/////////////////////////////////////////// // Running convGAN-majority-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: 323, 10 GAN fn, tp: 1, 12 GAN f1 score: 0.686 GAN cohens kappa score: 0.670 -> 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: 0.361 -> 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 1/5: 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: 3, 10 GAN f1 score: 0.741 GAN cohens kappa score: 0.730 -> test with 'LR' LR tn, fp: 294, 39 LR fn, tp: 1, 12 LR f1 score: 0.375 LR cohens kappa score: 0.335 LR average precision score: 0.304 -> 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: 314, 19 KNN fn, tp: 1, 12 KNN f1 score: 0.545 KNN cohens kappa score: 0.520 ------ 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: 326, 7 GAN fn, tp: 3, 10 GAN f1 score: 0.667 GAN cohens kappa score: 0.652 -> 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: 0.382 -> 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: 323, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ------ 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: 327, 6 GAN fn, tp: 2, 11 GAN f1 score: 0.733 GAN cohens kappa score: 0.721 -> test with 'LR' LR tn, fp: 293, 40 LR fn, tp: 0, 13 LR f1 score: 0.394 LR cohens kappa score: 0.355 LR average precision score: 0.372 -> 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: 320, 13 KNN fn, tp: 0, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ------ 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: 295, 36 LR fn, tp: 1, 12 LR f1 score: 0.393 LR cohens kappa score: 0.355 LR average precision score: 0.433 -> 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: 317, 14 KNN fn, tp: 0, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ====== 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: 328, 5 GAN fn, tp: 4, 9 GAN f1 score: 0.667 GAN cohens kappa score: 0.653 -> 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: 0.283 -> 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: 316, 17 KNN fn, tp: 0, 13 KNN f1 score: 0.605 KNN cohens kappa score: 0.583 ------ 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: 325, 8 GAN fn, tp: 1, 12 GAN f1 score: 0.727 GAN cohens kappa score: 0.714 -> test with 'LR' LR tn, fp: 275, 58 LR fn, tp: 0, 13 LR f1 score: 0.310 LR cohens kappa score: 0.263 LR average precision score: 0.290 -> 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: 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: 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: 2, 11 LR f1 score: 0.355 LR cohens kappa score: 0.314 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: 322, 11 KNN fn, tp: 0, 13 KNN f1 score: 0.703 KNN cohens kappa score: 0.687 ------ 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: 323, 10 GAN fn, tp: 1, 12 GAN f1 score: 0.686 GAN cohens kappa score: 0.670 -> 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: 0.284 -> 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: 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: 324, 7 GAN fn, tp: 0, 13 GAN f1 score: 0.788 GAN cohens kappa score: 0.778 -> 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: 0.532 -> 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: 322, 9 KNN fn, tp: 0, 13 KNN f1 score: 0.743 KNN cohens kappa score: 0.730 ====== 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: 6, 7 GAN f1 score: 0.583 GAN cohens kappa score: 0.568 -> test with 'LR' LR tn, fp: 296, 37 LR fn, tp: 1, 12 LR f1 score: 0.387 LR cohens kappa score: 0.348 LR average precision score: 0.310 -> 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: 3, 10 KNN f1 score: 0.645 KNN cohens kappa score: 0.629 ------ 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: 328, 5 GAN fn, tp: 2, 11 GAN f1 score: 0.759 GAN cohens kappa score: 0.748 -> 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: 0.439 -> 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: 330, 3 KNN fn, tp: 0, 13 KNN f1 score: 0.897 KNN cohens kappa score: 0.892 ------ 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: 320, 13 GAN fn, tp: 0, 13 GAN f1 score: 0.667 GAN cohens kappa score: 0.649 -> 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: 0.340 -> 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: 314, 19 KNN fn, tp: 0, 13 KNN f1 score: 0.578 KNN cohens kappa score: 0.554 ------ 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: 289, 44 LR fn, tp: 0, 13 LR f1 score: 0.371 LR cohens kappa score: 0.330 LR average precision score: 0.407 -> 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 3/5: 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: 