/////////////////////////////////////////// // Running ctGAN on folding_abalone9-18 /////////////////////////////////////////// Load 'data_input/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: 131, 7 LR fn, tp: 7, 2 LR f1 score: 0.222 LR cohens kappa score: 0.171 LR average precision score: 0.308 -> 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: 136, 2 GB fn, tp: 5, 4 GB f1 score: 0.533 GB cohens kappa score: 0.509 -> 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: 134, 4 LR fn, tp: 7, 2 LR f1 score: 0.267 LR cohens kappa score: 0.229 LR average precision score: 0.340 -> 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: 7, 2 GB f1 score: 0.333 GB cohens kappa score: 0.312 -> 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: 134, 4 LR fn, tp: 5, 4 LR f1 score: 0.471 LR cohens kappa score: 0.438 LR average precision score: 0.575 -> 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: 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: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 1/5: Slice 4/5 ------- -> 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: 0.377 -> 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: 137, 1 GB fn, tp: 6, 3 GB f1 score: 0.462 GB cohens kappa score: 0.440 -> 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 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 134, 3 LR fn, tp: 4, 2 LR f1 score: 0.364 LR cohens kappa score: 0.338 LR average precision score: 0.529 -> test with 'RF' RF tn, fp: 137, 0 RF fn, tp: 4, 2 RF f1 score: 0.500 RF cohens kappa score: 0.489 -> 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: 137, 0 KNN fn, tp: 5, 1 KNN f1 score: 0.286 KNN cohens kappa score: 0.277 ====== 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: 137, 1 LR fn, tp: 6, 3 LR f1 score: 0.462 LR cohens kappa score: 0.440 LR average precision score: 0.472 -> test with 'RF' RF tn, fp: 137, 1 RF fn, tp: 7, 2 RF f1 score: 0.333 RF cohens kappa score: 0.312 -> 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: 121, 17 LR fn, tp: 6, 3 LR f1 score: 0.207 LR cohens kappa score: 0.134 LR average precision score: 0.087 -> test with 'RF' RF tn, fp: 131, 7 RF fn, tp: 5, 4 RF f1 score: 0.400 RF cohens kappa score: 0.357 -> 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: 138, 0 KNN fn, tp: 7, 2 KNN f1 score: 0.364 KNN cohens kappa score: 0.349 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 112, 26 LR fn, tp: 6, 3 LR f1 score: 0.158 LR cohens kappa score: 0.071 LR average precision score: 0.194 -> 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: 134, 4 GB fn, tp: 6, 3 GB f1 score: 0.375 GB cohens kappa score: 0.340 -> 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 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 136, 2 LR fn, tp: 4, 5 LR f1 score: 0.625 LR cohens kappa score: 0.604 LR average precision score: 0.709 -> 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: 134, 4 GB fn, tp: 5, 4 GB f1 score: 0.471 GB cohens kappa score: 0.438 -> 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 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: 3, 3 LR f1 score: 0.353 LR cohens kappa score: 0.316 LR average precision score: 0.588 -> test with 'RF' RF tn, fp: 132, 5 RF fn, tp: 3, 3 RF f1 score: 0.429 RF cohens kappa score: 0.400 -> test with 'GB' GB tn, fp: 134, 3 GB fn, tp: 3, 3 GB f1 score: 0.500 GB cohens kappa score: 0.478 -> 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: 133, 5 LR fn, tp: 8, 1 LR f1 score: 0.133 LR cohens kappa score: 0.089 LR average precision score: 0.195 -> 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: 136, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.140 -> 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 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 136, 2 LR fn, tp: 6, 3 LR f1 score: 0.429 LR cohens kappa score: 0.402 LR average precision score: 0.545 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 8, 1 RF f1 score: 0.167 RF cohens kappa score: 0.140 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.163 -> 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: 7, 2 LR f1 score: 0.174 LR cohens kappa score: 0.107 LR average precision score: 0.201 -> 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: 137, 1 GB fn, tp: 6, 3 GB f1 score: 0.462 GB cohens kappa score: 0.440 -> 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 4/5 ------- -> 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: 0.347 -> 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: 135, 3 GB fn, tp: 5, 4 GB f1 score: 0.500 GB cohens kappa score: 0.472 -> 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 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 136, 1 LR fn, tp: 3, 3 LR f1 score: 0.600 LR cohens kappa score: 0.586 LR average precision score: 0.587 -> test with 'RF' RF tn, fp: 135, 2 RF fn, tp: 4, 2 RF f1 score: 0.400 RF cohens kappa score: 0.379 -> test with 'GB' GB tn, fp: 134, 3 GB fn, tp: 3, 3 GB