.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/swissmetro/plot_b19individual_level_parameters.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_b19individual_level_parameters.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_swissmetro_plot_b19individual_level_parameters.py:


Calculation of individual level parameters
==========================================

Calculation of the individual level parameters for the model defined
in :ref:`plot_b05normal_mixture`.

:author: Michel Bierlaire, EPFL
:date: Mon Apr 10 12:17:12 2023

.. GENERATED FROM PYTHON SOURCE LINES 13-20

.. code-block:: default


    import os
    import pickle
    import biogeme.biogeme as bio
    from biogeme import models
    from biogeme.expressions import Beta, bioDraws, MonteCarlo








.. GENERATED FROM PYTHON SOURCE LINES 21-22

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

.. GENERATED FROM PYTHON SOURCE LINES 22-36

.. code-block:: default

    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,
    )








.. GENERATED FROM PYTHON SOURCE LINES 37-38

Parameters. The initial value is irrelevant.

.. GENERATED FROM PYTHON SOURCE LINES 38-42

.. code-block:: default

    ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
    ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
    B_COST = Beta('B_COST', 0, None, None, 0)








.. GENERATED FROM PYTHON SOURCE LINES 43-45

Define a random parameter, normally distributed, designed to be used
for Monte-Carlo simulation.

.. GENERATED FROM PYTHON SOURCE LINES 45-49

.. code-block:: default

    B_TIME = Beta('B_TIME', 0, None, None, 0)
    B_TIME_S = Beta('B_TIME_S', 1, None, None, 0)
    B_TIME_RND = B_TIME + B_TIME_S * bioDraws('B_TIME_RND', 'NORMAL')








.. GENERATED FROM PYTHON SOURCE LINES 50-51

Define values for these parameters

.. GENERATED FROM PYTHON SOURCE LINES 51-59

.. code-block:: default

    beta_values = {
        'ASC_CAR': 0.137,
        'ASC_TRAIN': -0.402,
        'B_COST': -1.28,
        'B_TIME': -2.26,
        'B_TIME_S': 1.65,
    }








.. GENERATED FROM PYTHON SOURCE LINES 60-61

Definition of the utility functions.

.. GENERATED FROM PYTHON SOURCE LINES 61-65

.. code-block:: default

    V1 = ASC_TRAIN + B_TIME_RND * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED
    V2 = B_TIME_RND * SM_TT_SCALED + B_COST * SM_COST_SCALED
    V3 = ASC_CAR + B_TIME_RND * CAR_TT_SCALED + B_COST * CAR_CO_SCALED








.. GENERATED FROM PYTHON SOURCE LINES 66-67

Associate utility functions with the numbering of alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 67-69

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 70-71

Associate the availability conditions with the alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 71-73

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 74-75

Conditional on B_TIME_RND, we have a logit model (called the kernel).

.. GENERATED FROM PYTHON SOURCE LINES 75-77

.. code-block:: default

    prob_chosen = models.logit(V, av, CHOICE)








.. GENERATED FROM PYTHON SOURCE LINES 78-79

Numerator and denominator of the formula for individual parameters.

.. GENERATED FROM PYTHON SOURCE LINES 79-82

.. code-block:: default

    numerator = MonteCarlo(B_TIME_RND * prob_chosen)
    denominator = MonteCarlo(prob_chosen)








.. GENERATED FROM PYTHON SOURCE LINES 83-89

.. code-block:: default

    simulate = {
        'Numerator': numerator,
        'Denominator': denominator,
        'Choice': CHOICE,
    }








.. GENERATED FROM PYTHON SOURCE LINES 90-93

The results are saved in a picke file. The next time the script is
run, if the file exists, the results are simply loaded instead of
being re-calcuated.

.. GENERATED FROM PYTHON SOURCE LINES 93-105

.. code-block:: default

    PICKLE_FILE = 'b19individual_level_parameters.pickle'
    if os.path.isfile(PICKLE_FILE):
        with open(PICKLE_FILE, 'rb') as f:
            sim = pickle.load(f)
    else:
        biosim = bio.BIOGEME(database, simulate)
        sim = biosim.simulate(beta_values)
        sim['Individual-level parameters'] = sim['Numerator'] / sim['Denominator']
        with open(PICKLE_FILE, 'wb') as f:
            pickle.dump(sim, f)

    sim





.. 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>Numerator</th>
          <th>Denominator</th>
          <th>Choice</th>
          <th>Individual-level parameters</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>-1.776037</td>
          <td>0.644944</td>
          <td>2.0</td>
          <td>-2.753784</td>
        </tr>
        <tr>
          <th>1</th>
          <td>-1.725888</td>
          <td>0.658086</td>
          <td>2.0</td>
          <td>-2.622586</td>
        </tr>
        <tr>
          <th>2</th>
          <td>-1.759901</td>
          <td>0.623525</td>
          <td>2.0</td>
          <td>-2.822504</td>
        </tr>
        <tr>
          <th>3</th>
          <td>-1.045893</td>
          <td>0.434731</td>
          <td>2.0</td>
          <td>-2.405839</td>
        </tr>
        <tr>
          <th>4</th>
          <td>-1.582559</td>
          <td>0.628282</td>
          <td>2.0</td>
          <td>-2.518865</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>8446</th>
          <td>-0.231904</td>
          <td>0.156117</td>
          <td>1.0</td>
          <td>-1.485449</td>
        </tr>
        <tr>
          <th>8447</th>
          <td>-0.206029</td>
          <td>0.153192</td>
          <td>1.0</td>
          <td>-1.344902</td>
        </tr>
        <tr>
          <th>8448</th>
          <td>-0.181293</td>
          <td>0.137859</td>
          <td>1.0</td>
          <td>-1.315061</td>
        </tr>
        <tr>
          <th>8449</th>
          <td>-0.110605</td>
          <td>0.142277</td>
          <td>1.0</td>
          <td>-0.777392</td>
        </tr>
        <tr>
          <th>8450</th>
          <td>-0.244974</td>
          <td>0.161145</td>
          <td>1.0</td>
          <td>-1.520208</td>
        </tr>
      </tbody>
    </table>
    <p>6768 rows × 4 columns</p>
    </div>
    </div>
    <br />
    <br />


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

   **Total running time of the script:** (0 minutes 6.343 seconds)


.. _sphx_glr_download_auto_examples_swissmetro_plot_b19individual_level_parameters.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_b19individual_level_parameters.py <plot_b19individual_level_parameters.py>`

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

      :download:`Download Jupyter notebook: plot_b19individual_level_parameters.ipynb <plot_b19individual_level_parameters.ipynb>`


.. only:: html

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

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