.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/indicators/plot_b02estimation.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_indicators_plot_b02estimation.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_indicators_plot_b02estimation.py:


Estimation and simulation of a nested logit model
=================================================

 We estimate a nested logit model and we perform simulation using the
 estimated model.

:author: Michel Bierlaire, EPFL
:date: Wed Apr 12 21:05:16 2023

.. GENERATED FROM PYTHON SOURCE LINES 13-18

.. code-block:: default

    from biogeme import models
    import biogeme.biogeme as bio
    from optima_data import database
    from scenarios import scenario








.. GENERATED FROM PYTHON SOURCE LINES 19-21

Obtain the specification for the default scenario.
The definition of the scenarios is available in :ref:`scenarios`.

.. GENERATED FROM PYTHON SOURCE LINES 21-23

.. code-block:: default

    V, nests, Choice, _ = scenario()








.. GENERATED FROM PYTHON SOURCE LINES 24-26

The choice model is a nested logit, with availability conditions
For estimation, we need the log of the probability.

.. GENERATED FROM PYTHON SOURCE LINES 26-28

.. code-block:: default

    logprob = models.lognested(V, None, nests, Choice)








.. GENERATED FROM PYTHON SOURCE LINES 29-30

Create the Biogeme object for estimation.

.. GENERATED FROM PYTHON SOURCE LINES 30-33

.. code-block:: default

    the_biogeme = bio.BIOGEME(database, logprob)
    the_biogeme.modelName = 'b02estimation'








.. GENERATED FROM PYTHON SOURCE LINES 34-35

Estimate the parameters. Perform bootstrapping.

.. GENERATED FROM PYTHON SOURCE LINES 35-38

.. code-block:: default

    the_biogeme.bootstrap_samples = 100
    results = the_biogeme.estimate(run_bootstrap=True)