f1 score: 0.500 GB cohens kappa score: 0.478 -> 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 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: 135, 3 LR fn, tp: 6, 3 LR f1 score: 0.400 LR cohens kappa score: 0.369 LR average precision score: 0.352 -> 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: 135, 3 GB fn, tp: 5, 4 GB f1 score: 0.500 GB cohens kappa score: 0.472 -> 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 4/5: 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: 4, 5 LR f1 score: 0.500 LR cohens kappa score: 0.464 LR average precision score: 0.561 -> 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: 134, 4 GB fn, tp: 5, 4 GB f1 score: 0.471 GB cohens kappa score: 0.438 -> 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 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 135, 3 LR fn, tp: 5, 4 LR f1 score: 0.500 LR cohens kappa score: 0.472 LR average precision score: 0.343 -> 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: 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: 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: 134, 4 LR fn, tp: 6, 3 LR f1 score: 0.375 LR cohens kappa score: 0.340 LR average precision score: 0.471 -> test with 'RF' RF tn, fp: 137, 1 RF fn, tp: 7, 2 RF f1 score: 0.333 RF cohens kappa score: 0.312 -> 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: 136, 2 KNN fn, tp: 8, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.140 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 122, 15 LR fn, tp: 4, 2 LR f1 score: 0.174 LR cohens kappa score: 0.119 LR average precision score: 0.119 -> test with 'RF' RF tn, fp: 128, 9 RF fn, tp: 2, 4 RF f1 score: 0.421 RF cohens kappa score: 0.386 -> test with 'GB' GB tn, fp: 128, 9 GB fn, tp: 6, 0 GB f1 score: 0.000 GB cohens kappa score: -0.053 -> test with 'KNN' KNN tn, fp: 134, 3 KNN fn, tp: 5, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.172 ====== 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: 118, 20 LR fn, tp: 3, 6 LR f1 score: 0.343 LR cohens kappa score: 0.277 LR average precision score: 0.335 -> test with 'RF' RF tn, fp: 128, 10 RF fn, tp: 6, 3 RF f1 score: 0.273 RF cohens kappa score: 0.216 -> test with 'GB' GB tn, fp: 130, 8 GB fn, tp: 6, 3 GB f1 score: 0.300 GB cohens kappa score: 0.249 -> 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 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 122, 16 LR fn, tp: 6, 3 LR f1 score: 0.214 LR cohens kappa score: 0.143 LR average precision score: 0.338 -> 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: 136, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.281 -> 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 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 124, 14 LR fn, tp: 5, 4 LR f1 score: 0.296 LR cohens kappa score: 0.234 LR average precision score: 0.274 -> 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: 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 5/5: 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: 4, 5 LR f1 score: 0.667 LR cohens kappa score: 0.649 LR average precision score: 0.767 -> test with 'RF' RF tn, fp: 135, 3 RF fn, tp: 5, 4 RF f1 score: 0.500 RF cohens kappa score: 0.472 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 5, 4 GB f1 score: 0.533 GB cohens kappa score: 0.509 -> 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 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 132, 5 LR fn, tp: 6, 0 LR f1 score: 0.000 LR cohens kappa score: -0.040 LR average precision score: 0.032 -> test with 'RF' RF tn, fp: 136, 1 RF fn, tp: 3, 3 RF f1 score: 0.600 RF cohens kappa score: 0.586 -> 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, 26 LR fn, tp: 8, 6 LR f1 score: 0.667 LR cohens kappa score: 0.649 LR average precision score: 0.767 average: LR tn, fp: 129.2, 8.6 LR fn, tp: 5.2, 3.2 LR f1 score: 0.341 LR cohens kappa score: 0.296 LR average precision score: 0.386 minimum: LR tn, fp: 112, 1 LR fn, tp: 3, 0 LR f1 score: 0.000 LR cohens kappa score: -0.040 LR average precision score: 0.032 -----[ RF ]----- maximum: RF tn, fp: 137, 10 RF fn, tp: 8, 4 RF f1 score: 0.600 RF cohens kappa score: 0.586 average: RF tn, fp: 133.92, 3.88 RF fn, tp: 5.4, 3.0 RF f1 score: 0.398 RF cohens kappa score: 0.366 minimum: RF tn, fp: 128, 0 RF fn, tp: 2, 1 RF f1 score: 0.167 RF cohens kappa score: 0.140 -----[ GB ]----- maximum: GB tn, fp: 137, 9 GB fn, tp: 8, 4 GB f1 score: 0.667 GB cohens kappa score: 0.657 average: GB tn, fp: 134.88, 2.92 GB fn, tp: 5.6, 2.8 GB f1 score: 0.398 GB cohens kappa score: 0.370 minimum: GB tn, fp: 128, 0 GB fn, tp: 3, 0 GB f1 score: 0.000 GB cohens kappa score: -0.053 -----[ KNN ]----- maximum: KNN tn, fp: 138, 3 KNN fn, tp: 9, 3 KNN f1 score: 0.500 KNN cohens kappa score: 0.484 average: KNN tn, fp: 137.12, 0.68 KNN fn, tp: 7.32, 1.08 KNN f1 score: 0.212 KNN cohens kappa score: 0.197 minimum: KNN tn, fp: 134, 0 KNN fn, tp: 5, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.023