/////////////////////////////////////////// // Running convGAN-proximary-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: 291, 41 GAN fn, tp: 1, 13 GAN f1 score: 0.382 GAN cohens kappa score: 0.340 -> 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: 0.067 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 2, 12 GB f1 score: 0.828 GB cohens kappa score: 0.820 -> test with 'KNN' KNN tn, fp: 292, 40 KNN fn, tp: 0, 14 KNN f1 score: 0.412 KNN cohens kappa score: 0.371 ------ 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: 294, 38 GAN fn, tp: 4, 10 GAN f1 score: 0.323 GAN cohens kappa score: 0.277 -> test with 'LR' LR tn, fp: 194, 138 LR fn, tp: 3, 11 LR f1 score: 0.135 LR cohens kappa score: 0.066 LR average precision score: 0.088 -> 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: 315, 17 KNN fn, tp: 2, 12 KNN f1 score: 0.558 KNN cohens kappa score: 0.533 ------ 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: 306, 26 GAN fn, tp: 0, 14 GAN f1 score: 0.519 GAN cohens kappa score: 0.488 -> 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: 0.059 -> 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: 300, 32 KNN fn, tp: 1, 13 KNN f1 score: 0.441 KNN cohens kappa score: 0.404 ------ 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: 302, 30 GAN fn, tp: 2, 12 GAN f1 score: 0.429 GAN cohens kappa score: 0.392 -> 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: 0.080 -> test with 'GB' GB tn, fp: 328, 4 GB fn, tp: 3, 11 GB f1 score: 0.759 GB cohens kappa score: 0.748 -> test with 'KNN' KNN tn, fp: 304, 28 KNN fn, tp: 0, 14 KNN f1 score: 0.500 KNN cohens kappa score: 0.468 ------ 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: 298, 33 GAN fn, tp: 4, 9 GAN f1 score: 0.327 GAN cohens kappa score: 0.286 -> test with 'LR' LR tn, fp: 184, 147 LR fn, tp: 5, 8 LR f1 score: 0.095 LR cohens kappa score: 0.027 LR average precision score: 0.053 -> 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: 313, 18 KNN fn, tp: 1, 12 KNN f1 score: 0.558 KNN cohens kappa score: 0.534 ====== 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: 303, 29 GAN fn, tp: 6, 8 GAN f1 score: 0.314 GAN cohens kappa score: 0.271 -> test with 'LR' LR tn, fp: 166, 166 LR fn, tp: 4, 10 LR f1 score: 0.105 LR cohens kappa score: 0.033 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: 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: 294, 38 GAN fn, tp: 0, 14 GAN f1 score: 0.424 GAN cohens kappa score: 0.385 -> test with 'LR' LR tn, fp: 178, 154 LR fn, tp: 4, 10 LR f1 score: 0.112 LR cohens kappa score: 0.041 LR average precision score: 0.072 -> 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: 308, 24 KNN fn, tp: 1, 13 KNN f1 score: 0.510 KNN cohens kappa score: 0.479 ------ 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: 298, 34 GAN fn, tp: 2, 12 GAN f1 score: 0.400 GAN cohens kappa score: 0.360 -> 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: 0.074 -> 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: 316, 16 KNN fn, tp: 3, 11 KNN f1 score: 0.537 KNN cohens kappa score: 0.510 ------ 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: 287, 45 GAN fn, tp: 2, 12 GAN f1 score: 0.338 GAN cohens kappa score: 0.292 -> test with 'LR' LR tn, fp: 189, 143 LR fn, tp: 8, 6 LR f1 score: 0.074 LR cohens kappa score: -0.000 LR average precision score: 0.049 -> 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: 289, 43 KNN fn, tp: 1, 13 KNN f1 score: 0.371 KNN cohens kappa score: 0.328 ------ 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: 311, 20 GAN fn, tp: 3, 10 GAN f1 score: 0.465 GAN cohens kappa score: 0.435 -> test with 'LR' LR tn, fp: 193, 138 LR fn, tp: 6, 7 LR f1 score: 0.089 LR cohens kappa score: 0.021 LR