.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/sampling/plot_b03cnl.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_b03cnl.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_sampling_plot_b03cnl.py:


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

Estimation of a cross-nested logit model using sampling of alternatives.

:author: Michel Bierlaire
:date: Wed Nov  1 18:00:33 2023

.. GENERATED FROM PYTHON SOURCE LINES 11-34

.. code-block:: default

    import pandas as pd
    from biogeme.sampling_of_alternatives import (
        SamplingContext,
        ChoiceSetsGeneration,
        GenerateModel,
        generate_segment_size,
    )
    from biogeme.expressions import Beta
    import biogeme.biogeme_logging as blog
    import biogeme.biogeme as bio
    from biogeme.nests import OneNestForCrossNestedLogit, NestsForCrossNestedLogit
    from specification import V, combined_variables
    from compare import compare
    from alternatives import (
        alternatives,
        ID_COLUMN,
        partitions,
        all_alternatives,
        asian_and_downtown,
        only_downtown,
        only_asian,
    )








.. GENERATED FROM PYTHON SOURCE LINES 35-37

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 38-46

.. code-block:: default

    PARTITION = 'downtown'
    MEV_PARTITION = 'uniform_asian_or_downtown'
    SAMPLE_SIZE = 10  # out of 100 alternatives
    SAMPLE_SIZE_MEV = 63  # out of 63 alternatives
    CHOICE_COLUMN = 'cnl_3'
    MODEL_NAME = f'cnl_{SAMPLE_SIZE}_{SAMPLE_SIZE_MEV}'
    FILE_NAME = f'{MODEL_NAME}.dat'








.. GENERATED FROM PYTHON SOURCE LINES 47-51

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 52-56

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 57-58

We use all alternatives in the nest.

.. GENERATED FROM PYTHON SOURCE LINES 58-65

.. 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 66-67

Nests

.. GENERATED FROM PYTHON SOURCE LINES 69-70

Downtown

.. GENERATED FROM PYTHON SOURCE LINES 70-78

.. code-block:: default

    mu_downtown = Beta('mu_downtown', 1, 1, None, 0)
    downtown_alpha_dict = {i: 0.5 for i in asian_and_downtown} | {
        i: 1 for i in only_downtown
    }
    downtown_nest = OneNestForCrossNestedLogit(
        nest_param=mu_downtown, dict_of_alpha=downtown_alpha_dict, name='downtown'
    )








.. GENERATED FROM PYTHON SOURCE LINES 79-80

Asian

.. GENERATED FROM PYTHON SOURCE LINES 80-91

.. code-block:: default

    mu_asian = Beta('mu_asian', 1, 1, None, 0)
    asian_alpha_dict = {i: 0.5 for i in asian_and_downtown} | {i: 1.0 for i in only_asian}
    asian_nest = OneNestForCrossNestedLogit(
        nest_param=mu_asian, dict_of_alpha=asian_alpha_dict, name='asian'
    )

    cnl_nests = NestsForCrossNestedLogit(
        choice_set=all_alternatives,
        tuple_of_nests=(downtown_nest, asian_nest),
    )








.. GENERATED FROM PYTHON SOURCE LINES 92-94

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 95-110

.. 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,
        cnl_nests=cnl_nests,
    )








.. GENERATED FROM PYTHON SOURCE LINES 111-113

.. 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: 10: 5/46, 5/54
    Nbr of MEV alternatives: 63
    MEV partition: 1 segment(s) of size 63
    MEV sample: 63: 63/63
 




.. GENERATED FROM PYTHON SOURCE LINES 114-117

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 118-120

.. 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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    Define new variables 

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    File cnl_10_63.dat has been created. 




.. GENERATED FROM PYTHON SOURCE LINES 121-123

.. code-block:: default

    logprob = the_model_generation.get_cross_nested_logit()








.. GENERATED FROM PYTHON SOURCE LINES 124-127

.. 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 128-129

Calculate the null log likelihood for reporting.

.. GENERATED FROM PYTHON SOURCE LINES 129-131

.. 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


    -23025.850929942502



.. GENERATED FROM PYTHON SOURCE LINES 132-133

Estimate the parameters.

