/////////////////////////////////////////// // Running convGAN-majority-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: 330, 3 GAN fn, tp: 4, 9 GAN f1 score: 0.720 GAN cohens kappa score: 0.710 -> test with 'LR' LR tn, fp: 294, 39 LR fn, tp: 0, 13 LR f1 score: 0.400 LR cohens kappa score: 0.362 LR average precision score: 0.360 -> 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 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 328, 5 GAN fn, tp: 2, 11 GAN f1 score: 0.759 GAN cohens kappa score: 0.748 -> 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: 0.303 -> 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: 318, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ 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: 0, 13 GAN f1 score: 0.765 GAN cohens kappa score: 0.753 -> 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: 0.410 -> 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: 317, 16 KNN fn, tp: 0, 13 KNN f1 score: 0.619 KNN cohens kappa score: 0.598 ------ 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: 328, 5 GAN fn, tp: 3, 10 GAN f1 score: 0.714 GAN cohens kappa score: 0.702 -> 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: 0.366 -> 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: 330, 3 KNN fn, tp: 0, 13 KNN f1 score: 0.897 KNN cohens kappa score: 0.892 ------ 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: 328, 3 GAN fn, tp: 3, 10 GAN f1 score: 0.769 GAN cohens kappa score: 0.760 -> 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: 0.448 -> test with 'GB' GB tn, fp: 330, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> 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 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: 327, 6 GAN fn, tp: 4, 9 GAN f1 score: 0.643 GAN cohens kappa score: 0.628 -> test with 'LR' LR tn, fp: 302, 31 LR fn, tp: 2, 11 LR f1 score: 0.400 LR cohens kappa score: 0.363 LR average precision score: 0.288 -> 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: 325, 8 KNN fn, tp: 0, 13 KNN f1 score: 0.765 KNN cohens kappa score: 0.753 ------ 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: 280, 53 LR fn, tp: 0, 13 LR f1 score: 0.329 LR cohens kappa score: 0.284 LR average precision score: 0.367 -> 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: 318, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ 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: 1, 12 LR f1 score: 0.381 LR cohens kappa score: 0.342 LR average precision score: 0.333 -> 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: 328, 5 KNN fn, tp: 0, 13 KNN f1 score: 0.839 KNN cohens kappa score: 0.831 ------ 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: 2, 11 GAN f1 score: 0.688 GAN cohens kappa score: 0.673 -> test with 'LR' LR tn, fp: 295, 38 LR fn, tp: 0, 13 LR f1 score: 0.406 LR cohens kappa score: 0.368 LR average precision score: 0.318 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 3, 10 GB f1 score: 0.870 GB cohens kappa score: 0.865 -> 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 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with GAN.predict GAN tn, fp: 327, 4 GAN fn, tp: 3, 10 GAN f1 score: 0.741 GAN cohens kappa score: 0.730 -> 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: 0.522 -> 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: 4, 9 GAN f1 score: 0.692 GAN cohens kappa score: 0.680 -> 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: 325, 8 KNN fn, tp: 1, 12 KNN f1 score: 0.727 KNN cohens kappa score: 0.714 ------ 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: 330, 3 GAN fn, tp: 2, 11 GAN f1 score: 0.815 GAN cohens kappa score: 0.807 -> test with 'LR' LR tn, fp: 301, 32 LR fn, tp: 0, 13 LR f1 score: 0.448 LR cohens kappa score: 0.414 LR average precision score: 0.446 -> 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: 331, 2 KNN fn, tp: 0, 13 KNN f1 score: 0.929 KNN cohens kappa score: 0.926 ------ 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: 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: 0.330 -> 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: 326, 7 GAN fn, tp: 1, 12 GAN f1 score: 0.750 GAN cohens kappa score: 0.738 -> test with 'LR' LR tn, fp: 294, 39 LR fn, tp: 0, 13 LR f1 score: 0.400 LR cohens kappa score: 0.362 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: 325, 6 GAN fn, tp: 2, 11 GAN f1 score: 0.733 GAN cohens kappa score: 0.721 -> test with 'LR' LR tn, fp: 295, 36 LR fn, tp: 2, 11 LR f1 score: 0.367 LR cohens kappa score: 0.327 LR average precision score: 0.370 -> 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: 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: 