"""Check if we can delegate a method to the underlying estimator.

First, we check the fitted `final_estimator_` if available, otherwise we check the

unfitted `final_estimator`. We raise the original `AttributeError` if `attr` does

not exist. This function is used together with `available_if`.

"""

def check(self):

if hasattr(self, "final_estimator_"):

getattr(self.final_estimator_, attr)

else:

getattr(self.final_estimator, attr)

return True

"""Base class for stacking method."""

_parameter_constraints: dict = {

"estimators": [list],

"final_estimator": [None, HasMethods("fit")],

"cv": ["cv_object", StrOptions({"prefit"})],

"n_jobs": [None, Integral],

"passthrough": ["boolean"],

"verbose": ["verbose"],

}

@abstractmethod

def __init__(

self,

estimators,

final_estimator=None,

*,

cv=None,

stack_method="auto",

n_jobs=None,

verbose=0,

passthrough=False,

):

super().__init__(estimators=estimators)

self.final_estimator = final_estimator

self.cv = cv

self.stack_method = stack_method

self.n_jobs = n_jobs

self.verbose = verbose

self.passthrough = passthrough

def _clone_final_estimator(self, default):

if self.final_estimator is not None:

self.final_estimator_ = clone(self.final_estimator)

else:

self.final_estimator_ = clone(default)

def _concatenate_predictions(self, X, predictions):

"""Concatenate the predictions of each first layer learner and

possibly the input dataset `X`.

If `X` is sparse and `self.passthrough` is False, the output of

`transform` will be dense (the predictions). If `X` is sparse

and `self.passthrough` is True, the output of `transform` will

be sparse.

This helper is in charge of ensuring the predictions are 2D arrays and

it will drop one of the probability column when using probabilities

in the binary case. Indeed, the p(y|c=0) = 1 - p(y|c=1)

When `y` type is `"multilabel-indicator"`` and the method used is

`predict_proba`, `preds` can be either a `ndarray` of shape

`(n_samples, n_class)` or for some estimators a list of `ndarray`.

This function will drop one of the probability column in this situation as well.

"""

X_meta = []

for est_idx, preds in enumerate(predictions):

if isinstance(preds, list):

# `preds` is here a list of `n_targets` 2D ndarrays of

# `n_classes` columns. The k-th column contains the

# probabilities of the samples belonging the k-th class.

#

# Since those probabilities must sum to one for each sample,

# we can work with probabilities of `n_classes - 1` classes.

# Hence we drop the first column.

for pred in preds:

X_meta.append(pred[:, 1:])

elif preds.ndim == 1:

# Some estimator return a 1D array for predictions

# which must be 2-dimensional arrays.

X_meta.append(preds.reshape(-1, 1))

elif (

self.stack_method_[est_idx] == "predict_proba"

and len(self.classes_) == 2

):

# Remove the first column when using probabilities in

# binary classification because both features `preds` are perfectly

# collinear.

X_meta.append(preds[:, 1:])

else:

X_meta.append(preds)

self._n_feature_outs = [pred.shape[1] for pred in X_meta]

if self.passthrough:

X_meta.append(X)

if sparse.issparse(X):

return sparse.hstack(X_meta, format=X.format)

return np.hstack(X_meta)

@staticmethod

def _method_name(name, estimator, method):

if estimator == "drop":

return None

if method == "auto":

method = ["predict_proba", "decision_function", "predict"]

try:

method_name = _check_response_method(estimator, method).__name__

except AttributeError as e:

raise ValueError(

f"Underlying estimator {name} does not implement the method {method}."

) from e

return method_name

@_fit_context(

# estimators in Stacking*.estimators are not validated yet

prefer_skip_nested_validation=False

)

def fit(self, X, y, **fit_params):

"""Fit the estimators.

Parameters

----------

X : {array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, where `n_samples` is the number of samples and

`n_features` is the number of features.

y : array-like of shape (n_samples,)

Target values.

**fit_params : dict

Dict of metadata, potentially containing sample_weight as a

key-value pair. If sample_weight is not present, then samples are

equally weighted. Note that sample_weight is supported only if all

underlying estimators support sample weights.

