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