ensemble

Submodules

• sklearn_stacked_ensemble_base
• sklearn_stacked_ensemble_classifier
• sklearn_stacked_ensemble_regressor

Package Contents

Classes Summary

SklearnStackedEnsembleBase	Stacked Ensemble Base Class.
SklearnStackedEnsembleClassifier	Scikit-learn Stacked Ensemble Classifier.
SklearnStackedEnsembleRegressor	Scikit-learn 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)

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.

Attributes

model_family	ModelFamily.ENSEMBLE
modifies_features	True
modifies_target	False
predict_uses_y	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 component 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
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

prints and returns dictionary

Return type

None or dict

property feature_importance(self)

Not implemented for SklearnStackedEnsembleClassifier and SklearnStackedEnsembleRegressor

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

• y (list, pd.Series, np.ndarray, 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.

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, np.ndarray) – Data of shape [n_samples, n_features]

Returns

Predicted values

Return type

pd.Series

predict_proba(self, X)

Make probability estimates for labels.

Parameters

X (pd.DataFrame, or np.ndarray) – Features

Returns

Probability estimates

Return type

pd.Series

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.

Returns

None

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)

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,]

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 component to data
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
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

prints and returns dictionary

Return type

None or dict

property feature_importance(self)

Not implemented for SklearnStackedEnsembleClassifier and SklearnStackedEnsembleRegressor

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

• y (list, pd.Series, np.ndarray, 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.

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, np.ndarray) – Data of shape [n_samples, n_features]

Returns

Predicted values

Return type

pd.Series

predict_proba(self, X)

Make probability estimates for labels.

Parameters

X (pd.DataFrame, or np.ndarray) – Features

Returns

Probability estimates

Return type

pd.Series

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.

Returns

None

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

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,]

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 component to data
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
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

prints and returns dictionary

Return type

None or dict

property feature_importance(self)

Not implemented for SklearnStackedEnsembleClassifier and SklearnStackedEnsembleRegressor

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

• y (list, pd.Series, np.ndarray, 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.

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, np.ndarray) – Data of shape [n_samples, n_features]

Returns

Predicted values

Return type

pd.Series

predict_proba(self, X)

Make probability estimates for labels.

Parameters

X (pd.DataFrame, or np.ndarray) – Features

Returns

Probability estimates

Return type

pd.Series

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.

Returns

None

components sklearn_stacked_ensemble_base