stacked_ensemble_classifier¶
Stacked Ensemble Classifier.
Module Contents¶
Classes Summary¶
Stacked Ensemble Classifier. |
Contents¶
-
class
evalml.pipelines.components.ensemble.stacked_ensemble_classifier.
StackedEnsembleClassifier
(final_estimator=None, n_jobs=- 1, random_seed=0, **kwargs)[source]¶ Stacked Ensemble Classifier.
- Parameters
final_estimator (Estimator or subclass) – The classifier used to combine the base estimators. If None, uses ElasticNetClassifier.
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 below -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.classifiers.decision_tree_classifier import DecisionTreeClassifier >>> from evalml.pipelines.components.estimators.classifiers.elasticnet_classifier import ElasticNetClassifier ... >>> component_graph = { ... "Decision Tree": [DecisionTreeClassifier(random_seed=3), "X", "y"], ... "Decision Tree B": [DecisionTreeClassifier(random_seed=4), "X", "y"], ... "Stacked Ensemble": [ ... StackedEnsembleClassifier(n_jobs=1, final_estimator=DecisionTreeClassifier()), ... "Decision Tree.x", ... "Decision Tree B.x", ... "y", ... ], ... } ... >>> cg = ComponentGraph(component_graph) >>> assert cg.default_parameters == { ... 'Decision Tree Classifier': {'criterion': 'gini', ... 'max_features': 'auto', ... 'max_depth': 6, ... 'min_samples_split': 2, ... 'min_weight_fraction_leaf': 0.0}, ... 'Stacked Ensemble Classifier': {'final_estimator': ElasticNetClassifier, ... 'n_jobs': -1}}
Attributes
hyperparameter_ranges
{}
model_family
ModelFamily.ENSEMBLE
modifies_features
True
modifies_target
False
name
Stacked Ensemble Classifier
predict_uses_y
False
supported_problem_types
[ ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS,]
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 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.
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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
-
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
-
static
load
(file_path)¶ Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
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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.
-
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.