.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/hybrid_choice_models/plot_h08_mode_lv_parameter_overrides.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_hybrid_choice_models_plot_h08_mode_lv_parameter_overrides.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_hybrid_choice_models_plot_h08_mode_lv_parameter_overrides.py:

.. _plot_h08_mode_lv_parameter_overrides:

8. Build-only hybrid-choice example with explicit parameter overrides
======================================================================

This example follows the simultaneous Gaussian hybrid mode-choice model in
``plot_h04_mode_lv_gauss_simult.py``.  It deliberately stops before creating a
``BIOGEME`` estimation object and therefore does not estimate anything.

The purpose is to show where overrides belong in a hybrid-choice workflow:
the semantic specification is resolved, the latent-variable builder creates
the generated Biogeme parameters, and only then is the final likelihood
rewritten.  Here, one generated measurement loading is fixed by replacing it
with a fixed ``Beta`` having the same name.

Michel Bierlaire, EPFL

.. GENERATED FROM PYTHON SOURCE LINES 18-55

.. code-block:: Python


    from __future__ import annotations

    from choice_latent_variables import generate_availability, generate_utility_functions
    from likert_spec import likert_indicators, likert_types
    from one_latent_variable_spec import latent_variables
    from optima import Choice

    import biogeme.biogeme_logging as blog
    from biogeme.expressions import (
        Beta,
        MonteCarlo,
        ParameterOverrides,
        apply_parameter_overrides,
        list_of_all_betas_in_expression,
        log,
    )
    from biogeme.latent_variables import (
        BuildContext,
        EstimationMode,
        Fixing,
        IndicatorMeasurementSpec,
        MeasurementConfiguration,
        MeasurementIntercept,
        MeasurementLoading,
        MeasurementModel,
        NormalizationPlan,
        PositiveParameterSpec,
        PositivityMode,
        build_biogeme_model,
        resolve_model,
    )
    from biogeme.models import logit

    logger = blog.get_screen_logger(level=blog.INFO)
    logger.info('Example plot_h08_mode_lv_parameter_overrides.py')


.. GENERATED FROM PYTHON SOURCE LINES 56-57

Define the Gaussian measurement configuration used by H04.

.. GENERATED FROM PYTHON SOURCE LINES 57-68

.. code-block:: Python

    measurement_configuration = MeasurementConfiguration(
        specifications=[
            IndicatorMeasurementSpec(
                indicator_name=indicator.name,
                measurement_model=MeasurementModel.GAUSSIAN,
                measurement_sigma=PositiveParameterSpec(start=10.0),
            )
            for indicator in likert_indicators
        ]
    )


.. GENERATED FROM PYTHON SOURCE LINES 69-71

Resolve the semantic latent-variable specification.  The reference indicator
fixes the location and scale of the latent variable.

.. GENERATED FROM PYTHON SOURCE LINES 71-106

.. code-block:: Python

    default_context = BuildContext.default(EstimationMode.MAXIMUM_LIKELIHOOD)
    context = BuildContext(
        estimation_mode=default_context.estimation_mode,
        draw_type=default_context.draw_type,
        positivity_mode=PositivityMode.LOG_EXP,
        naming=default_context.naming,
        ordinal_eps=default_context.ordinal_eps,
        ordinal_enforce_order=default_context.ordinal_enforce_order,
    )

    normalization_plan = NormalizationPlan()
    normalization_plan.add(
        Fixing(
            MeasurementIntercept('Envir01'),
            0.0,
            note='reference indicator: location',
        )
    )
    normalization_plan.add(
        Fixing(
            MeasurementLoading('car_centric_attitude', 'Envir01'),
            -1.0,
            note='reference indicator: scale and orientation',
        )
    )

    resolved_model = resolve_model(
        latent_variables=latent_variables,
        likert_indicators=likert_indicators,
        likert_types=likert_types,
        measurement_configuration=measurement_configuration,
        context=context,
        normalization_plan=normalization_plan,
    )


.. GENERATED FROM PYTHON SOURCE LINES 107-109

Build the expressions.  At this point the builder has created names such as
``measurement_coefficient_car_centric_attitude_Envir02`` automatically.

.. GENERATED FROM PYTHON SOURCE LINES 109-117

.. code-block:: Python

    built_model = build_biogeme_model(resolved_model)
    utilities = generate_utility_functions(built_model.latent_expressions)
    availability = generate_availability()
    conditional_choice_likelihood = logit(utilities, availability, Choice)
    combined_conditional_likelihood = (
        built_model.conditional_likelihood * conditional_choice_likelihood
    )


.. GENERATED FROM PYTHON SOURCE LINES 118-121

Fix one automatically generated measurement loading.  The replacement uses
the exact generated Beta name, so the parameter remains visible as a fixed
coefficient while no estimation is performed in this example.

.. GENERATED FROM PYTHON SOURCE LINES 121-154

.. code-block:: Python

    loading_name = 'measurement_coefficient_car_centric_attitude_Envir02'
    overrides = ParameterOverrides()
    overrides.set(loading_name, Beta(loading_name, 0.5, None, None, 1))

    before = {
        beta.name
        for beta in list_of_all_betas_in_expression(combined_conditional_likelihood)
    }
    overridden_conditional_likelihood = apply_parameter_overrides(
        combined_conditional_likelihood, overrides
    )
    after = {
        beta.name
        for beta in list_of_all_betas_in_expression(overridden_conditional_likelihood)
    }
    overridden_loading = next(
        beta
        for beta in list_of_all_betas_in_expression(overridden_conditional_likelihood)
        if beta.name == loading_name
    )

    # Construct the final integrated likelihood only to demonstrate that the
    # rewritten expression is ready for estimation.  No database, BIOGEME object,
    # compilation, or estimation is performed.
    log_likelihood = log(MonteCarlo(overridden_conditional_likelihood))

    print(f'Overridden parameter: {loading_name}')
    print(f'Parameter present before override: {loading_name in before}')
    print(f'Parameter present after override: {loading_name in after}')
    print(f'Overridden parameter status: {overridden_loading.status}')
    print(f'Number of Betas before override: {len(before)}')
    print(f'Number of Betas after override: {len(after)}')
    print(f'Final expression type: {type(log_likelihood).__name__}')


.. _sphx_glr_download_auto_examples_hybrid_choice_models_plot_h08_mode_lv_parameter_overrides.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_h08_mode_lv_parameter_overrides.ipynb <plot_h08_mode_lv_parameter_overrides.ipynb>`

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

      :download:`Download Python source code: plot_h08_mode_lv_parameter_overrides.py <plot_h08_mode_lv_parameter_overrides.py>`

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

      :download:`Download zipped: plot_h08_mode_lv_parameter_overrides.zip <plot_h08_mode_lv_parameter_overrides.zip>`


.. only:: html

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