/////////////////////////////////////////// // Running convGAN-proximary-full 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 GAN.predict GAN tn, fp: 311, 22 GAN fn, tp: 0, 13 GAN f1 score: 0.542 GAN cohens kappa score: 0.515 -> test with 'LR' LR tn, fp: 292, 41 LR fn, tp: 0, 13 LR f1 score: 0.388 LR cohens kappa score: 0.349 LR average precision score: 0.355 -> 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: 325, 8 KNN fn, tp: 0, 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 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 327, 6 GAN fn, tp: 1, 12 GAN f1 score: 0.774 GAN cohens kappa score: 0.764 -> test with 'LR' LR tn, fp: 289, 44 LR fn, tp: 0, 13 LR f1 score: 0.371 LR cohens kappa score: 0.330 LR average precision score: 0.299 -> 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 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 321, 12 GAN fn, tp: 1, 12 GAN f1 score: 0.649 GAN cohens kappa score: 0.631 -> 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: 0.377 -> 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 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 323, 10 GAN fn, tp: 2, 11 GAN f1 score: 0.647 GAN cohens kappa score: 0.630 -> test with 'LR' LR tn, fp: 295, 38 LR fn, tp: 1, 12 LR f1 score: 0.381 LR cohens kappa score: 0.342 LR average precision score: 0.375 -> 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: 328, 5 KNN fn, tp: 0, 13 KNN f1 score: 0.839 KNN cohens kappa score: 0.831 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with GAN.predict GAN tn, fp: 321, 10 GAN fn, tp: 0, 13 GAN f1 score: 0.722 GAN cohens kappa score: 0.708 -> 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: 0.446 -> 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: 318, 13 KNN fn, tp: 0, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ====== 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 GAN.predict GAN tn, fp: 320, 13 GAN fn, tp: 0, 13 GAN f1 score: 0.667 GAN cohens kappa score: 0.649 -> test with 'LR' LR tn, fp: 296, 37 LR fn, tp: 0, 13 LR f1 score: 0.413 LR cohens kappa score: 0.375 LR average precision score: 0.282 -> 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: 323, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 317, 16 GAN fn, tp: 1, 12 GAN f1 score: 0.585 GAN cohens kappa score: 0.563 -> 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: 0.369 -> 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: 313, 20 KNN fn, tp: 0, 13 KNN f1 score: 0.565 KNN cohens kappa score: 0.540 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 313, 20 GAN fn, tp: 0, 13 GAN f1 score: 0.565 GAN cohens kappa score: 0.540 -> test with 'LR' LR tn, fp: 294, 39 LR fn, tp: 1, 12 LR f1 score: 0.375 LR cohens kappa score: 0.335 LR average precision score: 0.337 -> 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: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 311, 22 GAN fn, tp: 1, 12 GAN f1 score: 0.511 GAN cohens kappa score: 0.483 -> test with 'LR' LR tn, fp: 297, 36 LR fn, tp: 0, 13 LR f1 score: 0.419 LR cohens kappa score: 0.383 LR average precision score: 0.284 -> 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 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with GAN.predict GAN tn, fp: 311, 20 GAN fn, tp: 0, 13 GAN f1 score: 0.565 GAN cohens kappa score: 0.540 -> test with 'LR' LR tn, fp: 292, 39 LR fn, tp: 1, 12 LR f1 score: 0.375 LR cohens kappa score: 0.335 LR average precision score: 0.550 -> 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: 327, 4 KNN fn, tp: 0, 13 KNN f1 score: 0.867 KNN cohens kappa score: 0.861 ====== 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 GAN.predict GAN tn, fp: 318, 15 GAN fn, tp: 0, 13 GAN f1 score: 0.634 GAN cohens kappa score: 0.614 -> 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: 0.308 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 3, 10 GB f1 score: 0.870 GB cohens kappa score: 0.865 -> 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 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 319, 14 GAN fn, tp: 2, 11 GAN f1 score: 0.579 GAN cohens kappa score: 0.557 -> test with 'LR' LR tn, fp: 301, 32 LR fn, tp: 0, 13 LR f1 score: 0.448 LR cohens kappa score: 0.414 LR average precision score: 0.435 -> 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: 322, 11 KNN fn, tp: 0, 13 KNN f1 score: 0.703 KNN cohens kappa score: 0.687 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 315, 18 GAN fn, tp: 0, 13 GAN f1 score: 0.591 GAN cohens kappa score: 0.568 -> test with 'LR' LR tn, fp: 281, 52 LR fn, tp: 0, 13 LR f1 score: 0.333 LR cohens kappa score: 0.289 LR average precision score: 0.322 -> 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: 315, 18 KNN fn, tp: 0, 13 KNN f1 score: 0.591 KNN cohens kappa score: 0.568 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 319, 14 GAN fn, tp: 0, 13 GAN f1 score: 0.650 GAN cohens kappa score: 0.631 -> 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: 0.382 -> 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: 320, 13 KNN fn, tp: 0, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with GAN.predict