.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/swissmetro/plot_b21c_process_pareto.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_swissmetro_plot_b21c_process_pareto.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_swissmetro_plot_b21c_process_pareto.py:

.. _plot_b21c_process_pareto:

21c. 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_b21b_multiple_models_spec` .

Michel Bierlaire, EPFL
Sat Jun 28 2025, 20:58:22

.. GENERATED FROM PYTHON SOURCE LINES 15-37

.. code-block:: Python


    import biogeme.biogeme_logging as blog
    from biogeme.results_processing import compile_estimation_results

    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 plot_b21b_multiple_models_spec import the_biogeme, PARETO_FILE_NAME

    PATH_PARETO_FILE_NAME = f'saved_results/{PARETO_FILE_NAME}'

    logger = blog.get_screen_logger(blog.INFO)
    logger.info('Example b21c_process_pareto.py')

    CSV_FILE = 'b21_process_pareto.csv'
    SEP_CSV = ','





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    income_segmentation=INCOME: [{0: 'inc-zero', 1: 'inc-under50', 2: 'inc-50-100', 3: 'inc-100+', 4: 'inc-unknown'}] ref: inc-zero
    Example b21c_process_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=PATH_PARETO_FILE_NAME,
    )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Unable to read file saved_results/b21_multiple_models.pareto. Pareto set empty. 




.. 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: 0  
    Considered: 0  
    Removed: 0 




.. 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)








.. 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

    Empty DataFrame
    Columns: []
    Index: []




.. 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 b21_process_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)








.. 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_b21c_process_pareto_001.png
   :alt: plot b21c process pareto
   :srcset: /auto_examples/swissmetro/images/sphx_glr_plot_b21c_process_pareto_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    No plot available: Cannot plot an empty Pareto set





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.081 seconds)


.. _sphx_glr_download_auto_examples_swissmetro_plot_b21c_process_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_b21c_process_pareto.ipynb <plot_b21c_process_pareto.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_b21c_process_pareto.py <plot_b21c_process_pareto.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_b21c_process_pareto.zip <plot_b21c_process_pareto.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_