svm_regressor ========================================================================= .. py:module:: evalml.pipelines.components.estimators.regressors.svm_regressor .. autoapi-nested-parse:: Support Vector Machine Regressor. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: SVMRegressor(C=1.0, kernel='rbf', gamma='auto', random_seed=0, **kwargs) Support Vector Machine Regressor. :param C: The regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty. Defaults to 1.0. :type C: float :param kernel: Specifies the kernel type to be used in the algorithm. Defaults to "rbf". :type kernel: {"poly", "rbf", "sigmoid"} :param gamma: Kernel coefficient for "rbf", "poly" and "sigmoid". Defaults to "auto". - If gamma='scale' is passed then it uses 1 / (n_features * X.var()) as value of gamma - If "auto" (default), uses 1 / n_features :type gamma: {"scale", "auto"} or float :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** - { "C": Real(0, 10), "kernel": ["poly", "rbf", "sigmoid"], "gamma": ["scale", "auto"],} * - **model_family** - ModelFamily.SVM * - **modifies_features** - True * - **modifies_target** - False * - **name** - SVM Regressor * - **supported_problem_types** - [ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION,] * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.clone evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.default_parameters evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.describe evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.feature_importance evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.fit evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.get_prediction_intervals evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.load evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.needs_fitting evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.parameters evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.predict evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.predict_proba evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.save evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor.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:: feature_importance(self) :property: Feature importance of fitted SVM regresor. Only works with linear kernels. If the kernel isn't linear, we return a numpy array of zeros. :returns: The feature importance of the fitted SVM regressor, or an array of zeroes if the kernel is not linear. .. 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