.. versionadded:: 1.6

Returns

-------

self : object

"""

# all_estimators contains all estimators, the one to be fitted and the

# 'drop' string.

names, all_estimators = self._validate_estimators()

self._validate_final_estimator()

stack_method = [self.stack_method] * len(all_estimators)

if _routing_enabled():

routed_params = process_routing(self, "fit", **fit_params)

else:

routed_params = Bunch()

for name in names:

routed_params[name] = Bunch(fit={})

if "sample_weight" in fit_params:

routed_params[name].fit["sample_weight"] = fit_params[

"sample_weight"

]

if self.cv == "prefit":

self.estimators_ = []

for estimator in all_estimators:

if estimator != "drop":

check_is_fitted(estimator)

self.estimators_.append(estimator)

else:

# Fit the base estimators on the whole training data. Those

# base estimators will be used in transform, predict, and

# predict_proba. They are exposed publicly.

self.estimators_ = Parallel(n_jobs=self.n_jobs)(

delayed(_fit_single_estimator)(

clone(est), X, y, routed_params[name]["fit"]

)

for name, est in zip(names, all_estimators)

if est != "drop"

)

self.named_estimators_ = Bunch()

est_fitted_idx = 0

for name_est, org_est in zip(names, all_estimators):

if org_est != "drop":

current_estimator = self.estimators_[est_fitted_idx]

self.named_estimators_[name_est] = current_estimator

est_fitted_idx += 1

if hasattr(current_estimator, "feature_names_in_"):

self.feature_names_in_ = current_estimator.feature_names_in_

else:

self.named_estimators_[name_est] = "drop"

self.stack_method_ = [

self._method_name(name, est, meth)

for name, est, meth in zip(names, all_estimators, stack_method)

]

if self.cv == "prefit":

# Generate predictions from prefit models

predictions = [

getattr(estimator, predict_method)(X)

for estimator, predict_method in zip(all_estimators, self.stack_method_)

if estimator != "drop"

]

else:

# To train the meta-classifier using the most data as possible, we use

# a cross-validation to obtain the output of the stacked estimators.

# To ensure that the data provided to each estimator are the same,

# we need to set the random state of the cv if there is one and we

# need to take a copy.

cv = check_cv(self.cv, y=y, classifier=is_classifier(self))

if hasattr(cv, "random_state") and cv.random_state is None:

cv.random_state = np.random.RandomState()

predictions = Parallel(n_jobs=self.n_jobs)(

delayed(cross_val_predict)(

clone(est),

X,

y,

cv=deepcopy(cv),

method=meth,

n_jobs=self.n_jobs,

params=routed_params[name]["fit"],

verbose=self.verbose,

)

for name, est, meth in zip(names, all_estimators, self.stack_method_)

if est != "drop"

)

# Only not None or not 'drop' estimators will be used in transform.

# Remove the None from the method as well.

self.stack_method_ = [

meth

for (meth, est) in zip(self.stack_method_, all_estimators)

if est != "drop"

]

X_meta = self._concatenate_predictions(X, predictions)

_fit_single_estimator(self.final_estimator_, X_meta, y, fit_params=fit_params)

return self

@property

def n_features_in_(self):

"""Number of features seen during :term:`fit`."""

try:

check_is_fitted(self)

except NotFittedError as nfe:

raise AttributeError(

f"{self.__class__.__name__} object has no attribute n_features_in_"

) from nfe

return self.estimators_[0].n_features_in_

def _transform(self, X):

"""Concatenate and return the predictions of the estimators."""

check_is_fitted(self)

predictions = [

getattr(est, meth)(X)

for est, meth in zip(self.estimators_, self.stack_method_)

if est != "drop"

]

return self._concatenate_predictions(X, predictions)

def get_feature_names_out(self, input_features=None):

"""Get output feature names for transformation.

Parameters

----------

input_features : array-like of str or None, default=None

Input features. The input feature names are only used when `passthrough` is

`True`.

