533 -> 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: 0.892 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 4, 5 GB f1 score: 0.476 GB cohens kappa score: 0.437 -> test with 'KNN' KNN tn, fp: 125, 13 KNN fn, tp: 2, 7 KNN f1 score: 0.483 KNN cohens kappa score: 0.435 ------ 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: 130, 7 GAN fn, tp: 2, 4 GAN f1 score: 0.471 GAN cohens kappa score: 0.440 -> test with 'LR' LR tn, fp: 129, 8 LR fn, tp: 0, 6 LR f1 score: 0.600 LR cohens kappa score: 0.575 LR average precision score: 0.808 -> test with 'GB' GB tn, fp: 130, 7 GB fn, tp: 3, 3 GB f1 score: 0.375 GB cohens kappa score: 0.340 -> test with 'KNN' KNN tn, fp: 129, 8 KNN fn, tp: 2, 4 KNN f1 score: 0.444 KNN cohens kappa score: 0.412 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 132, 19 LR fn, tp: 4, 9 LR f1 score: 0.750 LR cohens kappa score: 0.729 LR average precision score: 0.928 average: LR tn, fp: 127.8, 10.0 LR fn, tp: 1.48, 6.92 LR f1 score: 0.548 LR cohens kappa score: 0.510 LR average precision score: 0.696 minimum: LR tn, fp: 119, 6 LR fn, tp: 0, 4 LR f1 score: 0.435 LR cohens kappa score: 0.388 LR average precision score: 0.488 -----[ GB ]----- maximum: GB tn, fp: 135, 12 GB fn, tp: 8, 7 GB f1 score: 0.737 GB cohens kappa score: 0.719 average: GB tn, fp: 131.36, 6.44 GB fn, tp: 5.2, 3.2 GB f1 score: 0.352 GB cohens kappa score: 0.311 minimum: GB tn, fp: 126, 3 GB fn, tp: 2, 1 GB f1 score: 0.111 GB cohens kappa score: 0.053 -----[ KNN ]----- maximum: KNN tn, fp: 129, 23 KNN fn, tp: 8, 9 KNN f1 score: 0.485 KNN cohens kappa score: 0.436 average: KNN tn, fp: 122.12, 15.68 KNN fn, tp: 3.2, 5.2 KNN f1 score: 0.355 KNN cohens kappa score: 0.298 minimum: KNN tn, fp: 115, 8 KNN fn, tp: 0, 1 KNN f1 score: 0.077 KNN cohens kappa score: -0.003 -----[ GAN ]----- maximum: GAN tn, fp: 135, 15 GAN fn, tp: 6, 9 GAN f1 score: 0.667 GAN cohens kappa score: 0.645 average: GAN tn, fp: 128.72, 9.08 GAN fn, tp: 2.76, 5.64 GAN f1 score: 0.486 GAN cohens kappa score: 0.446 minimum: GAN tn, fp: 123, 3 GAN fn, tp: 0, 3 GAN f1 score: 0.316 GAN cohens kappa score: 0.274