/////////////////////////////////////////// // Running CTGAN on folding_abalone9-18 /////////////////////////////////////////// Load 'folding_abalone9-18' 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 518 synthetic samples -> test with 'LR' LR tn, fp: 125, 13 LR fn, tp: 3, 6 LR f1 score: 0.429 LR cohens kappa score: 0.377 LR average precision score: 0.655 -> test with 'RF' RF tn, fp: 134, 4 RF fn, tp: 4, 5 RF f1 score: 0.556 RF cohens kappa score: 0.527 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 4, 5 GB f1 score: 0.526 GB cohens kappa score: 0.494 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 116, 22 LR fn, tp: 3, 6 LR f1 score: 0.324 LR cohens kappa score: 0.255 LR average precision score: 0.505 -> test with 'RF' RF tn, fp: 134, 4 RF fn, tp: 6, 3 RF f1 score: 0.375 RF cohens kappa score: 0.340 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 6, 3 GB f1 score: 0.429 GB cohens kappa score: 0.402 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 8, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.140 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 128, 10 LR fn, tp: 6, 3 LR f1 score: 0.273 LR cohens kappa score: 0.216 LR average precision score: 0.334 -> test with 'RF' RF tn, fp: 132, 6 RF fn, tp: 5, 4 RF f1 score: 0.421 RF cohens kappa score: 0.381 -> test with 'GB' GB tn, fp: 130, 8 GB fn, tp: 5, 4 GB f1 score: 0.381 GB cohens kappa score: 0.334 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 8, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.140 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 5, 4 LR f1 score: 0.444 LR cohens kappa score: 0.408 LR average precision score: 0.457 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 6, 3 RF f1 score: 0.429 RF cohens kappa score: 0.402 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 6, 3 GB f1 score: 0.400 GB cohens kappa score: 0.369 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 6, 3 KNN f1 score: 0.500 KNN cohens kappa score: 0.484 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 127, 10 LR fn, tp: 2, 4 LR f1 score: 0.400 LR cohens kappa score: 0.363 LR average precision score: 0.296 -> test with 'RF' RF tn, fp: 132, 5 RF fn, tp: 4, 2 RF f1 score: 0.308 RF cohens kappa score: 0.275 -> test with 'GB' GB tn, fp: 133, 4 GB fn, tp: 5, 1 GB f1 score: 0.182 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 135, 2 KNN fn, tp: 5, 1 KNN f1 score: 0.222 KNN cohens kappa score: 0.200 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 3, 6 LR f1 score: 0.545 LR cohens kappa score: 0.510 LR average precision score: 0.573 -> test with 'RF' RF tn, fp: 135, 3 RF fn, tp: 6, 3 RF f1 score: 0.400 RF cohens kappa score: 0.369 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 6, 3 GB f1 score: 0.462 GB cohens kappa score: 0.440 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 8, 1 KNN f1 score: 0.182 KNN cohens kappa score: 0.163 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 5, 4 LR f1 score: 0.444 LR cohens kappa score: 0.408 LR average precision score: 0.423 -> test with 'RF' RF tn, fp: 134, 4 RF fn, tp: 5, 4 RF f1 score: 0.471 RF cohens kappa score: 0.438 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 5, 4 GB f1 score: 0.421 GB cohens kappa score: 0.381 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 114, 24 LR fn, tp: 4, 5 LR f1 score: 0.263 LR cohens kappa score: 0.187 LR average precision score: 0.285 -> test with 'RF' RF tn, fp: 132, 6 RF fn, tp: 6, 3 RF f1 score: 0.333 RF cohens kappa score: 0.290 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 6, 3 GB f1 score: 0.375 GB cohens kappa score: 0.340 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 7, 2 KNN f1 score: 0.286 KNN cohens kappa score: 0.253 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 119, 19 LR fn, tp: 5, 4 LR f1 score: 0.250 LR cohens kappa score: 0.178 LR average precision score: 0.475 -> test with 'RF' RF tn, fp: 133, 5 RF fn, tp: 5, 4 RF f1 score: 0.444 RF cohens kappa score: 0.408 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 5, 4 GB f1 score: 0.444 GB cohens kappa score: 0.408 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 7, 2 KNN f1 score: 0.333 KNN cohens kappa score: 0.312 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 129, 8 LR fn, tp: 5, 1 