/////////////////////////////////////////// // Running convGAN-majority-full on folding_car_good /////////////////////////////////////////// Load 'data_input/folding_car_good' 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 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 325, 7 GAN fn, tp: 1, 13 GAN f1 score: 0.765 GAN cohens kappa score: 0.753 -> test with 'LR' LR tn, fp: 180, 152 LR fn, tp: 7, 7 LR f1 score: 0.081 LR cohens kappa score: 0.007 LR average precision score: 0.060 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 2, 12 GB f1 score: 0.857 GB cohens kappa score: 0.851 -> test with 'KNN' KNN tn, fp: 327, 5 KNN fn, tp: 1, 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 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 327, 5 GAN fn, tp: 1, 13 GAN f1 score: 0.813 GAN cohens kappa score: 0.804 -> test with 'LR' LR tn, fp: 188, 144 LR fn, tp: 4, 10 LR f1 score: 0.119 LR cohens kappa score: 0.048 LR average precision score: 0.083 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 4, 10 GB f1 score: 0.769 GB cohens kappa score: 0.760 -> test with 'KNN' KNN tn, fp: 319, 13 KNN fn, tp: 0, 14 KNN f1 score: 0.683 KNN cohens kappa score: 0.665 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 325, 7 GAN fn, tp: 2, 12 GAN f1 score: 0.727 GAN cohens kappa score: 0.714 -> test with 'LR' LR tn, fp: 179, 153 LR fn, tp: 7, 7 LR f1 score: 0.080 LR cohens kappa score: 0.007 LR average precision score: 0.056 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 5, 9 GB f1 score: 0.750 GB cohens kappa score: 0.741 -> test with 'KNN' KNN tn, fp: 319, 13 KNN fn, tp: 0, 14 KNN f1 score: 0.683 KNN cohens kappa score: 0.665 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 322, 10 GAN fn, tp: 3, 11 GAN f1 score: 0.629 GAN cohens kappa score: 0.610 -> test with 'LR' LR tn, fp: 185, 147 LR fn, tp: 4, 10 LR f1 score: 0.117 LR cohens kappa score: 0.046 LR average precision score: 0.077 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 8, 6 GB f1 score: 0.522 GB cohens kappa score: 0.506 -> test with 'KNN' KNN tn, fp: 320, 12 KNN fn, tp: 1, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.648 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 324, 7 GAN fn, tp: 4, 9 GAN f1 score: 0.621 GAN cohens kappa score: 0.604 -> test with 'LR' LR tn, fp: 181, 150 LR fn, tp: 5, 8 LR f1 score: 0.094 LR cohens kappa score: 0.026 LR average precision score: 0.055 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 2, 11 GB f1 score: 0.815 GB cohens kappa score: 0.807 -> test with 'KNN' KNN tn, fp: 318, 13 KNN fn, tp: 0, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 327, 5 GAN fn, tp: 1, 13 GAN f1 score: 0.813 GAN cohens kappa score: 0.804 -> test with 'LR' LR tn, fp: 169, 163 LR fn, tp: 5, 9 LR f1 score: 0.097 LR cohens kappa score: 0.024 LR average precision score: 0.066 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 2, 12 GB f1 score: 0.889 GB cohens kappa score: 0.884 -> test with 'KNN' KNN tn, fp: 324, 8 KNN fn, tp: 1, 13 KNN f1 score: 0.743 KNN cohens kappa score: 0.730 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 322, 10 GAN fn, tp: 1, 13 GAN f1 score: 0.703 GAN cohens kappa score: 0.687 -> test with 'LR' LR tn, fp: 175, 157 LR fn, tp: 3, 11 LR f1 score: 0.121 LR cohens kappa score: 0.050 LR average precision score: 0.069 