.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/swissmetro/debug.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_debug.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_swissmetro_debug.py:


Cross-nested logit
==================

 Example of a cross-nested logit model with two nests:

 - one with existing alternatives (car and train),
 - one with public transportation alternatives (train and Swissmetro)

:author: Michel Bierlaire, EPFL
:date: Sun Apr  9 18:06:44 2023

.. GENERATED FROM PYTHON SOURCE LINES 15-22

.. code-block:: Python


    import biogeme.biogeme_logging as blog
    import biogeme.biogeme as bio
    from biogeme import models
    from biogeme.expressions import Beta
    from biogeme.nests import OneNestForCrossNestedLogit, NestsForCrossNestedLogit


.. GENERATED FROM PYTHON SOURCE LINES 23-24

See the data processing script: :ref:`swissmetro_data`.

.. GENERATED FROM PYTHON SOURCE LINES 24-41

.. code-block:: Python

    from swissmetro_data import (
        database,
        CHOICE,
        SM_AV,
        CAR_AV_SP,
        TRAIN_AV_SP,
        TRAIN_TT_SCALED,
        TRAIN_COST_SCALED,
        SM_TT_SCALED,
        SM_COST_SCALED,
        CAR_TT_SCALED,
        CAR_CO_SCALED,
    )

    logger = blog.get_screen_logger(level=blog.INFO)
    logger.info('Example b11cnl.py')


.. GENERATED FROM PYTHON SOURCE LINES 42-43

Parameters to be estimated.

.. GENERATED FROM PYTHON SOURCE LINES 43-49

.. code-block:: Python

    ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
    ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
    ASC_SM = Beta('ASC_SM', 0, None, None, 1)
    B_TIME = Beta('B_TIME', 0, None, None, 0)
    B_COST = Beta('B_COST', 0, None, None, 0)


.. GENERATED FROM PYTHON SOURCE LINES 50-53

.. code-block:: Python

    MU_EXISTING = Beta('MU_EXISTING', 1, 1, 10, 0)
    MU_PUBLIC = Beta('MU_PUBLIC', 1, 1, 10, 0)


.. GENERATED FROM PYTHON SOURCE LINES 54-55

Nest membership parameters.

.. GENERATED FROM PYTHON SOURCE LINES 55-58

.. code-block:: Python

    ALPHA_EXISTING = Beta('ALPHA_EXISTING', 0.5, 0, 1, 0)
    ALPHA_PUBLIC = 1 - ALPHA_EXISTING


.. GENERATED FROM PYTHON SOURCE LINES 59-60

Definition of the utility functions

.. GENERATED FROM PYTHON SOURCE LINES 60-64

.. code-block:: Python

    V1 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED
    V2 = ASC_SM + B_TIME * SM_TT_SCALED + B_COST * SM_COST_SCALED
    V3 = ASC_CAR + B_TIME * CAR_TT_SCALED + B_COST * CAR_CO_SCALED


.. GENERATED FROM PYTHON SOURCE LINES 65-66

Associate utility functions with the numbering of alternatives

.. GENERATED FROM PYTHON SOURCE LINES 66-68

.. code-block:: Python

    V = {1: V1, 2: V2, 3: V3}


.. GENERATED FROM PYTHON SOURCE LINES 69-70

Associate the availability conditions with the alternatives

.. GENERATED FROM PYTHON SOURCE LINES 70-72

.. code-block:: Python

    av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}


.. GENERATED FROM PYTHON SOURCE LINES 73-74

Definition of nests.

.. GENERATED FROM PYTHON SOURCE LINES 74-89

.. code-block:: Python


    nest_existing = OneNestForCrossNestedLogit(
        nest_param=MU_EXISTING,
        dict_of_alpha={1: ALPHA_EXISTING, 2: 0.0, 3: 1.0},
        name='existing',
    )

    nest_public = OneNestForCrossNestedLogit(
        nest_param=MU_PUBLIC, dict_of_alpha={1: ALPHA_PUBLIC, 2: 1.0, 3: 0.0}, name='public'
    )

    nests = NestsForCrossNestedLogit(
        choice_set=[1, 2, 3], tuple_of_nests=(nest_existing, nest_public)
    )


.. GENERATED FROM PYTHON SOURCE LINES 90-91

The choice model is a cross-nested logit, with availability conditions.

.. GENERATED FROM PYTHON SOURCE LINES 91-93

.. code-block:: Python

    logprob = models.logcnl(V, av, nests, CHOICE)


.. GENERATED FROM PYTHON SOURCE LINES 94-95

Create the Biogeme object

.. GENERATED FROM PYTHON SOURCE LINES 95-98

.. code-block:: Python

    the_biogeme = bio.BIOGEME(database, logprob, number_of_threads=1)
    the_biogeme.modelName = 'b11cnl'


.. GENERATED FROM PYTHON SOURCE LINES 99-100

Estimate the parameters.

.. GENERATED FROM PYTHON SOURCE LINES 100-102

.. code-block:: Python

    results = the_biogeme.estimate()


.. GENERATED FROM PYTHON SOURCE LINES 103-105

.. code-block:: Python

    print(results.short_summary())


.. GENERATED FROM PYTHON SOURCE LINES 106-108

.. code-block:: Python

    pandas_results = results.get_estimated_parameters()
    pandas_results


.. _sphx_glr_download_auto_examples_swissmetro_debug.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: debug.ipynb <debug.ipynb>`

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

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

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

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


.. only:: html

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

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