/////////////////////////////////////////// // Running convGAN-proximary-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: 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: 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: 331, 1 GB fn, tp: 1, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 329, 3 KNN fn, tp: 0, 14 KNN f1 score: 0.903 KNN cohens kappa score: 0.899 ------ 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: 321, 11 GAN fn, tp: 4, 10 GAN f1 score: 0.571 GAN cohens kappa score: 0.550 -> test with 'LR' LR tn, fp: 172, 160 LR fn, tp: 2, 12 LR f1 score: 0.129 LR cohens kappa score: 0.059 LR average precision score: 0.069 -> 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: 300, 32 KNN fn, tp: 1, 13 KNN f1 score: 0.441 KNN cohens kappa score: 0.404 ------ 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: 324, 8 GAN fn, tp: 1, 13 GAN f1 score: 0.743 GAN cohens kappa score: 0.730 -> test with 'LR' LR tn, fp: 177, 155 LR fn, tp: 5, 9 LR f1 score: 0.101 LR cohens kappa score: 0.029 LR average precision score: 0.059 -> 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: 0, 14 KNN f1 score: 0.571 KNN cohens kappa score: 0.545 ------ 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: 319, 13 GAN fn, tp: 0, 14 GAN f1 score: 0.683 GAN cohens kappa score: 0.665 -> 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: 0.076 -> 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: 308, 24 KNN fn, tp: 1, 13 KNN f1 score: 0.510 KNN cohens kappa score: 0.479 ------ 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: 320, 11 GAN fn, tp: 2, 11 GAN f1 score: 0.629 GAN cohens kappa score: 0.610 -> test with 'LR' LR tn, fp: 177, 154 LR fn, tp: 5, 8 LR f1 score: 0.091 LR cohens kappa score: 0.023 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: 314, 17 KNN fn, tp: 1, 12 KNN f1 score: 0.571 KNN cohens kappa score: 0.548 ====== 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: 160, 172 LR fn, tp: 3, 11 LR f1 score: 0.112 LR cohens kappa score: 0.039 LR average precision score: 0.061 -> 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: 312, 20 KNN fn, tp: 1, 13 KNN f1 score: 0.553 KNN cohens kappa score: 0.526 ------ 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: 315, 17 GAN fn, tp: 0, 14 GAN f1 score: 0.622 GAN cohens kappa score: 0.600 -> test with 'LR' LR tn, fp: 183, 149 LR fn, tp: 4, 10 LR f1 score: 0.116 LR cohens kappa score: 0.045 LR average precision score: 0.061 -> test with 'GB' GB tn, fp: 327, 5 GB fn, tp: 4, 10 GB f1 score: 0.690 GB cohens kappa score: 0.676 -> test with 'KNN' KNN tn, fp: 312, 20 KNN fn, tp: 2, 12 KNN f1 score: 0.522 KNN cohens kappa score: 0.493 ------ 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: 314, 18 GAN fn, tp: 1, 13 GAN f1 score: 0.578 GAN cohens kappa score: 0.553 -> test with 'LR' LR tn, fp: 192, 140 LR fn, tp: 4, 10 LR f1 score: 0.122 LR cohens kappa score: 0.052 LR average precision score: 0.072 -> 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: 306, 26 KNN fn, tp: 1, 13 KNN f1 score: 0.491 KNN cohens kappa score: 0.458 ------ 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: 316, 16 GAN fn, tp: 1, 13 GAN f1 score: 0.605 GAN cohens kappa score: 0.582 -> test with 'LR' LR tn, fp: 190, 142 LR fn, tp: 9, 5 LR f1 score: 0.062 LR cohens kappa score: -0.013 LR average precision score: 0.051 -> 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: 315, 17 KNN fn, tp: 3, 11 KNN f1 score: 0.524 KNN cohens kappa score: 0.497 ------ 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: 3, 10 GAN f1 score: 0.690 GAN cohens kappa score: 0.676 -> test with 'LR' LR tn, fp: 185, 146 LR fn, tp: 5, 8 LR f1 score: 0.096 LR cohens kappa score: 0.028 LR average precision score: 0.074 -> test with 