.. GENERATED FROM PYTHON SOURCE LINES 133-135

.. 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 __cnl_10_63.iter 
    Cannot read file __cnl_10_63.iter. Statement is ignored. 
    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     mu_downtown     Function    Relgrad   Radius      Rho      
        0          -0.074          -0.091           0.095          -0.091             0.2          -0.065           -0.06              -1            0.74           -0.53            0.55             1.3             1.3      1.6e+04       0.15       10     0.94   ++ 
        1            0.74            0.51            0.92             1.2             1.6            0.84            0.78           -0.49             1.2           -0.41             0.8             1.9             1.5      1.4e+04      0.056    1e+02      0.9   ++ 
        2            0.83            0.52             0.8             1.1             1.3            0.85            0.75            -0.6             1.3           -0.43            0.75             2.1             1.8      1.4e+04      0.016    1e+03        1   ++ 
        3            0.78            0.53            0.78             1.1             1.3            0.82            0.76           -0.59             1.2           -0.42            0.75             2.1             1.9      1.4e+04     0.0027    1e+04      1.1   ++ 
        4            0.77            0.53            0.78             1.1             1.3            0.81            0.76           -0.59             1.2           -0.42            0.75             2.1             1.9      1.4e+04     0.0001    1e+05        1   ++ 
        5            0.77            0.53            0.78             1.1             1.3            0.81            0.76           -0.59             1.2           -0.42            0.75             2.1             1.9      1.4e+04    1.5e-07    1e+05        1   ++ 
    Results saved in file cnl_10_63~00.html 
    Results saved in file cnl_10_63~00.pickle 




.. GENERATED FROM PYTHON SOURCE LINES 136-138

.. code-block:: default

    print(results.short_summary())





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

 .. code-block:: none

    Results for model cnl_10_63
    Nbr of parameters:              13
    Sample size:                    10000
    Excluded data:                  0
    Null log likelihood:            -23025.85
    Final log likelihood:           -14092.76
    Likelihood ratio test (null):           17866.18
    Rho square (null):                      0.388
    Rho bar square (null):                  0.387
    Akaike Information Criterion:   28211.52
    Bayesian Information Criterion: 28305.26





.. GENERATED FROM PYTHON SOURCE LINES 139-142

.. 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>0.765983</td>
          <td>0.060207</td>
          <td>12.722451</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_ethiopian</th>
          <td>0.526412</td>
          <td>0.041496</td>
          <td>12.685779</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_french</th>
          <td>0.777741</td>
          <td>0.049470</td>
          <td>15.721391</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_indian</th>
          <td>1.097405</td>
          <td>0.052229</td>
          <td>21.011339</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_japanese</th>
          <td>1.296880</td>
          <td>0.046115</td>
          <td>28.122891</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_korean</th>
          <td>0.811152</td>
          <td>0.053094</td>
          <td>15.277640</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_lebanese</th>
          <td>0.755723</td>
          <td>0.048377</td>
          <td>15.621583</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_log_dist</th>
          <td>-0.586730</td>
          <td>0.012674</td>
          <td>-46.292851</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_mexican</th>
          <td>1.241028</td>
          <td>0.030040</td>
          <td>41.312755</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_price</th>
          <td>-0.420492</td>
          <td>0.012436</td>
          <td>-33.811212</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>beta_rating</th>
          <td>0.746966</td>
          <td>0.015039</td>
          <td>49.667319</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>mu_asian</th>
          <td>2.123777</td>
          <td>0.067253</td>
          <td>31.578949</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>mu_downtown</th>
          <td>1.930837</td>
          <td>0.030021</td>
          <td>64.316050</td>
          <td>0.0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 143-145

.. code-block:: default

    df, msg = compare(estimated_parameters)








.. GENERATED FROM PYTHON SOURCE LINES 146-148

.. 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         0.746966  0.201726
    1       beta_price       -0.40        -0.420492  1.647755
    2     beta_chinese        0.75         0.765983 -0.265467
    3    beta_japanese        1.25         1.296880 -1.016593
    4      beta_korean        0.75         0.811152 -1.151761
    5      beta_indian        1.00         1.097405 -1.864958
    6      beta_french        0.75         0.777741 -0.560769
    7     beta_mexican        1.25         1.241028  0.298658
    8    beta_lebanese        0.75         0.755723 -0.118305
    9   beta_ethiopian        0.50         0.526412 -0.636482
    10   beta_log_dist       -0.60        -0.586730 -1.046964
    11        mu_asian        2.00         2.123777 -1.840468
    12     mu_downtown        2.00         1.930837  2.303824




.. GENERATED FROM PYTHON SOURCE LINES 149-150

.. code-block:: default

    print(msg)








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

   **Total running time of the script:** (6 minutes 12.700 seconds)


.. _sphx_glr_download_auto_examples_sampling_plot_b03cnl.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_b03cnl.py <plot_b03cnl.py>`

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

      :download:`Download Jupyter notebook: plot_b03cnl.ipynb <plot_b03cnl.ipynb>`


.. only:: html

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

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