.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/swissmetro/plot_b09nested_old.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_b09nested_old.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_swissmetro_plot_b09nested_old.py:


Nested logit model
==================

Example of a nested logit model, using the original syntax for nests.
Since biogeme 3.13, a new syntax, more explicit, has been adopted.

:author: Michel Bierlaire, EPFL
:date: Tue Oct 24 13:37:27 2023

.. GENERATED FROM PYTHON SOURCE LINES 13-19

.. code-block:: Python


    import biogeme.biogeme_logging as blog
    import biogeme.biogeme as bio
    from biogeme import models
    from biogeme.expressions import Beta








.. GENERATED FROM PYTHON SOURCE LINES 20-21

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

.. GENERATED FROM PYTHON SOURCE LINES 21-38

.. code-block:: Python

    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 b09nested')





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

 .. code-block:: none

    Example b09nested 




.. GENERATED FROM PYTHON SOURCE LINES 39-40

Parameters to be estimated.

.. GENERATED FROM PYTHON SOURCE LINES 40-47

.. code-block:: Python

    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)
    MU = Beta('MU', 1, 1, 10, 0)








.. GENERATED FROM PYTHON SOURCE LINES 48-49

Definition of the utility functions.

.. GENERATED FROM PYTHON SOURCE LINES 49-53

.. code-block:: Python

    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 54-55

Associate utility functions with the numbering of alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 55-57

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 58-59

Associate the availability conditions with the alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 59-61

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 62-66

Definition of nests. In this example, we create a nest for the
existing modes, that is train (1) and car (3).  Each nest is
associated with a tuple containing (i) the nest parameter and (ii)
the list of alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 66-70

.. code-block:: Python

    existing = MU, [1, 3]
    future = 1.0, [2]
    nests = existing, future








.. GENERATED FROM PYTHON SOURCE LINES 71-74

Definition of the model. This is the contribution of each
observation to the log likelihood function.
The choice model is a nested logit, with availability conditions.

.. GENERATED FROM PYTHON SOURCE LINES 74-76

.. code-block:: Python

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





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

 .. code-block:: none

    It is recommended to define the nests of the nested logit model using the objects OneNestForNestedLogit and NestsForNestedLogit defined in biogeme.nests. 




.. GENERATED FROM PYTHON SOURCE LINES 77-78

Create the Biogeme object.

.. GENERATED FROM PYTHON SOURCE LINES 78-81

.. code-block:: Python

    the_biogeme = bio.BIOGEME(database, logprob)
    the_biogeme.modelName = "b09nested_old"





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

 .. code-block:: none

    Biogeme parameters read from biogeme.toml. 




.. GENERATED FROM PYTHON SOURCE LINES 82-83

Calculate the null log likelihood for reporting.

.. GENERATED FROM PYTHON SOURCE LINES 83-85

.. code-block:: Python

    the_biogeme.calculate_null_loglikelihood(av)





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

 .. code-block:: none


    np.float64(-6964.662979191462)



.. GENERATED FROM PYTHON SOURCE LINES 86-87

Estimate the parameters.

.. GENERATED FROM PYTHON SOURCE LINES 87-89

.. code-block:: Python

    results = the_biogeme.estimate()





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

 .. code-block:: none

    As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" 
    *** Initial values of the parameters are obtained from the file __b09nested_old.iter 
    Cannot read file __b09nested_old.iter. Statement is ignored. 
    As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" 
    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              MU     Function    Relgrad   Radius      Rho      
        0             0.1           -0.75              -1            -0.8             1.5      5.4e+03      0.082       10     0.92   ++ 
        1           -0.22           -0.28           -0.82           -0.86             2.2      5.3e+03      0.076       10     0.44    + 
        2           -0.17           -0.52            -0.7            -0.7             2.7      5.3e+03      0.023       10     0.72    + 
        3           -0.17           -0.52            -0.7            -0.7             2.7      5.3e+03      0.023     0.84     -4.6    - 
        4           -0.14           -0.52           -0.87           -0.92             1.8      5.2e+03      0.006     0.84     0.63    + 
        5           -0.16           -0.51           -0.87           -0.91               2      5.2e+03     0.0014      8.4      1.1   ++ 
        6           -0.16           -0.51           -0.87           -0.91               2      5.2e+03    8.1e-05      8.4        1   ++ 
    Results saved in file b09nested_old.html 
    Results saved in file b09nested_old.pickle 




.. GENERATED FROM PYTHON SOURCE LINES 90-92

.. code-block:: Python

    print(results.short_summary())





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

 .. code-block:: none

    Results for model b09nested_old
    Nbr of parameters:              5
    Sample size:                    6768
    Excluded data:                  3960
    Null log likelihood:            -6964.663
    Final log likelihood:           -5236.9
    Likelihood ratio test (null):           3455.525
    Rho square (null):                      0.248
    Rho bar square (null):                  0.247
    Akaike Information Criterion:   10483.8
    Bayesian Information Criterion: 10517.9





.. GENERATED FROM PYTHON SOURCE LINES 93-95

.. code-block:: Python

    pandas_results = results.get_estimated_parameters()
    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>-0.166892</td>
          <td>0.054518</td>
          <td>-3.061193</td>
          <td>2.204573e-03</td>
        </tr>
        <tr>
          <th>ASC_TRAIN</th>
          <td>-0.512028</td>
          <td>0.079123</td>
          <td>-6.471258</td>
          <td>9.719070e-11</td>
        </tr>
        <tr>
          <th>B_COST</th>
          <td>-0.857133</td>
          <td>0.060002</td>
          <td>-14.285004</td>
          <td>0.000000e+00</td>
        </tr>
        <tr>
          <th>B_TIME</th>
          <td>-0.899360</td>
          <td>0.107040</td>
          <td>-8.402065</td>
          <td>0.000000e+00</td>
        </tr>
        <tr>
          <th>MU</th>
          <td>2.051129</td>
          <td>0.163477</td>
          <td>12.546934</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 0.402 seconds)


.. _sphx_glr_download_auto_examples_swissmetro_plot_b09nested_old.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_b09nested_old.ipynb <plot_b09nested_old.ipynb>`

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

      :download:`Download Python source code: plot_b09nested_old.py <plot_b09nested_old.py>`

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

      :download:`Download zipped: plot_b09nested_old.zip <plot_b09nested_old.zip>`


.. only:: html

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

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