- If `input_features` is `None`, then `feature_names_in_` is

used as feature names in. If `feature_names_in_` is not defined,

then names are generated: `[x0, x1, ..., x(n_features_in_ - 1)]`.

- If `input_features` is an array-like, then `input_features` must

match `feature_names_in_` if `feature_names_in_` is defined.

If `passthrough` is `False`, then only the names of `estimators` are used

to generate the output feature names.

Returns

-------

feature_names_out : ndarray of str objects

Transformed feature names.

"""

check_is_fitted(self, "n_features_in_")

input_features = _check_feature_names_in(

self, input_features, generate_names=self.passthrough

)

class_name = self.__class__.__name__.lower()

non_dropped_estimators = (

name for name, est in self.estimators if est != "drop"

)

meta_names = []

for est, n_features_out in zip(non_dropped_estimators, self._n_feature_outs):

if n_features_out == 1:

meta_names.append(f"{class_name}_{est}")

else:

meta_names.extend(

f"{class_name}_{est}{i}" for i in range(n_features_out)

)

if self.passthrough:

return np.concatenate((meta_names, input_features))

return np.asarray(meta_names, dtype=object)

@available_if(_estimator_has("predict"))

def predict(self, X, **predict_params):

"""Predict target for X.

Parameters

----------

X : {array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, where `n_samples` is the number of samples and

`n_features` is the number of features.

**predict_params : dict of str -> obj

Parameters to the `predict` called by the `final_estimator`. Note

that this may be used to return uncertainties from some estimators

with `return_std` or `return_cov`. Be aware that it will only

account for uncertainty in the final estimator.

Returns

-------

y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)

Predicted targets.

"""

check_is_fitted(self)

return self.final_estimator_.predict(self.transform(X), **predict_params)

def _sk_visual_block_with_final_estimator(self, final_estimator):

names, estimators = zip(*self.estimators)

parallel = _VisualBlock("parallel", estimators, names=names, dash_wrapped=False)

# final estimator is wrapped in a parallel block to show the label:

# 'final_estimator' in the html repr

final_block = _VisualBlock(

"parallel", [final_estimator], names=["final_estimator"], dash_wrapped=False

)

return _VisualBlock("serial", (parallel, final_block), dash_wrapped=False)

def get_metadata_routing(self):

"""Get metadata routing of this object.

Please check :ref:`User Guide <metadata_routing>` on how the routing

mechanism works.

.. versionadded:: 1.6

Returns

-------

routing : MetadataRouter

A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating

routing information.

"""

router = MetadataRouter(owner=self.__class__.__name__)

# `self.estimators` is a list of (name, est) tuples

for name, estimator in self.estimators:

router.add(

**{name: estimator},

method_mapping=MethodMapping().add(callee="fit", caller="fit"),

)

try:

final_estimator_ = self.final_estimator_

except AttributeError:

final_estimator_ = self.final_estimator

router.add(

final_estimator_=final_estimator_,

method_mapping=MethodMapping().add(caller="predict", callee="predict"),

)

"""Stack of estimators with a final classifier.

Stacked generalization consists in stacking the output of individual

estimator and use a classifier to compute the final prediction. Stacking

allows to use the strength of each individual estimator by using their

output as input of a final estimator.

Note that `estimators_` are fitted on the full `X` while `final_estimator_`

is trained using cross-validated predictions of the base estimators using

`cross_val_predict`.

Read more in the :ref:`User Guide <stacking>`.

.. versionadded:: 0.22

Parameters

----------

estimators : list of (str, estimator)

Base estimators which will be stacked together. Each element of the

list is defined as a tuple of string (i.e. name) and an estimator

instance. An estimator can be set to 'drop' using `set_params`.

The type of estimator is generally expected to be a classifier.

However, one can pass a regressor for some use case (e.g. ordinal

regression).

final_estimator : estimator, default=None

A classifier which will be used to combine the base estimators.

