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 * - **is_multiseries** - False * - **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 evalml.pipelines.components.transformers.transformer.Transformer.update_parameters .. 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. .. py:method:: update_parameters(self, update_dict, reset_fit=True) Updates the parameter dictionary of the component. :param update_dict: A dict of parameters to update. :type update_dict: dict :param reset_fit: If True, will set `_is_fitted` to False. :type reset_fit: bool, optional