prophet_regressor ============================================================================= .. py:module:: evalml.pipelines.components.estimators.regressors.prophet_regressor .. autoapi-nested-parse:: Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: ProphetRegressor(time_index=None, changepoint_prior_scale=0.05, seasonality_prior_scale=10, holidays_prior_scale=10, seasonality_mode='additive', random_seed=0, stan_backend='CMDSTANPY', **kwargs) Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. More information here: https://facebook.github.io/prophet/ **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **hyperparameter_ranges** - { "changepoint_prior_scale": Real(0.001, 0.5), "seasonality_prior_scale": Real(0.01, 10), "holidays_prior_scale": Real(0.01, 10), "seasonality_mode": ["additive", "multiplicative"],} * - **model_family** - ModelFamily.PROPHET * - **modifies_features** - True * - **modifies_target** - False * - **name** - Prophet Regressor * - **supported_problem_types** - [ProblemTypes.TIME_SERIES_REGRESSION] * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.build_prophet_df evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.clone evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.default_parameters evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.describe evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.feature_importance evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.fit evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.get_params evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.load evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.needs_fitting evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.parameters evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.predict evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.predict_proba evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor.save .. py:method:: build_prophet_df(X, y=None, time_index='ds') :staticmethod: Build the Prophet data to pass fit and predict on. .. 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. :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: Returns array of 0's with len(1) as feature_importance is not defined for Prophet regressor. .. py:method:: fit(self, X, y=None) Fits Prophet regressor 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 :returns: self .. py:method:: get_params(self) Get parameters for the Prophet regressor. .. 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, y=None) Make predictions using fitted Prophet regressor. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :param y: Target data. Ignored. :type y: pd.Series :returns: Predicted values. :rtype: pd.Series .. py:method:: predict_proba(self, X) 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