/////////////////////////////////////////// // Running convGAN-majority-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: 126, 12 GAN fn, tp: 1, 8 GAN f1 score: 0.552 GAN cohens kappa score: 0.510 -> test with 'LR' LR tn, fp: 122, 16 LR fn, tp: 0, 9 LR f1 score: 0.529 LR cohens kappa score: 0.483 LR average precision score: 0.917 -> 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: 121, 17 KNN fn, tp: 4, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.258 ------ 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: 132, 6 GAN fn, tp: 3, 6 GAN f1 score: 0.571 GAN cohens kappa score: 0.539 -> 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: 0.546 -> 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: 122, 16 KNN fn, tp: 3, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.329 ------ 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: 129, 9 GAN fn, tp: 4, 5 GAN f1 score: 0.435 GAN cohens kappa score: 0.389 -> 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: 0.783 -> 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: 131, 7 GAN fn, tp: 2, 7 GAN f1 score: 0.609 GAN cohens kappa score: 0.577 -> 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: 0.643 -> 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: 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 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: 2, 4 LR f1 score: 0.444 LR cohens kappa score: 0.412 LR average precision score: 0.445 -> 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: 124, 13 KNN fn, tp: 2, 4 KNN f1 score: 0.348 KNN cohens kappa score: 0.305 ====== 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: 124, 14 GAN fn, tp: 4, 5 GAN f1 score: 0.357 GAN cohens kappa score: 0.299 -> 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: 0.638 -> 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: 124, 14 KNN fn, tp: 4, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.299 ------ 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: 133, 5 LR fn, tp: 1, 8 LR f1 score: 0.727 LR cohens kappa score: 0.706 LR average precision score: 0.804 -> 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: 127, 11 KNN fn, tp: 3, 6 KNN f1 score: 0.462 KNN cohens kappa score: 0.415 ------ 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: 124, 14 GAN fn, tp: 2, 7 GAN f1 score: 0.467 GAN cohens kappa score: 0.417 -> 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: 0.717 -> 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: 124, 14 KNN fn, tp: 2, 7 KNN f1 score: 0.467 KNN cohens kappa score: 0.417 ------ 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: 1, 8 GAN f1 score: 0.552 GAN cohens kappa score: 0.510 -> 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: 0.734 -> test with 'GB' GB tn, fp: 129, 9 GB fn, tp: 5, 4 GB f1 score: 0.364 GB cohens kappa score: 0.314 -> 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 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with GAN.predict GAN tn, fp: 124, 13 GAN fn, tp: 2, 4 GAN f1 score: 0.348 GAN cohens kappa score: 0.305 -> 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: 0.582 -> 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: 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 GAN.predict GAN tn, fp: 130, 8 GAN fn, tp: 4, 5 GAN f1 score: 0.455 GAN cohens kappa score: 0.412 -> 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: 0.545 -> test with 'GB' GB tn, fp: 129, 9 GB fn, tp: 8, 1 GB f1 score: 0.105 GB cohens kappa score: 0.044 -> test with 'KNN' KNN tn, fp: 123, 15 KNN fn, tp: 6, 3 KNN f1 score: 0.222 KNN cohens kappa score: 0.153 ------ 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: 131, 7 LR fn, tp: 0, 9 LR f1 score: 0.720 LR cohens kappa score: 0.696 LR average precision score: 0.906 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 1, 8 GB f1 score: 0.667 GB cohens kappa score: 0.639 -> test with 'KNN' KNN tn, fp: 121, 17 KNN fn, tp: 0, 9 KNN f1 score: 0.514 KNN cohens kappa score: 0.466 ------ 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: 126, 12 GAN fn, tp: 3, 6 GAN f1 score: 0.444 GAN cohens kappa score: 0.395 -> 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: 0.649 -> 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 GAN.predict GAN tn, fp: 120, 18 GAN fn, tp: 4, 5 GAN f1 score: 0.312 GAN cohens kappa score: 0.246 -> 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: 0.640 -> test with 'GB' GB tn, fp: 130, 8 GB fn, tp: 6, 3 GB f1 score: 0.300 GB cohens kappa score: 0.249 -> test with 'KNN' KNN tn, fp: 119, 19 KNN fn, tp: 4, 5 KNN f1 score: 0.303 KNN cohens kappa score: 0.235 ------ 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: 124, 13 GAN fn, tp: 2, 4 GAN f1 score: 0.348 GAN cohens