.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/swissmetro/plot_b11cnl.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.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_swissmetro_plot_b11cnl.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)

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

.. GENERATED FROM PYTHON SOURCE LINES 15-22

.. 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 23-24

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

.. GENERATED FROM PYTHON SOURCE LINES 24-41

.. 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 42-43

Parameters to be estimated.

.. GENERATED FROM PYTHON SOURCE LINES 43-49

.. 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 50-53

.. 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 54-55

Nest membership parameters.

.. GENERATED FROM PYTHON SOURCE LINES 55-58

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 59-60

Definition of the utility functions

.. GENERATED FROM PYTHON SOURCE LINES 60-64

.. 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 65-66

Associate utility functions with the numbering of alternatives

.. GENERATED FROM PYTHON SOURCE LINES 66-68

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 69-70

Associate the availability conditions with the alternatives

.. GENERATED FROM PYTHON SOURCE LINES 70-72

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 73-74

Definition of nests.

.. GENERATED FROM PYTHON SOURCE LINES 74-89

.. code-block:: default


    nest_existing = OneNestForCrossNestedLogit(
        nest_param=MU_EXISTING,
        dict_of_alpha={1: ALPHA_EXISTING, 2: 0.0, 3: 1.0},
        name='existing',
    )

    nest_public = OneNestForCrossNestedLogit(
        nest_param=MU_PUBLIC, dict_of_alpha={1: ALPHA_PUBLIC, 2: 1.0, 3: 0.0}, name='public'
    )

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








.. GENERATED FROM PYTHON SOURCE LINES 90-91

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

.. GENERATED FROM PYTHON SOURCE LINES 91-93

.. 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.py:91: 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 94-95

Create the Biogeme object

.. GENERATED FROM PYTHON SOURCE LINES 95-98

.. 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 99-100

Estimate the parameters.

.. GENERATED FROM PYTHON SOURCE LINES 100-102

.. 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 
    Cannot read file __b11cnl.iter. Statement is ignored. 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.  ALPHA_EXISTING         ASC_CAR       ASC_TRAIN          B_COST          B_TIME     MU_EXISTING       MU_PUBLIC     Function    Relgrad   Radius      Rho      
        0            0.57          -0.088           -0.83           -0.27              -1             1.5             1.5      5.6e+03      0.081        1     0.61    + 
        1            0.86           -0.29           -0.32            -1.3           -0.93             1.8             1.7      5.3e+03      0.049        1     0.63    + 
        2            0.86           -0.29           -0.32            -1.3           -0.93             1.8             1.7      5.3e+03      0.049      0.5    0.013    - 
        3            0.79           -0.12           -0.34           -0.77            -0.9               2             1.8      5.2e+03      0.012      0.5     0.79    + 
        4            0.56           -0.25            -0.1           -0.88            -0.8             2.5             2.2      5.2e+03      0.013      0.5     0.69    + 
        5            0.55           -0.25          -0.057           -0.86            -0.8             2.5             2.6      5.2e+03     0.0039        5      1.3   ++ 
        6            0.51           -0.25           0.049           -0.84           -0.79             2.5             3.3      5.2e+03     0.0054       50      1.2   ++ 
        7             0.5           -0.24           0.077           -0.83           -0.78             2.5             3.7      5.2e+03     0.0017    5e+02      1.2   ++ 
        8             0.5           -0.24           0.095           -0.82           -0.78             2.5               4      5.2e+03    0.00053    5e+03      1.1   ++ 
        9             0.5           -0.24           0.098           -0.82           -0.78             2.5             4.1      5.2e+03    3.3e-05    5e+04        1   ++ 
       10             0.5           -0.24           0.098           -0.82           -0.78             2.5             4.1      5.2e+03    1.5e-07    5e+04        1   ++ 
    Results saved in file b11cnl.html 
    Results saved in file b11cnl.pickle 




.. GENERATED FROM PYTHON SOURCE LINES 103-105

.. 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 106-108

.. 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 2.963 seconds)


.. _sphx_glr_download_auto_examples_swissmetro_plot_b11cnl.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.py <plot_b11cnl.py>`

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

      :download:`Download Jupyter notebook: plot_b11cnl.ipynb <plot_b11cnl.ipynb>`


.. only:: html

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

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