/////////////////////////////////////////// // Running convGAN-proxymary-full on folding_car-vgood /////////////////////////////////////////// Load 'data_input/folding_car-vgood' 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 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 319, 14 GAN fn, tp: 1, 12 GAN f1 score: 0.615 GAN cohens kappa score: 0.595 -> test with 'LR' LR tn, fp: 287, 46 LR fn, tp: 0, 13 LR f1 score: 0.361 LR cohens kappa score: 0.319 LR average precision score: 0.360 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 2, 11 GB f1 score: 0.846 GB cohens kappa score: 0.840 -> test with 'KNN' KNN tn, fp: 324, 9 KNN fn, tp: 0, 13 KNN f1 score: 0.743 KNN cohens kappa score: 0.730 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 330, 3 GAN fn, tp: 1, 12 GAN f1 score: 0.857 GAN cohens kappa score: 0.851 -> test with 'LR' LR tn, fp: 296, 37 LR fn, tp: 3, 10 LR f1 score: 0.333 LR cohens kappa score: 0.292 LR average precision score: 0.304 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 323, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 323, 10 GAN fn, tp: 1, 12 GAN f1 score: 0.686 GAN cohens kappa score: 0.670 -> test with 'LR' LR tn, fp: 283, 50 LR fn, tp: 0, 13 LR f1 score: 0.342 LR cohens kappa score: 0.298 LR average precision score: 0.402 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 1, 12 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> test with 'KNN' KNN tn, fp: 319, 14 KNN fn, tp: 0, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 326, 7 GAN fn, tp: 2, 11 GAN f1 score: 0.710 GAN cohens kappa score: 0.696 -> test with 'LR' LR tn, fp: 293, 40 LR fn, tp: 1, 12 LR f1 score: 0.369 LR cohens kappa score: 0.329 LR average precision score: 0.373 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 325, 8 KNN fn, tp: 0, 13 KNN f1 score: 0.765 KNN cohens kappa score: 0.753 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with GAN.predict GAN tn, fp: 329, 2 GAN fn, tp: 3, 10 GAN f1 score: 0.800 GAN cohens kappa score: 0.792 -> test with 'LR' LR tn, fp: 299, 32 LR fn, tp: 2, 11 LR f1 score: 0.393 LR cohens kappa score: 0.355 LR average precision score: 0.442 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 321, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 331, 2 GAN fn, tp: 6, 7 GAN f1 score: 0.636 GAN cohens kappa score: 0.625 -> test with 'LR' LR tn, fp: 300, 33 LR fn, tp: 2, 11 LR f1 score: 0.386 LR cohens kappa score: 0.348 LR average precision score: 0.285 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 323, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 329, 4 GAN fn, tp: 2, 11 GAN f1 score: 0.786 GAN cohens kappa score: 0.777 -> test with 'LR' LR tn, fp: 277, 56 LR fn, tp: 0, 13 LR f1 score: 0.317 LR cohens kappa score: 0.271 LR average precision score: 0.360 -> test with 'GB' GB tn, fp: 329, 4 GB fn, tp: 0, 13 GB f1 score: 0.867 GB cohens kappa score: 0.861 -> test with 'KNN' KNN tn, fp: 319, 14 KNN fn, tp: 0, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 329, 4 GAN fn, tp: 0, 13 GAN f1 score: 0.867 GAN cohens kappa score: 0.861 -> test with 'LR' LR tn, fp: 293, 40 LR fn, tp: 1, 12 LR f1 score: 0.369 LR cohens kappa score: 0.329 LR average precision score: 0.337 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 319, 14 KNN fn, tp: 0, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 324, 9 GAN fn, tp: 5, 8 GAN f1 score: 0.533 GAN cohens kappa score: 0.513 -> test with 'LR' LR tn, fp: 297, 36 LR fn, tp: 0, 13 LR f1 score: 0.419 LR cohens kappa score: 0.383 LR average precision score: 0.280 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 327, 6 KNN fn, tp: 1, 12 KNN f1 score: 0.774 KNN cohens kappa score: 0.764 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with GAN.predict GAN tn, fp: 323, 8 GAN