'GB' GB tn, fp: 327, 4 GB fn, tp: 8, 5 GB f1 score: 0.455 GB cohens kappa score: 0.437 -> test with 'KNN' KNN tn, fp: 316, 15 KNN fn, tp: 1, 12 KNN f1 score: 0.600 KNN cohens kappa score: 0.578 ====== 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: 316, 16 GAN fn, tp: 1, 13 GAN f1 score: 0.605 GAN cohens kappa score: 0.582 -> test with 'LR' LR tn, fp: 170, 162 LR fn, tp: 4, 10 LR f1 score: 0.108 LR cohens kappa score: 0.035 LR average precision score: 0.077 -> 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: 312, 20 KNN fn, tp: 2, 12 KNN f1 score: 0.522 KNN cohens kappa score: 0.493 ------ 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: 310, 22 GAN fn, tp: 1, 13 GAN f1 score: 0.531 GAN cohens kappa score: 0.502 -> test with 'LR' LR tn, fp: 189, 143 LR fn, tp: 3, 11 LR f1 score: 0.131 LR cohens kappa score: 0.061 LR average precision score: 0.066 -> 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: 301, 31 KNN fn, tp: 0, 14 KNN f1 score: 0.475 KNN cohens kappa score: 0.440 ------ 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: 311, 21 GAN fn, tp: 1, 13 GAN f1 score: 0.542 GAN cohens kappa score: 0.514 -> 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: 0.056 -> 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: 313, 19 KNN fn, tp: 1, 13 KNN f1 score: 0.565 KNN cohens kappa score: 0.539 ------ 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: 317, 15 GAN fn, tp: 2, 12 GAN f1 score: 0.585 GAN cohens kappa score: 0.562 -> test with 'LR' LR tn, fp: 173, 159 LR fn, tp: 1, 13 LR f1 score: 0.140 LR cohens kappa score: 0.070 LR average precision score: 0.077 -> 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: 315, 17 KNN fn, tp: 0, 14 KNN f1 score: 0.622 KNN cohens kappa score: 0.600 ------ 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: 1, 12 GAN f1 score: 0.706 GAN cohens kappa score: 0.691 -> test with 'LR' LR tn, fp: 169, 162 LR fn, tp: 5, 8 LR f1 score: 0.087 LR cohens kappa score: 0.019 LR average precision score: 0.052 -> 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: 308, 23 KNN fn, tp: 0, 13 KNN f1 score: 0.531 KNN cohens kappa score: 0.503 ====== 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: 319, 13 GAN fn, tp: 4, 10 GAN f1 score: 0.541 GAN cohens kappa score: 0.516 -> test with 'LR' LR tn, fp: 181, 151 LR fn, tp: 3, 11 LR f1 score: 0.125 LR cohens kappa score: 0.055 LR average precision score: 0.066 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 1, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> 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 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 319, 13 GAN fn, tp: 5, 9 GAN f1 score: 0.500 GAN cohens kappa score: 0.474 -> test with 'LR' LR tn, fp: 190, 142 LR fn, tp: 5, 9 LR f1 score: 0.109 LR cohens kappa score: 0.038 LR average precision score: 0.053 -> 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: 309, 23 KNN fn, tp: 1, 13 KNN f1 score: 0.520 KNN cohens kappa score: 0.490 ------ 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: 322, 10 GAN fn, tp: 2, 12 GAN f1 score: 0.667 GAN cohens kappa score: 0.649 -> 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: 0.064 -> 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: 302, 30 KNN fn, tp: 0, 14 KNN f1 score: 0.483 KNN cohens kappa score: 0.449 ------ 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: 328, 4 GAN fn, tp: 3, 11 GAN f1 score: 0.759 GAN cohens kappa score: 0.748 -> test with 'LR' LR tn, fp: 196, 136 LR fn, tp: 5, 9 LR f1 score: 0.113 LR cohens kappa score: 0.043 LR average precision score: 0.054 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 4, 10 GB f1 score: 0.741 GB cohens kappa score: 0.730 -> test with 'KNN' KNN tn, fp: 315, 17 KNN fn, tp: 0, 14 KNN