.. 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 transporation, 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.

:author: Michel Bierlaire, EPFL
:date: Wed Apr 12 20:51:37 2023

.. GENERATED FROM PYTHON SOURCE LINES 17-31

.. code-block:: default


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


.. GENERATED FROM PYTHON SOURCE LINES 32-33

List of parameters to be estimated.

.. GENERATED FROM PYTHON SOURCE LINES 33-43

.. code-block:: default

    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 44-47

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 47-59

.. code-block:: default

    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 60-62

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

.. GENERATED FROM PYTHON SOURCE LINES 62-112

.. code-block:: default

    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.
        :type factor: float

        :return: a dict with the utility functions, the nesting structure,
            and the choice expression.

        :rtype: dict(int: biogeme.expression), tuple(biogeme.expression,
            list(int)), biogeme.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_nocar = Beta('mu_nocar', 1.0, 1.0, None, 0)

        no_car_nest = OneNestForNestedLogit(
            nest_param=mu_nocar, list_of_alternatives=[0, 2], name='no_car'
        )
        nests = NestsForNestedLogit(choice_set=list(V), tuple_of_nests=(no_car_nest,))
        return V, nests, Choice, marginal_cost_scenario


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

   **Total running time of the script:** (0 minutes 0.000 seconds)


.. _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-python

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

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

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


.. only:: html

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

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