simple_imputer
==========================================================================

.. py:module:: evalml.pipelines.components.transformers.imputers.simple_imputer

.. autoapi-nested-parse::

   Component that imputes missing data according to a specified imputation strategy.



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

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

.. autoapisummary::

   evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: SimpleImputer(impute_strategy='most_frequent', fill_value=None, random_seed=0, **kwargs)



   Imputes missing data according to a specified imputation strategy.  Natural language columns are ignored.

   :param impute_strategy: Impute strategy to use. Valid values include "mean", "median", "most_frequent", "constant" for
                           numerical data, and "most_frequent", "constant" for object data types.
   :type impute_strategy: string
   :param fill_value: When impute_strategy == "constant", fill_value is used to replace missing data.
                      Defaults to 0 when imputing numerical data and "missing_value" for strings or object data types.
   :type fill_value: string
   :param random_seed: Seed for the random number generator. Defaults to 0.
   :type random_seed: int


   **Attributes**

   .. list-table::
      :widths: 15 85
      :header-rows: 0

      * - **hyperparameter_ranges**
        - {    "impute_strategy": ["mean", "median", "most_frequent"]}
      * - **modifies_features**
        - True
      * - **modifies_target**
        - False
      * - **name**
        - Simple Imputer
      * - **training_only**
        - False


   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer.clone
      evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer.default_parameters
      evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer.describe
      evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer.fit
      evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer.fit_transform
      evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer.load
      evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer.needs_fitting
      evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer.parameters
      evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer.save
      evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer.transform

   .. py:method:: clone(self)

      Constructs a new component with the same parameters and random state.

      :returns: A new instance of this component with identical parameters and random state.


   .. py:method:: default_parameters(cls)

      Returns the default parameters for this component.

      Our convention is that Component.default_parameters == Component().parameters.

      :returns: Default parameters for this component.
      :rtype: dict


   .. py:method:: describe(self, print_name=False, return_dict=False)

      Describe a component and its parameters.

      :param print_name: whether to print name of component
      :type print_name: bool, optional
      :param return_dict: whether to return description as dictionary in the format {"name": name, "parameters": parameters}
      :type return_dict: bool, optional

      :returns: Returns dictionary if return_dict is True, else None.
      :rtype: None or dict


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

      Fits imputer to data. 'None' values are converted to np.nan before imputation and are treated as the same.

      :param X: the input training data of shape [n_samples, n_features]
      :type X: pd.DataFrame or np.ndarray
      :param y: the target training data of length [n_samples]
      :type y: pd.Series, optional

      :returns: self

      :raises ValueError: if the SimpleImputer receives a dataframe with both Boolean and Categorical data.


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

      Fits on X and transforms X.

      :param X: Data to fit and transform
      :type X: pd.DataFrame
      :param y: Target data.
      :type y: pd.Series, optional

      :returns: Transformed X
      :rtype: pd.DataFrame


   .. py:method:: load(file_path)
      :staticmethod:

      Loads component at file path.

      :param file_path: Location to load file.
      :type file_path: str

      :returns: ComponentBase object


   .. py:method:: needs_fitting(self)

      Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

      This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

      :returns: True.


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

      Returns the parameters which were used to initialize the component.


   .. py:method:: save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)

      Saves component at file path.

      :param file_path: Location to save file.
      :type file_path: str
      :param pickle_protocol: The pickle data stream format.
      :type pickle_protocol: int


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

      Transforms input by imputing missing values. 'None' and np.nan values are treated as the same.

      :param X: Data to transform.
      :type X: pd.DataFrame
      :param y: Ignored.
      :type y: pd.Series, optional

      :returns: Transformed X
      :rtype: pd.DataFrame