f1 score: 0.622 KNN cohens kappa score: 0.600 ------ 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: 321, 10 GAN fn, tp: 2, 11 GAN f1 score: 0.647 GAN cohens kappa score: 0.630 -> test with 'LR' LR tn, fp: 177, 154 LR fn, tp: 2, 11 LR f1 score: 0.124 LR cohens kappa score: 0.058 LR average precision score: 0.077 -> 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: 310, 21 KNN fn, tp: 1, 12 KNN f1 score: 0.522 KNN cohens kappa score: 0.494 ====== 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: 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: 4, 10 GB f1 score: 0.800 GB cohens kappa score: 0.793 -> test with 'KNN' KNN tn, fp: 302, 30 KNN fn, tp: 2, 12 KNN f1 score: 0.429 KNN cohens kappa score: 0.392 ------ 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: 4, 10 GAN f1 score: 0.714 GAN cohens kappa score: 0.702 -> test with 'LR' LR tn, fp: 188, 144 LR fn, tp: 5, 9 LR f1 score: 0.108 LR cohens kappa score: 0.036 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: 329, 3 KNN fn, tp: 0, 14 KNN f1 score: 0.903 KNN cohens kappa score: 0.899 ------ 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: 314, 18 GAN fn, tp: 4, 10 GAN f1 score: 0.476 GAN cohens kappa score: 0.446 -> test with 'LR' LR tn, fp: 162, 170 LR fn, tp: 3, 11 LR f1 score: 0.113 LR cohens kappa score: 0.041 LR average precision score: 0.078 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 3, 11 GB f1 score: 0.786 GB cohens kappa score: 0.777 -> 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 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: 5, 9 GAN f1 score: 0.643 GAN cohens kappa score: 0.628 -> test with 'LR' LR tn, fp: 176, 156 LR fn, tp: 4, 10 LR f1 score: 0.111 LR cohens kappa score: 0.039 LR average precision score: 0.075 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 6, 8 GB f1 score: 0.696 GB cohens kappa score: 0.686 -> test with 'KNN' KNN tn, fp: 305, 27 KNN fn, tp: 1, 13 KNN f1 score: 0.481 KNN cohens kappa score: 0.448 ------ 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: 314, 17 GAN fn, tp: 2, 11 GAN f1 score: 0.537 GAN cohens kappa score: 0.511 -> test with 'LR' LR tn, fp: 178, 153 LR fn, tp: 4, 9 LR f1 score: 0.103 LR cohens kappa score: 0.035 LR average precision score: 0.065 -> 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: 299, 32 KNN fn, tp: 0, 13 KNN f1 score: 0.448 KNN cohens kappa score: 0.414 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 196, 172 LR fn, tp: 9, 13 LR f1 score: 0.140 LR cohens kappa score: 0.070 LR average precision score: 0.078 average: LR tn, fp: 179.36, 152.44 LR fn, tp: 4.32, 9.48 LR f1 score: 0.108 LR cohens kappa score: 0.037 LR average precision score: 0.065 minimum: LR tn, fp: 160, 136 LR fn, tp: 1, 5 LR f1 score: 0.062 LR cohens kappa score: -0.013 LR average precision score: 0.051 -----[ GB ]----- maximum: GB tn, fp: 332, 5 GB fn, tp: 9, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 average: GB tn, fp: 329.84, 1.96 GB fn, tp: 4.88, 8.92 GB f1 score: 0.714 GB cohens kappa score: 0.704 minimum: GB tn, fp: 327, 0 GB fn, tp: 1, 5 GB f1 score: 0.455 GB cohens kappa score: 0.437 -----[ KNN ]----- maximum: KNN tn, fp: 329, 32 KNN fn, tp: 3, 14 KNN f1 score: 0.903 KNN cohens kappa score: 0.899 average: KNN tn, fp: 311.16, 20.64 KNN fn, tp: 0.76, 13.04 KNN f1 score: 0.568 KNN cohens kappa score: 0.542 minimum: KNN tn, fp: 299, 3 KNN fn, tp: 0, 11 KNN f1 score: 0.429 KNN cohens kappa score: 0.392 -----[ GAN ]----- maximum: GAN tn, fp: 328, 22 GAN fn, tp: 5, 14 GAN f1 score: 0.774 GAN cohens kappa score: 0.764 average: GAN tn, fp: 319.56, 12.24 GAN fn, tp: 2.16, 11.64 GAN f1 score: 0.626 GAN cohens kappa score: 0.606 minimum: GAN tn, fp: 310, 4 GAN fn, tp: 0, 9 GAN f1 score: 0.476 GAN cohens kappa score: 0.446