.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "checks_gallery/tabular/integrity/plot_string_mismatch_comparison.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_string_mismatch_comparison.py>`
        to download the full example code

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

.. _sphx_glr_checks_gallery_tabular_integrity_plot_string_mismatch_comparison.py:


String Mismatch Comparison
**************************

This page provides an overview for using and understanding the "String Mismatch Comparison" check.

**Structure:**

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


What is the purpose of the check?
=================================
The check compares the same categorical column within train and test and checks whether there are variants of similar
strings that exists only in test and not in train.
Finding those mismatches is helpful to prevent errors when inferring on the test data. For example, in train data we
have category 'New York', and  in our test data we have 'new york'. We would like to be acknowledged that the test
data contain a new variant of the train data, so we can address the problem.

How String Mismatch Defined?
----------------------------
To recognize string mismatch, we transform each string to it's base form. The base form is the string with only its
alphanumeric characters in lowercase. (For example "Cat-9?!" base form is "cat9"). If two strings have the same base
form, they are considered to be the same.

.. GENERATED FROM PYTHON SOURCE LINES 29-32

.. code-block:: default


    import pandas as pd








.. GENERATED FROM PYTHON SOURCE LINES 33-35

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

.. GENERATED FROM PYTHON SOURCE LINES 35-44

.. code-block:: default

    from deepchecks.tabular.checks import StringMismatchComparison

    data = {'col1': ['Deep', 'deep', 'deep!!!', 'earth', 'foo', 'bar', 'foo?']}
    compared_data = {'col1': ['Deep', 'deep', '$deeP$', 'earth', 'foo', 'bar', 'foo?', '?deep']}

    check = StringMismatchComparison()
    result = check.run(pd.DataFrame(data=data), pd.DataFrame(data=compared_data))
    result






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    <h4><b>String Mismatch Comparison</b></h4><p>Detect different variants of string categories between the same categorical column in two datasets.</p><h5><b>Additional Outputs</b></h5><div>* showing only the top 10 columns, you can change it using n_top_columns param</div><style type="text/css">
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          <th id="T_953ed_level0_row1" class="row_heading level0 row1" >Variants only in test</th>
          <td id="T_953ed_row1_col0" class="data row1 col0" >['?deep', '$deeP$']</td>
        </tr>
        <tr>
          <th id="T_953ed_level0_row2" class="row_heading level0 row2" >% Unique variants out of all dataset samples (count)</th>
          <td id="T_953ed_row2_col0" class="data row2 col0" >25% (2)</td>
        </tr>
        <tr>
          <th id="T_953ed_level0_row3" class="row_heading level0 row3" >Variants only in train</th>
          <td id="T_953ed_row3_col0" class="data row3 col0" >['deep!!!']</td>
        </tr>
        <tr>
          <th id="T_953ed_level0_row4" class="row_heading level0 row4" >% Unique variants out of all baseline samples (count)</th>
          <td id="T_953ed_row4_col0" class="data row4 col0" >14.29% (1)</td>
        </tr>
      </tbody>
    </table>

    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 45-47

Define a Condition
==================

.. GENERATED FROM PYTHON SOURCE LINES 47-51

.. code-block:: default


    check = StringMismatchComparison().add_condition_no_new_variants()
    result = check.run(pd.DataFrame(data=data), pd.DataFrame(data=compared_data))
    result.show(show_additional_outputs=False)





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        ~ Copyright (C) 2021-2022 Deepchecks (https://www.deepchecks.com)
        ~
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        ~ Deepchecks is distributed under the terms of the GNU Affero General
        ~ Public License (version 3 or later).
        ~ You should have received a copy of the GNU Affero General Public License
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   **Total running time of the script:** ( 0 minutes  0.059 seconds)


.. _sphx_glr_download_checks_gallery_tabular_integrity_plot_string_mismatch_comparison.py:


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 .. container:: sphx-glr-footer
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     :download:`Download Jupyter notebook: plot_string_mismatch_comparison.ipynb <plot_string_mismatch_comparison.ipynb>`


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