The default classifier is a

:class:`~sklearn.linear_model.LogisticRegression`.

cv : int, cross-validation generator, iterable, or "prefit", default=None

Determines the cross-validation splitting strategy used in

`cross_val_predict` to train `final_estimator`. Possible inputs for

cv are:

* None, to use the default 5-fold cross validation,

* integer, to specify the number of folds in a (Stratified) KFold,

* An object to be used as a cross-validation generator,

* An iterable yielding train, test splits,

* `"prefit"` to assume the `estimators` are prefit. In this case, the

estimators will not be refitted.

For integer/None inputs, if the estimator is a classifier and y is

either binary or multiclass,

:class:`~sklearn.model_selection.StratifiedKFold` is used.

In all other cases, :class:`~sklearn.model_selection.KFold` is used.

These splitters are instantiated with `shuffle=False` so the splits

will be the same across calls.

Refer :ref:`User Guide <cross_validation>` for the various

cross-validation strategies that can be used here.

If "prefit" is passed, it is assumed that all `estimators` have

been fitted already. The `final_estimator_` is trained on the `estimators`

predictions on the full training set and are **not** cross validated

predictions. Please note that if the models have been trained on the same

data to train the stacking model, there is a very high risk of overfitting.

.. versionadded:: 1.1

The 'prefit' option was added in 1.1

.. note::

A larger number of split will provide no benefits if the number

of training samples is large enough. Indeed, the training time

will increase. ``cv`` is not used for model evaluation but for

prediction.

stack_method : {'auto', 'predict_proba', 'decision_function', 'predict'}, \

default='auto'

Methods called for each base estimator. It can be:

* if 'auto', it will try to invoke, for each estimator,

`'predict_proba'`, `'decision_function'` or `'predict'` in that

order.

* otherwise, one of `'predict_proba'`, `'decision_function'` or

`'predict'`. If the method is not implemented by the estimator, it

will raise an error.

n_jobs : int, default=None

The number of jobs to run in parallel all `estimators` `fit`.

`None` means 1 unless in a `joblib.parallel_backend` context. -1 means

using all processors. See Glossary for more details.

passthrough : bool, default=False

When False, only the predictions of estimators will be used as

training data for `final_estimator`. When True, the

`final_estimator` is trained on the predictions as well as the

original training data.

verbose : int, default=0

Verbosity level.

Attributes

----------

classes_ : ndarray of shape (n_classes,) or list of ndarray if `y` \

is of type `"multilabel-indicator"`.

Class labels.

estimators_ : list of estimators

The elements of the `estimators` parameter, having been fitted on the

training data. If an estimator has been set to `'drop'`, it

will not appear in `estimators_`. When `cv="prefit"`, `estimators_`

is set to `estimators` and is not fitted again.

named_estimators_ : :class:`~sklearn.utils.Bunch`

Attribute to access any fitted sub-estimators by name.

n_features_in_ : int

Number of features seen during :term:`fit`. Only defined if the

underlying classifier exposes such an attribute when fit.

.. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)

Names of features seen during :term:`fit`. Only defined if the

underlying estimators expose such an attribute when fit.

.. versionadded:: 1.0

final_estimator_ : estimator

The classifier which predicts given the output of `estimators_`.

stack_method_ : list of str

The method used by each base estimator.

See Also

--------

StackingRegressor : Stack of estimators with a final regressor.

Notes

-----

When `predict_proba` is used by each estimator (i.e. most of the time for

`stack_method='auto'` or specifically for `stack_method='predict_proba'`),

the first column predicted by each estimator will be dropped in the case

of a binary classification problem. Indeed, both feature will be perfectly

collinear.

In some cases (e.g. ordinal regression), one can pass regressors as the

first layer of the :class:`StackingClassifier`. However, note that `y` will

be internally encoded in a numerically increasing order or lexicographic

order. If this ordering is not adequate, one should manually numerically

encode the classes in the desired order.

References

----------

.. [1] Wolpert, David H. "Stacked generalization." Neural networks 5.2

(1992): 241-259.