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 1, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 318, 14 KNN fn, tp: 0, 14 KNN f1 score: 0.667 KNN cohens kappa score: 0.648 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 327, 5 GAN fn, tp: 4, 10 GAN f1 score: 0.690 GAN cohens kappa score: 0.676 -> test with 'LR' LR tn, fp: 189, 143 LR fn, tp: 4, 10 LR f1 score: 0.120 LR cohens kappa score: 0.049 LR average precision score: 0.073 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 5, 9 GB f1 score: 0.783 GB cohens kappa score: 0.775 -> test with 'KNN' KNN tn, fp: 325, 7 KNN fn, tp: 1, 13 KNN f1 score: 0.765 KNN cohens kappa score: 0.753 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 323, 9 GAN fn, tp: 3, 11 GAN f1 score: 0.647 GAN cohens kappa score: 0.629 -> test with 'LR' LR tn, fp: 192, 140 LR fn, tp: 9, 5 LR f1 score: 0.063 LR cohens kappa score: -0.012 LR average precision score: 0.050 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 7, 7 GB f1 score: 0.636 GB cohens kappa score: 0.625 -> test with 'KNN' KNN tn, fp: 308, 24 KNN fn, tp: 3, 11 KNN f1 score: 0.449 KNN cohens kappa score: 0.415 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 326, 5 GAN fn, tp: 6, 7 GAN f1 score: 0.560 GAN cohens kappa score: 0.543 -> test with 'LR' LR tn, fp: 192, 139 LR fn, tp: 5, 8 LR f1 score: 0.100 LR cohens kappa score: 0.033 LR average precision score: 0.074 -> test with 'GB' GB tn, fp: 327, 4 GB fn, tp: 3, 10 GB f1 score: 0.741 GB cohens kappa score: 0.730 -> test with 'KNN' KNN tn, fp: 302, 29 KNN fn, tp: 0, 13 KNN f1 score: 0.473 KNN cohens kappa score: 0.440 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 329, 3 GAN fn, tp: 3, 11 GAN f1 score: 0.786 GAN cohens kappa score: 0.777 -> test with 'LR' LR tn, fp: 168, 164 LR fn, tp: 3, 11 LR f1 score: 0.116 LR cohens kappa score: 0.045 LR average precision score: 0.078 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 3, 11 GB f1 score: 0.880 GB cohens kappa score: 0.876 -> test with 'KNN' KNN tn, fp: 322, 10 KNN fn, tp: 1, 13 KNN f1 score: 0.703 KNN cohens kappa score: 0.687 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 324, 8 GAN fn, tp: 3, 11 GAN f1 score: 0.667 GAN cohens kappa score: 0.650 -> test with 'LR' LR tn, fp: 195, 137 LR fn, tp: 5, 9 LR f1 score: 0.112 LR cohens kappa score: 0.042 LR average precision score: 0.071 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 3, 11 GB f1 score: 0.815 GB cohens kappa score: 0.807 -> test with 'KNN' KNN tn, fp: 318, 14 KNN fn, tp: 0, 14 KNN f1 score: 0.667 KNN cohens kappa score: 0.648 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 326, 6 GAN fn, tp: 5, 9 GAN f1 score: 0.621 GAN cohens kappa score: 0.604 -> test with 'LR' LR tn, fp: 186, 146 LR fn, tp: 6, 8 LR f1 score: 0.095 LR cohens kappa score: 0.023 LR average precision score: 0.056 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 9, 5 GB f1 score: 0.455 GB cohens kappa score: 0.438 -> test with 'KNN' KNN tn, fp: 320, 12 KNN fn, tp: 2, 12 KNN f1 score: 0.632 KNN cohens kappa score: 0.612 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 327, 5 GAN fn, tp: 2, 12 GAN f1 score: 0.774 GAN cohens kappa score: 0.764 -> test with 'LR' LR tn, fp: 179, 153 LR fn, tp: 2, 12 LR f1 score: 0.134 LR cohens kappa score: 0.064 LR average precision score: 0.087 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 4, 