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
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.