transformer
==============================================================

.. py:module:: evalml.pipelines.components.transformers.transformer

.. autoapi-nested-parse::

   A component that may or may not need fitting that transforms data. These components are used before an estimator.



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

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

.. autoapisummary::

   evalml.pipelines.components.transformers.transformer.Transformer





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: Transformer(parameters=None, component_obj=None, random_seed=0, **kwargs)



   A component that may or may not need fitting that transforms data. These components are used before an estimator.

   To implement a new Transformer, define your own class which is a subclass of Transformer, including
   a name and a list of acceptable ranges for any parameters to be tuned during the automl search (hyperparameters).
   Define an `__init__` method which sets up any necessary state and objects. Make sure your `__init__` only
   uses standard keyword arguments and calls `super().__init__()` with a parameters dict. You may also override the
   `fit`, `transform`, `fit_transform` and other methods in this class if appropriate.

   To see some examples, check out the definitions of any Transformer component.

   :param parameters: Dictionary of parameters for the component. Defaults to None.
   :type parameters: dict
   :param component_obj: Third-party objects useful in component implementation. Defaults to None.
   :type component_obj: obj
   :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

      * - **modifies_features**
        - True
      * - **modifies_target**
        - False
      * - **training_only**
        - False


   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.pipelines.components.transformers.transformer.Transformer.clone
      evalml.pipelines.components.transformers.transformer.Transformer.default_parameters
      evalml.pipelines.components.transformers.transformer.Transformer.describe
      evalml.pipelines.components.transformers.transformer.Transformer.fit
      evalml.pipelines.components.transformers.transformer.Transformer.fit_transform
      evalml.pipelines.components.transformers.transformer.Transformer.load
      evalml.pipelines.components.transformers.transformer.Transformer.name
      evalml.pipelines.components.transformers.transformer.Transformer.needs_fitting
      evalml.pipelines.components.transformers.transformer.Transformer.parameters
      evalml.pipelines.components.transformers.transformer.Transformer.save
      evalml.pipelines.components.transformers.transformer.Transformer.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 component to data.

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

      :returns: self

      :raises MethodPropertyNotFoundError: If component does not have a fit method or a component_obj that implements fit.


   .. 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

      :returns: Transformed X.
      :rtype: pd.DataFrame

      :raises MethodPropertyNotFoundError: If transformer does not have a transform method or a component_obj that implements transform.


   .. 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:: name(cls)
      :property:

      Returns string name of this component.


   .. 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)
      :abstractmethod:

      Transforms data X.

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

      :returns: Transformed X
      :rtype: pd.DataFrame

      :raises MethodPropertyNotFoundError: If transformer does not have a transform method or a component_obj that implements transform.