.. 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.

Michel Bierlaire
Sat Jul 26 2025, 13:01:22

.. GENERATED FROM PYTHON SOURCE LINES 11-34

.. code-block:: Python


    import pandas as pd
    from alternatives import ID_COLUMN, all_alternatives, alternatives, asian, partitions
    from compare import compare
    from IPython.core.display_functions import display
    from specification_sampling import V, combined_variables

    import biogeme.biogeme_logging as blog
    from biogeme.biogeme import BIOGEME
    from biogeme.expressions import Beta
    from biogeme.nests import NestsForNestedLogit, OneNestForNestedLogit
    from biogeme.results_processing import (
        EstimationResults,
        get_pandas_estimated_parameters,
    )
    from biogeme.sampling_of_alternatives import (
        ChoiceSetsGeneration,
        GenerateModel,
        SamplingContext,
        generate_segment_size,
    )
    from biogeme.tools import timeit





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

 .. code-block:: none

        ID  rating  price  ...   rest_lon    distance  downtown
    0    0       1      4  ...  42.220972   71.735518       1.0
    1    1       2      2  ...  50.549434  106.267205       0.0
    2    2       3      3  ...  97.830520  136.298409       0.0
    3    3       4      1  ...  69.152206   85.941147       0.0
    4    4       4      3  ...  89.145620   96.773021       0.0
    ..  ..     ...    ...  ...        ...         ...       ...
    95  95       4      3  ...   9.511387   84.166441       0.0
    96  96       1      1  ...  92.144641   95.601366       0.0
    97  97       4      2  ...  27.657518   30.440555       1.0
    98  98       4      4  ...  32.303213   45.027143       1.0
    99  99       4      1  ...  13.672495   25.703295       1.0

    [100 rows x 16 columns]
    Number of asian restaurants: 33




.. GENERATED FROM PYTHON SOURCE LINES 35-37

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 38-46

.. code-block:: Python

    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 47-51

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 52-54

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 55-56

We use all alternatives in the nest.

.. GENERATED FROM PYTHON SOURCE LINES 56-61

.. code-block:: Python

    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 62-64

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 65-79

.. code-block:: Python

    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 80-82

.. code-block:: Python

    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 83-86

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 87-89

.. code-block:: Python

    biogeme_database = the_data_generation.sample_and_merge(recycle=False)





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

 .. code-block:: none

    Generating 20 + 33 alternatives for 10000 observations 

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    File nested_downtown_20.dat has been created. 




.. GENERATED FROM PYTHON SOURCE LINES 90-91

Definition of the nest.

.. GENERATED FROM PYTHON SOURCE LINES 91-100

.. code-block:: Python

    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,),
    )





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

 .. code-block:: none

    The following elements do not appear in any nest and are assumed each to be alone in a separate nest: {2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 32, 35, 36, 38, 39, 41, 42, 43, 44, 46, 48, 49, 52, 53, 54, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 71, 73, 74, 75, 77, 82, 83, 84, 85, 86, 88, 90, 93, 95, 96, 97, 99}. If it is not the intention, check the assignment of alternatives to nests. 




.. GENERATED FROM PYTHON SOURCE LINES 101-103

.. code-block:: Python

    log_probability = the_model_generation.get_nested_logit(nests)








.. GENERATED FROM PYTHON SOURCE LINES 104-107

.. code-block:: Python

    the_biogeme = BIOGEME(biogeme_database, log_probability)
    the_biogeme.model_name = MODEL_NAME





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

 .. code-block:: none

    Biogeme parameters read from biogeme.toml. 




.. GENERATED FROM PYTHON SOURCE LINES 108-109

Calculate the null log likelihood for reporting.

.. GENERATED FROM PYTHON SOURCE LINES 109-113

.. code-block:: Python

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





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

 .. code-block:: none


    -29957.32273553991



.. GENERATED FROM PYTHON SOURCE LINES 114-115

Estimate the parameters.

.. GENERATED FROM PYTHON SOURCE LINES 115-123

.. code-block:: Python

    try:
        results = EstimationResults.from_yaml_file(
            filename=f'saved_results/{the_biogeme.model_name}.yaml'
        )
    except FileNotFoundError:
        with timeit(f'Estimate of model {the_biogeme.model_name}'):
            results = the_biogeme.estimate()



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

.. code-block:: pytb

    Traceback (most recent call last):
      File "/Users/bierlair/MyFiles/github/biogeme/docs/source/examples/sampling/plot_b02nested.py", line 116, in <module>
        results = EstimationResults.from_yaml_file(
            filename=f'saved_results/{the_biogeme.model_name}.yaml'
        )
      File "/Users/bierlair/MyFiles/github/biogeme/src/biogeme/results_processing/estimation_results.py", line 115, in from_yaml_file
        restored_results: RawEstimationResults = deserialize_from_yaml(
                                                 ~~~~~~~~~~~~~~~~~~~~~^
            filename=filename
            ^^^^^^^^^^^^^^^^^
        )
        ^
      File "/Users/bierlair/MyFiles/github/biogeme/src/biogeme/results_processing/raw_estimation_results.py", line 106, in deserialize_from_yaml
        if data['bootstrap_time'] is not None
           ~~~~^^^^^^^^^^^^^^^^^^
    TypeError: 'NoneType' object is not subscriptable




.. GENERATED FROM PYTHON SOURCE LINES 124-126

.. code-block:: Python

    print(results.short_summary())


.. GENERATED FROM PYTHON SOURCE LINES 127-131

.. code-block:: Python

    parameters_tables = get_pandas_estimated_parameters(estimation_results=results)
    estimated_parameters = parameters_tables['Estimated parameters']
    display(estimated_parameters)


.. GENERATED FROM PYTHON SOURCE LINES 132-134

.. code-block:: Python

    df, msg = compare(estimated_parameters)


.. GENERATED FROM PYTHON SOURCE LINES 135-137

.. code-block:: Python

    print(df)


.. GENERATED FROM PYTHON SOURCE LINES 138-139

.. code-block:: Python

    print(msg)


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

   **Total running time of the script:** (0 minutes 18.961 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-jupyter

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

    .. 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-zip

      :download:`Download zipped: plot_b02nested.zip <plot_b02nested.zip>`


.. only:: html

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

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