.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/swissmetro/plot_b21process_pareto.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_swissmetro_plot_b21process_pareto.py: .. _plot_b21process_pareto: Re-estimate the Pareto optimal models ===================================== The assisted specification algorithm generates a file containing the pareto optimal specification. This script is designed to re-estimate the Pareto optimal models. The catalog of specifications is defined in :ref:`plot_b21multiple_models_spec` . :author: Michel Bierlaire, EPFL :date: Wed Apr 12 17:46:14 2023 .. GENERATED FROM PYTHON SOURCE LINES 15-37 .. code-block:: Python import biogeme.biogeme_logging as blog try: import matplotlib.pyplot as plt can_plot = True except ModuleNotFoundError: can_plot = False from biogeme_optimization.exceptions import OptimizationError from biogeme.assisted import ParetoPostProcessing from biogeme.results import compile_estimation_results from plot_b21multiple_models_spec import the_biogeme PARETO_FILE_NAME = 'saved_results/b21multiple_models.pareto' logger = blog.get_screen_logger(blog.INFO) logger.info('Example b21process_pareto.py') CSV_FILE = 'b21process_pareto.csv' SEP_CSV = ',' .. rst-class:: sphx-glr-script-out .. code-block:: none Example b21process_pareto.py .. GENERATED FROM PYTHON SOURCE LINES 38-43 The constructor of the Pareto post processing object takes two arguments: - the biogeme object, - the name of the file where the algorithm has stored the estimated models. .. GENERATED FROM PYTHON SOURCE LINES 43-48 .. code-block:: Python the_pareto_post = ParetoPostProcessing( biogeme_object=the_biogeme, pareto_file_name=PARETO_FILE_NAME, ) .. rst-class:: sphx-glr-script-out .. code-block:: none Pareto set initialized from file with 36 elements [8 Pareto] and 0 invalid elements. .. GENERATED FROM PYTHON SOURCE LINES 49-51 .. code-block:: Python the_pareto_post.log_statistics() .. rst-class:: sphx-glr-script-out .. code-block:: none Pareto: 8 Considered: 36 Removed: 4 .. GENERATED FROM PYTHON SOURCE LINES 52-54 Complete re-estimation of the best models, including the calculation of the statistics. .. GENERATED FROM PYTHON SOURCE LINES 54-56 .. code-block:: Python all_results = the_pareto_post.reestimate(recycle=False) .. rst-class:: sphx-glr-script-out .. code-block:: none Biogeme parameters provided by the user. As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" *** Initial values of the parameters are obtained from the file __b21multiple_models_000000.iter Parameter values restored from __b21multiple_models_000000.iter As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME lambda_time Function Relgrad Radius Rho 0 -0.062 -0.34 -0.94 1.9 -1.1 -1.7 0.36 5e+03 0.0063 10 1 ++ 1 -0.064 -0.31 -1 2 -1.1 -1.7 0.38 5e+03 0.00022 1e+02 1 ++ 2 -0.064 -0.31 -1 2 -1.1 -1.7 0.38 5e+03 2.2e-07 1e+02 1 ++ Results saved in file b21multiple_models_000000~00.html Results saved in file b21multiple_models_000000~00.pickle Biogeme parameters provided by the user. As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" *** Initial values of the parameters are obtained from the file __b21multiple_models_000001.iter Parameter values restored from __b21multiple_models_000001.iter As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_CAR_GA ASC_CAR_male ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_male B_COST B_TIME lambda_time Function Relgrad Radius Rho 0 -0.42 -0.45 0.41 -0.22 2 -1.1 -1.1 -1.7 0.34 4.9e+03 0.0002 10 1 ++ 1 -0.42 -0.45 0.41 -0.22 2 -1.1 -1.1 -1.7 0.34 4.9e+03 1.9e-07 10 1 ++ Results saved in file b21multiple_models_000001~00.html Results saved in file b21multiple_models_000001~00.pickle Biogeme