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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_checks_gallery_tabular_methodology_plot_identifier_leakage.py>`
        to download the full example code

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

.. _sphx_glr_checks_gallery_tabular_methodology_plot_identifier_leakage.py:


Identifier Leakage
******************

.. GENERATED FROM PYTHON SOURCE LINES 8-10

Imports
=======

.. GENERATED FROM PYTHON SOURCE LINES 10-18

.. code-block:: default


    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd

    from deepchecks.tabular import Dataset
    from deepchecks.tabular.checks.methodology import *








.. GENERATED FROM PYTHON SOURCE LINES 19-20

Generating Data

.. GENERATED FROM PYTHON SOURCE LINES 20-27

.. code-block:: default


    np.random.seed(42)
    df = pd.DataFrame(np.random.randn(100, 3), columns=['x1', 'x2', 'x3'])
    df['x4'] = df['x1'] * 0.05 + df['x2']
    df['x5'] = df['x2']*121 + 0.01 * df['x1']
    df['label'] = df['x5'].apply(lambda x: 0 if x < 0 else 1)








.. GENERATED FROM PYTHON SOURCE LINES 28-31

.. code-block:: default


    dataset = Dataset(df, label='label', index_name='x1', datetime_name='x2')








.. GENERATED FROM PYTHON SOURCE LINES 32-34

Running ``identifier_leakage`` check
====================================

.. GENERATED FROM PYTHON SOURCE LINES 34-37

.. code-block:: default


    IdentifierLeakage().run(dataset)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">

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        src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js"
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    <h4><b>Identifier Leakage</b></h4><p>Check if identifiers (Index/Date) can be used to predict the label.</p><h5><b>Additional Outputs</b></h5><div>                            <div id="86e093cf-dcf7-40e9-91ca-72c834928ece" class="plotly-graph-div" style="height:400px; width:700px;"></div>            <script type="text/javascript">                require(["plotly"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById("86e093cf-dcf7-40e9-91ca-72c834928ece")) {                    Plotly.newPlot(                        "86e093cf-dcf7-40e9-91ca-72c834928ece",                        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power score (PPS)"},"fixedrange":true,"range":[0,1]},"coloraxis":{"colorbar":{"title":{"text":"ppscore"}},"colorscale":[[0,"rgb(255, 255, 255)"],[0.1,"rgb(255,155,100)"],[0.2,"rgb(255, 50, 50)"],[0.3,"rgb(200, 0, 0)"],[1,"rgb(55, 0, 0)"]],"cmin":0,"cmax":1},"legend":{"tracegroupgap":0},"margin":{"t":60},"barmode":"relative","width":700,"height":400},                        {"responsive": true}                    ).then(function(){
                            
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                            })                };                });            </script>        </div><div>The PPS represents the ability of a feature to single-handedly predict another feature or label.</div><div>For Identifier columns (Index/Date) PPS should be nearly 0, otherwise date and index have some predictive effect on the label.</div>
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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 38-40

Using the ``SingleFeatureContribution`` check class
===================================================

.. GENERATED FROM PYTHON SOURCE LINES 40-43

.. code-block:: default


    my_check = IdentifierLeakage(ppscore_params={'sample': 10})
    my_check.run(dataset=dataset)





.. raw:: html

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    <h4><b>Identifier Leakage</b></h4><p>Check if identifiers (Index/Date) can be used to predict the label.</p><h5><b>Additional Outputs</b></h5><div>                            <div id="22c5a6a7-906d-4826-96a7-c158475daa28" class="plotly-graph-div" style="height:400px; width:700px;"></div>            <script type="text/javascript">                require(["plotly"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById("22c5a6a7-906d-4826-96a7-c158475daa28")) {                    Plotly.newPlot(                        "22c5a6a7-906d-4826-96a7-c158475daa28",                        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.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.126 seconds)


.. _sphx_glr_download_checks_gallery_tabular_methodology_plot_identifier_leakage.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



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

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



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

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


.. only:: html

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

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