10 GB f1 score: 0.833 GB cohens kappa score: 0.828 -> test with 'KNN' KNN tn, fp: 317, 15 KNN fn, tp: 0, 14 KNN f1 score: 0.651 KNN cohens kappa score: 0.631 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 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: 170, 161 LR fn, tp: 5, 8 LR f1 score: 0.088 LR cohens kappa score: 0.019 LR average precision score: 0.054 -> test with 'GB' GB tn, fp: 327, 4 GB fn, tp: 2, 11 GB f1 score: 0.786 GB cohens kappa score: 0.777 -> test with 'KNN' KNN tn, fp: 304, 27 KNN fn, tp: 1, 12 KNN f1 score: 0.462 KNN cohens kappa score: 0.429 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 324, 8 GAN fn, tp: 3, 11 GAN f1 score: 0.667 GAN cohens kappa score: 0.650 -> test with 'LR' LR tn, fp: 180, 152 LR fn, tp: 3, 11 LR f1 score: 0.124 LR cohens kappa score: 0.054 LR average precision score: 0.065 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 4, 10 GB f1 score: 0.833 GB cohens kappa score: 0.828 -> test with 'KNN' KNN tn, fp: 324, 8 KNN fn, tp: 0, 14 KNN f1 score: 0.778 KNN cohens kappa score: 0.766 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 324, 8 GAN fn, tp: 6, 8 GAN f1 score: 0.533 GAN cohens kappa score: 0.512 -> test with 'LR' LR tn, fp: 179, 153 LR fn, tp: 6, 8 LR f1 score: 0.091 LR cohens kappa score: 0.018 LR average precision score: 0.063 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 5, 9 GB f1 score: 0.720 GB cohens kappa score: 0.710 -> test with 'KNN' KNN tn, fp: 313, 19 KNN fn, tp: 0, 14 KNN f1 score: 0.596 KNN cohens kappa score: 0.571 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 326, 6 GAN fn, tp: 6, 8 GAN f1 score: 0.571 GAN cohens kappa score: 0.553 -> test with 'LR' LR tn, fp: 176, 156 LR fn, tp: 5, 9 LR f1 score: 0.101 LR cohens kappa score: 0.028 LR average precision score: 0.069 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 5, 9 GB f1 score: 0.783 GB cohens kappa score: 0.775 -> test with 'KNN' KNN tn, fp: 312, 20 KNN fn, tp: 0, 14 KNN f1 score: 0.583 KNN cohens kappa score: 0.558 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 327, 5 GAN fn, tp: 2, 12 GAN f1 score: 0.774 GAN cohens kappa score: 0.764 -> test with 'LR' LR tn, fp: 191, 141 LR fn, tp: 6, 8 LR f1 score: 0.098 LR cohens kappa score: 0.026 LR average precision score: 0.055 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 6, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 323, 9 KNN fn, tp: 0, 14 KNN f1 score: 0.757 KNN cohens kappa score: 0.744 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 325, 6 GAN fn, tp: 3, 10 GAN f1 score: 0.690 GAN cohens kappa score: 0.676 -> test with 'LR' LR tn, fp: 180, 151 LR fn, tp: 2, 11 LR f1 score: 0.126 LR cohens kappa score: 0.060 LR average precision score: 0.080 -> test with 'GB' GB tn, fp: 325, 6 GB fn, tp: 7, 6 GB f1 score: 0.480 GB cohens kappa score: 0.460 -> test with 'KNN' KNN tn, fp: 312, 19 KNN fn, tp: 1, 12 KNN f1 score: 0.545 KNN cohens kappa score: 0.520 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 319, 13 GAN fn, tp: 1, 13 GAN f1 score: 0.650 GAN cohens kappa score: 0.631 -> test with 'LR' LR tn, fp: 187, 145 LR fn, tp: 8, 6 LR f1 score: 0.073 LR cohens kappa score: -0.001 LR average precision score: 0.052 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 8, 6 GB f1 score: 0.571 GB cohens kappa score: 0.560 -> test with 'KNN' KNN tn, fp: 314, 18 