/////////////////////////////////////////// // Running convGAN-majority-5 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: 328, 4 GAN fn, tp: 3, 11 GAN f1 score: 0.759 GAN cohens kappa score: 0.748 -> 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: 0.060 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 1, 13 GB f1 score: 0.897 GB cohens kappa score: 0.892 -> test with 'KNN' KNN tn, fp: 327, 5 KNN fn, tp: 0, 14 KNN f1 score: 0.848 KNN cohens kappa score: 0.841 ------ 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: 2, 12 GAN f1 score: 0.774 GAN cohens kappa score: 0.764 -> test with 'LR' LR tn, fp: 180, 152 LR fn, tp: 4, 10 LR f1 score: 0.114 LR cohens kappa score: 0.042 LR average precision score: 0.083 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 4, 10 GB f1 score: 0.800 GB cohens kappa score: 0.793 -> 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 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 323, 9 GAN fn, tp: 2, 12 GAN f1 score: 0.686 GAN cohens kappa score: 0.670 -> test with 'LR' LR tn, fp: 179, 153 LR fn, tp: 4, 10 LR f1 score: 0.113 LR cohens kappa score: 0.042 LR average precision score: 0.056 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 4, 10 GB f1 score: 0.800 GB cohens kappa score: 0.793 -> test with 'KNN' KNN tn, fp: 308, 24 KNN fn, tp: 0, 14 KNN f1 score: 0.538 KNN cohens kappa score: 0.509 ------ 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: 323, 9 GAN fn, tp: 3, 11 GAN f1 score: 0.647 GAN cohens kappa score: 0.629 -> test with 'LR' LR tn, fp: 178, 154 LR fn, tp: 3, 11 LR f1 score: 0.123 LR cohens kappa score: 0.052 LR average precision score: 0.076 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 7, 7 GB f1 score: 0.609 GB cohens kappa score: 0.596 -> test with 'KNN' KNN tn, fp: 315, 17 KNN fn, tp: 2, 12 KNN f1 score: 0.558 KNN cohens kappa score: 0.533 ------ 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: 322, 9 GAN fn, tp: 3, 10 GAN f1 score: 0.625 GAN cohens kappa score: 0.607 -> test with 'LR' LR tn, fp: 182, 149 LR fn, tp: 2, 11 LR f1 score: 0.127 LR cohens kappa score: 0.062 LR average precision score: 0.060 -> test with 'GB' GB tn, fp: 326, 5 GB fn, tp: 2, 11 GB f1 score: 0.759 GB cohens kappa score: 0.748 -> test with 'KNN' KNN tn, fp: 308, 23 KNN fn, tp: 1, 12 KNN f1 score: 0.500 KNN cohens kappa score: 0.471 ====== 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: 324, 8 GAN fn, tp: 2, 12 GAN f1 score: 0.706 GAN cohens kappa score: 0.691 -> test with 'LR' LR tn, fp: 150, 182 LR fn, tp: 5, 9 LR f1 score: 0.088 LR cohens kappa score: 0.013 LR average precision score: 0.062 -> 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: 310, 22 KNN fn, tp: 1, 13 KNN f1 score: 0.531 KNN cohens kappa score: 0.502 ------ 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: 328, 4 GAN fn, tp: 2, 12 GAN f1 score: 0.800 GAN cohens kappa score: 0.791 -> test with 'LR' LR tn, fp: 173, 159 LR fn, tp: 3, 11 LR f1 score: 0.120 LR cohens kappa score: 0.048 LR average precision score: 0.071 -> 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: 322, 10 KNN fn, tp: 0, 14 KNN f1 score: 0.737 KNN cohens kappa score: 0.723 ------ 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: 322, 10 GAN fn, tp: 4, 10 GAN f1 score: 0.588 GAN cohens kappa score: 0.568 -> test with 'LR' LR tn, fp: 199, 133 LR fn, tp: 3, 11 LR f1 score: 0.139 LR cohens kappa score: 0.071 LR average precision score: 0.072 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 8, 6 GB f1 score: 0.600 GB cohens kappa score: 0.590 -> test with 'KNN' KNN tn, fp: 317, 15 KNN fn, tp: 3, 11 KNN f1 score: 0.550 KNN cohens kappa score: 0.525 ------ 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: 185, 147 LR fn, tp: 9, 5 LR f1 score: 0.060 LR cohens kappa score: -0.015 LR average precision score: 0.050 -> 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: 295, 37 KNN fn, tp: 1, 13 KNN f1 score: 0.406 KNN cohens kappa score: 0.366 ------ 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: 324, 7 GAN fn, tp: 