2, 11 GAN f1 score: 0.786 GAN cohens kappa score: 0.777 -> test with 'LR' LR tn, fp: 293, 38 LR fn, tp: 2, 11 LR f1 score: 0.355 LR cohens kappa score: 0.314 LR average precision score: 0.338 -> 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: 324, 7 KNN fn, tp: 2, 11 KNN f1 score: 0.710 KNN cohens kappa score: 0.696 ====== 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: 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: 0.359 -> 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: 1, 12 KNN f1 score: 0.800 KNN cohens kappa score: 0.791 ------ 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: 327, 6 GAN fn, tp: 3, 10 GAN f1 score: 0.690 GAN cohens kappa score: 0.676 -> test with 'LR' LR tn, fp: 285, 48 LR fn, tp: 1, 12 LR f1 score: 0.329 LR cohens kappa score: 0.285 LR average precision score: 0.533 -> 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: 316, 17 KNN fn, tp: 0, 13 KNN f1 score: 0.605 KNN cohens kappa score: 0.583 ------ 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: 288, 45 LR fn, tp: 0, 13 LR f1 score: 0.366 LR cohens kappa score: 0.325 LR average precision score: 0.276 -> 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: 327, 6 GAN fn, tp: 7, 6 GAN f1 score: 0.480 GAN cohens kappa score: 0.461 -> test with 'LR' LR tn, fp: 298, 35 LR fn, tp: 1, 12 LR f1 score: 0.400 LR cohens kappa score: 0.362 LR average precision score: 0.270 -> 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 4/5: 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: 4, 9 GAN f1 score: 0.720 GAN cohens kappa score: 0.709 -> 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: 0.359 -> 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: 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: 324, 9 GAN fn, tp: 1, 12 GAN f1 score: 0.706 GAN cohens kappa score: 0.692 -> test with 'LR' LR tn, fp: 275, 58 LR fn, tp: 0, 13 LR f1 score: 0.310 LR cohens kappa score: 0.263 LR average precision score: 0.328 -> 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: 323, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ------ 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: 3, 10 GAN f1 score: 0.690 GAN cohens kappa score: 0.676 -> 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: 0.340 -> 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: 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: 327, 6 GAN fn, tp: 4, 9 GAN f1 score: 0.643 GAN cohens kappa score: 0.628 -> test with 'LR' LR tn, fp: 303, 30 LR fn, tp: 1, 12 LR f1 score: 0.436 LR cohens kappa score: 0.402 LR average precision score: 0.356 -> 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: 321, 12 KNN fn, tp: 1, 12 KNN f1 score: 0.649 KNN cohens kappa score: 0.631 ------ 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: 324, 9 GAN fn, tp: 2, 11 GAN f1 score: 0.667 GAN cohens kappa score: 0.651 -> 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: 0.274 -> 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 5/5: 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: 1, 12 GAN f1 score: 0.800 GAN cohens kappa score: 0.791 -> 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: 0.471 -> 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: 326, 5 KNN fn, tp: 1, 12 KNN f1 score: 0.800 KNN cohens kappa score: 0.791 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 303, 58 LR fn, tp: 3, 13 LR f1 score: 0.448 LR cohens kappa score: 0.414 LR average precision score: 0.533 average: LR tn, fp: 291.56, 41.04 LR fn, tp: 0.52, 12.48 LR f1 score: 0.379 LR cohens kappa score: 0.339 LR average precision score: 0.359 minimum: LR tn, fp: 275, 30 LR fn, tp: 0, 10 LR f1 score: 0.310 LR cohens kappa score: 0.263 LR average precision score: 0.270 -----[ 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: 331.88, 0.72 GB fn, tp: 0.6, 12.4 GB f1 score: 0.950 GB cohens kappa score: 0.948 minimum: GB tn, fp: 329, 0 GB fn, tp: 0, 10 GB f1 score: 0.867 GB cohens kappa score: 0.861 -----[ KNN ]----- maximum: KNN tn, fp: 330, 20 KNN fn, tp: 3, 13 KNN f1 score: 0.897 KNN cohens kappa score: 0.892 average: KNN tn, fp: 321.16, 11.44 KNN fn, tp: 0.44, 12.56 KNN f1 score: 0.689 KNN cohens kappa score: 0.673 minimum: KNN tn, fp: 313, 3 KNN fn, tp: 0, 10 KNN f1 score: 0.545 KNN cohens kappa score: 0.520 -----[ GAN ]----- maximum: GAN tn, fp: 330, 13 GAN fn, tp: 7, 13 GAN f1 score: 0.815 GAN cohens kappa score: 0.807 average: GAN tn, fp: 326.16, 6.44 GAN fn, tp: 2.36, 10.64 GAN f1 score: 0.706 GAN cohens kappa score: 0.693 minimum: GAN tn, fp: 320, 3 GAN fn, tp: 0, 6 GAN f1 score: 0.480 GAN cohens kappa score: 0.461