/////////////////////////////////////////// // Running Repeater on folding_car-vgood /////////////////////////////////////////// Load 'data_input/folding_car-vgood' 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 1278 synthetic samples -> test with 'LR' LR tn, fp: 286, 47 LR fn, tp: 0, 13 LR f1 score: 0.356 LR cohens kappa score: 0.314 LR average precision score: 0.363 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 303, 30 KNN fn, tp: 0, 13 KNN f1 score: 0.464 KNN cohens kappa score: 0.431 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 286, 47 LR fn, tp: 0, 13 LR f1 score: 0.356 LR cohens kappa score: 0.314 LR average precision score: 0.304 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 294, 39 KNN fn, tp: 0, 13 KNN f1 score: 0.400 KNN cohens kappa score: 0.362 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 278, 55 LR fn, tp: 0, 13 LR f1 score: 0.321 LR cohens kappa score: 0.275 LR average precision score: 0.392 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 286, 47 KNN fn, tp: 0, 13 KNN f1 score: 0.356 KNN cohens kappa score: 0.314 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 290, 43 LR fn, tp: 0, 13 LR f1 score: 0.377 LR cohens kappa score: 0.336 LR average precision score: 0.367 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 294, 39 KNN fn, tp: 0, 13 KNN f1 score: 0.400 KNN cohens kappa score: 0.362 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 294, 37 LR fn, tp: 1, 12 LR f1 score: 0.387 LR cohens kappa score: 0.348 LR average precision score: 0.447 -> test with 'GB' GB tn, fp: 326, 5 GB fn, tp: 0, 13 GB f1 score: 0.839 GB cohens kappa score: 0.831 -> test with 'KNN' KNN tn, fp: 300, 31 KNN fn, tp: 0, 13 KNN f1 score: 0.456 KNN cohens kappa score: 0.422 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 290, 43 LR fn, tp: 0, 13 LR f1 score: 0.377 LR cohens kappa score: 0.336 LR average precision score: 0.289 -> test with 'GB' GB tn, fp: 330, 3 GB fn, tp: 0, 13 GB f1 score: 0.897 GB cohens kappa score: 0.892 -> test with 'KNN' KNN tn, fp: 297, 36 KNN fn, tp: 0, 13 KNN f1 score: 0.419 KNN cohens kappa score: 0.383 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 274, 59 LR fn, tp: 0, 13 LR f1 score: 0.306 LR cohens kappa score: 0.259 LR average precision score: 0.366 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 287, 46 KNN fn, tp: 0, 13 KNN f1 score: 0.361 KNN cohens kappa score: 0.319 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 293, 40 LR fn, tp: 1, 12 LR f1 score: 0.369 LR cohens kappa score: 0.329 LR average precision score: 0.345 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 298, 35 KNN fn, tp: 0, 13 KNN f1 score: 0.426 KNN cohens kappa score: 0.390 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 295, 38 LR fn, tp: 0, 13 LR f1 score: 0.406 LR cohens kappa score: 0.368 LR average precision score: 0.286 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 293, 40 KNN fn, tp: 0, 13 KNN f1 score: 0.394 KNN cohens kappa score: 0.355 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 284, 47 LR fn, tp: 0, 13 LR f1 score: 0.356 LR cohens kappa score: 0.314 LR average precision score: 0.555 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 303, 28 KNN fn, tp: 0, 13 KNN f1 score: 0.481 KNN cohens kappa score: 0.450 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 288, 45 LR fn, tp: 0, 13 LR f1 score: 0.366 LR cohens kappa score: 0.325 LR average precision score: 0.308 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 293, 40 KNN fn, tp: 0, 13 KNN f1 score: 0.394 KNN cohens kappa score: 0.355 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 294, 39 LR fn, tp: 0, 13 LR f1 score: 0.400 LR cohens kappa score: 0.362 LR average precision score: 0.436 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 298, 35 KNN fn, tp: 0, 13 KNN f1 score: 0.426 KNN cohens kappa score: 0.390 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 280, 53 LR fn, tp: 0, 13 LR f1 score: 0.329 LR cohens kappa score: 0.284 LR average precision score: 0.314 -> test with 'GB' GB tn, fp: 328, 5 GB fn, tp: 0, 13 GB f1 score: 0.839 GB cohens kappa score: 0.831 -> test with 'KNN' KNN tn, fp: 295, 38 KNN fn, tp: 0, 13 KNN f1 score: 0.406 KNN cohens kappa score: 0.368 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 291, 42 LR fn, tp: 0, 13 LR f1 score: 0.382 LR cohens kappa score: 