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

 .. code-block:: none


      0%|          | 0/100 [00:00<?, ?it/s]
      1%|          | 1/100 [00:00<00:18,  5.36it/s]
      2%|▏         | 2/100 [00:00<00:28,  3.46it/s]
      3%|▎         | 3/100 [00:00<00:26,  3.63it/s]
      4%|▍         | 4/100 [00:01<00:29,  3.21it/s]
      5%|▌         | 5/100 [00:01<00:28,  3.34it/s]
      6%|▌         | 6/100 [00:01<00:28,  3.34it/s]
      7%|▋         | 7/100 [00:01<00:26,  3.56it/s]
      8%|▊         | 8/100 [00:02<00:28,  3.28it/s]
      9%|▉         | 9/100 [00:02<00:24,  3.75it/s]
     10%|█         | 10/100 [00:02<00:26,  3.42it/s]
     11%|█         | 11/100 [00:03<00:26,  3.41it/s]
     12%|█▏        | 12/100 [00:03<00:26,  3.31it/s]
     13%|█▎        | 13/100 [00:03<00:23,  3.69it/s]
     14%|█▍        | 14/100 [00:03<00:23,  3.65it/s]
     15%|█▌        | 15/100 [00:04<00:23,  3.68it/s]
     16%|█▌        | 16/100 [00:04<00:21,  3.93it/s]
     17%|█▋        | 17/100 [00:04<00:26,  3.15it/s]
     18%|█▊        | 18/100 [00:05<00:24,  3.30it/s]
     19%|█▉        | 19/100 [00:05<00:25,  3.19it/s]
     20%|██        | 20/100 [00:05<00:26,  3.05it/s]
     21%|██        | 21/100 [00:06<00:26,  3.04it/s]
     22%|██▏       | 22/100 [00:06<00:23,  3.26it/s]
     23%|██▎       | 23/100 [00:06<00:25,  3.06it/s]
     24%|██▍       | 24/100 [00:07<00:23,  3.21it/s]
     25%|██▌       | 25/100 [00:07<00:22,  3.34it/s]
     26%|██▌       | 26/100 [00:07<00:21,  3.37it/s]
     27%|██▋       | 27/100 [00:07<00:19,  3.70it/s]
     28%|██▊       | 28/100 [00:08<00:19,  3.66it/s]
     29%|██▉       | 29/100 [00:08<00:21,  3.24it/s]
     30%|███       | 30/100 [00:08<00:22,  3.06it/s]
     31%|███       | 31/100 [00:09<00:25,  2.75it/s]
     32%|███▏      | 32/100 [00:09<00:25,  2.66it/s]
     33%|███▎      | 33/100 [00:10<00:23,  2.82it/s]
     34%|███▍      | 34/100 [00:10<00:28,  2.34it/s]
     35%|███▌      | 35/100 [00:11<00:28,  2.32it/s]
     36%|███▌      | 36/100 [00:11<00:26,  2.43it/s]
     37%|███▋      | 37/100 [00:12<00:29,  2.17it/s]
     38%|███▊      | 38/100 [00:12<00:28,  2.18it/s]
     39%|███▉      | 39/100 [00:13<00:30,  2.02it/s]
     40%|████      | 40/100 [00:13<00:28,  2.09it/s]
     41%|████      | 41/100 [00:13<00:25,  2.27it/s]
     42%|████▏     | 42/100 [00:14<00:22,  2.56it/s]
     43%|████▎     | 43/100 [00:14<00:19,  2.87it/s]
     44%|████▍     | 44/100 [00:14<00:17,  3.15it/s]
     45%|████▌     | 45/100 [00:15<00:17,  3.09it/s]
     46%|████▌     | 46/100 [00:15<00:18,  2.97it/s]
     47%|████▋     | 47/100 [00:15<00:19,  2.69it/s]
     48%|████▊     | 48/100 [00:16<00:21,  2.38it/s]
     49%|████▉     | 49/100 [00:16<00:20,  2.50it/s]
     50%|█████     | 50/100 [00:17<00:18,  2.68it/s]
     51%|█████     | 51/100 [00:17<00:19,  2.57it/s]
     52%|█████▏    | 52/100 [00:17<00:17,  2.80it/s]
     53%|█████▎    | 53/100 [00:18<00:16,  2.91it/s]
     54%|█████▍    | 54/100 [00:18<00:16,  2.81it/s]
     55%|█████▌    | 55/100 [00:18<00:16,  2.70it/s]
     56%|█████▌    | 56/100 [00:19<00:16,  2.68it/s]
     57%|█████▋    | 57/100 [00:19<00:15,  2.72it/s]
     58%|█████▊    | 58/100 [00:20<00:17,  2.43it/s]
     59%|█████▉    | 59/100 [00:20<00:18,  2.23it/s]
     60%|██████    | 60/100 [00:20<00:16,  2.43it/s]
     61%|██████    | 61/100 [00:21<00:14,  2.74it/s]
     62%|██████▏   | 62/100 [00:21<00:14,  2.62it/s]
     63%|██████▎   | 63/100 [00:22<00:14,  2.47it/s]
     64%|██████▍   | 64/100 [00:22<00:14,  2.52it/s]
     65%|██████▌   | 65/100 [00:22<00:13,  2.68it/s]
     66%|██████▌   | 66/100 [00:23<00:14,  2.42it/s]
     67%|██████▋   | 67/100 [00:23<00:14,  2.35it/s]
     68%|██████▊   | 68/100 [00:24<00:14,  2.22it/s]
     69%|██████▉   | 69/100 [00:24<00:13,  2.27it/s]
     70%|███████   | 70/100 [00:25<00:12,  2.40it/s]
     71%|███████   | 71/100 [00:25<00:12,  2.38it/s]
     72%|███████▏  | 72/100 [00:25<00:12,  2.21it/s]
     73%|███████▎  | 73/100 [00:26<00:12,  2.24it/s]
     74%|███████▍  | 74/100 [00:26<00:10,  2.37it/s]
     75%|███████▌  | 75/100 [00:27<00:09,  2.54it/s]
     76%|███████▌  | 76/100 [00:27<00:09,  2.59it/s]
     77%|███████▋  | 77/100 [00:27<00:08,  2.82it/s]
     78%|███████▊  | 78/100 [00:28<00:08,  2.73it/s]
     79%|███████▉  | 79/100 [00:28<00:06,  3.05it/s]
     80%|████████  | 80/100 [00:28<00:06,  2.97it/s]
     81%|████████  | 81/100 [00:29<00:06,  2.96it/s]
     82%|████████▏ | 82/100 [00:29<00:06,  2.86it/s]
     83%|████████▎ | 83/100 [00:29<00:05,  3.03it/s]
     84%|████████▍ | 84/100 [00:30<00:05,  2.94it/s]
     85%|████████▌ | 85/100 [00:30<00:05,  2.90it/s]
     86%|████████▌ | 86/100 [00:30<00:05,  2.66it/s]
     87%|████████▋ | 87/100 [00:31<00:04,  3.05it/s]
     88%|████████▊ | 88/100 [00:31<00:03,  3.10it/s]
     89%|████████▉ | 89/100 [00:31<00:03,  2.85it/s]
     90%|█████████ | 90/100 [00:32<00:03,  2.70it/s]
     91%|█████████ | 91/100 [00:32<00:03,  2.61it/s]
     92%|█████████▏| 92/100 [00:33<00:02,  2.77it/s]
     93%|█████████▎| 93/100 [00:33<00:02,  2.93it/s]
     94%|█████████▍| 94/100 [00:33<00:01,  3.20it/s]
     95%|█████████▌| 95/100 [00:33<00:01,  2.87it/s]
     96%|█████████▌| 96/100 [00:34<00:01,  2.82it/s]
     97%|█████████▋| 97/100 [00:34<00:01,  2.81it/s]
     98%|█████████▊| 98/100 [00:35<00:00,  2.85it/s]
     99%|█████████▉| 99/100 [00:35<00:00,  2.69it/s]
    100%|██████████| 100/100 [00:35<00:00,  2.63it/s]
    100%|██████████| 100/100 [00:35<00:00,  2.79it/s]




