stacked_ensemble_regressor ========================================================================= .. py:module:: evalml.pipelines.components.ensemble.stacked_ensemble_regressor .. autoapi-nested-parse:: Stacked Ensemble Regressor. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: StackedEnsembleRegressor(final_estimator=None, n_jobs=-1, random_seed=0, **kwargs) Stacked Ensemble Regressor. :param final_estimator: The regressor used to combine the base estimators. If None, uses ElasticNetRegressor. :type final_estimator: Estimator or subclass :param n_jobs: Integer describing level of parallelism used for pipelines. None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs greater than -1, (n_cpus + 1 + n_jobs) are used. Defaults to -1. - Note: there could be some multi-process errors thrown for values of `n_jobs != 1`. If this is the case, please use `n_jobs = 1`. :type n_jobs: int or None :param random_seed: Seed for the random number generator. Defaults to 0. :type random_seed: int .. rubric:: Example >>> from evalml.pipelines.component_graph import ComponentGraph >>> from evalml.pipelines.components.estimators.regressors.rf_regressor import RandomForestRegressor >>> from evalml.pipelines.components.estimators.regressors.elasticnet_regressor import ElasticNetRegressor ... >>> component_graph = { ... "Random Forest": [RandomForestRegressor(random_seed=3), "X", "y"], ... "Random Forest B": [RandomForestRegressor(random_seed=4), "X", "y"], ... "Stacked Ensemble": [ ... StackedEnsembleRegressor(n_jobs=1, final_estimator=RandomForestRegressor()), ... "Random Forest.x", ... "Random Forest B.x", ... "y", ... ], ... } ... >>> cg = ComponentGraph(component_graph) >>> assert cg.default_parameters == { ... 'Random Forest Regressor': {'n_estimators': 100, ... 'max_depth': 6, ... 'n_jobs': -1}, ... 'Stacked Ensemble Regressor': {'final_estimator': ElasticNetRegressor, ... 'n_jobs': -1}} **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **hyperparameter_ranges** - {} * - **model_family** - ModelFamily.ENSEMBLE * - **modifies_features** - True * - **modifies_target** - False * - **name** - Stacked Ensemble Regressor * - **supported_problem_types** - [ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION,] * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.clone evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.default_parameters evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.describe evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.feature_importance evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.fit evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.get_prediction_intervals evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.load evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.needs_fitting evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.parameters evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.predict evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.predict_proba evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.save evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor.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 stacked ensemble classes. :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:: feature_importance(self) :property: Not implemented for StackedEnsembleClassifier and StackedEnsembleRegressor. .. py:method:: fit(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None) Fits estimator 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 .. py:method:: get_prediction_intervals(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None, coverage: List[float] = None, predictions: pandas.Series = None) -> Dict[str, pandas.Series] Find the prediction intervals using the fitted regressor. This function takes the predictions of the fitted estimator and calculates the rolling standard deviation across all predictions using a window size of 5. The lower and upper predictions are determined by taking the percent point (quantile) function of the lower tail probability at each bound multiplied by the rolling standard deviation. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :param y: Target data. Ignored. :type y: pd.Series :param coverage: A list of floats between the values 0 and 1 that the upper and lower bounds of the prediction interval should be calculated for. :type coverage: list[float] :param predictions: Optional list of predictions to use. If None, will generate predictions using `X`. :type predictions: pd.Series :returns: Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper. :rtype: dict :raises MethodPropertyNotFoundError: If the estimator does not support Time Series Regression as a problem type. .. 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:: predict(self, X: pandas.DataFrame) -> pandas.Series Make predictions using selected features. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted values. :rtype: pd.Series :raises MethodPropertyNotFoundError: If estimator does not have a predict method or a component_obj that implements predict. .. py:method:: predict_proba(self, X: pandas.DataFrame) -> pandas.Series Make probability estimates for labels. :param X: Features. :type X: pd.DataFrame :returns: Probability estimates. :rtype: pd.Series :raises MethodPropertyNotFoundError: If estimator does not have a predict_proba method or a component_obj that implements predict_proba. .. 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:: 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