kappa score: 0.305 -> 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: 0.592 -> 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: 118, 19 KNN fn, tp: 3, 3 KNN f1 score: 0.214 KNN cohens kappa score: 0.159 ====== 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: 124, 14 GAN fn, tp: 3, 6 GAN f1 score: 0.414 GAN cohens kappa score: 0.360 -> 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: 0.520 -> 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: 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: 126, 12 LR fn, tp: 3, 6 LR f1 score: 0.444 LR cohens kappa score: 0.395 LR average precision score: 0.619 -> test with 'GB' GB tn, fp: 125, 13 GB fn, tp: 4, 5 GB f1 score: 0.370 GB cohens kappa score: 0.314 -> 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 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with GAN.predict GAN tn, fp: 125, 13 GAN fn, tp: 2, 7 GAN f1 score: 0.483 GAN cohens kappa score: 0.435 -> 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: 0.696 -> 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: 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 GAN.predict GAN tn, fp: 125, 13 GAN fn, tp: 0, 9 GAN f1 score: 0.581 GAN cohens kappa score: 0.541 -> 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: 0.963 -> test with 'GB' GB tn, fp: 126, 12 GB fn, tp: 6, 3 GB f1 score: 0.250 GB cohens kappa score: 0.188 -> test with 'KNN' KNN tn, fp: 123, 15 KNN fn, tp: 2, 7 KNN f1 score: 0.452 KNN cohens kappa score: 0.399 ------ 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: 130, 7 GAN fn, tp: 2, 4 GAN f1 score: 0.471 GAN cohens kappa score: 0.440 -> 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: 0.604 -> 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: 122, 15 KNN fn, tp: 2, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.274 ====== 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: 4, 5 GAN f1 score: 0.400 GAN cohens kappa score: 0.349 -> 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: 0.692 -> 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: 121, 17 KNN fn, tp: 5, 4 KNN f1 score: 0.267 KNN cohens kappa score: 0.198 ------ 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: 120, 18 GAN fn, tp: 4, 5 GAN f1 score: 0.312 GAN cohens kappa score: 0.246 -> 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: 0.702 -> 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: 5, 4 KNN f1 score: 0.216 KNN cohens kappa score: 0.136 ------ 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: 127, 11 GAN fn, tp: 4, 5 GAN f1 score: 0.400 GAN cohens kappa score: 0.349 -> 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: 0.532 -> 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: 6, 3 KNN f1 score: 0.222 KNN cohens kappa score: 0.153 ------ 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: 123, 15 GAN fn, tp: 1, 8 GAN f1 score: 0.500 GAN cohens kappa score: 0.452 -> 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: 0.869 -> 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: 123, 15 KNN fn, tp: 1, 8 KNN f1 score: 0.500 KNN cohens kappa score: 0.452 ------ 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: 126, 11 GAN fn, tp: 0, 6 GAN f1 score: 0.522 GAN cohens kappa score: 0.490 -> 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: 0.813 -> 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: 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: 133, 19 LR fn, tp: 4, 9 LR f1 score: 0.727 LR cohens kappa score: 0.706 LR average precision score: 0.963 average: LR tn, fp: 127.96, 9.84 LR fn, tp: 1.6, 6.8 LR f1 score: 0.546 LR cohens kappa score: 0.509 LR average precision score: 0.686 minimum: LR tn, fp: 119, 5 LR fn, tp: 0, 4 LR f1 score: 0.417 LR cohens kappa score: 0.368 LR average precision score: 0.445 -----[ GB ]----- maximum: GB tn, fp: 134, 13 GB fn, tp: 8, 8 GB f1 score: 0.667 GB cohens kappa score: 0.639 average: GB tn, fp: 130.44, 7.36 GB fn, tp: 4.88, 3.52 GB f1 score: 0.360 GB cohens kappa score: 0.316 minimum: GB tn, fp: 125, 4 GB fn, tp: 1, 1 GB f1 score: 0.105 GB cohens kappa score: 0.044 -----[ KNN ]----- maximum: KNN tn, fp: 127, 24 KNN fn, tp: 6, 9 KNN f1 score: 0.514 KNN cohens kappa score: 0.466 average: KNN tn, fp: 122.48, 15.32 KNN fn, tp: 3.28, 5.12 KNN f1 score: 0.354 KNN cohens kappa score: 0.297 minimum: KNN tn, fp: 114, 11 KNN fn, tp: 0, 3 KNN f1 score: 0.214 KNN cohens kappa score: 0.136 -----[ GAN ]----- maximum: GAN tn, fp: 132, 18 GAN fn, tp: 4, 9 GAN f1 score: 0.643 GAN cohens kappa score: 0.610 average: GAN tn, fp: 126.16, 11.64 GAN fn, tp: 2.32, 6.08 GAN f1 score: 0.467 GAN cohens kappa score: 0.423 minimum: GAN tn, fp: 120, 6 GAN fn, tp: 0, 4 GAN f1 score: 0.312 GAN cohens kappa score: 0.246