0.342 LR average precision score: 0.385 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 288, 45 KNN fn, tp: 0, 13 KNN f1 score: 0.366 KNN cohens kappa score: 0.325 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 286, 45 LR fn, tp: 1, 12 LR f1 score: 0.343 LR cohens kappa score: 0.300 LR average precision score: 0.376 -> 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: 297, 34 KNN fn, tp: 0, 13 KNN f1 score: 0.433 KNN cohens kappa score: 0.398 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 291, 42 LR fn, tp: 0, 13 LR f1 score: 0.382 LR cohens kappa score: 0.342 LR average precision score: 0.419 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 296, 37 KNN fn, tp: 0, 13 KNN f1 score: 0.413 KNN cohens kappa score: 0.375 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 284, 49 LR fn, tp: 0, 13 LR f1 score: 0.347 LR cohens kappa score: 0.303 LR average precision score: 0.511 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 299, 34 KNN fn, tp: 0, 13 KNN f1 score: 0.433 KNN cohens kappa score: 0.398 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 280, 53 LR fn, tp: 0, 13 LR f1 score: 0.329 LR cohens kappa score: 0.284 LR average precision score: 0.320 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 291, 42 KNN fn, tp: 0, 13 KNN f1 score: 0.382 KNN cohens kappa score: 0.342 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 290, 43 LR fn, tp: 1, 12 LR f1 score: 0.353 LR cohens kappa score: 0.311 LR average precision score: 0.275 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 293, 40 KNN fn, tp: 0, 13 KNN f1 score: 0.394 KNN cohens kappa score: 0.355 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 289, 42 LR fn, tp: 0, 13 LR f1 score: 0.382 LR cohens kappa score: 0.342 LR average precision score: 0.321 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> 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 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 279, 54 LR fn, tp: 0, 13 LR f1 score: 0.325 LR cohens kappa score: 0.280 LR average precision score: 0.292 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 293, 40 KNN fn, tp: 0, 13 KNN f1 score: 0.394 KNN cohens kappa score: 0.355 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 293, 40 LR fn, tp: 1, 12 LR f1 score: 0.369 LR cohens kappa score: 0.329 LR average precision score: 0.359 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 303, 30 KNN fn, tp: 0, 13 KNN f1 score: 0.464 KNN cohens kappa score: 0.431 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 298, 35 LR fn, tp: 0, 13 LR f1 score: 0.426 LR cohens kappa score: 0.390 LR average precision score: 0.336 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 298, 35 KNN fn, tp: 0, 13 KNN f1 score: 0.426 KNN cohens kappa score: 0.390 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with 'LR' LR tn, fp: 282, 51 LR fn, tp: 0, 13 LR f1 score: 0.338 LR cohens kappa score: 0.294 LR average precision score: 0.295 -> test with 'GB' GB tn, fp: 327, 6 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 288, 45 KNN fn, tp: 0, 13 KNN f1 score: 0.366 KNN cohens kappa score: 0.325 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with 'LR' LR tn, fp: 287, 44 LR fn, tp: 0, 13 LR f1 score: 0.371 LR cohens kappa score: 0.330 LR average precision score: 0.488 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> 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: 298, 59 LR fn, tp: 1, 13 LR f1 score: 0.426 LR cohens kappa score: 0.390 LR average precision score: 0.555 average: LR tn, fp: 287.28, 45.32 LR fn, tp: 0.2, 12.8 LR f1 score: 0.362 LR cohens kappa score: 0.320 LR average precision score: 0.366 minimum: LR tn, fp: 274, 35 LR fn, tp: 0, 12 LR f1 score: 0.306 LR cohens kappa score: 0.259 LR average precision score: 0.275 -----[ GB ]----- maximum: GB tn, fp: 333, 6 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 330.8, 1.8 GB fn, tp: 0.0, 13.0 GB f1 score: 0.938 GB cohens kappa score: 0.935 minimum: GB tn, fp: 326, 0 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.804 -----[ KNN ]----- maximum: KNN tn, fp: 303, 47 KNN fn, tp: 0, 13 KNN f1 score: 0.481 KNN cohens kappa score: 0.450 average: KNN tn, fp: 295.2, 37.4 KNN fn, tp: 0.0, 13.0 KNN f1 score: 0.413 KNN cohens kappa score: 0.375 minimum: KNN tn, fp: 286, 28 KNN fn, tp: 0, 13 KNN f1 score: 0.356 KNN cohens kappa score: 0.314