varmax_regressor#

Vector Autoregressive Moving Average with eXogenous regressors model. The two parameters (p, q) are the AR order and the MA order. More information here: https://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.varmax.VARMAX.html.

Module Contents#

Classes Summary#

VARMAXRegressor

Contents#

class evalml.pipelines.components.estimators.regressors.varmax_regressor.VARMAXRegressor(time_index: Optional[Hashable] = None, p: int = 1, q: int = 0, trend: Optional[str] = 'c', random_seed: Union[int, float] = 0, maxiter: int = 10, use_covariates: bool = True, **kwargs)[source]#

Currently VARMAXRegressor isn’t supported via conda install. It’s recommended that it be installed via PyPI.

Parameters

time_index (str) – Specifies the name of the column in X that provides the datetime objects. Defaults to None.
p (int) – Maximum Autoregressive order. Defaults to 1.
q (int) – Maximum Moving Average order. Defaults to 0.
trend (str) – Controls the deterministic trend. Options are [‘n’, ‘c’, ‘t’, ‘ct’] where ‘c’ is a constant term, ‘t’ indicates a linear trend, and ‘ct’ is both. Can also be an iterable when defining a polynomial, such as [1, 1, 0, 1].
random_seed (int) – Seed for the random number generator. Defaults to 0.
max_iter (int) – Maximum number of iterations for solver. Defaults to 10.
use_covariates (bool) – If True, will pass exogenous variables in fit/predict methods. If False, forecasts will solely be based off of the datetimes and target values. Defaults to True.

Attributes

hyperparameter_ranges	{ “p”: Integer(1, 10), “q”: Integer(1, 10), “trend”: Categorical([‘n’, ‘c’, ‘t’, ‘ct’]),}
is_multiseries	ModelFamily.VARMAX
model_family	None
modifies_features	True
modifies_target	False
name	VARMAX Regressor
supported_problem_types	[ProblemTypes.TIME_SERIES_REGRESSION]
training_only	False

Methods

`clone`	Constructs a new component with the same parameters and random state.
`default_parameters`	Returns the default parameters for this component.
`describe`	Describe a component and its parameters.
`feature_importance`	Returns array of 0's with a length of 1 as feature_importance is not defined for VARMAX regressor.
`fit`	Fits VARMAX regressor to data.
`get_prediction_intervals`	Find the prediction intervals using the fitted VARMAXRegressor.
`load`	Loads component at file path.
`needs_fitting`	Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
`parameters`	Returns the parameters which were used to initialize the component.
`predict`	Make predictions using fitted VARMAX regressor.
`predict_proba`	Make probability estimates for labels.
`save`	Saves component at file path.
`update_parameters`	Updates the parameter dictionary of the component.

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.

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.
Return type: dict

describe(self, print_name=False, return_dict=False)#

Describe a component and its parameters.

Parameters

print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

property feature_importance(self) → numpy.ndarray#: Returns array of 0’s with a length of 1 as feature_importance is not defined for VARMAX regressor.

fit(self, X: pandas.DataFrame, y: Optional[pandas.DataFrame] = None)[source]#

Fits VARMAX regressor to data.

Parameters

X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.DataFrane) – The target training data of shape [n_samples, n_series_id_values].

Returns

self

Raises

ValueError – If y was not passed in.

abstract get_prediction_intervals(self, X: pandas.DataFrame, y: pandas.DataFrame = None, coverage: List[float] = None, predictions: pandas.Series = None) → Dict[str, pandas.Series][source]#

Find the prediction intervals using the fitted VARMAXRegressor.

Parameters

X (pd.DataFrame) – Data of shape [n_samples, n_features].
y (pd.DataFrame) – Target data of shape [n_samples, n_series_id_values]. Optional.
coverage (list[float]) – A list of floats between the values 0 and 1 that the upper and lower bounds of the prediction interval should be calculated for.
predictions (pd.Series) – Not used for VARMAX regressor.

Returns

Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper.

Return type

dict

static load(file_path)#

Loads component at file path.

Parameters: file_path (str) – Location to load file.
Returns: ComponentBase object

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.

property parameters(self)#: Returns the parameters which were used to initialize the component.

predict(self, X: pandas.DataFrame, y: Optional[pandas.DataFrame] = None) → pandas.Series[source]#

Make predictions using fitted VARMAX regressor.

Parameters

X (pd.DataFrame) – Data of shape [n_samples, n_features].
y (pd.DataFrame) – Target data of shape [n_samples, n_series_id_values].

Returns

Predicted values.

Return type

pd.Series

Raises

ValueError – If X was passed to fit but not passed in predict.

predict_proba(self, X: pandas.DataFrame) → pandas.Series#

Make probability estimates for labels.

Parameters: X (pd.DataFrame) – Features.
Returns: Probability estimates.
Return type: pd.Series
Raises: MethodPropertyNotFoundError – If estimator does not have a predict_proba method or a component_obj that implements predict_proba.

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#

Saves component at file path.

Parameters

file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.

update_parameters(self, update_dict, reset_fit=True)#

Updates the parameter dictionary of the component.

Parameters

update_dict (dict) – A dict of parameters to update.
reset_fit (bool, optional) – If True, will set _is_fitted to False.