average precision score: 0.077 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 1, 12 GB f1 score: 0.857 GB cohens kappa score: 0.851 -> test with 'KNN' KNN tn, fp: 310, 21 KNN fn, tp: 0, 13 KNN f1 score: 0.553 KNN cohens kappa score: 0.527 ====== 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: 304, 28 GAN fn, tp: 3, 11 GAN f1 score: 0.415 GAN cohens kappa score: 0.378 -> test with 'LR' LR tn, fp: 174, 158 LR fn, tp: 2, 12 LR f1 score: 0.130 LR cohens kappa score: 0.060 LR average precision score: 0.078 -> 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: 313, 19 KNN fn, tp: 1, 13 KNN f1 score: 0.565 KNN cohens kappa score: 0.539 ------ 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: 306, 26 GAN fn, tp: 0, 14 GAN f1 score: 0.519 GAN cohens kappa score: 0.488 -> 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: 329, 3 GB fn, tp: 0, 14 GB f1 score: 0.903 GB cohens kappa score: 0.899 -> test with 'KNN' KNN tn, fp: 308, 24 KNN fn, tp: 2, 12 KNN f1 score: 0.480 KNN cohens kappa score: 0.448 ------ 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: 309, 23 GAN fn, tp: 3, 11 GAN f1 score: 0.458 GAN cohens kappa score: 0.425 -> test with 'LR' LR tn, fp: 188, 144 LR fn, tp: 6, 8 LR f1 score: 0.096 LR cohens kappa score: 0.024 LR average precision score: 0.058 -> 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: 310, 22 KNN fn, tp: 3, 11 KNN f1 score: 0.468 KNN cohens kappa score: 0.436 ------ 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: 301, 31 GAN fn, tp: 2, 12 GAN f1 score: 0.421 GAN cohens kappa score: 0.383 -> test with 'LR' LR tn, fp: 181, 151 LR fn, tp: 2, 12 LR f1 score: 0.136 LR cohens kappa score: 0.066 LR average precision score: 0.083 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 2, 12 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> test with 'KNN' KNN tn, fp: 301, 31 KNN fn, tp: 1, 13 KNN f1 score: 0.448 KNN cohens kappa score: 0.412 ------ 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: 301, 30 GAN fn, tp: 1, 12 GAN f1 score: 0.436 GAN cohens kappa score: 0.402 -> test with 'LR' LR tn, fp: 180, 151 LR fn, tp: 5, 8 LR f1 score: 0.093 LR cohens kappa score: 0.025 LR average precision score: 0.059 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 1, 12 GB f1 score: 0.889 GB cohens kappa score: 0.884 -> 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 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: 305, 27 GAN fn, tp: 2, 12 GAN f1 score: 0.453 GAN cohens kappa score: 0.418 -> 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: 0.069 -> 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: 326, 6 KNN fn, tp: 2, 12 KNN f1 score: 0.750 KNN cohens kappa score: 0.738 ------ 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: 303, 29 GAN fn, tp: 3, 11 GAN f1 score: 0.407 GAN cohens kappa score: 0.370 -> test with 'LR' LR tn, fp: 182, 150 LR fn, tp: 5, 9 LR f1 score: 0.104 LR cohens kappa score: 0.032 LR average precision score: 0.067 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 2, 12 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> 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 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with GAN.predict GAN tn, fp: 294, 38 GAN fn, tp: 1, 13 GAN f1 score: 0.400 GAN cohens kappa score: 0.359 -> 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: 0.067 -> 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: 300, 32 KNN fn, tp: 0, 14 KNN f1 score: 0.467 KNN cohens kappa score: 0.431 ------ 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: 297, 35 GAN fn, tp: 1, 13 GAN f1 score: 0.419 GAN cohens kappa score: 0.381 -> test with 'LR' LR tn, fp: 200, 132 LR fn, tp: 6, 8 LR f1 score: 0.104 LR cohens kappa score: 0.033 