fn, tp: 1, 12 GAN f1 score: 0.727 GAN cohens kappa score: 0.714 -> test with 'LR' LR tn, fp: 293, 38 LR fn, tp: 1, 12 LR f1 score: 0.381 LR cohens kappa score: 0.341 LR average precision score: 0.536 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 329, 2 KNN fn, tp: 0, 13 KNN f1 score: 0.929 KNN cohens kappa score: 0.926 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 326, 7 GAN fn, tp: 2, 11 GAN f1 score: 0.710 GAN cohens kappa score: 0.696 -> test with 'LR' LR tn, fp: 290, 43 LR fn, tp: 1, 12 LR f1 score: 0.353 LR cohens kappa score: 0.311 LR average precision score: 0.309 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 2, 11 GB f1 score: 0.880 GB cohens kappa score: 0.876 -> test with 'KNN' KNN tn, fp: 321, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 324, 9 GAN fn, tp: 2, 11 GAN f1 score: 0.667 GAN cohens kappa score: 0.651 -> test with 'LR' LR tn, fp: 296, 37 LR fn, tp: 0, 13 LR f1 score: 0.413 LR cohens kappa score: 0.375 LR average precision score: 0.446 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 324, 9 KNN fn, tp: 0, 13 KNN f1 score: 0.743 KNN cohens kappa score: 0.730 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 323, 10 GAN fn, tp: 0, 13 GAN f1 score: 0.722 GAN cohens kappa score: 0.708 -> test with 'LR' LR tn, fp: 284, 49 LR fn, tp: 0, 13 LR f1 score: 0.347 LR cohens kappa score: 0.303 LR average precision score: 0.329 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 310, 23 KNN fn, tp: 0, 13 KNN f1 score: 0.531 KNN cohens kappa score: 0.503 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 326, 7 GAN fn, tp: 1, 12 GAN f1 score: 0.750 GAN cohens kappa score: 0.738 -> test with 'LR' LR tn, fp: 297, 36 LR fn, tp: 0, 13 LR f1 score: 0.419 LR cohens kappa score: 0.383 LR average precision score: 0.386 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 318, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with GAN.predict GAN tn, fp: 320, 11 GAN fn, tp: 2, 11 GAN f1 score: 0.629 GAN cohens kappa score: 0.610 -> test with 'LR' LR tn, fp: 298, 33 LR fn, tp: 2, 11 LR f1 score: 0.386 LR cohens kappa score: 0.348 LR average precision score: 0.372 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 328, 3 KNN fn, tp: 0, 13 KNN f1 score: 0.897 KNN cohens kappa score: 0.892 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 330, 3 GAN fn, tp: 1, 12 GAN f1 score: 0.857 GAN cohens kappa score: 0.851 -> test with 'LR' LR tn, fp: 297, 36 LR fn, tp: 0, 13 LR f1 score: 0.419 LR cohens kappa score: 0.383 LR average precision score: 0.368 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 327, 6 KNN fn, tp: 0, 13 KNN f1 score: 0.813 KNN cohens kappa score: 0.804 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 330, 3 GAN fn, tp: 3, 10 GAN f1 score: 0.769 GAN cohens kappa score: 0.760 -> test with 'LR' LR tn, fp: 290, 43 LR fn, tp: 1, 12 LR f1 score: 0.353 LR cohens kappa score: 0.311 LR average precision score: 0.506 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 319, 14 KNN fn, tp: 0, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 323, 10 GAN fn, tp: 1, 12 GAN f1 score: 0.686 GAN cohens kappa score: 0.670 -> test with 'LR' LR tn, fp: 288, 45 LR fn, tp: 0, 13 LR f1 score: 0.366 LR cohens kappa score: 0.325 LR average precision score: 0.313 -> test with 'GB' GB tn, fp: 329, 4 GB fn, tp: 0, 13 GB f1 score: 0.867 GB cohens kappa score: 0.861 -> test with 'KNN' KNN tn, fp: 321, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 323, 10 GAN fn, tp: 5, 8 GAN f1 score: 0.516 GAN cohens kappa score: 0.494 -> test with 'LR' LR tn, fp: 299, 34 LR fn, tp: 2, 11 LR f1 score: 0.379 LR cohens kappa score: 0.341 LR average precision score: 0.271 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 326, 7 KNN fn, tp: 0, 13 KNN f1 score: 0.788 KNN