.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/montecarlo/plot_b07estimation_monte_carlo_mlhs_500.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_montecarlo_plot_b07estimation_monte_carlo_mlhs_500.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_montecarlo_plot_b07estimation_monte_carlo_mlhs_500.py:


Mixtures of logit with Monte-Carlo 500 MLHS draws
=================================================

Estimation of a mixtures of logit models where the integral is
approximated using MonteCarlo integration with MLHS draws.

:author: Michel Bierlaire, EPFL
:date: Thu Apr 13 23:37:44 2023

.. GENERATED FROM PYTHON SOURCE LINES 12-17

.. code-block:: default


    import biogeme.biogeme_logging as blog
    from biogeme.expressions import bioDraws
    from b07estimation_specification import get_biogeme








.. GENERATED FROM PYTHON SOURCE LINES 18-21

.. code-block:: default

    logger = blog.get_screen_logger(level=blog.INFO)
    logger.info('Example b07estimation_monte_carlo_mlhs_500.py')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Example b07estimation_monte_carlo_mlhs_500.py 




.. GENERATED FROM PYTHON SOURCE LINES 22-28

.. code-block:: default

    R = 500

    the_draws = bioDraws('B_TIME_RND', 'NORMAL_MLHS')
    the_biogeme = get_biogeme(the_draws=the_draws, number_of_draws=R)
    the_biogeme.modelName = 'b07estimation_monte_carlo_mlhs_500'





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    File /var/folders/rp/ppksq7xd6_x7p0jb0t73x7vw0000gq/T/tmpya5lp_yq/079077ab-a391-4ce5-9594-61feaa529541 has been parsed. 




.. GENERATED FROM PYTHON SOURCE LINES 29-31

.. code-block:: default

    results = the_biogeme.estimate()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    *** Initial values of the parameters are obtained from the file __b07estimation_monte_carlo_mlhs_500.iter 
    Parameter values restored from __b07estimation_monte_carlo_mlhs_500.iter 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         asc_car       asc_train          b_cost          b_time        b_time_s     Function    Relgrad   Radius      Rho      
        0             0.1           -0.43            -1.2            -2.1             1.4      5.2e+03     0.0074       10      1.1   ++ 
        1            0.13           -0.41            -1.3            -2.2             1.6      5.2e+03    0.00096    1e+02      1.1   ++ 
        2            0.13            -0.4            -1.3            -2.2             1.6      5.2e+03    1.6e-05    1e+03        1   ++ 
        3            0.13            -0.4            -1.3            -2.2             1.6      5.2e+03    4.6e-09    1e+03        1   ++ 




.. GENERATED FROM PYTHON SOURCE LINES 32-34

.. code-block:: default

    print(results.short_summary())





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Results for model b07estimation_monte_carlo_mlhs_500
    Nbr of parameters:              5
    Sample size:                    6768
    Excluded data:                  3960
    Final log likelihood:           -5217.204
    Akaike Information Criterion:   10444.41
    Bayesian Information Criterion: 10478.51





.. GENERATED FROM PYTHON SOURCE LINES 35-37

.. code-block:: default

    pandas_results = results.getEstimatedParameters()
    pandas_results





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Value</th>
          <th>Rob. Std err</th>
          <th>Rob. t-test</th>
          <th>Rob. p-value</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>asc_car</th>
          <td>0.134004</td>
          <td>0.051956</td>
          <td>2.579184</td>
          <td>9.903396e-03</td>
        </tr>
        <tr>
          <th>asc_train</th>
          <td>-0.404873</td>
          <td>0.066146</td>
          <td>-6.120903</td>
          <td>9.304664e-10</td>
        </tr>
        <tr>
          <th>b_cost</th>
          <td>-1.280591</td>
          <td>0.085999</td>
          <td>-14.890835</td>
          <td>0.000000e+00</td>
        </tr>
        <tr>
          <th>b_time</th>
          <td>-2.249351</td>
          <td>0.117630</td>
          <td>-19.122260</td>
          <td>0.000000e+00</td>
        </tr>
        <tr>
          <th>b_time_s</th>
          <td>1.647880</td>
          <td>0.135517</td>
          <td>12.159937</td>
          <td>0.000000e+00</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />


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

   **Total running time of the script:** (0 minutes 51.636 seconds)


.. _sphx_glr_download_auto_examples_montecarlo_plot_b07estimation_monte_carlo_mlhs_500.py:

.. only:: html

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




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

      :download:`Download Python source code: plot_b07estimation_monte_carlo_mlhs_500.py <plot_b07estimation_monte_carlo_mlhs_500.py>`

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

      :download:`Download Jupyter notebook: plot_b07estimation_monte_carlo_mlhs_500.ipynb <plot_b07estimation_monte_carlo_mlhs_500.ipynb>`


.. only:: html

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

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