LR average precision score: 0.057 -> 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: 308, 24 KNN fn, tp: 1, 13 KNN f1 score: 0.510 KNN cohens kappa score: 0.479 ------ 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: 293, 38 GAN fn, tp: 2, 11 GAN f1 score: 0.355 GAN cohens kappa score: 0.314 -> test with 'LR' LR tn, fp: 176, 155 LR fn, tp: 1, 12 LR f1 score: 0.133 LR cohens kappa score: 0.068 LR average precision score: 0.077 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 6, 7 GB f1 score: 0.609 GB cohens kappa score: 0.595 -> test with 'KNN' KNN tn, fp: 301, 30 KNN fn, tp: 1, 12 KNN f1 score: 0.436 KNN cohens kappa score: 0.402 ====== 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: 293, 39 GAN fn, tp: 2, 12 GAN f1 score: 0.369 GAN cohens kappa score: 0.326 -> test with 'LR' LR tn, fp: 182, 150 LR fn, tp: 8, 6 LR f1 score: 0.071 LR cohens kappa score: -0.004 LR average precision score: 0.055 -> 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 2/5 ------- -> 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: 196, 136 LR fn, tp: 6, 8 LR f1 score: 0.101 LR cohens kappa score: 0.030 LR average precision score: 0.081 -> 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: 311, 21 KNN fn, tp: 0, 14 KNN f1 score: 0.571 KNN cohens kappa score: 0.545 ------ 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: 306, 26 GAN fn, tp: 2, 12 GAN f1 score: 0.462 GAN cohens kappa score: 0.428 -> 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: 0.085 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 1, 13 GB f1 score: 0.867 GB cohens kappa score: 0.861 -> 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: 309, 23 GAN fn, tp: 3, 11 GAN f1 score: 0.458 GAN cohens kappa score: 0.425 -> 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: 0.081 -> 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: 301, 31 KNN fn, tp: 0, 14 KNN f1 score: 0.475 KNN cohens kappa score: 0.440 ------ 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: 301, 30 GAN fn, tp: 3, 10 GAN f1 score: 0.377 GAN cohens kappa score: 0.340 -> test with 'LR' LR tn, fp: 184, 147 LR fn, tp: 4, 9 LR f1 score: 0.107 LR cohens kappa score: 0.039 LR average precision score: 0.067 -> test with 'GB' GB tn, fp: 330, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 287, 44 KNN fn, tp: 0, 13 KNN f1 score: 0.371 KNN cohens kappa score: 0.330 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 200, 166 LR fn, tp: 8, 12 LR f1 score: 0.136 LR cohens kappa score: 0.068 LR average precision score: 0.088 average: LR tn, fp: 184.24, 147.56 LR fn, tp: 4.64, 9.16 LR f1 score: 0.107 LR cohens kappa score: 0.037 LR average precision score: 0.070 minimum: LR tn, fp: 166, 132 LR fn, tp: 1, 6 LR f1 score: 0.071 LR cohens kappa score: -0.004 LR average precision score: 0.049 -----[ GB ]----- maximum: GB tn, fp: 332, 4 GB fn, tp: 7, 14 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 330.16, 1.64 GB fn, tp: 2.4, 11.4 GB f1 score: 0.845 GB cohens kappa score: 0.839 minimum: GB tn, fp: 328, 0 GB fn, tp: 0, 7 GB f1 score: 0.609 GB cohens kappa score: 0.595 -----[ KNN ]----- maximum: KNN tn, fp: 326, 44 KNN fn, tp: 3, 14 KNN f1 score: 0.750 KNN cohens kappa score: 0.738 average: KNN tn, fp: 305.2, 26.6 KNN fn, tp: 0.92, 12.88 KNN f1 score: 0.495 KNN cohens kappa score: 0.464 minimum: KNN tn, fp: 287, 6 KNN fn, tp: 0, 11 KNN f1 score: 0.371 KNN cohens kappa score: 0.328 -----[ GAN ]----- maximum: GAN tn, fp: 313, 45 GAN fn, tp: 6, 14 GAN f1 score: 0.519 GAN cohens kappa score: 0.488 average: GAN tn, fp: 300.76, 31.04 GAN fn, tp: 2.2, 11.6 GAN f1 score: 0.415 GAN cohens kappa score: 0.377 minimum: GAN tn, fp: 287, 19 GAN fn, tp: 0, 8 GAN f1 score: 0.314 GAN cohens kappa score: 0.271