LR f1 score: 0.133 LR cohens kappa score: 0.087 LR average precision score: 0.091 -> test with 'RF' RF tn, fp: 130, 7 RF fn, tp: 3, 3 RF f1 score: 0.375 RF cohens kappa score: 0.340 -> test with 'GB' GB tn, fp: 132, 5 GB fn, tp: 4, 2 GB f1 score: 0.308 GB cohens kappa score: 0.275 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 5, 1 KNN f1 score: 0.286 KNN cohens kappa score: 0.277 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 95, 43 LR fn, tp: 2, 7 LR f1 score: 0.237 LR cohens kappa score: 0.149 LR average precision score: 0.322 -> test with 'RF' RF tn, fp: 131, 7 RF fn, tp: 6, 3 RF f1 score: 0.316 RF cohens kappa score: 0.269 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 6, 3 GB f1 score: 0.333 GB cohens kappa score: 0.290 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 5, 4 KNN f1 score: 0.615 KNN cohens kappa score: 0.600 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 3, 6 LR f1 score: 0.571 LR cohens kappa score: 0.539 LR average precision score: 0.641 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 5, 4 RF f1 score: 0.533 RF cohens kappa score: 0.509 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 4, 5 GB f1 score: 0.667 GB cohens kappa score: 0.649 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.023 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 126, 12 LR fn, tp: 6, 3 LR f1 score: 0.250 LR cohens kappa score: 0.188 LR average precision score: 0.426 -> test with 'RF' RF tn, fp: 132, 6 RF fn, tp: 6, 3 RF f1 score: 0.333 RF cohens kappa score: 0.290 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.313 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 8, 1 KNN f1 score: 0.154 KNN cohens kappa score: 0.121 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 123, 15 LR fn, tp: 6, 3 LR f1 score: 0.222 LR cohens kappa score: 0.153 LR average precision score: 0.146 -> test with 'RF' RF tn, fp: 134, 4 RF fn, tp: 4, 5 RF f1 score: 0.556 RF cohens kappa score: 0.527 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 5, 4 GB f1 score: 0.471 GB cohens kappa score: 0.438 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 6, 3 KNN f1 score: 0.400 KNN cohens kappa score: 0.369 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 127, 10 LR fn, tp: 4, 2 LR f1 score: 0.222 LR cohens kappa score: 0.176 LR average precision score: 0.402 -> test with 'RF' RF tn, fp: 130, 7 RF fn, tp: 4, 2 RF f1 score: 0.267 RF cohens kappa score: 0.228 -> test with 'GB' GB tn, fp: 133, 4 GB fn, tp: 4, 2 GB f1 score: 0.333 GB cohens kappa score: 0.304 -> test with 'KNN' KNN tn, fp: 136, 1 KNN fn, tp: 5, 1 KNN f1 score: 0.250 KNN cohens kappa score: 0.234 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 4, 5 LR f1 score: 0.455 LR cohens kappa score: 0.412 LR average precision score: 0.648 -> test with 'RF' RF tn, fp: 135, 3 RF fn, tp: 6, 3 RF f1 score: 0.400 RF cohens kappa score: 0.369 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 6, 3 GB f1 score: 0.400 GB cohens kappa score: 0.369 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 6, 3 KNN f1 score: 0.500 KNN cohens kappa score: 0.484 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 126, 12 LR fn, tp: 5, 4 LR f1 score: 0.320 LR cohens kappa score: 0.262 LR average precision score: 0.329 -> test with 'RF' RF tn, fp: 127, 11 RF fn, tp: 4, 5 RF f1 score: 0.400 RF cohens kappa score: 0.349 -> test with 'GB' GB tn, fp: 132, 6 GB fn, tp: 5, 4 GB f1 score: 0.421 GB cohens kappa score: 0.381 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 6, 3 KNN f1 score: 0.462 KNN cohens kappa score: 0.440 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 102, 36 LR fn, tp: 8, 1 LR f1 score: 0.043 LR cohens kappa score: -0.061 LR average precision score: 0.084 -> test with 'RF' RF tn, fp: 131, 7 RF fn, tp: 6, 3 RF f1 score: 0.316 RF cohens kappa score: 0.269 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 7, 2 GB f1 score: 0.250 GB cohens kappa score: 0.208 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 137, 1 LR fn, tp: 6, 3 LR f1 score: 0.462 LR cohens kappa score: 0.440 LR average precision score: 0.686 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 7, 2 RF f1 score: 0.308 RF cohens kappa score: 0.281 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 6, 3 GB f1 score: 0.400 GB