stacked_ensemble_regressor

Stacked Ensemble Regressor.

Module Contents

Classes Summary

StackedEnsembleRegressor

Stacked Ensemble Regressor.

Contents

class evalml.pipelines.components.ensemble.stacked_ensemble_regressor.StackedEnsembleRegressor(final_estimator=None, n_jobs=- 1, random_seed=0, **kwargs)[source]

Stacked Ensemble Regressor.

Parameters
  • final_estimator (Estimator or subclass) – The regressor used to combine the base estimators. If None, uses ElasticNetRegressor.

  • n_jobs (int or None) – Integer describing level of parallelism used for pipelines. None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs greater than -1, (n_cpus + 1 + n_jobs) are used. Defaults to -1. - Note: there could be some multi-process errors thrown for values of n_jobs != 1. If this is the case, please use n_jobs = 1.

  • random_seed (int) – Seed for the random number generator. Defaults to 0.

Example

>>> from evalml.pipelines.component_graph import ComponentGraph
>>> from evalml.pipelines.components.estimators.regressors.rf_regressor import RandomForestRegressor
>>> from evalml.pipelines.components.estimators.regressors.elasticnet_regressor import ElasticNetRegressor
...
>>> component_graph = {
...     "Random Forest": [RandomForestRegressor(random_seed=3), "X", "y"],
...     "Random Forest B": [RandomForestRegressor(random_seed=4), "X", "y"],
...     "Stacked Ensemble": [
...         StackedEnsembleRegressor(n_jobs=1, final_estimator=RandomForestRegressor()),
...         "Random Forest.x",
...         "Random Forest B.x",
...         "y",
...     ],
... }
...
>>> cg = ComponentGraph(component_graph)
>>> assert cg.default_parameters == {
...     'Random Forest Regressor': {'n_estimators': 100,
...                                 'max_depth': 6,
...                                 'n_jobs': -1},
...     'Stacked Ensemble Regressor': {'final_estimator': ElasticNetRegressor,
...                                    'n_jobs': -1}}

Attributes

hyperparameter_ranges

{}

model_family

ModelFamily.ENSEMBLE

modifies_features

True

modifies_target

False

name

Stacked Ensemble Regressor

predict_uses_y

False

supported_problem_types

[ ProblemTypes.REGRESSION, 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 stacked ensemble classes.

describe

Describe a component and its parameters.

feature_importance

Not implemented for StackedEnsembleClassifier and StackedEnsembleRegressor.

fit

Fits estimator to data.

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 selected features.

predict_proba

Make probability estimates for labels.

save

Saves component at file path.

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 stacked ensemble classes.

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)

Not implemented for StackedEnsembleClassifier and StackedEnsembleRegressor.

fit(self, X, y=None)

Fits estimator to data.

Parameters
  • X (pd.DataFrame) – The input training data of shape [n_samples, n_features].

  • y (pd.Series, optional) – The target training data of length [n_samples].

Returns

self

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)

Make predictions using selected features.

Parameters

X (pd.DataFrame) – Data of shape [n_samples, n_features].

Returns

Predicted values.

Return type

pd.Series

Raises

MethodPropertyNotFoundError – If estimator does not have a predict method or a component_obj that implements predict.

predict_proba(self, X)

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.