KNN fn, tp: 1, 13 KNN f1 score: 0.578 KNN cohens kappa score: 0.553 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 328, 4 GAN fn, tp: 2, 12 GAN f1 score: 0.800 GAN cohens kappa score: 0.791 -> test with 'LR' LR tn, fp: 184, 148 LR fn, tp: 4, 10 LR f1 score: 0.116 LR cohens kappa score: 0.045 LR average precision score: 0.069 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 8, 6 GB f1 score: 0.545 GB cohens kappa score: 0.532 -> test with 'KNN' KNN tn, fp: 317, 15 KNN fn, tp: 0, 14 KNN f1 score: 0.651 KNN cohens kappa score: 0.631 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 322, 10 GAN fn, tp: 4, 10 GAN f1 score: 0.588 GAN cohens kappa score: 0.568 -> test with 'LR' LR tn, fp: 163, 169 LR fn, tp: 2, 12 LR f1 score: 0.123 LR cohens kappa score: 0.052 LR average precision score: 0.072 -> test with 'GB' GB tn, fp: 328, 4 GB fn, tp: 1, 13 GB f1 score: 0.839 GB cohens kappa score: 0.831 -> test with 'KNN' KNN tn, fp: 318, 14 KNN fn, tp: 0, 14 KNN f1 score: 0.667 KNN cohens kappa score: 0.648 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 327, 5 GAN fn, tp: 6, 8 GAN f1 score: 0.593 GAN cohens kappa score: 0.576 -> test with 'LR' LR tn, fp: 168, 164 LR fn, tp: 4, 10 LR f1 score: 0.106 LR cohens kappa score: 0.034 LR average precision score: 0.072 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 4, 10 GB f1 score: 0.769 GB cohens kappa score: 0.760 -> test with 'KNN' KNN tn, fp: 308, 24 KNN fn, tp: 1, 13 KNN f1 score: 0.510 KNN cohens kappa score: 0.479 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 322, 9 GAN fn, tp: 0, 13 GAN f1 score: 0.743 GAN cohens kappa score: 0.730 -> test with 'LR' LR tn, fp: 177, 154 LR fn, tp: 4, 9 LR f1 score: 0.102 LR cohens kappa score: 0.035 LR average precision score: 0.064 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 4, 9 GB f1 score: 0.818 GB cohens kappa score: 0.812 -> test with 'KNN' KNN tn, fp: 304, 27 KNN fn, tp: 0, 13 KNN f1 score: 0.491 KNN cohens kappa score: 0.460 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 195, 169 LR fn, tp: 9, 12 LR f1 score: 0.134 LR cohens kappa score: 0.064 LR average precision score: 0.087 average: LR tn, fp: 180.52, 151.28 LR fn, tp: 4.72, 9.08 LR f1 score: 0.104 LR cohens kappa score: 0.033 LR average precision score: 0.067 minimum: LR tn, fp: 163, 137 LR fn, tp: 2, 5 LR f1 score: 0.063 LR cohens kappa score: -0.012 LR average precision score: 0.050 -----[ GB ]----- maximum: GB tn, fp: 332, 6 GB fn, tp: 9, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 average: GB tn, fp: 329.96, 1.84 GB fn, tp: 4.48, 9.32 GB f1 score: 0.739 GB cohens kappa score: 0.730 minimum: GB tn, fp: 325, 0 GB fn, tp: 1, 5 GB f1 score: 0.455 GB cohens kappa score: 0.438 -----[ KNN ]----- maximum: KNN tn, fp: 327, 29 KNN fn, tp: 3, 14 KNN f1 score: 0.813 KNN cohens kappa score: 0.804 average: KNN tn, fp: 316.24, 15.56 KNN fn, tp: 0.56, 13.24 KNN f1 score: 0.635 KNN cohens kappa score: 0.614 minimum: KNN tn, fp: 302, 5 KNN fn, tp: 0, 11 KNN f1 score: 0.449 KNN cohens kappa score: 0.415 -----[ GAN ]----- maximum: GAN tn, fp: 329, 13 GAN fn, tp: 6, 13 GAN f1 score: 0.813 GAN cohens kappa score: 0.804 average: GAN tn, fp: 324.92, 6.88 GAN fn, tp: 2.92, 10.88 GAN f1 score: 0.688 GAN cohens kappa score: 0.673 minimum: GAN tn, fp: 319, 3 GAN fn, tp: 0, 7 GAN f1 score: 0.533 GAN cohens kappa score: 0.512