2, 11 GAN f1 score: 0.710 GAN cohens kappa score: 0.696 -> test with 'LR' LR tn, fp: 195, 136 LR fn, tp: 5, 8 LR f1 score: 0.102 LR cohens kappa score: 0.035 LR average precision score: 0.072 -> 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: 294, 37 KNN fn, tp: 0, 13 KNN f1 score: 0.413 KNN cohens kappa score: 0.375 ====== 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: 324, 8 GAN fn, tp: 2, 12 GAN f1 score: 0.706 GAN cohens kappa score: 0.691 -> test with 'LR' LR tn, fp: 174, 158 LR fn, tp: 3, 11 LR f1 score: 0.120 LR cohens kappa score: 0.049 LR average precision score: 0.077 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 3, 11 GB f1 score: 0.846 GB cohens kappa score: 0.840 -> 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 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 321, 11 GAN fn, tp: 0, 14 GAN f1 score: 0.718 GAN cohens kappa score: 0.703 -> test with 'LR' LR tn, fp: 198, 134 LR fn, tp: 5, 9 LR f1 score: 0.115 LR cohens kappa score: 0.044 LR average precision score: 0.069 -> 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: 307, 25 KNN fn, tp: 0, 14 KNN f1 score: 0.528 KNN cohens kappa score: 0.498 ------ 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: 323, 9 GAN fn, tp: 0, 14 GAN f1 score: 0.757 GAN cohens kappa score: 0.744 -> test with 'LR' LR tn, fp: 180, 152 LR fn, tp: 6, 8 LR f1 score: 0.092 LR cohens kappa score: 0.019 LR average precision score: 0.056 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 9, 5 GB f1 score: 0.476 GB cohens kappa score: 0.462 -> 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 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 328, 4 GAN fn, tp: 4, 10 GAN f1 score: 0.714 GAN cohens kappa score: 0.702 -> test with 'LR' LR tn, fp: 185, 147 LR fn, tp: 1, 13 LR f1 score: 0.149 LR cohens kappa score: 0.081 LR average precision score: 0.070 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 3, 11 GB f1 score: 0.846 GB cohens kappa score: 0.840 -> test with 'KNN' KNN tn, fp: 303, 29 KNN fn, tp: 0, 14 KNN f1 score: 0.491 KNN cohens kappa score: 0.458 ------ 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: 323, 8 GAN fn, tp: 1, 12 GAN f1 score: 0.727 GAN cohens kappa score: 0.714 -> test with 'LR' LR tn, fp: 165, 166 LR fn, tp: 5, 8 LR f1 score: 0.086 LR cohens kappa score: 0.016 LR average precision score: 0.049 -> test with 'GB' GB tn, fp: 327, 4 GB fn, tp: 6, 7 GB f1 score: 0.583 GB cohens kappa score: 0.568 -> test with 'KNN' KNN tn, fp: 290, 41 KNN fn, tp: 0, 13 KNN f1 score: 0.388 KNN cohens kappa score: 0.348 ====== 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: 322, 10 GAN fn, tp: 2, 12 GAN f1 score: 0.667 GAN cohens kappa score: 0.649 -> test with 'LR' LR tn, fp: 176, 156 LR fn, tp: 3, 11 LR f1 score: 0.122 LR cohens kappa score: 0.051 LR average precision score: 0.065 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 4, 10 GB f1 score: 0.800 GB cohens kappa score: 0.793 -> 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 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 326, 6 GAN fn, tp: 7, 7 GAN f1 score: 0.519 GAN cohens kappa score: 0.499 -> 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: 0.062 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 9, 5 GB f1 score: 0.476 GB cohens kappa score: 0.462 -> test with 'KNN' KNN tn, fp: 307, 25 KNN fn, tp: 1, 13 KNN f1 score: 0.500 KNN cohens kappa score: 0.469 ------ 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: 3, 11 GAN f1 score: 0.710 GAN cohens kappa score: 0.696 -> 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: 0.061 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 4, 10 GB f1 score: 0.800 GB cohens kappa score: 0.793 -> test with 'KNN' KNN tn, fp: 307, 25 KNN fn, tp: 0, 14 KNN f1 score: 0.528 KNN cohens kappa score: 0.498 ------ 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: 1, 13 GAN f1 score: 0.813 GAN cohens kappa score: 0.804 -> test with 'LR' LR tn, fp: 198, 134 LR fn, tp: 7, 7 LR f1 score: 0.090 LR cohens kappa score: 0.018 LR average precision