.. GENERATED FROM PYTHON SOURCE LINES 39-40

Get the results in a pandas table

.. GENERATED FROM PYTHON SOURCE LINES 40-44

.. 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>ASC_CAR</th>
          <td>0.261291</td>
          <td>0.100112</td>
          <td>2.609977</td>
          <td>9.054838e-03</td>
        </tr>
        <tr>
          <th>ASC_SM</th>
          <td>0.059020</td>
          <td>0.216597</td>
          <td>0.272489</td>
          <td>7.852463e-01</td>
        </tr>
        <tr>
          <th>BETA_COST</th>
          <td>-0.716192</td>
          <td>0.138371</td>
          <td>-5.175890</td>
          <td>2.268270e-07</td>
        </tr>
        <tr>
          <th>BETA_DIST_FEMALE</th>
          <td>-0.831209</td>
          <td>0.192866</td>
          <td>-4.309772</td>
          <td>1.634229e-05</td>
        </tr>
        <tr>
          <th>BETA_DIST_MALE</th>
          <td>-0.686327</td>
          <td>0.160823</td>
          <td>-4.267581</td>
          <td>1.976044e-05</td>
        </tr>
        <tr>
          <th>BETA_DIST_UNREPORTED</th>
          <td>-0.702974</td>
          <td>0.196307</td>
          <td>-3.581000</td>
          <td>3.422819e-04</td>
        </tr>
        <tr>
          <th>BETA_TIME_FULLTIME</th>
          <td>-1.597087</td>
          <td>0.332796</td>
          <td>-4.798996</td>
          <td>1.594633e-06</td>
        </tr>
        <tr>
          <th>BETA_TIME_OTHER</th>
          <td>-0.577362</td>
          <td>0.296477</td>
          <td>-1.947409</td>
          <td>5.148566e-02</td>
        </tr>
        <tr>
          <th>mu_nocar</th>
          <td>1.528532</td>
          <td>0.305659</td>
          <td>5.000776</td>
          <td>5.709995e-07</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 45-46

Simulation

.. GENERATED FROM PYTHON SOURCE LINES 46-49

.. code-block:: default

    simulated_choices = logprob.getValue_c(betas=results.getBetaValues(), database=database)
    simulated_choices





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

 .. code-block:: none


    array([-0.65048784, -1.4326262 , -0.13259011, ..., -0.37239372,
           -0.29534586, -0.26726783])



.. GENERATED FROM PYTHON SOURCE LINES 50-57

.. code-block:: default

    loglikelihood = logprob.getValue_c(
        betas=results.getBetaValues(),
        database=database,
        aggregation=True,
    )
    print(f'Final log likelihood:     {results.data.logLike}')
    print(f'Simulated log likelihood: {loglikelihood}')




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

 .. code-block:: none

    Final log likelihood:     -1298.4982789719456
    Simulated log likelihood: -1298.4982789719452





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

   **Total running time of the script:** (0 minutes 37.367 seconds)


.. _sphx_glr_download_auto_examples_indicators_plot_b02estimation.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_b02estimation.py <plot_b02estimation.py>`

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

      :download:`Download Jupyter notebook: plot_b02estimation.ipynb <plot_b02estimation.ipynb>`


.. only:: html

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

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