.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/sampling/plot_b02nested.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_sampling_plot_b02nested.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_sampling_plot_b02nested.py:


Nested logit
============

Estimation of a nested logit model using sampling of alternatives.

:author: Michel Bierlaire
:date: Wed Nov  1 18:00:15 2023

.. GENERATED FROM PYTHON SOURCE LINES 11-32

.. code-block:: default

    import pandas as pd
    from biogeme.sampling_of_alternatives import (
        SamplingContext,
        ChoiceSetsGeneration,
        GenerateModel,
        generate_segment_size,
    )
    from biogeme.expressions import Beta
    from biogeme.nests import OneNestForNestedLogit, NestsForNestedLogit
    import biogeme.biogeme_logging as blog
    import biogeme.biogeme as bio
    from specification import V, combined_variables
    from compare import compare
    from alternatives import (
        alternatives,
        ID_COLUMN,
        partitions,
        asian,
        all_alternatives,
    )








.. GENERATED FROM PYTHON SOURCE LINES 33-35

.. code-block:: default

    logger = blog.get_screen_logger(level=blog.INFO)








.. GENERATED FROM PYTHON SOURCE LINES 36-44

.. code-block:: default

    SAMPLE_SIZE = 20  # out of 100
    SAMPLE_SIZE_MEV = 33  # out of 33
    CHOICE_COLUMN = 'nested_0'
    PARTITION = 'downtown'
    MEV_PARTITION = 'uniform_asian'
    MODEL_NAME = f'nested_{PARTITION}_{SAMPLE_SIZE}'
    FILE_NAME = f'{MODEL_NAME}.dat'








.. GENERATED FROM PYTHON SOURCE LINES 45-49

.. code-block:: default

    the_partition = partitions.get(PARTITION)
    if the_partition is None:
        raise ValueError(f'Unknown partition: {PARTITION}')








.. GENERATED FROM PYTHON SOURCE LINES 50-52

.. code-block:: default

    segment_sizes = generate_segment_size(SAMPLE_SIZE, the_partition.number_of_segments())








.. GENERATED FROM PYTHON SOURCE LINES 53-54

We use all alternatives in the nest.

.. GENERATED FROM PYTHON SOURCE LINES 54-59

.. code-block:: default

    mev_partition = partitions.get(MEV_PARTITION)
    if mev_partition is None:
        raise ValueError(f'Unknown partition: {MEV_PARTITION}')
    mev_segment_sizes = [SAMPLE_SIZE_MEV]








.. GENERATED FROM PYTHON SOURCE LINES 60-62

.. code-block:: default

    observations = pd.read_csv('obs_choice.dat')








.. GENERATED FROM PYTHON SOURCE LINES 63-77

.. code-block:: default

    context = SamplingContext(
        the_partition=the_partition,
        sample_sizes=segment_sizes,
        individuals=observations,
        choice_column=CHOICE_COLUMN,
        alternatives=alternatives,
        id_column=ID_COLUMN,
        biogeme_file_name=FILE_NAME,
        utility_function=V,
        combined_variables=combined_variables,
        mev_partition=mev_partition,
        mev_sample_sizes=mev_segment_sizes,
    )








.. GENERATED FROM PYTHON SOURCE LINES 78-80

.. code-block:: default

    logger.info(context.reporting())





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

 .. code-block:: none

    Size of the choice set: 100
    Main partition: 2 segment(s) of size 46, 54
    Main sample: 20: 10/46, 10/54
    Nbr of MEV alternatives: 33
    MEV partition: 1 segment(s) of size 33
    MEV sample: 33: 33/33
 




.. GENERATED FROM PYTHON SOURCE LINES 81-84

.. code-block:: default

    the_data_generation = ChoiceSetsGeneration(context=context)
    the_model_generation = GenerateModel(context=context)








.. GENERATED FROM PYTHON SOURCE LINES 85-87

.. code-block:: default

    biogeme_database = the_data_generation.sample_and_merge(recycle=False)





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

 .. code-block:: none

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    Defining new variables...: 100%|██████████| 20/20 [00:25<00:00,  1.29s/it]
    File nested_downtown_20.dat has been created. 




.. GENERATED FROM PYTHON SOURCE LINES 88-89

Definition of the nest.

.. GENERATED FROM PYTHON SOURCE LINES 89-98

.. code-block:: default

    mu_asian = Beta('mu_asian', 1.0, 1.0, None, 0)
    nest_asian = OneNestForNestedLogit(
        nest_param=mu_asian, list_of_alternatives=asian, name='asian'
    )
    nests = NestsForNestedLogit(
        choice_set=all_alternatives,
        tuple_of_nests=(nest_asian,),
    )








.. GENERATED FROM PYTHON SOURCE LINES 99-101

.. code-block:: default

    logprob = the_model_generation.get_nested_logit(nests)








.. GENERATED FROM PYTHON SOURCE LINES 102-105

.. code-block:: default

    the_biogeme = bio.BIOGEME(biogeme_database, logprob)
    the_biogeme.modelName = MODEL_NAME





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

 .. code-block:: none

    File biogeme.toml has been parsed. 




.. GENERATED FROM PYTHON SOURCE LINES 106-107

Calculate the null log likelihood for reporting.