parameters provided by the user. As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" *** Initial values of the parameters are obtained from the file __b21multiple_models_000002.iter Parameter values restored from __b21multiple_models_000002.iter As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Results saved in file b21multiple_models_000002~00.html Results saved in file b21multiple_models_000002~00.pickle Biogeme parameters provided by the user. As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" *** Initial values of the parameters are obtained from the file __b21multiple_models_000003.iter Parameter values restored from __b21multiple_models_000003.iter As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_CAR_GA ASC_CAR_male ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_male B_COST B_COST_GA B_TIME lambda_time Function Relgrad Radius Rho 0 -0.4 -0.8 0.39 -0.25 1.9 -1.1 -1 0.89 -1.6 0.39 4.9e+03 0.012 10 1 ++ 1 -0.42 -1 0.41 -0.22 2 -1.2 -1.1 0.92 -1.7 0.33 4.9e+03 0.0004 1e+02 1 ++ 2 -0.42 -1 0.41 -0.22 2 -1.2 -1.1 0.92 -1.7 0.33 4.9e+03 2.5e-06 1e+02 1 ++ Results saved in file b21multiple_models_000003~00.html Results saved in file b21multiple_models_000003~00.pickle Biogeme parameters provided by the user. As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" *** Initial values of the parameters are obtained from the file __b21multiple_models_000004.iter Parameter values restored from __b21multiple_models_000004.iter As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_CAR_GA ASC_CAR_male ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_male B_COST B_COST_inc-100+ B_COST_inc-50-1 B_COST_inc-unde B_COST_inc-unkn B_TIME lambda_time Function Relgrad Radius Rho 0 -0.46 -0.32 0.45 -0.28 2 -1.1 -1.5 0.58 0.2 -0.62 0.79 -1.7 0.33 4.9e+03 0.0021 10 1 ++ 1 -0.46 -0.32 0.45 -0.28 2 -1.1 -1.5 0.58 0.2 -0.62 0.79 -1.7 0.33 4.9e+03 3.5e-05 10 1 ++ Results saved in file b21multiple_models_000004~00.html Results saved in file b21multiple_models_000004~00.pickle Biogeme parameters provided by the user. As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" *** Initial values of the parameters are obtained from the file __b21multiple_models_000005.iter Parameter values restored from __b21multiple_models_000005.iter As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME Function Relgrad Radius Rho 0 -0.072 -0.73 -0.93 -1.2 5.3e+03 0.017 1 0.82 + 1 -0.15 -0.71 -1.1 -1.3 5.3e+03 0.0009 10 1 ++ 2 -0.15 -0.71 -1.1 -1.3 5.3e+03 3.9e-06 10 1 ++ Results saved in file b21multiple_models_000005~00.html Results saved in file b21multiple_models_000005~00.pickle Biogeme parameters provided by the user. As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" *** Initial values of the parameters are obtained from the file __b21multiple_models_000006.iter Parameter values restored from __b21multiple_models_000006.iter As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME lambda_time Function Relgrad Radius Rho 0 -0.0036 -0.37 -1.1 -1.7 0.5 5.3e+03 0.016 1 0.83 + 1 -0.0049 -0.48 -1.1 -1.7 0.51 5.3e+03 0.00057 10 1 ++ 2 -0.0049 -0.48 -1.1 -1.7 0.51 5.3e+03 8.2e-07 10 1 ++ Results saved in file b21multiple_models_000006~00.html Results saved in file b21multiple_models_000006~00.pickle Biogeme parameters provided by the user. As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" *** Initial values of the parameters are obtained from the file __b21multiple_models_000007.iter Parameter values restored from __b21multiple_models_000007.iter As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_CAR_GA ASC_CAR_male ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_male B_COST B_TIME Function Relgrad Radius Rho 0 -0.42 -0.45 0.41 -0.22 2 -1.2 -1.1 -1.7 4.9e+03 3.7e-05 1 1 Results saved in file b21multiple_models_000007~00.html Results saved in file b21multiple_models_000007~00.pickle .. GENERATED FROM PYTHON SOURCE LINES 57-60 .. code-block:: Python summary, description = compile_estimation_results(all_results, use_short_names=True) print(summary) .. rst-class:: sphx-glr-script-out .. code-block:: none Model_000000 ... Model_000007 Number of estimated parameters 7 ... 