/////////////////////////////////////////// // Running convGAN-proximary-5 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: 322, 11 GAN fn, tp: 1, 12 GAN f1 score: 0.667 GAN cohens kappa score: 0.650 -> test with 'LR' LR tn, fp: 286, 47 LR fn, tp: 0, 13 LR f1 score: 0.356 LR cohens kappa score: 0.314 LR average precision score: 0.353 -> 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: 318, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ 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: 2, 11 GAN f1 score: 0.815 GAN cohens kappa score: 0.807 -> test with 'LR' LR tn, fp: 292, 41 LR fn, tp: 0, 13 LR f1 score: 0.388 LR cohens kappa score: 0.349 LR average precision score: 0.300 -> 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: 315, 18 KNN fn, tp: 1, 12 KNN f1 score: 0.558 KNN cohens kappa score: 0.534 ------ 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: 325, 8 GAN fn, tp: 4, 9 GAN f1 score: 0.600 GAN cohens kappa score: 0.582 -> 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: 0.369 -> 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: 329, 4 GAN fn, tp: 3, 10 GAN f1 score: 0.741 GAN cohens kappa score: 0.730 -> test with 'LR' LR tn, fp: 292, 41 LR fn, tp: 0, 13 LR f1 score: 0.388 LR cohens kappa score: 0.349 LR average precision score: 0.353 -> 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: 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: 326, 5 GAN fn, tp: 2, 11 GAN f1 score: 0.759 GAN cohens kappa score: 0.748 -> test with 'LR' LR tn, fp: 298, 33 LR fn, tp: 1, 12 LR f1 score: 0.414 LR cohens kappa score: 0.377 LR average precision score: 0.445 -> 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: 2, 11 KNN f1 score: 0.647 KNN cohens kappa score: 0.630 ====== 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: 329, 4 GAN fn, tp: 2, 11 GAN f1 score: 0.786 GAN cohens kappa score: 0.777 -> test with 'LR' LR tn, fp: 291, 42 LR fn, tp: 0, 13 LR f1 score: 0.382 LR cohens kappa score: 0.342 LR average precision score: 0.289 -> 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: 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: 331, 2 GAN fn, tp: 3, 10 GAN f1 score: 0.800 GAN cohens kappa score: 0.793 -> test with 'LR' LR tn, fp: 278, 55 LR fn, tp: 0, 13 LR f1 score: 0.321 LR cohens kappa score: 0.275 LR average precision score: 0.365 -> 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: 313, 20 KNN fn, tp: 0, 13 KNN f1 score: 0.565 KNN cohens kappa score: 0.540 ------ 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: 330, 3 GAN fn, tp: 1, 12 GAN f1 score: 0.857 GAN cohens kappa score: 0.851 -> test with 'LR' LR tn, fp: 295, 38 LR fn, tp: 2, 11 LR f1 score: 0.355 LR cohens kappa score: 0.314 LR average precision score: 0.332 -> 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: 316, 17 KNN fn, tp: 0, 13 KNN f1 score: 0.605 KNN cohens kappa score: 0.583 ------ 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: 325, 8 GAN fn, tp: 4, 9 GAN f1 score: 0.600 GAN cohens kappa score: 0.582 -> 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: 0.294 -> 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: 325, 8 KNN fn, tp: 0, 13 KNN f1 score: 0.765 KNN cohens kappa score: 0.753 ------ 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: 321, 10 GAN fn, tp: 2, 11 GAN f1 score: 0.647 GAN cohens kappa score: 0.630 -> test with 'LR' LR tn, fp: 291, 40 LR fn, tp: 0, 13 LR f1 score: 0.394 LR cohens kappa score: 0.355 LR average precision score: 0.549 -> 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: 324, 7 KNN fn, tp: 0, 13 KNN f1 score: 0.788 KNN cohens kappa score: 0.778 ====== 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: 329, 4 GAN fn, tp: 3, 10 GAN f1 score: 0.741 GAN cohens kappa score: 0.730 -> test with 'LR' LR tn, fp: 295, 38 LR fn, tp: 1, 12 LR f1 score: 0.381 LR cohens kappa score: 0.342 LR average precision score: 0.279 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 3, 10 GB f1 score: 0.833 GB cohens kappa score: 0.827 -> test with 'KNN' KNN tn, fp: 322, 11 KNN fn, tp: 1, 12 KNN f1 score: 0.667 KNN cohens kappa score: 0.650 ------ 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: 327, 6 GAN fn, tp: 2, 11 GAN f1 score: 0.733 GAN cohens kappa score: 0.721 -> 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: 0.402 -> 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: 319, 14 KNN fn, tp: 0, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ------ 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: 326, 7 GAN fn, tp: 0, 13 GAN f1 score: 0.788 GAN cohens kappa score: 0.778 -> 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: 0.314 -> 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: 321, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ------ 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: 329, 4 GAN fn, tp: 1, 12 GAN f1 score: 0.828 GAN cohens kappa score: 0.820 -> 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: 0.389 -> 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 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with GAN.predict GAN tn, fp: 322, 9 GAN fn, tp: 2, 11 GAN f1 score: 0.667 GAN cohens kappa score: 0.651 -> test with 'LR' LR tn, fp: 