decomposer =========================================================================== .. py:module:: evalml.pipelines.components.transformers.preprocessing.decomposer .. autoapi-nested-parse:: Component that removes trends from time series and returns the decomposed components. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: Decomposer(parameters=None, component_obj=None, random_seed=0, **kwargs) Component that removes trends and seasonality from time series and returns the decomposed components. :param parameters: Dictionary of parameters to pass to component object. :type parameters: dict :param component_obj: Instance of a detrender/deseasonalizer class. :type component_obj: class :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** - None * - **modifies_features** - False * - **modifies_target** - True * - **name** - Decomposer * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.clone evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.default_parameters evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.describe evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.determine_periodicity evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.fit evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.fit_transform evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.get_trend_dataframe evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.inverse_transform evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.load evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.needs_fitting evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.parameters evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.save evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.set_seasonal_period evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer.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:: determine_periodicity(self, X, y, method='autocorrelation') Function that uses autocorrelative methods to determine the first, signficant period of the seasonal signal. :param X: The feature data of the time series problem. :type X: pandas.DataFrame :param y: The target data of a time series problem. :type y: pandas.Series :param method: Either "autocorrelation" or "partial-autocorrelation". The method by which to determine the first period of the seasonal part of the target signal. "partial-autocorrelation" should currently not be used. Defaults to "autocorrelation". :type method: str :returns: The integer numbers of entries in time series data over which the seasonal part of the target data repeats. If the time series data is in days, then this is the number of days that it takes the target's seasonal signal to repeat. Note: the target data can contain multiple seasonal signals. This function will only return the first, and thus, shortest period. E.g. if the target has both weekly and yearly seasonality, the function will only return "7" and not return "365". If no period is detected, returns [None]. :rtype: (list[int]) .. 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:: get_trend_dataframe(self, y) :abstractmethod: Return a list of dataframes, each with 3 columns: trend, seasonality, residual. .. py:method:: inverse_transform(self, y) :abstractmethod: Add the trend + seasonality back to y. .. 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:: set_seasonal_period(self, X, y) Function to set the component's seasonal period based on the target's seasonality. :param X: The feature data of the time series problem. :type X: pandas.DataFrame :param y: The target data of a time series problem. :type y: pandas.Series .. 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.