score: 0.056 -> 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: 318, 14 KNN fn, tp: 0, 14 KNN f1 score: 0.667 KNN cohens kappa score: 0.648 ------ 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: 320, 11 GAN fn, tp: 3, 10 GAN f1 score: 0.588 GAN cohens kappa score: 0.568 -> test with 'LR' LR tn, fp: 168, 163 LR fn, tp: 2, 11 LR f1 score: 0.118 LR cohens kappa score: 0.051 LR average precision score: 0.079 -> test with 'GB' GB tn, fp: 327, 4 GB fn, tp: 7, 6 GB f1 score: 0.522 GB cohens kappa score: 0.505 -> test with 'KNN' KNN tn, fp: 317, 14 KNN fn, tp: 1, 12 KNN f1 score: 0.615 KNN cohens kappa score: 0.595 ====== 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: 318, 14 GAN fn, tp: 3, 11 GAN f1 score: 0.564 GAN cohens kappa score: 0.540 -> test with 'LR' LR tn, fp: 185, 147 LR fn, tp: 8, 6 LR f1 score: 0.072 LR cohens kappa score: -0.002 LR average precision score: 0.051 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 7, 7 GB f1 score: 0.609 GB cohens kappa score: 0.596 -> test with 'KNN' KNN tn, fp: 311, 21 KNN fn, tp: 4, 10 KNN f1 score: 0.444 KNN cohens kappa score: 0.412 ------ 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: 323, 9 GAN fn, tp: 2, 12 GAN f1 score: 0.686 GAN cohens kappa score: 0.670 -> 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: 0.067 -> 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: 309, 23 KNN fn, tp: 0, 14 KNN f1 score: 0.549 KNN cohens kappa score: 0.521 ------ 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: 325, 7 GAN fn, tp: 3, 11 GAN f1 score: 0.688 GAN cohens kappa score: 0.673 -> test with 'LR' LR tn, fp: 164, 168 LR fn, tp: 4, 10 LR f1 score: 0.104 LR cohens kappa score: 0.032 LR average precision score: 0.075 -> test with 'GB' GB tn, fp: 327, 5 GB fn, tp: 2, 12 GB f1 score: 0.774 GB cohens kappa score: 0.764 -> test with 'KNN' KNN tn, fp: 300, 32 KNN fn, tp: 0, 14 KNN f1 score: 0.467 KNN cohens kappa score: 0.431 ------ 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: 330, 2 GAN fn, tp: 3, 11 GAN f1 score: 0.815 GAN cohens kappa score: 0.807 -> test with 'LR' LR tn, fp: 177, 155 LR fn, tp: 4, 10 LR f1 score: 0.112 LR cohens kappa score: 0.040 LR average precision score: 0.073 -> 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: 310, 22 KNN fn, tp: 1, 13 KNN f1 score: 0.531 KNN cohens kappa score: 0.502 ------ 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: 321, 10 GAN fn, tp: 0, 13 GAN f1 score: 0.722 GAN cohens kappa score: 0.708 -> test with 'LR' LR tn, fp: 183, 148 LR fn, tp: 4, 9 LR f1 score: 0.106 LR cohens kappa score: 0.039 LR average precision score: 0.062 -> 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: 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: 199, 182 LR fn, tp: 9, 13 LR f1 score: 0.149 LR cohens kappa score: 0.081 LR average precision score: 0.083 average: LR tn, fp: 179.36, 152.44 LR fn, tp: 4.4, 9.4 LR f1 score: 0.107 LR cohens kappa score: 0.036 LR average precision score: 0.065 minimum: LR tn, fp: 150, 133 LR fn, tp: 1, 5 LR f1 score: 0.060 LR cohens kappa score: -0.015 LR average precision score: 0.049 -----[ GB ]----- maximum: GB tn, fp: 332, 5 GB fn, tp: 9, 13 GB f1 score: 0.897 GB cohens kappa score: 0.892 average: GB tn, fp: 329.88, 1.92 GB fn, tp: 4.8, 9.0 GB f1 score: 0.718 GB cohens kappa score: 0.709 minimum: GB tn, fp: 326, 0 GB fn, tp: 1, 5 GB f1 score: 0.476 GB cohens kappa score: 0.462 -----[ KNN ]----- maximum: KNN tn, fp: 327, 41 KNN fn, tp: 4, 14 KNN f1 score: 0.848 KNN cohens kappa score: 0.841 average: KNN tn, fp: 309.32, 22.48 KNN fn, tp: 0.68, 13.12 KNN f1 score: 0.545 KNN cohens kappa score: 0.517 minimum: KNN tn, fp: 290, 5 KNN fn, tp: 0, 10 KNN f1 score: 0.388 KNN cohens kappa score: 0.348 -----[ GAN ]----- maximum: GAN tn, fp: 330, 14 GAN fn, tp: 7, 14 GAN f1 score: 0.815 GAN cohens kappa score: 0.807 average: GAN tn, fp: 324.04, 7.76 GAN fn, tp: 2.4, 11.4 GAN f1 score: 0.693 GAN cohens kappa score: 0.678 minimum: GAN tn, fp: 318, 2 GAN fn, tp: 0, 7 GAN f1 score: 0.519 GAN cohens kappa score: 0.499