/////////////////////////////////////////// // Running convGAN-proxymary-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: 326, 6 GAN fn, tp: 2, 12 GAN f1 score: 0.750 GAN cohens kappa score: 0.738 -> 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: 0.058 -> test with 'GB' GB tn, fp: 328, 4 GB fn, tp: 4, 10 GB f1 score: 0.714 GB cohens kappa score: 0.702 -> 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 1/5: 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: 7, 7 GAN f1 score: 0.452 GAN cohens kappa score: 0.426 -> test with 'LR' LR tn, fp: 182, 150 LR fn, tp: 4, 10 LR f1 score: 0.115 LR cohens kappa score: 0.044 LR average precision score: 0.085 -> 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: 323, 9 KNN fn, tp: 0, 14 KNN f1 score: 0.757 KNN cohens kappa score: 0.744 ------ 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: 321, 11 GAN fn, tp: 1, 13 GAN f1 score: 0.684 GAN cohens kappa score: 0.667 -> test with 'LR' LR tn, fp: 174, 158 LR fn, tp: 7, 7 LR f1 score: 0.078 LR cohens kappa score: 0.004 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: 319, 13 KNN fn, tp: 1, 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 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 317, 15 GAN fn, tp: 2, 12 GAN f1 score: 0.585 GAN cohens kappa score: 0.562 -> 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: 330, 2 GB fn, tp: 8, 6 GB f1 score: 0.545 GB cohens kappa score: 0.532 -> test with 'KNN' KNN tn, fp: 318, 14 KNN fn, tp: 2, 12 KNN f1 score: 0.600 KNN cohens kappa score: 0.578 ------ 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: 323, 8 GAN fn, tp: 3, 10 GAN f1 score: 0.645 GAN cohens kappa score: 0.629 -> test with 'LR' LR tn, fp: 179, 152 LR fn, tp: 5, 8 LR f1 score: 0.092 LR cohens kappa score: 0.024 LR average precision score: 0.056 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 3, 10 GB f1 score: 0.800 GB cohens kappa score: 0.792 -> 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 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: 322, 10 GAN fn, tp: 0, 14 GAN f1 score: 0.737 GAN cohens kappa score: 0.723 -> test with 'LR' LR tn, fp: 168, 164 LR fn, tp: 5, 9 LR f1 score: 0.096 LR cohens kappa score: 0.023 LR average precision score: 0.064 -> 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: 319, 13 KNN fn, tp: 1, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ------ 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: 317, 15 GAN fn, tp: 1, 13 GAN f1 score: 0.619 GAN cohens kappa score: 0.597 -> 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: 0.069 -> 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: 0, 14 KNN f1 score: 0.778 KNN cohens kappa score: 0.766 ------ 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: 318, 14 GAN fn, tp: 2, 12 GAN f1 score: 0.600 GAN cohens kappa score: 0.578 -> test with 'LR' LR tn, fp: 190, 142 LR fn, tp: 4, 10 LR f1 score: 0.120 LR cohens kappa score: 0.050 LR average precision score: 0.072 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 6, 8 GB f1 score: 0.727 GB cohens kappa score: 0.719 -> test with 'KNN' KNN tn, fp: 321, 11 KNN fn, tp: 2, 12 KNN f1 score: 0.649 KNN cohens kappa score: 0.630 ------ 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: 317, 15 GAN fn, tp: 3, 11 GAN f1 score: 0.550 GAN cohens kappa score: 0.525 -> test with 'LR' LR tn, fp: 190, 142 LR fn, tp: 7, 7 LR f1 score: 0.086 LR cohens kappa score: 0.013 LR average precision score: 0.051 -> 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: 311, 21 KNN fn, tp: 2, 12 KNN f1 score: 0.511 KNN cohens kappa score: 0.481 ------ 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: 325, 6 GAN fn, tp: 5, 8 GAN f1 score: 0.593 GAN cohens