Examples

--------

>>> from sklearn.datasets import load_iris

>>> from sklearn.ensemble import RandomForestClassifier

>>> from sklearn.svm import LinearSVC

>>> from sklearn.linear_model import LogisticRegression

>>> from sklearn.preprocessing import StandardScaler

>>> from sklearn.pipeline import make_pipeline

>>> from sklearn.ensemble import StackingClassifier

>>> X, y = load_iris(return_X_y=True)

>>> estimators = [

... ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),

... ('svr', make_pipeline(StandardScaler(),

... LinearSVC(random_state=42)))

... ]

>>> clf = StackingClassifier(

... estimators=estimators, final_estimator=LogisticRegression()

... )

>>> from sklearn.model_selection import train_test_split

>>> X_train, X_test, y_train, y_test = train_test_split(

... X, y, stratify=y, random_state=42

... )

>>> clf.fit(X_train, y_train).score(X_test, y_test)

0.9...

"""

_parameter_constraints: dict = {

**_BaseStacking._parameter_constraints,

"stack_method": [

StrOptions({"auto", "predict_proba", "decision_function", "predict"})

],

}

def __init__(

self,

estimators,

final_estimator=None,

*,

cv=None,

stack_method="auto",

n_jobs=None,

passthrough=False,

verbose=0,

):

super().__init__(

estimators=estimators,

final_estimator=final_estimator,

cv=cv,

stack_method=stack_method,

n_jobs=n_jobs,

passthrough=passthrough,

verbose=verbose,

)

def _validate_final_estimator(self):

self._clone_final_estimator(default=LogisticRegression())

if not is_classifier(self.final_estimator_):

raise ValueError(

"'final_estimator' parameter should be a classifier. Got {}".format(

self.final_estimator_

)

)

def _validate_estimators(self):

"""Overload the method of `_BaseHeterogeneousEnsemble` to be more

lenient towards the type of `estimators`.

Regressors can be accepted for some cases such as ordinal regression.

"""

if len(self.estimators) == 0:

raise ValueError(

"Invalid 'estimators' attribute, 'estimators' should be a "

"non-empty list of (string, estimator) tuples."

)

names, estimators = zip(*self.estimators)

self._validate_names(names)

has_estimator = any(est != "drop" for est in estimators)

if not has_estimator:

raise ValueError(

"All estimators are dropped. At least one is required "

"to be an estimator."

)

return names, estimators

# TODO(1.7): remove `sample_weight` from the signature after deprecation

# cycle; pop it from `fit_params` before the `_raise_for_params` check and

# reinsert afterwards, for backwards compatibility

@_deprecate_positional_args(version="1.7")

def fit(self, X, y, *, sample_weight=None, **fit_params):

"""Fit the estimators.

Parameters

----------

X : {array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, where `n_samples` is the number of samples and

`n_features` is the number of features.

y : array-like of shape (n_samples,)

Target values. Note that `y` will be internally encoded in

numerically increasing order or lexicographic order. If the order

matter (e.g. for ordinal regression), one should numerically encode

the target `y` before calling :term:`fit`.

sample_weight : array-like of shape (n_samples,), default=None

Sample weights. If None, then samples are equally weighted.

Note that this is supported only if all underlying estimators

support sample weights.

**fit_params : dict

Parameters to pass to the underlying estimators.

.. versionadded:: 1.6

Only available if `enable_metadata_routing=True`, which can be

set by using ``sklearn.set_config(enable_metadata_routing=True)``.

See :ref:`Metadata Routing User Guide <metadata_routing>` for

more details.

Returns

-------

self : object

Returns a fitted instance of estimator.

"""

_raise_for_params(fit_params, self, "fit")

check_classification_targets(y)

if type_of_target(y) == "multilabel-indicator":

self._label_encoder = [LabelEncoder().fit(yk) for yk in y.T]

self.classes_ = [le.classes_ for le in self._label_encoder]

y_encoded = np.array(

[

self._label_encoder[target_idx].transform(target)

for target_idx, target in enumerate(y.T)

]

).T

else:

self._label_encoder = LabelEncoder().fit(y)

self.classes_ = self._label_encoder.classes_

y_encoded = self._label_encoder.transform(y)

if sample_weight is not None:

fit_params["sample_weight"] = sample_weight

return super().fit(X, y_encoded, **fit_params)

@available_if(_estimator_has("predict"))

def predict(self, X, **predict_params):

"""Predict target for X.

