.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/swissmetro/plot_b11cnl_sparse.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_swissmetro_plot_b11cnl_sparse.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_swissmetro_plot_b11cnl_sparse.py:


Cross-nested logit
==================

 Example of a cross-nested logit model with two nests:

 - one with existing alternatives (car and train),
 - one with public transportation alternatives (train and Swissmetro)

This illustrates the possibility to ignore all membership parameters that are 0.

:author: Michel Bierlaire, EPFL
:date: Sun Apr  9 18:06:44 2023

.. GENERATED FROM PYTHON SOURCE LINES 17-24

.. code-block:: default


    import biogeme.biogeme_logging as blog
    import biogeme.biogeme as bio
    from biogeme import models
    from biogeme.expressions import Beta
    from biogeme.nests import OneNestForCrossNestedLogit, NestsForCrossNestedLogit








.. GENERATED FROM PYTHON SOURCE LINES 25-26

See the data processing script: :ref:`swissmetro_data`.

.. GENERATED FROM PYTHON SOURCE LINES 26-43

.. code-block:: default

    from swissmetro_data import (
        database,
        CHOICE,
        SM_AV,
        CAR_AV_SP,
        TRAIN_AV_SP,
        TRAIN_TT_SCALED,
        TRAIN_COST_SCALED,
        SM_TT_SCALED,
        SM_COST_SCALED,
        CAR_TT_SCALED,
        CAR_CO_SCALED,
    )

    logger = blog.get_screen_logger(level=blog.INFO)
    logger.info('Example b11cnl.py')





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

 .. code-block:: none

    Example b11cnl.py 




.. GENERATED FROM PYTHON SOURCE LINES 44-45

Parameters to be estimated.

.. GENERATED FROM PYTHON SOURCE LINES 45-51

.. code-block:: default

    ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
    ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
    ASC_SM = Beta('ASC_SM', 0, None, None, 1)
    B_TIME = Beta('B_TIME', 0, None, None, 0)
    B_COST = Beta('B_COST', 0, None, None, 0)








.. GENERATED FROM PYTHON SOURCE LINES 52-55

.. code-block:: default

    MU_EXISTING = Beta('MU_EXISTING', 1, 1, 10, 0)
    MU_PUBLIC = Beta('MU_PUBLIC', 1, 1, 10, 0)








.. GENERATED FROM PYTHON SOURCE LINES 56-57

Nest membership parameters.

.. GENERATED FROM PYTHON SOURCE LINES 57-60

.. code-block:: default

    ALPHA_EXISTING = Beta('ALPHA_EXISTING', 0.5, 0, 1, 0)
    ALPHA_PUBLIC = 1 - ALPHA_EXISTING








.. GENERATED FROM PYTHON SOURCE LINES 61-62

Definition of the utility functions

.. GENERATED FROM PYTHON SOURCE LINES 62-66

.. code-block:: default

    V1 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED
    V2 = ASC_SM + B_TIME * SM_TT_SCALED + B_COST * SM_COST_SCALED
    V3 = ASC_CAR + B_TIME * CAR_TT_SCALED + B_COST * CAR_CO_SCALED








.. GENERATED FROM PYTHON SOURCE LINES 67-68

Associate utility functions with the numbering of alternatives

.. GENERATED FROM PYTHON SOURCE LINES 68-70

.. code-block:: default

    V = {1: V1, 2: V2, 3: V3}








.. GENERATED FROM PYTHON SOURCE LINES 71-72

Associate the availability conditions with the alternatives

.. GENERATED FROM PYTHON SOURCE LINES 72-74

.. code-block:: default

    av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}








.. GENERATED FROM PYTHON SOURCE LINES 75-76

Definition of nests.

