training_validation_split
=======================================================================

.. py:module:: evalml.preprocessing.data_splitters.training_validation_split

.. autoapi-nested-parse::

   Training Validation Split class.



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

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

.. autoapisummary::

   evalml.preprocessing.data_splitters.training_validation_split.TrainingValidationSplit





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: TrainingValidationSplit(test_size=None, train_size=None, shuffle=False, stratify=None, random_seed=0)



   Split the training data into training and validation sets.

   :param test_size: What percentage of data points should be included in the validation
                     set. Defalts to the complement of `train_size` if `train_size` is set, and 0.25 otherwise.
   :type test_size: float
   :param train_size: What percentage of data points should be included in the training set.
                      Defaults to the complement of `test_size`
   :type train_size: float
   :param shuffle: Whether to shuffle the data before splitting. Defaults to False.
   :type shuffle: boolean
   :param stratify: Splits the data in a stratified fashion, using this argument as class labels.
                    Defaults to None.
   :type stratify: list
   :param random_seed: The seed to use for random sampling. Defaults to 0.
   :type random_seed: int

   .. rubric:: Examples

   >>> import numpy as np
   >>> import pandas as pd
   ...
   >>> X = pd.DataFrame([i for i in range(10)], columns=["First"])
   >>> y = pd.Series([i for i in range(10)])
   ...
   >>> tv_split = TrainingValidationSplit()
   >>> split_ = next(tv_split.split(X, y))
   >>> assert (split_[0] == np.array([0, 1, 2, 3, 4, 5, 6])).all()
   >>> assert (split_[1] == np.array([7, 8, 9])).all()
   ...
   ...
   >>> tv_split = TrainingValidationSplit(test_size=0.5)
   >>> split_ = next(tv_split.split(X, y))
   >>> assert (split_[0] == np.array([0, 1, 2, 3, 4])).all()
   >>> assert (split_[1] == np.array([5, 6, 7, 8, 9])).all()
   ...
   ...
   >>> tv_split = TrainingValidationSplit(shuffle=True)
   >>> split_ = next(tv_split.split(X, y))
   >>> assert (split_[0] == np.array([9, 1, 6, 7, 3, 0, 5])).all()
   >>> assert (split_[1] == np.array([2, 8, 4])).all()
   ...
   ...
   >>> y = pd.Series([i % 3 for i in range(10)])
   >>> tv_split = TrainingValidationSplit(shuffle=True, stratify=y)
   >>> split_ = next(tv_split.split(X, y))
   >>> assert (split_[0] == np.array([1, 9, 3, 2, 8, 6, 7])).all()
   >>> assert (split_[1] == np.array([0, 4, 5])).all()




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.preprocessing.data_splitters.training_validation_split.TrainingValidationSplit.get_metadata_routing
      evalml.preprocessing.data_splitters.training_validation_split.TrainingValidationSplit.get_n_splits
      evalml.preprocessing.data_splitters.training_validation_split.TrainingValidationSplit.is_cv
      evalml.preprocessing.data_splitters.training_validation_split.TrainingValidationSplit.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()
      :staticmethod:

      Return the number of splits of this object.

      :returns: Always returns 1.
      :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)

      Divide the data into training and testing sets.

      :param X: Dataframe of points to split
      :type X: pd.DataFrame
      :param y: Series of points to split
      :type y: pd.Series

      :returns: Indices to split data into training and test set
      :rtype: list