sk_splitters
==========================================================

.. py:module:: evalml.preprocessing.data_splitters.sk_splitters

.. autoapi-nested-parse::

   SKLearn data splitter wrapper classes.



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

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

.. autoapisummary::

   evalml.preprocessing.data_splitters.sk_splitters.KFold
   evalml.preprocessing.data_splitters.sk_splitters.StratifiedKFold





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: KFold(n_splits=5, *, shuffle=False, random_state=None)



   Wrapper class for sklearn's KFold splitter.




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.preprocessing.data_splitters.sk_splitters.KFold.get_metadata_routing
      evalml.preprocessing.data_splitters.sk_splitters.KFold.get_n_splits
      evalml.preprocessing.data_splitters.sk_splitters.KFold.is_cv
      evalml.preprocessing.data_splitters.sk_splitters.KFold.split

   .. py:method:: get_metadata_routing(self)

      Get metadata routing of this object.

      Please check :ref:`User Guide <metadata_routing>` on how the routing
      mechanism works.

      :returns: **routing** -- A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating
                routing information.
      :rtype: MetadataRequest


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

      Returns the number of splitting iterations in the cross-validator

      :param X: Always ignored, exists for compatibility.
      :type X: object
      :param y: Always ignored, exists for compatibility.
      :type y: object
      :param groups: Always ignored, exists for compatibility.
      :type groups: object

      :returns: **n_splits** -- Returns the number of splitting iterations in the cross-validator.
      :rtype: int


   .. py:method:: is_cv(self)
      :property:

      Returns whether or not the data splitter is a cross-validation data splitter.

      :returns: If the splitter is a cross-validation data splitter
      :rtype: bool


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

      Generate indices to split data into training and test set.

      :param X: Training data, where `n_samples` is the number of samples
                and `n_features` is the number of features.
      :type X: array-like of shape (n_samples, n_features)
      :param y: The target variable for supervised learning problems.
      :type y: array-like of shape (n_samples,), default=None
      :param groups: Group labels for the samples used while splitting the dataset into
                     train/test set.
      :type groups: array-like of shape (n_samples,), default=None

      :Yields: * **train** (*ndarray*) -- The training set indices for that split.
               * **test** (*ndarray*) -- The testing set indices for that split.



.. py:class:: StratifiedKFold(n_splits=5, *, shuffle=False, random_state=None)



   Wrapper class for sklearn's Stratified KFold splitter.




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.preprocessing.data_splitters.sk_splitters.StratifiedKFold.get_metadata_routing
      evalml.preprocessing.data_splitters.sk_splitters.StratifiedKFold.get_n_splits
      evalml.preprocessing.data_splitters.sk_splitters.StratifiedKFold.is_cv
      evalml.preprocessing.data_splitters.sk_splitters.StratifiedKFold.split

   .. py:method:: get_metadata_routing(self)

      Get metadata routing of this object.

      Please check :ref:`User Guide <metadata_routing>` on how the routing
      mechanism works.

      :returns: **routing** -- A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating
                routing information.
      :rtype: MetadataRequest


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

      Returns the number of splitting iterations in the cross-validator

      :param X: Always ignored, exists for compatibility.
      :type X: object
      :param y: Always ignored, exists for compatibility.
      :type y: object
      :param groups: Always ignored, exists for compatibility.
      :type groups: object

      :returns: **n_splits** -- Returns the number of splitting iterations in the cross-validator.
      :rtype: int


   .. py:method:: is_cv(self)
      :property:

      Returns whether or not the data splitter is a cross-validation data splitter.

      :returns: If the splitter is a cross-validation data splitter
      :rtype: bool


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

      Generate indices to split data into training and test set.

      :param X: Training data, where `n_samples` is the number of samples
                and `n_features` is the number of features.

                Note that providing ``y`` is sufficient to generate the splits and
                hence ``np.zeros(n_samples)`` may be used as a placeholder for
                ``X`` instead of actual training data.
      :type X: array-like of shape (n_samples, n_features)
      :param y: The target variable for supervised learning problems.
                Stratification is done based on the y labels.
      :type y: array-like of shape (n_samples,)
      :param groups: Always ignored, exists for compatibility.
      :type groups: object

      :Yields: * **train** (*ndarray*) -- The training set indices for that split.
               * **test** (*ndarray*) -- The testing set indices for that split.

      .. rubric:: Notes

      Randomized CV splitters may return different results for each call of
      split. You can make the results identical by setting `random_state`
      to an integer.