cohens kappa score: 0.778 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with GAN.predict GAN tn, fp: 326, 5 GAN fn, tp: 3, 10 GAN f1 score: 0.714 GAN cohens kappa score: 0.702 -> test with 'LR' LR tn, fp: 292, 39 LR fn, tp: 0, 13 LR f1 score: 0.400 LR cohens kappa score: 0.361 LR average precision score: 0.323 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.958 -> test with 'KNN' KNN tn, fp: 315, 16 KNN fn, tp: 0, 13 KNN f1 score: 0.619 KNN cohens kappa score: 0.598 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 323, 10 GAN fn, tp: 3, 10 GAN f1 score: 0.606 GAN cohens kappa score: 0.587 -> test with 'LR' LR tn, fp: 277, 56 LR fn, tp: 0, 13 LR f1 score: 0.317 LR cohens kappa score: 0.271 LR average precision score: 0.299 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 320, 13 KNN fn, tp: 0, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 324, 9 GAN fn, tp: 3, 10 GAN f1 score: 0.625 GAN cohens kappa score: 0.607 -> test with 'LR' LR tn, fp: 299, 34 LR fn, tp: 3, 10 LR f1 score: 0.351 LR cohens kappa score: 0.311 LR average precision score: 0.357 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 327, 6 KNN fn, tp: 0, 13 KNN f1 score: 0.813 KNN cohens kappa score: 0.804 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 328, 5 GAN fn, tp: 3, 10 GAN f1 score: 0.714 GAN cohens kappa score: 0.702 -> test with 'LR' LR tn, fp: 306, 27 LR fn, tp: 2, 11 LR f1 score: 0.431 LR cohens kappa score: 0.398 LR average precision score: 0.336 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 329, 4 KNN fn, tp: 0, 13 KNN f1 score: 0.867 KNN cohens kappa score: 0.861 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 328, 5 GAN fn, tp: 1, 12 GAN f1 score: 0.800 GAN cohens kappa score: 0.791 -> test with 'LR' LR tn, fp: 287, 46 LR fn, tp: 0, 13 LR f1 score: 0.361 LR cohens kappa score: 0.319 LR average precision score: 0.273 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 322, 11 KNN fn, tp: 0, 13 KNN f1 score: 0.703 KNN cohens kappa score: 0.687 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with GAN.predict GAN tn, fp: 329, 2 GAN fn, tp: 2, 11 GAN f1 score: 0.846 GAN cohens kappa score: 0.840 -> test with 'LR' LR tn, fp: 296, 35 LR fn, tp: 0, 13 LR f1 score: 0.426 LR cohens kappa score: 0.390 LR average precision score: 0.536 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 327, 4 KNN fn, tp: 1, 12 KNN f1 score: 0.828 KNN cohens kappa score: 0.820 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 306, 56 LR fn, tp: 3, 13 LR f1 score: 0.431 LR cohens kappa score: 0.398 LR average precision score: 0.536 average: LR tn, fp: 292.56, 40.04 LR fn, tp: 0.84, 12.16 LR f1 score: 0.376 LR cohens kappa score: 0.336 LR average precision score: 0.364 minimum: LR tn, fp: 277, 27 LR fn, tp: 0, 10 LR f1 score: 0.317 LR cohens kappa score: 0.271 LR average precision score: 0.271 -----[ GB ]----- maximum: GB tn, fp: 333, 4 GB fn, tp: 2, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 331.76, 0.84 GB fn, tp: 0.52, 12.48 GB f1 score: 0.949 GB cohens kappa score: 0.947 minimum: GB tn, fp: 329, 0 GB fn, tp: 0, 11 GB f1 score: 0.846 GB cohens kappa score: 0.840 -----[ KNN ]----- maximum: KNN tn, fp: 329, 23 KNN fn, tp: 1, 13 KNN f1 score: 0.929 KNN cohens kappa score: 0.926 average: KNN tn, fp: 322.52, 10.08 KNN fn, tp: 0.08, 12.92 KNN f1 score: 0.730 KNN cohens kappa score: 0.716 minimum: KNN tn, fp: 310, 2 KNN fn, tp: 0, 12 KNN f1 score: 0.531 KNN cohens kappa score: 0.503 -----[ GAN ]----- maximum: GAN tn, fp: 331, 14 GAN fn, tp: 6, 13 GAN f1 score: 0.867 GAN cohens kappa score: 0.861 average: GAN tn, fp: 325.84, 6.76 GAN fn, tp: 2.16, 10.84 GAN f1 score: 0.713 GAN cohens kappa score: 0.700 minimum: GAN tn, fp: 319, 2 GAN fn, tp: 0, 7 GAN f1 score: 0.516 GAN cohens kappa score: 0.494