kappa score: 0.576 -> test with 'LR' LR tn, fp: 190, 141 LR fn, tp: 5, 8 LR f1 score: 0.099 LR cohens kappa score: 0.031 LR average precision score: 0.073 -> 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: 312, 19 KNN fn, tp: 0, 13 KNN f1 score: 0.578 KNN cohens kappa score: 0.554 ====== 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: 319, 13 GAN fn, tp: 3, 11 GAN f1 score: 0.579 GAN cohens kappa score: 0.556 -> test with 'LR' LR tn, fp: 174, 158 LR fn, tp: 4, 10 LR f1 score: 0.110 LR cohens kappa score: 0.038 LR average precision score: 0.073 -> 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: 318, 14 KNN fn, tp: 1, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ 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: 318, 14 GAN fn, tp: 2, 12 GAN f1 score: 0.600 GAN cohens kappa score: 0.578 -> test with 'LR' LR tn, fp: 181, 151 LR fn, tp: 5, 9 LR f1 score: 0.103 LR cohens kappa score: 0.031 LR average precision score: 0.067 -> 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: 317, 15 KNN fn, tp: 0, 14 KNN f1 score: 0.651 KNN cohens kappa score: 0.631 ------ 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: 322, 10 GAN fn, tp: 3, 11 GAN f1 score: 0.629 GAN cohens kappa score: 0.610 -> test with 'LR' LR tn, fp: 189, 143 LR fn, tp: 5, 9 LR f1 score: 0.108 LR cohens kappa score: 0.037 LR average precision score: 0.055 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 6, 8 GB f1 score: 0.640 GB cohens kappa score: 0.627 -> test with 'KNN' KNN tn, fp: 322, 10 KNN fn, tp: 2, 12 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ------ 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: 325, 7 GAN fn, tp: 4, 10 GAN f1 score: 0.645 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: 0.086 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 6, 8 GB f1 score: 0.727 GB cohens kappa score: 0.719 -> test with 'KNN' KNN tn, fp: 321, 11 KNN fn, tp: 0, 14 KNN f1 score: 0.718 KNN cohens kappa score: 0.703 ------ 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: 322, 9 GAN fn, tp: 2, 11 GAN f1 score: 0.667 GAN cohens kappa score: 0.651 -> test with 'LR' LR tn, fp: 167, 164 LR fn, tp: 5, 8 LR f1 score: 0.086 LR cohens kappa score: 0.017 LR average precision score: 0.052 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 5, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 312, 19 KNN fn, tp: 0, 13 KNN f1 score: 0.578 KNN cohens kappa score: 0.554 ====== 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: 321, 11 GAN fn, tp: 3, 11 GAN f1 score: 0.611 GAN cohens kappa score: 0.591 -> 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: 332, 0 GB fn, tp: 0, 14 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 325, 7 KNN fn, tp: 0, 14 KNN f1 score: 0.800 KNN cohens kappa score: 0.790 ------ 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: 315, 17 GAN fn, tp: 3, 11 GAN f1 score: 0.524 GAN cohens kappa score: 0.497 -> 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: 0.061 -> 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: 311, 21 KNN fn, tp: 0, 14 KNN f1 score: 0.571 KNN cohens kappa score: 0.545 ------ 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: 318, 14 GAN fn, tp: 5, 9 GAN f1 score: 0.486 GAN cohens kappa score: 0.459 -> test with 'LR' LR tn, fp: 174, 158 LR fn, tp: 5, 9 LR f1 score: 0.099 LR cohens kappa score: 0.027 LR average precision score: 0.070 -> 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: 314, 18 KNN fn, tp: 0, 14 KNN f1 score: 0.609 KNN cohens kappa score: 0.585 ------ 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: 326, 6 GAN fn, tp: 2, 12 GAN f1 score: 0.750 GAN cohens kappa score: 0.738 -> test with 'LR' LR tn, fp: 191, 141 LR fn, tp: 5, 9 LR f1 