Parameters

----------

X : {array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, where `n_samples` is the number of samples and

`n_features` is the number of features.

**predict_params : dict of str -> obj

Parameters to the `predict` called by the `final_estimator`. Note

that this may be used to return uncertainties from some estimators

with `return_std` or `return_cov`. Be aware that it will only

account for uncertainty in the final estimator.

- If `enable_metadata_routing=False` (default):

Parameters directly passed to the `predict` method of the

`final_estimator`.

- If `enable_metadata_routing=True`: Parameters safely routed to

the `predict` method of the `final_estimator`. See :ref:`Metadata

Routing User Guide <metadata_routing>` for more details.

.. versionchanged:: 1.6

`**predict_params` can be routed via metadata routing API.

Returns

-------

y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)

Predicted targets.

"""

if _routing_enabled():

routed_params = process_routing(self, "predict", **predict_params)

else:

# TODO(SLEP6): remove when metadata routing cannot be disabled.

routed_params = Bunch()

routed_params.final_estimator_ = Bunch(predict={})

routed_params.final_estimator_.predict = predict_params

y_pred = super().predict(X, **routed_params.final_estimator_["predict"])

if isinstance(self._label_encoder, list):

# Handle the multilabel-indicator case

y_pred = np.array(

[

self._label_encoder[target_idx].inverse_transform(target)

for target_idx, target in enumerate(y_pred.T)

]

).T

else:

y_pred = self._label_encoder.inverse_transform(y_pred)

return y_pred

@available_if(_estimator_has("predict_proba"))

def predict_proba(self, X):

"""Predict class probabilities for `X` using the final estimator.

Parameters

----------

X : {array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, where `n_samples` is the number of samples and

`n_features` is the number of features.

Returns

-------

probabilities : ndarray of shape (n_samples, n_classes) or \

list of ndarray of shape (n_output,)

The class probabilities of the input samples.

"""

check_is_fitted(self)

y_pred = self.final_estimator_.predict_proba(self.transform(X))

if isinstance(self._label_encoder, list):

# Handle the multilabel-indicator cases

y_pred = np.array([preds[:, 0] for preds in y_pred]).T

return y_pred

@available_if(_estimator_has("decision_function"))

def decision_function(self, X):

"""Decision function for samples in `X` using the final estimator.

Parameters

----------

X : {array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, where `n_samples` is the number of samples and

`n_features` is the number of features.

Returns

-------

decisions : ndarray of shape (n_samples,), (n_samples, n_classes), \

or (n_samples, n_classes * (n_classes-1) / 2)

The decision function computed the final estimator.

"""

check_is_fitted(self)

return self.final_estimator_.decision_function(self.transform(X))

def transform(self, X):

"""Return class labels or probabilities for X for each estimator.

Parameters

----------

X : {array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, where `n_samples` is the number of samples and

`n_features` is the number of features.

Returns

-------

y_preds : ndarray of shape (n_samples, n_estimators) or \

(n_samples, n_classes * n_estimators)

Prediction outputs for each estimator.

"""

return self._transform(X)

def _sk_visual_block_(self):

# If final_estimator's default changes then this should be

# updated.

if self.final_estimator is None:

final_estimator = LogisticRegression()

else:

final_estimator = self.final_estimator

"""Stack of estimators with a final regressor.

Stacked generalization consists in stacking the output of individual

estimator and use a regressor to compute the final prediction. Stacking

allows to use the strength of each individual estimator by using their

output as input of a final estimator.

Note that `estimators_` are fitted on the full `X` while `final_estimator_`

is trained using cross-validated predictions of the base estimators using

`cross_val_predict`.

Read more in the :ref:`User Guide <stacking>`.