296, 35 LR fn, tp: 2, 11 LR f1 score: 0.373 LR cohens kappa score: 0.334 LR average precision score: 0.374 -> 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: 326, 5 KNN fn, tp: 1, 12 KNN f1 score: 0.800 KNN cohens kappa score: 0.791 ====== 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: 0, 13 GAN f1 score: 0.897 GAN cohens kappa score: 0.892 -> test with 'LR' LR tn, fp: 300, 33 LR fn, tp: 1, 12 LR f1 score: 0.414 LR cohens kappa score: 0.378 LR average precision score: 0.336 -> 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: 325, 8 KNN fn, tp: 1, 12 KNN f1 score: 0.727 KNN cohens kappa score: 0.714 ------ 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: 291, 42 LR fn, tp: 1, 12 LR f1 score: 0.358 LR cohens kappa score: 0.317 LR average precision score: 0.515 -> 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: 322, 11 KNN fn, tp: 0, 13 KNN f1 score: 0.703 KNN cohens kappa score: 0.687 ------ 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: 324, 9 GAN fn, tp: 1, 12 GAN f1 score: 0.706 GAN cohens kappa score: 0.692 -> test with 'LR' LR tn, fp: 285, 48 LR fn, tp: 0, 13 LR f1 score: 0.351 LR cohens kappa score: 0.309 LR average precision score: 0.288 -> test with 'GB' GB tn, fp: 330, 3 GB fn, tp: 0, 13 GB f1 score: 0.897 GB cohens kappa score: 0.892 -> 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 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: 6, 7 GAN f1 score: 0.467 GAN cohens kappa score: 0.443 -> test with 'LR' LR tn, fp: 295, 38 LR fn, tp: 1, 12 LR f1 score: 0.381 LR cohens kappa score: 0.342 LR average precision score: 0.274 -> 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: 317, 16 KNN fn, tp: 0, 13 KNN f1 score: 0.619 KNN cohens kappa score: 0.598 ------ 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: 324, 7 GAN fn, tp: 2, 11 GAN f1 score: 0.710 GAN cohens kappa score: 0.696 -> test with 'LR' LR tn, fp: 298, 33 LR fn, tp: 0, 13 LR f1 score: 0.441 LR cohens kappa score: 0.406 LR average precision score: 0.325 -> 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: 316, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ====== 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: 324, 9 GAN fn, tp: 5, 8 GAN f1 score: 0.533 GAN cohens kappa score: 0.513 -> test with 'LR' LR tn, fp: 271, 62 LR fn, tp: 0, 13 LR f1 score: 0.295 LR cohens kappa score: 0.247 LR average precision score: 0.341 -> 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: 315, 18 KNN fn, tp: 0, 13 KNN f1 score: 0.591 KNN cohens kappa score: 0.568 ------ 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: 325, 8 GAN fn, tp: 3, 10 GAN f1 score: 0.645 GAN cohens kappa score: 0.629 -> test with 'LR' LR tn, fp: 306, 27 LR fn, tp: 3, 10 LR f1 score: 0.400 LR cohens kappa score: 0.365 LR average precision score: 0.326 -> 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: 320, 13 KNN fn, tp: 1, 12 KNN f1 score: 0.632 KNN cohens kappa score: 0.612 ------ 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: 330, 3 GAN fn, tp: 4, 9 GAN f1 score: 0.720 GAN cohens kappa score: 0.710 -> test with 'LR' LR tn, fp: 305, 28 LR fn, tp: 1, 12 LR f1 score: 0.453 LR cohens kappa score: 0.420 LR average precision score: 0.348 -> 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 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: 0.281 -> 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: 321, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ------ 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: 3, 10 GAN f1 score: 0.800 GAN cohens kappa score: 0.792 -> test with 'LR' LR tn, fp: 288, 43 LR fn, tp: 0, 13 LR f1 score: 0.377 LR cohens kappa score: 0.336 LR average precision score: 0.444 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 1, 12 GB f1 score: 0.889 GB cohens kappa score: 0.884 -> test with 'KNN' KNN tn, fp: 317, 14 KNN fn, tp: 0, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 306, 62 LR fn, tp: 3, 13 LR f1 score: 0.453 LR cohens kappa score: 0.420 LR average precision score: 0.549 average: LR tn, fp: 291.88, 40.72 LR fn, tp: 0.52, 12.48 LR f1 score: 0.381 LR cohens kappa score: 0.341 LR average precision score: 0.355 minimum: LR tn, fp: 271, 27 LR fn, tp: 0, 10 LR f1 score: 0.295 LR cohens kappa score: 0.247 LR average precision score: 0.274 -----[ GB ]----- maximum: GB tn, fp: 333, 4 GB fn, tp: 3, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 331.68, 0.92 GB fn, tp: 0.76, 12.24 GB f1 score: 0.936 GB cohens kappa score: 0.933 minimum: GB tn, fp: 329, 0 GB fn, tp: 0, 10 GB f1 score: 0.833 GB cohens kappa score: 0.827 -----[ KNN ]----- maximum: KNN tn, fp: 327, 20 KNN fn, tp: 2, 13 KNN f1 score: 0.813 KNN cohens kappa score: 0.804 average: KNN tn, fp: 320.36, 12.24 KNN fn, tp: 0.28, 12.72 KNN f1 score: 0.677 KNN cohens kappa score: 0.660 minimum: KNN tn, fp: 313, 5 KNN fn, tp: 0, 11 KNN f1 score: 0.558 KNN cohens kappa score: 0.534 -----[ GAN ]----- maximum: GAN tn, fp: 331, 11 GAN fn, tp: 6, 13 GAN f1 score: 0.897 GAN cohens kappa score: 0.892 average: GAN tn, fp: 326.72, 5.88 GAN fn, tp: 2.4, 10.6 GAN f1 score: 0.723 GAN cohens kappa score: 0.711 minimum: GAN tn, fp: 321, 2 GAN fn, tp: 0, 7 GAN f1 score: 0.467 GAN cohens kappa score: 0.443