.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/timing/plot02_cnl.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_timing_plot02_cnl.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_timing_plot02_cnl.py:


Timing of a cross-nested logit model
====================================

Michel Bierlaire
Tue Jul 2 14:49:25 2024

.. GENERATED FROM PYTHON SOURCE LINES 9-12

.. code-block:: Python


    from tabulate import tabulate


.. GENERATED FROM PYTHON SOURCE LINES 13-14

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

.. GENERATED FROM PYTHON SOURCE LINES 14-32

.. code-block:: Python

    from biogeme.data.swissmetro import (
        CAR_AV_SP,
        CAR_CO_SCALED,
        CAR_TT_SCALED,
        CHOICE,
        SM_AV,
        SM_COST_SCALED,
        SM_TT_SCALED,
        TRAIN_AV_SP,
        TRAIN_COST_SCALED,
        TRAIN_TT_SCALED,
        read_data,
    )
    from biogeme.expressions import Beta, Expression
    from biogeme.models import logcnl
    from biogeme.nests import NestsForCrossNestedLogit, OneNestForCrossNestedLogit
    from timing_expression import timing_expression


.. GENERATED FROM PYTHON SOURCE LINES 33-34

Parameters to be estimated.

.. GENERATED FROM PYTHON SOURCE LINES 34-40

.. 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 41-44

.. code-block:: Python

    MU_EXISTING = Beta('MU_EXISTING', 1.1, 1, 10, 0)
    MU_PUBLIC = Beta('MU_PUBLIC', 1.1, 1, 10, 0)


.. GENERATED FROM PYTHON SOURCE LINES 45-46

Nest membership parameters.

.. GENERATED FROM PYTHON SOURCE LINES 46-49

.. code-block:: Python

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


.. GENERATED FROM PYTHON SOURCE LINES 50-51

Definition of the utility functions.

.. GENERATED FROM PYTHON SOURCE LINES 51-55

.. 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 56-57

Associate utility functions with the numbering of alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 57-59

.. code-block:: Python

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


.. GENERATED FROM PYTHON SOURCE LINES 60-61

Associate the availability conditions with the alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 61-63

.. code-block:: Python

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


.. GENERATED FROM PYTHON SOURCE LINES 64-65

Definition of nests.

.. GENERATED FROM PYTHON SOURCE LINES 65-80

.. 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 81-83

Definition of the model.
This is the contribution of each observation to the log likelihood function.

.. GENERATED FROM PYTHON SOURCE LINES 83-85

.. code-block:: Python

    log_probability: Expression = logcnl(V, av, nests, CHOICE)


.. GENERATED FROM PYTHON SOURCE LINES 86-88

.. code-block:: Python

    database = read_data()


.. GENERATED FROM PYTHON SOURCE LINES 89-90

Timing

.. GENERATED FROM PYTHON SOURCE LINES 90-95

.. code-block:: Python

    timing_results = timing_expression(
        the_expression=log_probability, the_database=database
    )
    results = [[k, f'{v:.3g}'] for k, v in timing_results.items()]
    print(tabulate(results, headers=['', 'Time (in sec.)'], tablefmt='github'))


.. _sphx_glr_download_auto_examples_timing_plot02_cnl.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot02_cnl.ipynb <plot02_cnl.ipynb>`

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

      :download:`Download Python source code: plot02_cnl.py <plot02_cnl.py>`

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

      :download:`Download zipped: plot02_cnl.zip <plot02_cnl.zip>`


.. only:: html

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

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