datetime_featurizer
====================================================================================

.. py:module:: evalml.pipelines.components.transformers.preprocessing.datetime_featurizer

.. autoapi-nested-parse::

   Transformer that can automatically extract features from datetime columns.



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

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

.. autoapisummary::

   evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: DateTimeFeaturizer(features_to_extract=None, encode_as_categories=False, time_index=None, random_seed=0, **kwargs)



   Transformer that can automatically extract features from datetime columns.

   :param features_to_extract: List of features to extract. Valid options include "year", "month", "day_of_week", "hour". Defaults to None.
   :type features_to_extract: list
   :param encode_as_categories: Whether day-of-week and month features should be encoded as pandas "category" dtype.
                                This allows OneHotEncoders to encode these features. Defaults to False.
   :type encode_as_categories: bool
   :param time_index: Name of the column containing the datetime information used to order the data. Ignored.
   :type time_index: str
   :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**
        - {}
      * - **modifies_features**
        - True
      * - **modifies_target**
        - False
      * - **name**
        - DateTime Featurizer
      * - **training_only**
        - False


   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer.clone
      evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer.default_parameters
      evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer.describe
      evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer.fit
      evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer.fit_transform
      evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer.get_feature_names
      evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer.load
      evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer.needs_fitting
      evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer.parameters
      evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer.save
      evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer.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)

      Fit the datetime featurizer component.

      :param X: Input features.
      :type X: pd.DataFrame
      :param y: Target data. Ignored.
      :type y: pd.Series, optional

      :returns: self


   .. 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:: get_feature_names(self)

      Gets the categories of each datetime feature.

      :returns:

                Dictionary, where each key-value pair is a column name and a dictionary
                    mapping the unique feature values to their integer encoding.
      :rtype: dict


   .. 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 data X by creating new features using existing DateTime columns, and then dropping those DateTime columns.

      :param X: Input features.
      :type X: pd.DataFrame
      :param y: Ignored.
      :type y: pd.Series, optional

      :returns: Transformed X
      :rtype: pd.DataFrame