.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tabular/auto_checks/model_evaluation/plot_performance_bias.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_tabular_auto_checks_model_evaluation_plot_performance_bias.py>`
        to download the full example code

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

.. _sphx_glr_tabular_auto_checks_model_evaluation_plot_performance_bias.py:


.. _tabular__performance_bias:

Performance Bias
****************

This notebook provides an overview for using and understanding the Performance Bias check.

**Structure:**

* `What is the purpose of the check? <#what-is-the-purpose-of-the-check>`__
* `Generate data & model <#generate-data-model>`__
* `Run the check <#run-the-check>`__
* `Define a condition <#define-a-condition>`__

What is the purpose of the check?
==================================

The check is designed to help you identify subgroups for which the model has a much lower performance score than its
baseline score (its overall performance). The subgroups are defined by a chosen *protected* feature (e.g., "sex",
"race") and you can specify a *control* feature (e.g., "education") by which to group the data before computing
performance differences. This is primarily useful for fairness analyses, but can also be used to identify other types
of performance disparities.

Large performance disparities can indicate a problem with the model. The training data may not be sufficient for
certain subgroups or may contain biases, or the model may need to be re-calibrated when applied to certain subgroups.
When using appropriate scoring functions, looking at performance disparities can help uncover issues of these kinds.

Remember that this check relies on labeled data provided in the dataset. As such, it can only assess performance
disparities to the extent that the labeled data is accurate and representative of the population of interest. Using
scoring functions that are robust to class imbalance or that are computed for each model class can help mitigate this
issue. 

.. GENERATED FROM PYTHON SOURCE LINES 37-39

Generate data & model
=====================

.. GENERATED FROM PYTHON SOURCE LINES 39-46

.. code-block:: default


    from deepchecks.tabular.datasets.classification.adult import (
        load_data, load_fitted_model)

    train_dataset, test_dataset = load_data()
    model = load_fitted_model()








.. GENERATED FROM PYTHON SOURCE LINES 47-66

Run the check
=============

The check requires the argument ``protected_feature`` identifying a column that defines the subgroups
for which performance disparities are assessed. In addition, the check has several optional parameters
that affect its behavior and output.

* ``control_feature``: Column to use to split the data by groups prior to computing performance disparities.

* ``scorer``: Scoring function to measure performance. Default to "accuracy" for classification tasks 
  and "r2" for regression tasks.

* ``max_subgroups_per_control_cat_to_display``: Maximum number of subgroups (per ``control_feature`` category)
  to display.

* ``max_control_cat_to_display``: Maximum number of ``control_feature`` categories to display.

see :class:`API reference <deepchecks.tabular.checks.model_evaluation.PerformanceBias>` for
more details.

.. GENERATED FROM PYTHON SOURCE LINES 66-77

.. code-block:: default


    from deepchecks.tabular.checks.model_evaluation import PerformanceBias

    check = PerformanceBias(
       protected_feature="race",
       control_feature="education",
       scorer="accuracy",
       max_segments=3)
    result = check.run(test_dataset, model)
    result.show()






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That performance disparity is highlighted by a red line for negative\n                differences and a green line for positive differences. Only the top 3\n                categories of the control feature with the largest performance differences are displayed. 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.. GENERATED FROM PYTHON SOURCE LINES 78-84

Observe the check's output
--------------------------

We see in the results that the check identified the largest performance disparity for the subgroup 
"Others" within the category of "HS-grad" for the control feature "education". The model performance 
on this subgroup is 0.095 versus 0.258 for this entire education category.

.. GENERATED FROM PYTHON SOURCE LINES 84-87

.. code-block:: default


    result.value['scores_df'].head(3)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
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          <th>_baseline_count</th>
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          <td>Black</td>
          <td>Masters</td>
          <td>accuracy</td>
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          <td>0.770878</td>
          <td>934</td>
          <td>57</td>
          <td>-0.086667</td>
        </tr>
        <tr>
          <th>26</th>
          <td>Others</td>
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          <td>accuracy</td>
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          <td>0.900067</td>
          <td>1501</td>
          <td>99</td>
          <td>-0.051582</td>
        </tr>
        <tr>
          <th>22</th>
          <td>Others</td>
          <td>Assoc-voc</td>
          <td>accuracy</td>
          <td>0.766667</td>
          <td>0.807069</td>
          <td>679</td>
          <td>30</td>
          <td>-0.040403</td>
        </tr>
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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 88-95

Define a condition
==================

We can define on our check a condition that will validate all performance disparities fall within
a certain threshold. If the condition is not met, the check will fail.

Let's add a condition and re-run the check:

.. GENERATED FROM PYTHON SOURCE LINES 95-100

.. code-block:: default


    check.add_condition_bounded_performance_difference(lower_bound=-0.1)
    result = check.run(test_dataset, model)
    result.show(show_additional_outputs=False)






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

   **Total running time of the script:** (0 minutes 7.689 seconds)


.. _sphx_glr_download_tabular_auto_checks_model_evaluation_plot_performance_bias.py:

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    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_performance_bias.py <plot_performance_bias.py>`

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


.. only:: html

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

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