/////////////////////////////////////////// // Running CTAB-GAN 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 0%| | 0/10 [00:00 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: 0.325 -> 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: 321, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 286, 47 LR fn, tp: 2, 11 LR f1 score: 0.310 LR cohens kappa score: 0.265 LR average precision score: 0.294 -> 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: 314, 19 KNN fn, tp: 0, 13 KNN f1 score: 0.578 KNN cohens kappa score: 0.554 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 276, 57 LR fn, tp: 0, 13 LR f1 score: 0.313 LR cohens kappa score: 0.267 LR average precision score: 0.378 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 323, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.394 -> 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: 315, 18 KNN fn, tp: 1, 12 KNN f1 score: 0.558 KNN cohens kappa score: 0.534 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1280 synthetic samples -> test with 'LR' LR tn, fp: 286, 45 LR fn, tp: 0, 13 LR f1 score: 0.366 LR cohens kappa score: 0.324 LR average precision score: 0.388 -> 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: 322, 9 KNN fn, tp: 0, 13 KNN f1 score: 0.743 KNN cohens kappa score: 0.730 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 316, 17 LR fn, tp: 8, 5 LR f1 score: 0.286 LR cohens kappa score: 0.250 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: 309, 24 KNN fn, tp: 0, 13 KNN f1 score: 0.520 KNN cohens kappa score: 0.492 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 272, 61 LR fn, tp: 0, 13 LR f1 score: 0.299 LR cohens kappa score: 0.251 LR average precision score: 0.397 -> 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: 316, 17 KNN fn, tp: 3, 10 KNN f1 score: 0.500 KNN cohens kappa score: 0.473 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 291, 42 LR fn, tp: 1, 12 LR f1 score: 0.358 LR cohens kappa score: 0.317 LR average precision score: 0.355 -> 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: 321, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 318, 15 LR fn, tp: 7, 6 LR f1 score: 0.353 LR cohens kappa score: 0.321 LR average precision score: 0.355 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 321, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1280 synthetic samples -> test with 'LR' LR tn, fp: 319, 12 LR fn, tp: 4, 9 LR f1 score: 0.529 LR cohens kappa score: 0.506 LR average precision score: 0.433 -> 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: 326, 5 KNN fn, tp: 6, 7 KNN f1 score: 0.560 KNN cohens kappa score: 0.543 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 302, 31 LR fn, tp: 1, 12 LR f1 score: 0.429 LR cohens kappa score: 0.394 LR average precision score: 0.291 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 321, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.446 -> 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: 321, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 316, 17 LR fn, tp: 6, 7 LR f1 score: 0.378 LR cohens kappa score: 0.347 LR average precision score: 0.329 -> 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: 307, 26 KNN fn, tp: 0, 13 KNN f1 score: 0.500 KNN cohens kappa score: 0.470 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.390 -> 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: 323, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1280 synthetic samples -> test with 'LR' LR tn, fp: 275, 56 LR fn, tp: 0, 13 LR f1 score: 0.317 LR cohens kappa score: 0.271 LR average precision score: 0.429 -> 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: 323, 8 KNN fn, tp: 0, 13 KNN f1 score: 0.765 KNN cohens kappa score: 0.753 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 318, 15 LR fn, tp: 5, 8 LR f1 score: 0.444 LR cohens kappa score: 0.416 LR average precision score: 0.386 -> 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: 319, 14 KNN fn, tp: 0, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.386 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 320, 13 KNN fn, tp: 3, 10 KNN f1 score: 0.556 KNN cohens kappa score: 0.533 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 275, 58 LR fn, tp: 0, 13 LR f1 score: 0.310 LR cohens kappa score: 0.263 LR average precision score: 0.316 -> 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: 326, 7 KNN fn, tp: 0, 13 KNN f1 score: 0.788 KNN cohens kappa score: 0.778 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 317, 16 LR fn, tp: 7, 6 LR f1 score: 0.343 LR cohens kappa score: 0.310 LR average precision score: 0.288 -> 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: 317, 16 KNN fn, tp: 0, 13 KNN f1 score: 0.619 KNN cohens kappa score: 0.598 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.337 -> 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: 316, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 277, 56 LR fn, tp: 0, 13 LR f1 score: 0.317 LR cohens kappa score: 0.271 LR average precision score: 0.265 -> 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: 318, 15 KNN fn, tp: 1, 12 KNN f1 score: 0.600 KNN cohens kappa score: 0.579 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.341 -> 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: 319, 14 KNN fn, tp: 0, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 318, 15 LR fn, tp: 4, 9 LR f1 score: 0.486 LR cohens kappa score: 0.460 LR average precision score: 0.379 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 315, 18 KNN fn, tp: 0, 13 KNN f1 score: 0.591 KNN cohens kappa score: 0.568 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1278 synthetic samples -> test with 'LR' LR tn, fp: 277, 56 LR fn, tp: 0, 13 LR f1 score: 0.317 LR cohens kappa score: 0.271 LR average precision score: 0.285 -> 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: 301, 32 KNN fn, tp: 0, 13 KNN f1 score: 0.448 KNN cohens kappa score: 0.414 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1280 synthetic samples -> test with 'LR' LR tn, fp: 297, 34 LR fn, tp: 1, 12 LR f1 score: 0.407 LR cohens kappa score: 0.370 LR average precision score: 0.463 -> 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: 321, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 319, 61 LR fn, tp: 8, 13 LR f1 score: 0.529 LR cohens kappa score: 0.506 LR average precision score: 0.463 average: LR tn, fp: 294.2, 38.4 LR fn, tp: 1.84, 11.16 LR f1 score: 0.367 LR cohens kappa score: 0.328 LR average precision score: 0.357 minimum: LR tn, fp: 272, 12 LR fn, tp: 0, 5 LR f1 score: 0.286 LR cohens kappa score: 0.250 LR average precision score: 0.265 -----[ GB ]----- maximum: GB tn, fp: 333, 2 GB fn, tp: 2, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 332.16, 0.44 GB fn, tp: 0.24, 12.76 GB f1 score: 0.974 GB cohens kappa score: 0.973 minimum: GB tn, fp: 329, 0 GB fn, tp: 0, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -----[ KNN ]----- maximum: KNN tn, fp: 326, 32 KNN fn, tp: 6, 13 KNN f1 score: 0.788 KNN cohens kappa score: 0.778 average: KNN tn, fp: 318.2, 14.4 KNN fn, tp: 0.56, 12.44 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 minimum: KNN tn, fp: 301, 5 KNN fn, tp: 0, 7 KNN f1 score: 0.448 KNN cohens kappa score: 0.414