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

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

.. _sphx_glr_auto_examples_indicators_scenarios.py:


.. _scenarios:

Specification of a nested logit model
=====================================

Specification of a nested logit model, that will be estimated, and
 used for simulation.  Three alternatives: public transportation, car
 and slow modes.  RP data.  Based on the Optima data.  It contains a
 function that generates scenarios where the current cost of public
 transportation is multiplied by a factor.

Michel Bierlaire, EPFL
Sat Jun 28 2025, 15:52:11

.. GENERATED FROM PYTHON SOURCE LINES 16-30

.. code-block:: Python


    from biogeme.data.optima import (
        Choice,
        CostCarCHF,
        Gender,
        MarginalCostPT,
        OccupStat,
        TimeCar,
        TimePT,
        distance_km,
    )
    from biogeme.expressions import Beta, Expression
    from biogeme.nests import NestsForNestedLogit, OneNestForNestedLogit


.. GENERATED FROM PYTHON SOURCE LINES 31-32

List of parameters to be estimated.

.. GENERATED FROM PYTHON SOURCE LINES 32-42

.. code-block:: Python

    asc_car = Beta('asc_car', 0, None, None, 0)
    asc_pt = Beta('asc_pt', 0, None, None, 1)
    asc_sm = Beta('asc_sm', 0, None, None, 0)
    beta_time_fulltime = Beta('beta_time_fulltime', 0, None, None, 0)
    beta_time_other = Beta('beta_time_other', 0, None, None, 0)
    beta_dist_male = Beta('beta_dist_male', 0, None, None, 0)
    beta_dist_female = Beta('beta_dist_female', 0, None, None, 0)
    beta_dist_unreported = Beta('beta_dist_unreported', 0, None, None, 0)
    beta_cost = Beta('beta_cost', 0, None, None, 0)


.. GENERATED FROM PYTHON SOURCE LINES 43-46

Definition of variables:
For numerical reasons, it is good practice to scale the data to
that the values of the parameters are around 1.0.

.. GENERATED FROM PYTHON SOURCE LINES 46-58

.. code-block:: Python

    TimePT_scaled = TimePT / 200
    TimeCar_scaled = TimeCar / 200
    CostCarCHF_scaled = CostCarCHF / 10
    distance_km_scaled = distance_km / 5
    male = Gender == 1
    female = Gender == 2
    unreportedGender = Gender == -1

    fulltime = OccupStat == 1
    notfulltime = OccupStat != 1



.. GENERATED FROM PYTHON SOURCE LINES 59-61

Model specification as a function of the multiplication factor for
the price of public transportation.

.. GENERATED FROM PYTHON SOURCE LINES 61-112

.. code-block:: Python

    def scenario(
        factor: float = 1.0,
    ) -> tuple[dict[int, Expression], NestsForNestedLogit, Expression, float]:
        """Provide the model specification for a scenario with the price of
            public transportation is multiplied by a factor

        :param factor: factor that multiples the price of public transportation.
        :return: a dict with the utility functions, the nesting structure,
            and the choice expression.

        """
        marginal_cost_scenario = MarginalCostPT * factor
        marginal_cost_pt_scaled = marginal_cost_scenario / 10
        # Definition of utility functions:
        v_pt = (
            asc_pt
            + beta_time_fulltime * TimePT_scaled * fulltime
            + beta_time_other * TimePT_scaled * notfulltime
            + beta_cost * marginal_cost_pt_scaled
        )
        v_car = (
            asc_car
            + beta_time_fulltime * TimeCar_scaled * fulltime
            + beta_time_other * TimeCar_scaled * notfulltime
            + beta_cost * CostCarCHF_scaled
        )
        v_sm = (
            asc_sm
            + beta_dist_male * distance_km_scaled * male
            + beta_dist_female * distance_km_scaled * female
            + beta_dist_unreported * distance_km_scaled * unreportedGender
        )

        # Associate utility functions with the numbering of alternatives
        v = {0: v_pt, 1: v_car, 2: v_sm}

        # Definition of the nests:
        # 1: nests parameter
        # 2: list of alternatives
        mu_no_car = Beta('mu_no_car', 1, 1, 2, 0)

        no_car_nest = OneNestForNestedLogit(
            nest_param=mu_no_car, list_of_alternatives=[0, 2], name='no_car'
        )
        car_nest = OneNestForNestedLogit(
            nest_param=1.0, list_of_alternatives=[1], name='car'
        )
        nests = NestsForNestedLogit(
            choice_set=list(v), tuple_of_nests=(no_car_nest, car_nest)
        )
        return v, nests, Choice, marginal_cost_scenario


.. _sphx_glr_download_auto_examples_indicators_scenarios.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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