Source code for evalml.pipelines.multiseries_regression_pipeline

"""Pipeline base class for time series regression problems."""
from woodwork.statistics_utils import infer_frequency

from evalml.pipelines.time_series_regression_pipeline import (
    TimeSeriesRegressionPipeline,
)
from evalml.problem_types import ProblemTypes


[docs]class MultiseriesRegressionPipeline(TimeSeriesRegressionPipeline): """Pipeline base class for multiseries time series regression problems. Args: component_graph (ComponentGraph, list, dict): ComponentGraph instance, list of components in order, or dictionary of components. parameters (dict): Dictionary with component names as keys and dictionary of that component's parameters as values. An empty dictionary {} implies using all default values for component parameters. Pipeline-level parameters such as time_index, gap, and max_delay must be specified with the "pipeline" key. For example: Pipeline(parameters={"pipeline": {"time_index": "Date", "max_delay": 4, "gap": 2}}). custom_name (str): Custom name for the pipeline. Defaults to None. random_seed (int): Seed for the random number generator. Defaults to 0. """ problem_type = ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION """ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION""" def __init__( self, component_graph, parameters=None, custom_name=None, random_seed=0, ): if not parameters or "pipeline" not in parameters: raise ValueError( "time_index, gap, max_delay, and forecast_horizon parameters cannot be omitted from the parameters dict. " "Please specify them as a dictionary with the key 'pipeline'.", ) if "series_id" not in parameters["pipeline"]: raise ValueError( "series_id must be defined for multiseries time series pipelines. Please specify it as a key in the pipeline " "parameters dict.", ) self.series_id = parameters["pipeline"]["series_id"] super().__init__( component_graph, custom_name=custom_name, parameters=parameters, random_seed=random_seed, )
[docs] def fit(self, X, y): """Fit a multiseries time series pipeline. Args: X (pd.DataFrame): The input training data of shape [n_samples, n_features]. y (pd.Series): The target training targets of length [n_samples*n_series]. Returns: self Raises: ValueError: If the target is not numeric. """ self._fit(X, y) return self
def _fit(self, X, y): from evalml.pipelines.utils import unstack_multiseries self.input_target_name = y.name X_unstacked, y_unstacked = unstack_multiseries( X, y, self.series_id, self.time_index, self.input_target_name, ) self.frequency = infer_frequency(X_unstacked[self.time_index]) self.component_graph.fit(X_unstacked, y_unstacked) self.input_feature_names = self.component_graph.input_feature_names
[docs] def predict_in_sample( self, X, y, X_train, y_train, objective=None, calculating_residuals=False, ): """Predict on future data where the target is known, e.g. cross validation. Args: X (pd.DataFrame or np.ndarray): Future data of shape [n_samples, n_features] y (pd.Series, np.ndarray): Future target of shape [n_samples] X_train (pd.DataFrame, np.ndarray): Data the pipeline was trained on of shape [n_samples_train, n_feautures] y_train (pd.Series, np.ndarray): Targets used to train the pipeline of shape [n_samples_train] objective (ObjectiveBase, str, None): Objective used to threshold predicted probabilities, optional. calculating_residuals (bool): Whether we're calling predict_in_sample to calculate the residuals. This means the X and y arguments are not future data, but actually the train data. Returns: pd.Series: Estimated labels. Raises: ValueError: If final component is not an Estimator. """ from evalml.pipelines.utils import stack_data, unstack_multiseries X_unstacked, y_unstacked = unstack_multiseries( X, y, self.series_id, self.time_index, self.input_target_name, ) X_train_unstacked, y_train_unstacked = unstack_multiseries( X_train, y_train, self.series_id, self.time_index, self.input_target_name, ) unstacked_predictions = super().predict_in_sample( X_unstacked, y_unstacked, X_train_unstacked, y_train_unstacked, objective, calculating_residuals, ) stacked_predictions = stack_data(unstacked_predictions) # Index will start at the unstacked index, so we need to reset it to the original index stacked_predictions.index = X.index return stacked_predictions