target_distribution_data_check
===========================================================

.. py:module:: evalml.data_checks.target_distribution_data_check

.. autoapi-nested-parse::

   Data check that checks if the target data contains certain distributions that may need to be transformed prior training to improve model performance.



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

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

.. autoapisummary::

   evalml.data_checks.target_distribution_data_check.TargetDistributionDataCheck





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: TargetDistributionDataCheck



   Check if the target data contains certain distributions that may need to be transformed prior training to improve model performance. Uses the Shapiro-Wilks test when the dataset is <=5000 samples, otherwise uses Jarque-Bera.




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.data_checks.target_distribution_data_check.TargetDistributionDataCheck.name
      evalml.data_checks.target_distribution_data_check.TargetDistributionDataCheck.validate

   .. py:method:: name(cls)

      Return a name describing the data check.


   .. py:method:: validate(self, X, y)

      Check if the target data has a certain distribution.

      :param X: Features. Ignored.
      :type X: pd.DataFrame, np.ndarray
      :param y: Target data to check for underlying distributions.
      :type y: pd.Series, np.ndarray

      :returns: List with DataCheckErrors if certain distributions are found in the target data.
      :rtype: dict (DataCheckError)

      .. rubric:: Examples

      >>> import pandas as pd

      Targets that exhibit a lognormal distribution will raise a warning for the user to transform the target.

      >>> y = [0.946, 0.972, 1.154, 0.954, 0.969, 1.222, 1.038, 0.999, 0.973, 0.897]
      >>> target_check = TargetDistributionDataCheck()
      >>> assert target_check.validate(None, y) == [
      ...     {
      ...         "message": "Target may have a lognormal distribution.",
      ...         "data_check_name": "TargetDistributionDataCheck",
      ...         "level": "warning",
      ...         "code": "TARGET_LOGNORMAL_DISTRIBUTION",
      ...         "details": {"normalization_method": "shapiro", "statistic": 0.8, "p-value": 0.045, "columns": None, "rows": None},
      ...         "action_options": [
      ...             {
      ...                 "code": "TRANSFORM_TARGET",
      ...                 "data_check_name": "TargetDistributionDataCheck",
      ...                 "parameters": {},
      ...                 "metadata": {
      ...                     "transformation_strategy": "lognormal",
      ...                     "is_target": True,
      ...                     "columns": None,
      ...                     "rows": None
      ...                 }
      ...             }
      ...         ]
      ...     }
      ... ]
      ...
      >>> y = pd.Series([1, 1, 1, 2, 2, 3, 4, 4, 5, 5, 5])
      >>> assert target_check.validate(None, y) == []
      ...
      ...
      >>> y = pd.Series(pd.date_range("1/1/21", periods=10))
      >>> assert target_check.validate(None, y) == [
      ...     {
      ...         "message": "Target is unsupported datetime type. Valid Woodwork logical types include: integer, double",
      ...         "data_check_name": "TargetDistributionDataCheck",
      ...         "level": "error",
      ...         "details": {"columns": None, "rows": None, "unsupported_type": "datetime"},
      ...         "code": "TARGET_UNSUPPORTED_TYPE",
      ...         "action_options": []
      ...     }
      ... ]