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

.. only:: html

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

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

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

.. _sphx_glr_checks_gallery_tabular_data_integrity_plot_outlier_sample_detection.py:


Outlier Sample Detection
***************

This notebooks provides an overview for using and understanding the Outlier Sample Detection check.

**Structure:**

* `How deepchecks detects outliers <#How-deepchecks-detects-outliers>`__
* `Prepare data <#prepare-data>`__
* `Run the check <#run-the-check>`__
* `Define a condition <#define-a-condition>`__

How deepchecks detects outliers
========================
Outlier Sample Detection searches for outliers samples (jointly across all features) using the LoOP algorithm.
The LoOP algorithm is a robust method for detecting outliers in a dataset across multiple variables by comparing
the density in the area of a sample with the densities in the areas of its nearest neighbors
(see `link <https://www.dbs.ifi.lmu.de/Publikationen/Papers/LoOP1649.pdf>`_ for further details).

LoOP relies on a distance matrix. In our implementation we use the Gower distance that averages the distances
per feature between samples. For numeric features it calculates the absolute distance divided by the range of the
feature and for categorical features it is an indicator whether the values are the same
(see `link <https://www.jstor.org/stable/2528823>`_ for further details).

.. GENERATED FROM PYTHON SOURCE LINES 29-31

Imports
=======

.. GENERATED FROM PYTHON SOURCE LINES 31-38

.. code-block:: default


    import pandas as pd
    from sklearn.datasets import load_iris

    from deepchecks.tabular import Dataset
    from deepchecks.tabular.checks import OutlierSampleDetection








.. GENERATED FROM PYTHON SOURCE LINES 39-41

Prepare data
=========

.. GENERATED FROM PYTHON SOURCE LINES 41-45

.. code-block:: default


    iris = pd.DataFrame(load_iris().data)
    iris.describe()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
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          <th>count</th>
          <td>150.000000</td>
          <td>150.000000</td>
          <td>150.000000</td>
          <td>150.000000</td>
        </tr>
        <tr>
          <th>mean</th>
          <td>5.843333</td>
          <td>3.057333</td>
          <td>3.758000</td>
          <td>1.199333</td>
        </tr>
        <tr>
          <th>std</th>
          <td>0.828066</td>
          <td>0.435866</td>
          <td>1.765298</td>
          <td>0.762238</td>
        </tr>
        <tr>
          <th>min</th>
          <td>4.300000</td>
          <td>2.000000</td>
          <td>1.000000</td>
          <td>0.100000</td>
        </tr>
        <tr>
          <th>25%</th>
          <td>5.100000</td>
          <td>2.800000</td>
          <td>1.600000</td>
          <td>0.300000</td>
        </tr>
        <tr>
          <th>50%</th>
          <td>5.800000</td>
          <td>3.000000</td>
          <td>4.350000</td>
          <td>1.300000</td>
        </tr>
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          <td>6.400000</td>
          <td>3.300000</td>
          <td>5.100000</td>
          <td>1.800000</td>
        </tr>
        <tr>
          <th>max</th>
          <td>7.900000</td>
          <td>4.400000</td>
          <td>6.900000</td>
          <td>2.500000</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 46-47

Add an outlier:

.. GENERATED FROM PYTHON SOURCE LINES 47-53

.. code-block:: default


    outlier_sample = [1, 10, 50, 100]
    iris.loc[len(iris.index)] = outlier_sample
    print(iris.tail())
    modified_iris = Dataset(iris, cat_features=[])





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

           0     1     2      3
    146  6.3   2.5   5.0    1.9
    147  6.5   3.0   5.2    2.0
    148  6.2   3.4   5.4    2.3
    149  5.9   3.0   5.1    1.8
    150  1.0  10.0  50.0  100.0




.. GENERATED FROM PYTHON SOURCE LINES 54-57

Run the Check
=============
We define the nearest_neighbors_percent and the extent parameters for the LoOP algorithm.

.. GENERATED FROM PYTHON SOURCE LINES 57-61

.. code-block:: default


    check = OutlierSampleDetection(nearest_neighbors_percent=0.01, extent_parameter=3)
    check.run(modified_iris)






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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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.. GENERATED FROM PYTHON SOURCE LINES 62-65

Define a condition
==================
Now, we define a condition that enforces that the ratio of outlier samples in out dataset is below 0.001.

.. GENERATED FROM PYTHON SOURCE LINES 65-68

.. code-block:: default


    check = OutlierSampleDetection()
    check.add_condition_outlier_ratio_not_greater_than(max_outliers_ratio=0.001, outlier_score_threshold=0.9)
    check.run(modified_iris)




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            "value": "<div><span>\n                    The Outlier Probability Score is calculated by the LoOP algorithm which measures the local deviation\n                    of density of a given sample with respect to its neighbors. These outlier scores are directly\n                    interpretable as a probability of an object being an outlier (see\n                    <a href=\"https://www.dbs.ifi.lmu.de/Publikationen/Papers/LoOP1649.pdf\"\n                    target=\"_blank\" rel=\"noopener noreferrer\">link</a> for more information).<br><br>\n                    </span></div>"
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.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.200 seconds)


.. _sphx_glr_download_checks_gallery_tabular_data_integrity_plot_outlier_sample_detection.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_outlier_sample_detection.py <plot_outlier_sample_detection.py>`



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

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


.. only:: html

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

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