scalers ========================================================== .. py:module:: evalml.pipelines.components.transformers.scalers .. autoapi-nested-parse:: Components that scale input data. Submodules ---------- .. toctree:: :titlesonly: :maxdepth: 1 standard_scaler/index.rst Package Contents ---------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.transformers.scalers.StandardScaler Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: StandardScaler(random_seed=0, **kwargs) A transformer that standardizes input features by removing the mean and scaling to unit variance. :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** - Standard Scaler * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.transformers.scalers.StandardScaler.clone evalml.pipelines.components.transformers.scalers.StandardScaler.default_parameters evalml.pipelines.components.transformers.scalers.StandardScaler.describe evalml.pipelines.components.transformers.scalers.StandardScaler.fit evalml.pipelines.components.transformers.scalers.StandardScaler.fit_transform evalml.pipelines.components.transformers.scalers.StandardScaler.load evalml.pipelines.components.transformers.scalers.StandardScaler.needs_fitting evalml.pipelines.components.transformers.scalers.StandardScaler.parameters evalml.pipelines.components.transformers.scalers.StandardScaler.save evalml.pipelines.components.transformers.scalers.StandardScaler.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) Fits the standard scalar on the given 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:: fit_transform(self, X, y=None) Fit and transform data using the standard scaler component. :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: Transformed data. :rtype: pd.DataFrame .. 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) Transform data using the fitted standard scaler. :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: Transformed data. :rtype: pd.DataFrame