stacked_ensemble_base¶
Stacked Ensemble Base.
Module Contents¶
Classes Summary¶
Stacked Ensemble Base Class. |
Contents¶
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class
evalml.pipelines.components.ensemble.stacked_ensemble_base.
StackedEnsembleBase
(final_estimator=None, n_jobs=- 1, random_seed=0, **kwargs)[source]¶ Stacked Ensemble Base Class.
- Parameters
final_estimator (Estimator or subclass) – The estimator used to combine the base estimators.
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.
Attributes
model_family
ModelFamily.ENSEMBLE
modifies_features
True
modifies_target
False
predict_uses_y
False
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for stacked ensemble classes.
Describe a component and its parameters.
Not implemented for StackedEnsembleClassifier and StackedEnsembleRegressor.
Fits estimator to data.
Loads component at file path.
Returns string name of this component.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Make predictions using selected features.
Make probability estimates for labels.
Saves component at file path.
Problem types this estimator supports.
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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.
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default_parameters
(cls)¶ Returns the default parameters for stacked ensemble classes.
- Returns
default parameters for this component.
- Return type
dict
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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
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property
feature_importance
(self)¶ Not implemented for StackedEnsembleClassifier and StackedEnsembleRegressor.
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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
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static
load
(file_path)¶ Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
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property
name
(cls)¶ Returns string name of this component.
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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.
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property
parameters
(self)¶ Returns the parameters which were used to initialize the component.
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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.
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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.
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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.
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property
supported_problem_types
(cls)¶ Problem types this estimator supports.