cohens kappa score: 0.369 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.012 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 123, 14 LR fn, tp: 2, 4 LR f1 score: 0.333 LR cohens kappa score: 0.289 LR average precision score: 0.304 -> test with 'RF' RF tn, fp: 127, 10 RF fn, tp: 3, 3 RF f1 score: 0.316 RF cohens kappa score: 0.274 -> test with 'GB' GB tn, fp: 126, 11 GB fn, tp: 3, 3 GB f1 score: 0.300 GB cohens kappa score: 0.256 -> test with 'KNN' KNN tn, fp: 134, 3 KNN fn, tp: 4, 2 KNN f1 score: 0.364 KNN cohens kappa score: 0.338 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 5, 4 LR f1 score: 0.364 LR cohens kappa score: 0.314 LR average precision score: 0.305 -> test with 'RF' RF tn, fp: 133, 5 RF fn, tp: 7, 2 RF f1 score: 0.250 RF cohens kappa score: 0.208 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 8, 1 GB f1 score: 0.118 GB cohens kappa score: 0.064 -> test with 'KNN' KNN tn, fp: 134, 4 KNN fn, tp: 6, 3 KNN f1 score: 0.375 KNN cohens kappa score: 0.340 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 123, 15 LR fn, tp: 6, 3 LR f1 score: 0.222 LR cohens kappa score: 0.153 LR average precision score: 0.283 -> test with 'RF' RF tn, fp: 130, 8 RF fn, tp: 6, 3 RF f1 score: 0.300 RF cohens kappa score: 0.249 -> test with 'GB' GB tn, fp: 131, 7 GB fn, tp: 6, 3 GB f1 score: 0.316 GB cohens kappa score: 0.269 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 7, 2 KNN f1 score: 0.308 KNN cohens kappa score: 0.281 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 127, 11 LR fn, tp: 5, 4 LR f1 score: 0.333 LR cohens kappa score: 0.278 LR average precision score: 0.351 -> test with 'RF' RF tn, fp: 133, 5 RF fn, tp: 6, 3 RF f1 score: 0.353 RF cohens kappa score: 0.313 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 7, 2 GB f1 score: 0.267 GB cohens kappa score: 0.229 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 7, 2 KNN f1 score: 0.286 KNN cohens kappa score: 0.253 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 128, 10 LR fn, tp: 1, 8 LR f1 score: 0.593 LR cohens kappa score: 0.556 LR average precision score: 0.731 -> test with 'RF' RF tn, fp: 133, 5 RF fn, tp: 5, 4 RF f1 score: 0.444 RF cohens kappa score: 0.408 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 4, 5 GB f1 score: 0.667 GB cohens kappa score: 0.649 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 7, 2 KNN f1 score: 0.364 KNN cohens kappa score: 0.349 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 131, 6 LR fn, tp: 3, 3 LR f1 score: 0.400 LR cohens kappa score: 0.368 LR average precision score: 0.638 -> test with 'RF' RF tn, fp: 137, 0 RF fn, tp: 3, 3 RF f1 score: 0.667 RF cohens kappa score: 0.657 -> test with 'GB' GB tn, fp: 137, 0 GB fn, tp: 3, 3 GB f1 score: 0.667 GB cohens kappa score: 0.657 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 5, 1 KNN f1 score: 0.286 KNN cohens kappa score: 0.277 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 137, 43 LR fn, tp: 8, 8 LR f1 score: 0.593 LR cohens kappa score: 0.556 LR average precision score: 0.731 average: LR tn, fp: 124.56, 13.24 LR fn, tp: 4.28, 4.12 LR f1 score: 0.341 LR cohens kappa score: 0.288 LR average precision score: 0.416 minimum: LR tn, fp: 95, 1 LR fn, tp: 1, 1 LR f1 score: 0.043 LR cohens kappa score: -0.061 LR average precision score: 0.084 -----[ RF ]----- maximum: RF tn, fp: 137, 11 RF fn, tp: 7, 5 RF f1 score: 0.667 RF cohens kappa score: 0.657 average: RF tn, fp: 132.68, 5.12 RF fn, tp: 5.12, 3.28 RF f1 score: 0.395 RF cohens kappa score: 0.359 minimum: RF tn, fp: 127, 0 RF fn, tp: 3, 2 RF f1 score: 0.250 RF cohens kappa score: 0.208 -----[ GB ]----- maximum: GB tn, fp: 137, 11 GB fn, tp: 8, 5 GB f1 score: 0.667 GB cohens kappa score: 0.657 average: GB tn, fp: 133.4, 4.4 GB fn, tp: 5.28, 3.12 GB f1 score: 0.396 GB cohens kappa score: 0.362 minimum: GB tn, fp: 126, 0 GB fn, tp: 3, 1 GB f1 score: 0.118 GB cohens kappa score: 0.064 -----[ KNN ]----- maximum: KNN tn, fp: 138, 4 KNN fn, tp: 9, 4 KNN f1 score: 0.615 KNN cohens kappa score: 0.600 average: KNN tn, fp: 136.44, 1.36 KNN fn, tp: 6.76, 1.64 KNN f1 score: 0.276 KNN cohens kappa score: 0.256 minimum: KNN tn, fp: 134, 0 KNN fn, tp: 4, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.023 wall time: 00:04:04s, process time: 00:31:24s