score: 0.110 LR cohens kappa score: 0.039 LR average precision score: 0.055 -> 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: 313, 19 KNN fn, tp: 0, 14 KNN f1 score: 0.596 KNN cohens kappa score: 0.571 ------ 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: 318, 13 GAN fn, tp: 1, 12 GAN f1 score: 0.632 GAN cohens kappa score: 0.612 -> test with 'LR' LR tn, fp: 170, 161 LR fn, tp: 2, 11 LR f1 score: 0.119 LR cohens kappa score: 0.052 LR average precision score: 0.082 -> test with 'GB' GB tn, fp: 327, 4 GB fn, tp: 5, 8 GB f1 score: 0.640 GB cohens kappa score: 0.626 -> test with 'KNN' KNN tn, fp: 319, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ====== 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: 308, 24 GAN fn, tp: 2, 12 GAN f1 score: 0.480 GAN cohens kappa score: 0.448 -> 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: 320, 12 KNN fn, tp: 1, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.648 ------ 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: 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: 5, 9 LR f1 score: 0.106 LR cohens kappa score: 0.034 LR average precision score: 0.069 -> 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 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 313, 19 GAN fn, tp: 3, 11 GAN f1 score: 0.500 GAN cohens kappa score: 0.471 -> test with 'LR' LR tn, fp: 164, 168 LR fn, tp: 3, 11 LR f1 score: 0.114 LR cohens kappa score: 0.042 LR average precision score: 0.074 -> test with 'GB' GB tn, fp: 328, 4 GB fn, tp: 2, 12 GB f1 score: 0.800 GB cohens kappa score: 0.791 -> 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 5/5: Slice 4/5 ------- -> 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: 170, 162 LR fn, tp: 3, 11 LR f1 score: 0.118 LR cohens kappa score: 0.046 LR average precision score: 0.065 -> 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: 316, 16 KNN fn, tp: 1, 13 KNN f1 score: 0.605 KNN cohens kappa score: 0.582 ------ 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: 312, 19 GAN fn, tp: 2, 11 GAN f1 score: 0.512 GAN cohens kappa score: 0.484 -> test with 'LR' LR tn, fp: 180, 151 LR fn, tp: 4, 9 LR f1 score: 0.104 LR cohens kappa score: 0.037 LR average precision score: 0.065 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 308, 23 KNN fn, tp: 0, 13 KNN f1 score: 0.531 KNN cohens kappa score: 0.503 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 191, 168 LR fn, tp: 8, 11 LR f1 score: 0.123 LR cohens kappa score: 0.052 LR average precision score: 0.086 average: LR tn, fp: 179.32, 152.48 LR fn, tp: 4.64, 9.16 LR f1 score: 0.104 LR cohens kappa score: 0.033 LR average precision score: 0.066 minimum: LR tn, fp: 164, 141 LR fn, tp: 2, 6 LR f1 score: 0.073 LR cohens kappa score: -0.001 LR average precision score: 0.051 -----[ GB ]----- maximum: GB tn, fp: 332, 4 GB fn, tp: 9, 14 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 330.08, 1.72 GB fn, tp: 4.72, 9.08 GB f1 score: 0.730 GB cohens kappa score: 0.721 minimum: GB tn, fp: 327, 0 GB fn, tp: 0, 5 GB f1 score: 0.476 GB cohens kappa score: 0.462 -----[ KNN ]----- maximum: KNN tn, fp: 325, 23 KNN fn, tp: 2, 14 KNN f1 score: 0.800 KNN cohens kappa score: 0.790 average: KNN tn, fp: 317.68, 14.12 KNN fn, tp: 0.6, 13.2 KNN f1 score: 0.649 KNN cohens kappa score: 0.629 minimum: KNN tn, fp: 308, 7 KNN fn, tp: 0, 12 KNN f1 score: 0.511 KNN cohens kappa score: 0.481 -----[ GAN ]----- maximum: GAN tn, fp: 326, 24 GAN fn, tp: 7, 14 GAN f1 score: 0.750 GAN cohens kappa score: 0.738 average: GAN tn, fp: 319.72, 12.08 GAN fn, tp: 2.76, 11.04 GAN f1 score: 0.603 GAN cohens kappa score: 0.582 minimum: GAN tn, fp: 308, 6 GAN fn, tp: 0, 7 GAN f1 score: 0.452 GAN cohens kappa score: 0.426