.. GENERATED FROM PYTHON SOURCE LINES 107-109

.. code-block:: default

    the_biogeme.calculateNullLoglikelihood({i: 1 for i in range(context.total_sample_size)})





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

 .. code-block:: none


    -29957.32273553647



.. GENERATED FROM PYTHON SOURCE LINES 110-111

Estimate the parameters

.. GENERATED FROM PYTHON SOURCE LINES 111-113

.. code-block:: default

    results = the_biogeme.estimate(recycle=False)





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

 .. code-block:: none

    *** Initial values of the parameters are obtained from the file __nested_downtown_20.iter 
    Parameter values restored from __nested_downtown_20.iter 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.    beta_chinese  beta_ethiopian     beta_french     beta_indian   beta_japanese     beta_korean   beta_lebanese   beta_log_dist    beta_mexican      beta_price     beta_rating        mu_asian     Function    Relgrad   Radius      Rho      
        0          -0.096           -0.04           0.018           -0.12            0.12          -0.067          0.0058              -1            0.67           -0.51            0.47             1.2      2.4e+04      0.076       10     0.93   ++ 
        1            0.12            0.51            0.78            0.34            0.96             0.2            0.79           -0.63             1.2            -0.4            0.76             1.4      2.3e+04      0.023    1e+02        1   ++ 
        2            0.45             0.5            0.72            0.78             1.1            0.51            0.73           -0.64             1.2           -0.42            0.79             1.7      2.3e+04      0.012    1e+03      1.1   ++ 
        3            0.57             0.5            0.71            0.86             1.2            0.59            0.73           -0.61             1.2           -0.41            0.78             1.9      2.3e+04     0.0031    1e+04      1.2   ++ 
        4            0.63             0.5            0.71            0.92             1.2            0.65            0.73            -0.6             1.2            -0.4            0.77               2      2.3e+04    0.00039    1e+05      1.1   ++ 
        5            0.63             0.5            0.71            0.92             1.2            0.65            0.73            -0.6             1.2            -0.4            0.77               2      2.3e+04    5.5e-06    1e+05        1   ++ 
    Results saved in file nested_downtown_20~00.html 
    Results saved in file nested_downtown_20~00.pickle 




.. GENERATED FROM PYTHON SOURCE LINES 114-116

.. code-block:: default

    print(results.short_summary())





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

 .. code-block:: none

    Results for model nested_downtown_20
    Nbr of parameters:              12
    Sample size:                    10000
    Excluded data:                  0
    Null log likelihood:            -29957.32
    Final log likelihood:           -22979.05
    Likelihood ratio test (null):           13956.55
    Rho square (null):                      0.233
    Rho bar square (null):                  0.233
    Akaike Information Criterion:   45982.09
    Bayesian Information Criterion: 46068.62





.. GENERATED FROM PYTHON SOURCE LINES 117-120

.. code-block:: default

    estimated_parameters = results.getEstimatedParameters()
    estimated_parameters






.. 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>beta_chinese</th>
          <td>0.636120</td>
          <td>0.072040</td>
          <td>8.830092</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_ethiopian</th>
          <td>0.499967</td>
          <td>0.040207</td>
          <td>12.434724</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_french</th>
          <td>0.710729</td>
          <td>0.049090</td>
          <td>14.478023</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_indian</th>
          <td>0.926366</td>
          <td>0.064207</td>
          <td>14.427884</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_japanese</th>
          <td>1.197669</td>
          <td>0.054417</td>
          <td>22.009107</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_korean</th>
          <td>0.651077</td>
          <td>0.061969</td>
          <td>10.506438</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_lebanese</th>
          <td>0.726548</td>
          <td>0.049740</td>
          <td>14.606902</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_log_dist</th>
          <td>-0.603541</td>
          <td>0.013011</td>
          <td>-46.388565</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_mexican</th>
          <td>1.199156</td>
          <td>0.029119</td>
          <td>41.181569</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_price</th>
          <td>-0.403833</td>
          <td>0.012157</td>
          <td>-33.218725</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_rating</th>
          <td>0.768725</td>
          <td>0.015337</td>
          <td>50.123186</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>mu_asian</th>
          <td>1.978174</td>
          <td>0.057582</td>
          <td>34.354012</td>
          <td>0.0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 121-123

.. code-block:: default

    df, msg = compare(estimated_parameters)








.. GENERATED FROM PYTHON SOURCE LINES 124-126

.. code-block:: default

    print(df)





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

 .. code-block:: none

                  Name  True Value  Estimated Value    T-Test
    0      beta_rating        0.75         0.768725 -1.220940
    1       beta_price       -0.40        -0.403833  0.315295
    2     beta_chinese        0.75         0.636120  1.580784
    3    beta_japanese        1.25         1.197669  0.961672
    4      beta_korean        0.75         0.651077  1.596322
    5      beta_indian        1.00         0.926366  1.146833
    6      beta_french        0.75         0.710729  0.799967
    7     beta_mexican        1.25         1.199156  1.746107
    8    beta_lebanese        0.75         0.726548  0.471487
    9   beta_ethiopian        0.50         0.499967  0.000816
    10   beta_log_dist       -0.60        -0.603541  0.272169
    11        mu_asian        2.00         1.978174  0.379049




.. GENERATED FROM PYTHON SOURCE LINES 127-128

.. code-block:: default

    print(msg)




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

 .. code-block:: none

    Parameters not estimated: ['mu_downtown']





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

   **Total running time of the script:** (2 minutes 13.658 seconds)


.. _sphx_glr_download_auto_examples_sampling_plot_b02nested.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_b02nested.py <plot_b02nested.py>`

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

      :download:`Download Jupyter notebook: plot_b02nested.ipynb <plot_b02nested.ipynb>`


.. only:: html

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

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