GAN tn, fp: 326, 5 GAN fn, tp: 0, 13 GAN f1 score: 0.839 GAN cohens kappa score: 0.831 -> test with 'LR' LR tn, fp: 295, 36 LR fn, tp: 2, 11 LR f1 score: 0.367 LR cohens kappa score: 0.327 LR average precision score: 0.378 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.958 -> test with 'KNN' KNN tn, fp: 324, 7 KNN fn, tp: 0, 13 KNN f1 score: 0.788 KNN cohens kappa score: 0.778 ====== 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 GAN.predict GAN tn, fp: 326, 7 GAN fn, tp: 2, 11 GAN f1 score: 0.710 GAN cohens kappa score: 0.696 -> 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: 0.409 -> 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 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 325, 8 GAN fn, tp: 0, 13 GAN f1 score: 0.765 GAN cohens kappa score: 0.753 -> 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: 0.487 -> 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: 326, 7 KNN fn, tp: 0, 13 KNN f1 score: 0.788 KNN cohens kappa score: 0.778 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 322, 11 GAN fn, tp: 0, 13 GAN f1 score: 0.703 GAN cohens kappa score: 0.687 -> test with 'LR' LR tn, fp: 285, 48 LR fn, tp: 0, 13 LR f1 score: 0.351 LR cohens kappa score: 0.309 LR average precision score: 0.323 -> 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 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 315, 18 GAN fn, tp: 0, 13 GAN f1 score: 0.591 GAN cohens kappa score: 0.568 -> test with 'LR' LR tn, fp: 294, 39 LR fn, tp: 1, 12 LR f1 score: 0.375 LR cohens kappa score: 0.335 LR average precision score: 0.272 -> 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: 0, 13 KNN f1 score: 0.591 KNN cohens kappa score: 0.568 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with GAN.predict GAN tn, fp: 325, 6 GAN fn, tp: 0, 13 GAN f1 score: 0.813 GAN cohens kappa score: 0.804 -> test with 'LR' LR tn, fp: 299, 32 LR fn, tp: 1, 12 LR f1 score: 0.421 LR cohens kappa score: 0.385 LR average precision score: 0.325 -> 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: 321, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ====== 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 GAN.predict GAN tn, fp: 316, 17 GAN fn, tp: 1, 12 GAN f1 score: 0.571 GAN cohens kappa score: 0.548 -> test with 'LR' LR tn, fp: 281, 52 LR fn, tp: 0, 13 LR f1 score: 0.333 LR cohens kappa score: 0.289 LR average precision score: 0.289 -> 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: 318, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 321, 12 GAN fn, tp: 2, 11 GAN f1 score: 0.611 GAN cohens kappa score: 0.591 -> test with 'LR' LR tn, fp: 300, 33 LR fn, tp: 3, 10 LR f1 score: 0.357 LR cohens kappa score: 0.318 LR average precision score: 0.358 -> 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 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 316, 17 GAN fn, tp: 2, 11 GAN f1 score: 0.537 GAN cohens kappa score: 0.512 -> 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: 0.334 -> 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 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> test with GAN.predict GAN tn, fp: 313, 20 GAN fn, tp: 0, 13 GAN f1 score: 0.565 GAN cohens kappa score: 0.540 -> 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: 0.292 -> 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: 322, 11 KNN fn, tp: 0, 13 KNN f1 score: 0.703 KNN cohens kappa score: 0.687 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> test with GAN.predict GAN tn, fp: 316, 15 GAN fn, tp: 1, 12 GAN f1 score: 0.600 GAN cohens kappa score: 0.578 -> test with 'LR' LR tn, fp: 292, 39 LR fn, tp: 0, 13 LR f1 score: 0.400 LR cohens kappa score: 0.361 LR average precision score: 0.474 -> 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: 319, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 302, 55 LR fn, tp: 3, 13 LR f1 score: 0.448 LR cohens kappa score: 0.414 LR average precision score: 0.550 average: LR tn, fp: 292.0, 40.6 LR fn, tp: 0.56, 12.44 LR f1 score: 0.380 LR cohens kappa score: 0.340 LR average precision score: 0.362 minimum: LR tn, fp: 278, 31 LR fn, tp: 0, 10 LR f1 score: 0.321 LR cohens kappa score: 0.275 LR average precision score: 0.272 -----[ GB ]----- maximum: GB tn, fp: 333, 3 GB fn, tp: 3, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 332.24, 0.36 GB fn, tp: 0.24, 12.76 GB f1 score: 0.977 GB cohens kappa score: 0.976 minimum: GB tn, fp: 330, 0 GB fn, tp: 0, 10 GB f1 score: 0.870 GB cohens kappa score: 0.865 -----[ KNN ]----- maximum: KNN tn, fp: 328, 20 KNN fn, tp: 0, 13 KNN f1 score: 0.867 KNN cohens kappa score: 0.861 average: KNN tn, fp: 321.24, 11.36 KNN fn, tp: 0.0, 13.0 KNN f1 score: 0.705 KNN cohens kappa score: 0.689 minimum: KNN tn, fp: 313, 4 KNN fn, tp: 0, 13 KNN f1 score: 0.565 KNN cohens kappa score: 0.540 -----[ GAN ]----- maximum: GAN tn, fp: 327, 22 GAN fn, tp: 2, 13 GAN f1 score: 0.839 GAN cohens kappa score: 0.831 average: GAN tn, fp: 318.68, 13.92 GAN fn, tp: 0.64, 12.36 GAN f1 score: 0.639 GAN cohens kappa score: 0.620 minimum: GAN tn, fp: 311, 5 GAN fn, tp: 0, 11 GAN f1 score: 0.511 GAN cohens kappa score: 0.483