.. versionadded:: 0.22

Parameters

----------

estimators : list of (str, estimator)

Base estimators which will be stacked together. Each element of the

list is defined as a tuple of string (i.e. name) and an estimator

instance. An estimator can be set to 'drop' using `set_params`.

final_estimator : estimator, default=None

A regressor which will be used to combine the base estimators.

The default regressor is a :class:`~sklearn.linear_model.RidgeCV`.

cv : int, cross-validation generator, iterable, or "prefit", default=None

Determines the cross-validation splitting strategy used in

`cross_val_predict` to train `final_estimator`. Possible inputs for

cv are:

* None, to use the default 5-fold cross validation,

* integer, to specify the number of folds in a (Stratified) KFold,

* An object to be used as a cross-validation generator,

* An iterable yielding train, test splits.

* "prefit" to assume the `estimators` are prefit, and skip cross validation

For integer/None inputs, if the estimator is a classifier and y is

either binary or multiclass,

:class:`~sklearn.model_selection.StratifiedKFold` is used.

In all other cases, :class:`~sklearn.model_selection.KFold` is used.

These splitters are instantiated with `shuffle=False` so the splits

will be the same across calls.

Refer :ref:`User Guide <cross_validation>` for the various

cross-validation strategies that can be used here.

If "prefit" is passed, it is assumed that all `estimators` have

been fitted already. The `final_estimator_` is trained on the `estimators`

predictions on the full training set and are **not** cross validated

predictions. Please note that if the models have been trained on the same

data to train the stacking model, there is a very high risk of overfitting.

.. versionadded:: 1.1

The 'prefit' option was added in 1.1

.. note::

A larger number of split will provide no benefits if the number

of training samples is large enough. Indeed, the training time

will increase. ``cv`` is not used for model evaluation but for

prediction.

n_jobs : int, default=None

The number of jobs to run in parallel for `fit` of all `estimators`.

`None` means 1 unless in a `joblib.parallel_backend` context. -1 means

using all processors. See Glossary for more details.

passthrough : bool, default=False

When False, only the predictions of estimators will be used as

training data for `final_estimator`. When True, the

`final_estimator` is trained on the predictions as well as the

original training data.

verbose : int, default=0

Verbosity level.

Attributes

----------

estimators_ : list of estimator

The elements of the `estimators` parameter, having been fitted on the

training data. If an estimator has been set to `'drop'`, it

will not appear in `estimators_`. When `cv="prefit"`, `estimators_`

is set to `estimators` and is not fitted again.

named_estimators_ : :class:`~sklearn.utils.Bunch`

Attribute to access any fitted sub-estimators by name.

n_features_in_ : int

Number of features seen during :term:`fit`. Only defined if the

underlying regressor exposes such an attribute when fit.

.. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)

Names of features seen during :term:`fit`. Only defined if the

underlying estimators expose such an attribute when fit.

.. versionadded:: 1.0

final_estimator_ : estimator

The regressor to stacked the base estimators fitted.

stack_method_ : list of str

The method used by each base estimator.

See Also

--------

StackingClassifier : Stack of estimators with a final classifier.

References

----------

.. [1] Wolpert, David H. "Stacked generalization." Neural networks 5.2

(1992): 241-259.

Examples

--------

>>> from sklearn.datasets import load_diabetes

>>> from sklearn.linear_model import RidgeCV

>>> from sklearn.svm import LinearSVR

>>> from sklearn.ensemble import RandomForestRegressor

>>> from sklearn.ensemble import StackingRegressor

>>> X, y = load_diabetes(return_X_y=True)

>>> estimators = [

... ('lr', RidgeCV()),

... ('svr', LinearSVR(random_state=42))

... ]

>>> reg = StackingRegressor(

... estimators=estimators,

... final_estimator=RandomForestRegressor(n_estimators=10,

... random_state=42)

... )

>>> from sklearn.model_selection import train_test_split

>>> X_train, X_test, y_train, y_test = train_test_split(

... X, y, random_state=42

... )

>>> reg.fit(X_train, y_train).score(X_test, y_test)

0.3...

"""

def __init__(

self,

estimators,

final_estimator=None,