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

.. only:: html

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

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

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

.. _sphx_glr_checks_gallery_tabular_integrity_plot_conflicting_labels.py:


Conflicting Labels
******************

This notebooks provides an overview for using and understanding the conflicting labels check.

**Structure:**

* `What are Conflicting Labels? <#what-are-conflicting-labels>`__
* `Load Data <#load-data>`__
* `Run the Check <#run-the-check>`__
* `Define a Condition <#define-a-condition>`__

What are Conflicting Labels?
============================
The check searches for identical samples with different labels. This can
occur due to either mislabeled data, or when the data collected is missing
features necessary to separate the labels. If the data is mislabled, it can
confuse the model and can result in lower performance of the model.

.. GENERATED FROM PYTHON SOURCE LINES 22-25

.. code-block:: default

    import pandas as pd

    from deepchecks.tabular import Dataset







.. GENERATED FROM PYTHON SOURCE LINES 26-29

.. code-block:: default

    from deepchecks.tabular.checks.integrity import ConflictingLabels
    from deepchecks.tabular.datasets.classification.phishing import load_data








.. GENERATED FROM PYTHON SOURCE LINES 30-32

Load Data
=========

.. GENERATED FROM PYTHON SOURCE LINES 32-37

.. code-block:: default



    phishing_dataframe = load_data(as_train_test=False, data_format='Dataframe')
    phishing_dataset = Dataset(phishing_dataframe, label='target', features=['urlLength', 'numDigits', 'numParams', 'num_%20', 'num_@', 'bodyLength', 'numTitles', 'numImages', 'numLinks', 'specialChars'])








.. GENERATED FROM PYTHON SOURCE LINES 38-40

Run the Check
=============

.. GENERATED FROM PYTHON SOURCE LINES 40-43

.. code-block:: default


    ConflictingLabels().run(phishing_dataset)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">

    <script
        src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js"
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        }
    </script>
    <h4><b>Conflicting Labels</b></h4><p>Find samples which have the exact same features' values but different labels.</p><h5><b>Additional Outputs</b></h5><div>Each row in the table shows an example of a data sample and the its observed labels as found in the dataset. Showing top 5 of 17</div><table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>urlLength</th>
          <th>numDigits</th>
          <th>numParams</th>
          <th>num_%20</th>
          <th>num_@</th>
          <th>bodyLength</th>
          <th>numTitles</th>
          <th>numImages</th>
          <th>numLinks</th>
          <th>specialChars</th>
        </tr>
        <tr>
          <th>Observed Labels</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>(0, 1)</th>
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          <td>6</td>
          <td>0</td>
          <td>0</td>
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        </tr>
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    </table>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 44-45

We can also check label ambiguity on a subset of the features:

.. GENERATED FROM PYTHON SOURCE LINES 45-48

.. code-block:: default


    ConflictingLabels(n_to_show=1).run(phishing_dataset)






.. raw:: html

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            console.log('requirejs is not present');
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    </script>
    <h4><b>Conflicting Labels</b></h4><p>Find samples which have the exact same features' values but different labels.</p><h5><b>Additional Outputs</b></h5><div>Each row in the table shows an example of a data sample and the its observed labels as found in the dataset. Showing top 1 of 17</div><style type="text/css">
    #T_244c9 table {
      text-align: left;
      white-space: pre-wrap;
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          <th id="T_244c9_level0_col0" class="col_heading level0 col0" >urlLength</th>
          <th id="T_244c9_level0_col1" class="col_heading level0 col1" >numDigits</th>
          <th id="T_244c9_level0_col2" class="col_heading level0 col2" >numParams</th>
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          <th id="T_244c9_level0_col4" class="col_heading level0 col4" >num_@</th>
          <th id="T_244c9_level0_col5" class="col_heading level0 col5" >bodyLength</th>
          <th id="T_244c9_level0_col6" class="col_heading level0 col6" >numTitles</th>
          <th id="T_244c9_level0_col7" class="col_heading level0 col7" >numImages</th>
          <th id="T_244c9_level0_col8" class="col_heading level0 col8" >numLinks</th>
          <th id="T_244c9_level0_col9" class="col_heading level0 col9" >specialChars</th>
        </tr>
        <tr>
          <th class="index_name level0" >Observed Labels</th>
          <th class="blank col0" >&nbsp;</th>
          <th class="blank col1" >&nbsp;</th>
          <th class="blank col2" >&nbsp;</th>
          <th class="blank col3" >&nbsp;</th>
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    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 49-52

.. code-block:: default


    ConflictingLabels(columns=['urlLength', 'numDigits']).run(phishing_dataset)






.. raw:: html

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    <h4><b>Conflicting Labels</b></h4><p>Find samples which have the exact same features' values but different labels.</p><h5><b>Additional Outputs</b></h5><div>Each row in the table shows an example of a data sample and the its observed labels as found in the dataset. Showing top 5 of 78</div><table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
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    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 53-58

Define a Condition
==================
Now, we define a condition that enforces that the ratio of samples with conflicting labels
should be 0. A condition is deepchecks' way to validate model and data quality,
and let you know if anything goes wrong.

.. GENERATED FROM PYTHON SOURCE LINES 58-63

.. code-block:: default


    check = ConflictingLabels()
    check.add_condition_ratio_of_conflicting_labels_not_greater_than(0)
    result = check.run(phishing_dataset)
    result.show(show_additional_outputs=False)





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

   **Total running time of the script:** ( 0 minutes  3.578 seconds)


.. _sphx_glr_download_checks_gallery_tabular_integrity_plot_conflicting_labels.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_conflicting_labels.py <plot_conflicting_labels.py>`



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

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


.. only:: html

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

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