328, 5 GAN fn, tp: 2, 11 GAN f1 score: 0.759 GAN cohens kappa score: 0.748 -> test with 'LR' LR tn, fp: 299, 34 LR fn, tp: 0, 13 LR f1 score: 0.433 LR cohens kappa score: 0.398 LR average precision score: 0.370 -> 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: 324, 9 KNN fn, tp: 0, 13 KNN f1 score: 0.743 KNN cohens kappa score: 0.730 ------ 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: 329, 4 GAN fn, tp: 4, 9 GAN f1 score: 0.692 GAN cohens kappa score: 0.680 -> 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: 328, 5 KNN fn, tp: 0, 13 KNN f1 score: 0.839 KNN cohens kappa score: 0.831 ------ 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: 321, 12 GAN fn, tp: 1, 12 GAN f1 score: 0.649 GAN cohens kappa score: 0.631 -> 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: 0.321 -> 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: 325, 8 KNN fn, tp: 0, 13 KNN f1 score: 0.765 KNN cohens kappa score: 0.753 ------ 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: 326, 7 GAN fn, tp: 6, 7 GAN f1 score: 0.519 GAN cohens kappa score: 0.499 -> test with 'LR' LR tn, fp: 298, 35 LR fn, tp: 2, 11 LR f1 score: 0.373 LR cohens kappa score: 0.334 LR average precision score: 0.272 -> 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: 1, 12 KNN f1 score: 0.750 KNN cohens kappa score: 0.738 ------ 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: 325, 6 GAN fn, tp: 1, 12 GAN f1 score: 0.774 GAN cohens kappa score: 0.764 -> 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: 0.346 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 3, 10 GB f1 score: 0.870 GB cohens kappa score: 0.865 -> test with 'KNN' KNN tn, fp: 317, 14 KNN fn, tp: 0, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ====== 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: 332, 1 GAN fn, tp: 4, 9 GAN f1 score: 0.783 GAN cohens kappa score: 0.775 -> test with 'LR' LR tn, fp: 282, 51 LR fn, tp: 0, 13 LR f1 score: 0.338 LR cohens kappa score: 0.294 LR average precision score: 0.291 -> 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 5/5: 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: 297, 36 LR fn, tp: 2, 11 LR f1 score: 0.367 LR cohens kappa score: 0.327 LR average precision score: 0.357 -> 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: 327, 6 KNN fn, tp: 1, 12 KNN f1 score: 0.774 KNN cohens kappa score: 0.764 ------ 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: 327, 6 GAN fn, tp: 5, 8 GAN f1 score: 0.593 GAN cohens kappa score: 0.576 -> test with 'LR' LR tn, fp: 307, 26 LR fn, tp: 2, 11 LR f1 score: 0.440 LR cohens kappa score: 0.407 LR average precision score: 0.332 -> 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: 328, 5 KNN fn, tp: 0, 13 KNN f1 score: 0.839 KNN cohens kappa score: 0.831 ------ 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: 326, 7 GAN fn, tp: 2, 11 GAN f1 score: 0.710 GAN cohens kappa score: 0.696 -> test with 'LR' LR tn, fp: 289, 44 LR fn, tp: 0, 13 LR f1 score: 0.371 LR cohens kappa score: 0.330 LR average precision score: 0.291 -> 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 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: 293, 38 LR fn, tp: 0, 13 LR f1 score: 0.406 LR cohens kappa score: 0.368 LR average precision score: 0.455 -> 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: 324, 7 KNN fn, tp: 0, 13 KNN f1 score: 0.788 KNN cohens kappa score: 0.778 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 307, 53 LR fn, tp: 2, 13 LR f1 score: 0.448 LR cohens kappa score: 0.414 LR average precision score: 0.522 average: LR tn, fp: 293.0, 39.6 LR fn, tp: 0.64, 12.36 LR f1 score: 0.383 LR cohens kappa score: 0.344 LR average precision score: 0.363 minimum: LR tn, fp: 280, 26 LR fn, tp: 0, 11 LR f1 score: 0.329 LR cohens kappa score: 0.284 LR average precision score: 0.272 -----[ GB ]----- maximum: GB tn, fp: 333, 2 GB fn, tp: 3, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 332.0, 0.6 GB fn, tp: 0.48, 12.52 GB f1 score: 0.958 GB cohens kappa score: 0.956 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: 331, 16 KNN fn, tp: 1, 13 KNN f1 score: 0.929 KNN cohens kappa score: 0.926 average: KNN tn, fp: 324.6, 8.0 KNN fn, tp: 0.12, 12.88 KNN f1 score: 0.769 KNN cohens kappa score: 0.758 minimum: KNN tn, fp: 317, 2 KNN fn, tp: 0, 12 KNN f1 score: 0.619 KNN cohens kappa score: 0.598 -----[ GAN ]----- maximum: GAN tn, fp: 332, 12 GAN fn, tp: 6, 13 GAN f1 score: 0.857 GAN cohens kappa score: 0.851 average: GAN tn, fp: 327.24, 5.36 GAN fn, tp: 2.52, 10.48 GAN f1 score: 0.728 GAN cohens kappa score: 0.716 minimum: GAN tn, fp: 321, 1 GAN fn, tp: 0, 7 GAN f1 score: 0.519 GAN cohens kappa score: 0.499