8 Sample size 6768 ... 6768 Final log likelihood -4995.755387 ... -4900.883444 Akaike Information Criterion 10005.510775 ... 9817.766888 Bayesian Information Criterion 10053.250501 ... 9872.326575 ASC_CAR (t-test) -0.064 (-1.22) ... -0.389 (-3.95) ASC_CAR_GA (t-test) -0.313 (-1.59) ... -0.415 (-2.02) ASC_TRAIN (t-test) -1.03 (-13.9) ... -0.203 (-2.23) ASC_TRAIN_GA (t-test) 2.04 (22.8) ... 2.03 (22.4) B_COST (t-test) -1.1 (-14.8) ... -1.06 (-15.2) B_TIME (t-test) -1.67 (-21.3) ... -1.7 (-21.5) lambda_time (t-test) 0.382 (5.18) ... ASC_CAR_male (t-test) ... 0.377 (3.65) ASC_TRAIN_male (t-test) ... -1.2 (-14.1) B_COST_GA (t-test) ... B_COST_inc-100+ (t-test) ... B_COST_inc-50-100 (t-test) ... B_COST_inc-under50 (t-test) ... B_COST_inc-unknown (t-test) ... [19 rows x 8 columns] .. GENERATED FROM PYTHON SOURCE LINES 61-64 .. code-block:: Python print(f'Summary table available in {CSV_FILE}') summary.to_csv(CSV_FILE, sep=SEP_CSV) .. rst-class:: sphx-glr-script-out .. code-block:: none Summary table available in b21process_pareto.csv .. GENERATED FROM PYTHON SOURCE LINES 65-66 Explanation of the short names of the models. .. GENERATED FROM PYTHON SOURCE LINES 66-73 .. code-block:: Python with open(CSV_FILE, 'a', encoding='utf-8') as f: print('\n\n', file=f) for k, v in description.items(): if k != v: print(f'{k}: {v}') print(f'{k}{SEP_CSV}{v}', file=f) .. rst-class:: sphx-glr-script-out .. code-block:: none Model_000000: ASC:GA;B_COST:no_seg;TRAIN_TT:boxcox Model_000001: ASC:MALE-GA;B_COST:no_seg;TRAIN_TT:boxcox Model_000002: ASC:GA;B_COST:no_seg;TRAIN_TT:log Model_000003: ASC:MALE-GA;B_COST:GA;TRAIN_TT:boxcox Model_000004: ASC:MALE-GA;B_COST:INCOME;TRAIN_TT:boxcox Model_000005: ASC:no_seg;B_COST:no_seg;TRAIN_TT:linear Model_000006: ASC:no_seg;B_COST:no_seg;TRAIN_TT:boxcox Model_000007: ASC:MALE-GA;B_COST:no_seg;TRAIN_TT:log .. GENERATED FROM PYTHON SOURCE LINES 74-82 The following plot illustrates all models that have been estimated. Each dot corresponds to a model. The x-coordinate corresponds to the negative log-likelihood. The y-coordinate corresponds to the number of parameters. If the shape of the dot is a circle, it means that it corresponds to a Pareto optimal model. If the shape is a cross, it means that the model has been Pareto optimal at some point during the algorithm and later removed as a new model dominating it has been found. .. GENERATED FROM PYTHON SOURCE LINES 82-90 .. code-block:: Python if can_plot: try: _ = the_pareto_post.plot( label_x='Negative loglikelihood', label_y='Number of parameters' ) plt.show() except OptimizationError as e: print(f'No plot available: {e}') .. image-sg:: /auto_examples/swissmetro/images/sphx_glr_plot_b21process_pareto_001.png :alt: plot b21process pareto :srcset: /auto_examples/swissmetro/images/sphx_glr_plot_b21process_pareto_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.547 seconds) .. _sphx_glr_download_auto_examples_swissmetro_plot_b21process_pareto.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_b21process_pareto.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_b21process_pareto.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_b21process_pareto.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_