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

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

.. _sphx_glr_auto_examples_timing_timing_expression.py:


Timing of any expression
========================

Michel Bierlaire
Sun Jul 27 2025, 16:55:31

.. GENERATED FROM PYTHON SOURCE LINES 9-136

.. code-block:: Python


    from biogeme.calculator import CompiledFormulaEvaluator
    from biogeme.database import Database

    from biogeme.expressions import Expression, collect_init_values
    from biogeme.model_elements import ModelElements
    from biogeme.second_derivatives import SecondDerivativesMode
    from biogeme.tools.time import Timing


    def timing_expression(
        the_expression: Expression,
        the_database: Database,
        number_of_draws=0,
        warm_up_runs=10,
        num_runs=10,
    ) -> dict[str, float]:
        print(f'Timing expression calculated {num_runs} times')
        the_timing = Timing(warm_up_runs=warm_up_runs, num_runs=num_runs)

        free_beta_values = collect_init_values(the_expression)
        model_elements_jit = ModelElements.from_expression_and_weight(
            log_like=the_expression,
            weight=None,
            database=the_database,
            number_of_draws=number_of_draws,
            use_jit=True,
        )
        function_jit = CompiledFormulaEvaluator(
            model_elements=model_elements_jit,
            second_derivatives_mode=SecondDerivativesMode.ANALYTICAL,
            numerically_safe=False,
        )
        model_elements_no_jit = ModelElements.from_expression_and_weight(
            log_like=the_expression,
            weight=None,
            database=the_database,
            number_of_draws=number_of_draws,
            use_jit=False,
        )
        function_no_jit = CompiledFormulaEvaluator(
            model_elements=model_elements_no_jit,
            second_derivatives_mode=SecondDerivativesMode.ANALYTICAL,
            numerically_safe=False,
        )

        results = {}

        # Timing when only the expression is needed
        average_time_function_only = the_timing.time_function(
            function_jit.evaluate,
            kwargs={
                'the_betas': free_beta_values,
                'gradient': False,
                'hessian': False,
                'bhhh': False,
            },
        )

        results['Function only (with JIT)'] = average_time_function_only

        average_time_function_only_no_jit = the_timing.time_function(
            function_no_jit.evaluate,
            kwargs={
                'the_betas': free_beta_values,
                'gradient': False,
                'hessian': False,
                'bhhh': False,
            },
        )

        results['Function only (without JIT)'] = average_time_function_only_no_jit

        # Timing when the log likelihood and the gradient
        average_time_function_gradient = the_timing.time_function(
            function_jit.evaluate,
            kwargs={
                'the_betas': free_beta_values,
                'gradient': True,
                'hessian': False,
                'bhhh': False,
            },
        )
        results['Function and gradient (with JIT)'] = average_time_function_gradient

        # Timing when the log likelihood and the gradient
        average_time_function_gradient_no_jit = the_timing.time_function(
            function_no_jit.evaluate,
            kwargs={
                'the_betas': free_beta_values,
                'gradient': True,
                'hessian': False,
                'bhhh': False,
            },
        )
        results['Function and gradient (without JIT)'] = (
            average_time_function_gradient_no_jit
        )

        # Timing when the log likelihood, the gradient and hessian are needed
        average_time_function_gradient_hessian = the_timing.time_function(
            function_jit.evaluate,
            kwargs={
                'the_betas': free_beta_values,
                'gradient': True,
                'hessian': True,
                'bhhh': False,
            },
        )
        results['Function, gradient and hessian (with JIT)'] = (
            average_time_function_gradient_hessian
        )

        average_time_function_gradient_hessian_no_jit = the_timing.time_function(
            function_no_jit.evaluate,
            kwargs={
                'the_betas': free_beta_values,
                'gradient': True,
                'hessian': True,
                'bhhh': False,
            },
        )
        results['Function, gradient and hessian (without JIT)'] = (
            average_time_function_gradient_hessian_no_jit
        )

        return results


.. _sphx_glr_download_auto_examples_timing_timing_expression.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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