ensemble¶

Ensemble components.

Submodules¶

Package Contents¶

Classes Summary¶

`SklearnStackedEnsembleBase`	Stacked Ensemble Base Class.
`SklearnStackedEnsembleClassifier`	Scikit-learn Stacked Ensemble Classifier.
`SklearnStackedEnsembleRegressor`	Scikit-learn Stacked Ensemble Regressor.
`StackedEnsembleBase`	Stacked Ensemble Base Class.
`StackedEnsembleClassifier`	Stacked Ensemble Classifier.
`StackedEnsembleRegressor`	Stacked Ensemble Regressor.

Contents¶

class evalml.pipelines.components.ensemble.SklearnStackedEnsembleBase(input_pipelines=None, final_estimator=None, cv=None, n_jobs=- 1, random_seed=0, **kwargs)[source]¶

Stacked Ensemble Base Class.

Parameters

input_pipelines (list(PipelineBase or subclass obj)) – List of pipeline instances to use as the base estimators. This must not be None or an empty list or else EnsembleMissingPipelinesError will be raised.
final_estimator (Estimator or subclass) – The estimator used to combine the base estimators.
cv (int, cross-validation generator or an iterable) –
Determines the cross-validation splitting strategy used to train final_estimator. For int/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. Possible inputs for cv are:
- None: 5-fold cross validation
- int: the number of folds in a (Stratified) KFold
- An scikit-learn cross-validation generator object
- An iterable yielding (train, test) splits
n_jobs (int or None) – Non-negative 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.

Raises

EnsembleMissingPipelinesError – If input_pipelines is None or an empty list.
ValueError – If any of the input pipelines cannot be used in a stacked ensemble.

Attributes

model_family	ModelFamily.ENSEMBLE
modifies_features	True
modifies_target	False
predict_uses_y	False
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 SklearnStackedEnsembleClassifier and SklearnStackedEnsembleRegressor.
`fit`	Fits estimator to data.
`load`	Loads component at file path.
`name`	Returns string name of this component.
`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.
`supported_problem_types`	Problem types this estimator supports.

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 SklearnStackedEnsembleClassifier and SklearnStackedEnsembleRegressor.

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

property name(cls)¶: Returns string name of this component.

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.

property supported_problem_types(cls)¶: Problem types this estimator supports.

class evalml.pipelines.components.ensemble.SklearnStackedEnsembleClassifier(input_pipelines=None, final_estimator=None, cv=None, n_jobs=- 1, random_seed=0, **kwargs)[source]¶

Scikit-learn Stacked Ensemble Classifier.

Parameters

input_pipelines (list(PipelineBase or subclass obj)) – List of pipeline instances to use as the base estimators. This must not be None or an empty list or else EnsembleMissingPipelinesError will be raised.
final_estimator (Estimator or subclass) – The classifier used to combine the base estimators. If None, uses LogisticRegressionClassifier.
cv (int, cross-validation generator or an iterable) –
Determines the cross-validation splitting strategy used to train final_estimator. For int/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. Defaults to None. Possible inputs for cv are:
- None: 3-fold cross validation
- int: the number of folds in a (Stratified) KFold
- An scikit-learn cross-validation generator object
- An iterable yielding (train, test) splits
n_jobs (int or None) – Non-negative 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.

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.ENSEMBLE
modifies_features	True
modifies_target	False
name	Sklearn 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

`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 SklearnStackedEnsembleClassifier and SklearnStackedEnsembleRegressor.
`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 SklearnStackedEnsembleClassifier and SklearnStackedEnsembleRegressor.

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.

class evalml.pipelines.components.ensemble.SklearnStackedEnsembleRegressor(input_pipelines=None, final_estimator=None, cv=None, n_jobs=- 1, random_seed=0, **kwargs)[source]¶

Scikit-learn Stacked Ensemble Regressor.

Parameters

input_pipelines (list(PipelineBase or subclass obj)) – List of pipeline instances to use as the base estimators. This must not be None or an empty list or else EnsembleMissingPipelinesError will be raised.
final_estimator (Estimator or subclass) – The regressor used to combine the base estimators. If None, uses LinearRegressor.
cv (int, cross-validation generator or an iterable) –
Determines the cross-validation splitting strategy used to train final_estimator. For int/None inputs, KFold is used. Defaults to None. Possible inputs for cv are:
- None: 3-fold cross validation
- int: the number of folds in a (Stratified) KFold
- An scikit-learn cross-validation generator object
- An iterable yielding (train, test) splits
n_jobs (int or None) – Non-negative 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.

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.ENSEMBLE
modifies_features	True
modifies_target	False
name	Sklearn 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 SklearnStackedEnsembleClassifier and SklearnStackedEnsembleRegressor.
`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 SklearnStackedEnsembleClassifier and SklearnStackedEnsembleRegressor.

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.

class evalml.pipelines.components.ensemble.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

`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.
`name`	Returns string name of this component.
`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.
`supported_problem_types`	Problem types this estimator supports.

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

property name(cls)¶: Returns string name of this component.

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.

property supported_problem_types(cls)¶: Problem types this estimator supports.

class evalml.pipelines.components.ensemble.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.

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

`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.

class evalml.pipelines.components.ensemble.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.

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.

components

sklearn_stacked_ensemble_base