component_base
====================================================

.. py:module:: evalml.pipelines.components.component_base

.. autoapi-nested-parse::

   Base class for all components.



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

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

.. autoapisummary::

   evalml.pipelines.components.component_base.ComponentBase





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



   Base class for all components.

   :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




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.pipelines.components.component_base.ComponentBase.clone
      evalml.pipelines.components.component_base.ComponentBase.default_parameters
      evalml.pipelines.components.component_base.ComponentBase.describe
      evalml.pipelines.components.component_base.ComponentBase.fit
      evalml.pipelines.components.component_base.ComponentBase.load
      evalml.pipelines.components.component_base.ComponentBase.modifies_features
      evalml.pipelines.components.component_base.ComponentBase.modifies_target
      evalml.pipelines.components.component_base.ComponentBase.name
      evalml.pipelines.components.component_base.ComponentBase.needs_fitting
      evalml.pipelines.components.component_base.ComponentBase.parameters
      evalml.pipelines.components.component_base.ComponentBase.save
      evalml.pipelines.components.component_base.ComponentBase.training_only

   .. 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:: 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:: modifies_features(cls)
      :property:

      Returns whether this component modifies (subsets or transforms) the features variable during transform.

      For Estimator objects, this attribute determines if the return
      value from `predict` or `predict_proba` should be used as
      features or targets.


   .. py:method:: modifies_target(cls)
      :property:

      Returns whether this component modifies (subsets or transforms) the target variable during transform.

      For Estimator objects, this attribute determines if the return
      value from `predict` or `predict_proba` should be used as
      features or targets.


   .. 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:: training_only(cls)
      :property:

      Returns whether or not this component should be evaluated during training-time only, or during both training and prediction time.