.. GENERATED FROM PYTHON SOURCE LINES 76-91

.. code-block:: default


    nest_existing = OneNestForCrossNestedLogit(
        nest_param=MU_EXISTING,
        dict_of_alpha={1: ALPHA_EXISTING, 3: 1.0},
        name='existing',
    )

    nest_public = OneNestForCrossNestedLogit(
        nest_param=MU_PUBLIC, dict_of_alpha={1: ALPHA_PUBLIC, 2: 1.0}, name='public'
    )

    nests = NestsForCrossNestedLogit(
        choice_set=[1, 2, 3], tuple_of_nests=(nest_existing, nest_public)
    )








.. GENERATED FROM PYTHON SOURCE LINES 92-93

The choice model is a cross-nested logit, with availability conditions.

.. GENERATED FROM PYTHON SOURCE LINES 93-95

.. code-block:: default

    logprob = models.logcnl_avail(V, av, nests, CHOICE)





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

 .. code-block:: none

    /Users/bierlair/OnlineFiles/FilesOnGoogleDrive/github/biogeme/docs/examples/swissmetro/plot_b11cnl_sparse.py:93: DeprecationWarning: The function logcnl_avail is deprecated. It has been replaced by the function logcnl
      logprob = models.logcnl_avail(V, av, nests, CHOICE)




.. GENERATED FROM PYTHON SOURCE LINES 96-97

Create the Biogeme object

.. GENERATED FROM PYTHON SOURCE LINES 97-100

.. code-block:: default

    the_biogeme = bio.BIOGEME(database, logprob)
    the_biogeme.modelName = 'b11cnl'





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

 .. code-block:: none

    File biogeme.toml has been parsed. 




.. GENERATED FROM PYTHON SOURCE LINES 101-102

Estimate the parameters.

.. GENERATED FROM PYTHON SOURCE LINES 102-104

.. 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 __b11cnl.iter 
    Parameter values restored from __b11cnl.iter 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Results saved in file b11cnl~01.html 
    Results saved in file b11cnl~01.pickle 




.. GENERATED FROM PYTHON SOURCE LINES 105-107

.. code-block:: default

    print(results.short_summary())





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

 .. code-block:: none

    Results for model b11cnl
    Nbr of parameters:              7
    Sample size:                    6768
    Excluded data:                  3960
    Final log likelihood:           -5214.049
    Akaike Information Criterion:   10442.1
    Bayesian Information Criterion: 10489.84





.. GENERATED FROM PYTHON SOURCE LINES 108-110

.. 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>ALPHA_EXISTING</th>
          <td>0.495072</td>
          <td>0.034752</td>
          <td>14.246000</td>
          <td>0.000000e+00</td>
        </tr>
        <tr>
          <th>ASC_CAR</th>
          <td>-0.240459</td>
          <td>0.053450</td>
          <td>-4.498764</td>
          <td>6.834958e-06</td>
        </tr>
        <tr>
          <th>ASC_TRAIN</th>
          <td>0.098277</td>
          <td>0.069978</td>
          <td>1.404403</td>
          <td>1.601989e-01</td>
        </tr>
        <tr>
          <th>B_COST</th>
          <td>-0.818885</td>
          <td>0.058971</td>
          <td>-13.886130</td>
          <td>0.000000e+00</td>
        </tr>
        <tr>
          <th>B_TIME</th>
          <td>-0.776846</td>
          <td>0.102380</td>
          <td>-7.587854</td>
          <td>3.241851e-14</td>
        </tr>
        <tr>
          <th>MU_EXISTING</th>
          <td>2.514876</td>
          <td>0.248325</td>
          <td>10.127363</td>
          <td>0.000000e+00</td>
        </tr>
        <tr>
          <th>MU_PUBLIC</th>
          <td>4.113595</td>
          <td>0.496722</td>
          <td>8.281479</td>
          <td>2.220446e-16</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />


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

   **Total running time of the script:** (0 minutes 0.238 seconds)


.. _sphx_glr_download_auto_examples_swissmetro_plot_b11cnl_sparse.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_b11cnl_sparse.py <plot_b11cnl_sparse.py>`

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

      :download:`Download Jupyter notebook: plot_b11cnl_sparse.ipynb <plot_b11cnl_sparse.ipynb>`


.. only:: html

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

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