.. 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:


.. _plot_tabular_outlier_sample_detection:

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

This notebook 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 the `LoOp paper <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 for whether the values are the same
(see `link <https://www.jstor.org/stable/2528823>`__ for further details).

.. GENERATED FROM PYTHON SOURCE LINES 32-34

Imports
=======

.. GENERATED FROM PYTHON SOURCE LINES 34-41

.. 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 42-44

Prepare data
============

.. GENERATED FROM PYTHON SOURCE LINES 44-48

.. 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>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
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          <th>0</th>
          <th>1</th>
          <th>2</th>
          <th>3</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <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>
        <tr>
          <th>75%</th>
          <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 49-50

Add an outlier:

.. GENERATED FROM PYTHON SOURCE LINES 50-56

.. 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

 .. 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 57-60

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

.. GENERATED FROM PYTHON SOURCE LINES 60-64

.. 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
        ~ along with Deepchecks.  If not, see <http://www.gnu.org/licenses/>.
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.. GENERATED FROM PYTHON SOURCE LINES 65-68

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 68-71

.. code-block:: default


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




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"@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5064f27fd3b24328b468b4ce1806e53b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0b753a34254c4d298c5d7108dd6d1390": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_240797b25b79423bb1fd53391eaf1f83", "style": "IPY_MODEL_d3eb5563cb21461fb0ed099db0378f3e", "value": "<p>Detects outliers in a dataset using the LoOP algorithm. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/data_integrity/plot_outlier_sample_detection.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "240797b25b79423bb1fd53391eaf1f83": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d3eb5563cb21461fb0ed099db0378f3e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "af1ebe12e0114cef8f73b2eb7e2f803d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b411ff7505ca4bbdad38a2722617fc1b", "style": "IPY_MODEL_957ae9b031904404adfcb19b5753ec5f", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_5edee_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5edee_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5edee_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5edee_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5edee_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5edee_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_5edee_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_5edee_row0_col1\" class=\"data row0 col1\" >Ratio of samples exceeding the outlier score threshold 0.9 is less or equal to 0.1%</td>\n      <td id=\"T_5edee_row0_col2\" class=\"data row0 col2\" >0.6% of dataset samples above outlier threshold</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b411ff7505ca4bbdad38a2722617fc1b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "957ae9b031904404adfcb19b5753ec5f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c794cef5136346aa87d8196849f0f110": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_10ceedf4cb1c4fa5bb6c8cb68b4b9b37", "IPY_MODEL_f874200f7db54e9bb070d1a8ce972efd", "IPY_MODEL_9148fb894dcc47ae879b3ecf2e2b2766"], "layout": "IPY_MODEL_67107dc708b9431ab7959c1e05c5118f"}}, "10ceedf4cb1c4fa5bb6c8cb68b4b9b37": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a6e3e257785a4b19ad7999a13fd41e25", "style": "IPY_MODEL_40cb080883ef4d598091144a60b65a61", "value": "<h5><b>Additional Outputs</b></h5>"}}, "a6e3e257785a4b19ad7999a13fd41e25": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "40cb080883ef4d598091144a60b65a61": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f874200f7db54e9bb070d1a8ce972efd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b7b1de661346489b89bdfa6f34a5f8d0", "style": "IPY_MODEL_251e659743344be4a984ebc3776ce632", "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>"}}, "b7b1de661346489b89bdfa6f34a5f8d0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "251e659743344be4a984ebc3776ce632": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9148fb894dcc47ae879b3ecf2e2b2766": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_311c53fc13794b62894075a0b27dadf2", "style": "IPY_MODEL_1f0db9ab97144a6783a3a464a8992252", "value": "<style type=\"text/css\">\n#T_0b58a_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0b58a_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0b58a_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0b58a_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0b58a_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_0b58a_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >Outlier Probability Score</th>\n      <th class=\"col_heading level0 col1\" >0</th>\n      <th class=\"col_heading level0 col2\" >1</th>\n      <th class=\"col_heading level0 col3\" >2</th>\n      <th class=\"col_heading level0 col4\" >3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_0b58a_level0_row0\" class=\"row_heading level0 row0\" >150</th>\n      <td id=\"T_0b58a_row0_col0\" class=\"data row0 col0\" >1.00</td>\n      <td id=\"T_0b58a_row0_col1\" class=\"data row0 col1\" >1.00</td>\n      <td id=\"T_0b58a_row0_col2\" class=\"data row0 col2\" >10.00</td>\n      <td id=\"T_0b58a_row0_col3\" class=\"data row0 col3\" >50.00</td>\n      <td id=\"T_0b58a_row0_col4\" class=\"data row0 col4\" >100.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b58a_level0_row1\" class=\"row_heading level0 row1\" >41</th>\n      <td id=\"T_0b58a_row1_col0\" class=\"data row1 col0\" >0.67</td>\n      <td id=\"T_0b58a_row1_col1\" class=\"data row1 col1\" >4.50</td>\n      <td id=\"T_0b58a_row1_col2\" class=\"data row1 col2\" >2.30</td>\n      <td id=\"T_0b58a_row1_col3\" class=\"data row1 col3\" >1.30</td>\n      <td id=\"T_0b58a_row1_col4\" class=\"data row1 col4\" >0.30</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b58a_level0_row2\" class=\"row_heading level0 row2\" >108</th>\n      <td id=\"T_0b58a_row2_col0\" class=\"data row2 col0\" >0.50</td>\n      <td id=\"T_0b58a_row2_col1\" class=\"data row2 col1\" >6.70</td>\n      <td id=\"T_0b58a_row2_col2\" class=\"data row2 col2\" >2.50</td>\n      <td id=\"T_0b58a_row2_col3\" class=\"data row2 col3\" >5.80</td>\n      <td id=\"T_0b58a_row2_col4\" class=\"data row2 col4\" >1.80</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b58a_level0_row3\" class=\"row_heading level0 row3\" >109</th>\n      <td id=\"T_0b58a_row3_col0\" class=\"data row3 col0\" >0.50</td>\n      <td id=\"T_0b58a_row3_col1\" class=\"data row3 col1\" >7.20</td>\n      <td id=\"T_0b58a_row3_col2\" class=\"data row3 col2\" >3.60</td>\n      <td id=\"T_0b58a_row3_col3\" class=\"data row3 col3\" >6.10</td>\n      <td id=\"T_0b58a_row3_col4\" class=\"data row3 col4\" >2.50</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b58a_level0_row4\" class=\"row_heading level0 row4\" >22</th>\n      <td id=\"T_0b58a_row4_col0\" class=\"data row4 col0\" >0.44</td>\n      <td id=\"T_0b58a_row4_col1\" class=\"data row4 col1\" >4.60</td>\n      <td id=\"T_0b58a_row4_col2\" class=\"data row4 col2\" >3.60</td>\n      <td id=\"T_0b58a_row4_col3\" class=\"data row4 col3\" >1.00</td>\n      <td id=\"T_0b58a_row4_col4\" class=\"data row4 col4\" >0.20</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "311c53fc13794b62894075a0b27dadf2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1f0db9ab97144a6783a3a464a8992252": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "67107dc708b9431ab7959c1e05c5118f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "93ce9d3d27d14a08a837b10ebbdd7173": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}}}
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   **Total running time of the script:** ( 0 minutes  0.128 seconds)


.. _sphx_glr_download_checks_gallery_tabular_data_integrity_plot_outlier_sample_detection.py:

.. only:: html

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


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

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


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

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