outliers_data_check
================================================

.. py:module:: evalml.data_checks.outliers_data_check

.. autoapi-nested-parse::

   Data check that checks if there are any outliers in input data by using IQR to determine score anomalies.



Module Contents
---------------

Classes Summary
~~~~~~~~~~~~~~~

.. autoapisummary::

   evalml.data_checks.outliers_data_check.OutliersDataCheck





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: OutliersDataCheck



   Checks if there are any outliers in input data by using IQR to determine score anomalies.

   Columns with score anomalies are considered to contain outliers.




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.data_checks.outliers_data_check.OutliersDataCheck.get_boxplot_data
      evalml.data_checks.outliers_data_check.OutliersDataCheck.name
      evalml.data_checks.outliers_data_check.OutliersDataCheck.validate

   .. py:method:: get_boxplot_data(data_)
      :staticmethod:

      Returns box plot information for the given data.

      :param data_: Input data.
      :type data_: pd.Series, np.ndarray

      :returns: A payload of box plot statistics.
      :rtype: dict

      .. rubric:: Examples

      >>> import pandas as pd
      ...
      >>> df = pd.DataFrame({
      ...     "x": [1, 2, 3, 4, 5],
      ...     "y": [6, 7, 8, 9, 10],
      ...     "z": [-1, -2, -3, -1201, -4]
      ... })
      >>> box_plot_data = OutliersDataCheck.get_boxplot_data(df["z"])
      >>> box_plot_data["score"] = round(box_plot_data["score"], 2)
      >>> assert box_plot_data == {
      ...     "score": 0.89,
      ...     "pct_outliers": 0.2,
      ...     "values": {"q1": -4.0,
      ...                "median": -3.0,
      ...                "q3": -2.0,
      ...                "low_bound": -7.0,
      ...                "high_bound": -1.0,
      ...                "low_values": [-1201],
      ...                "high_values": [],
      ...                "low_indices": [3],
      ...                "high_indices": []}
      ...     }


   .. py:method:: name(cls)

      Return a name describing the data check.


   .. py:method:: validate(self, X, y=None)

      Check if there are any outliers in a dataframe by using IQR to determine column anomalies. Column with anomalies are considered to contain outliers.

      :param X: Input features.
      :type X: pd.DataFrame, np.ndarray
      :param y: Ignored. Defaults to None.
      :type y: pd.Series, np.ndarray

      :returns: A dictionary with warnings if any columns have outliers.
      :rtype: dict

      .. rubric:: Examples

      >>> import pandas as pd

      The column "z" has an outlier so a warning is added to alert the user of its location.

      >>> df = pd.DataFrame({
      ...     "x": [1, 2, 3, 4, 5],
      ...     "y": [6, 7, 8, 9, 10],
      ...     "z": [-1, -2, -3, -1201, -4]
      ... })
      ...
      >>> outliers_check = OutliersDataCheck()
      >>> assert outliers_check.validate(df) == [
      ...     {
      ...         "message": "Column(s) 'z' are likely to have outlier data.",
      ...         "data_check_name": "OutliersDataCheck",
      ...         "level": "warning",
      ...         "code": "HAS_OUTLIERS",
      ...         "details": {"columns": ["z"], "rows": [3], "column_indices": {"z": [3]}},
      ...         "action_options": [
      ...             {
      ...                 "code": "DROP_ROWS",
      ...                  "data_check_name": "OutliersDataCheck",
      ...                  "parameters": {},
      ...                  "metadata": {"rows": [3], "columns": None}
      ...             }
      ...         ]
      ...     }
      ... ]