_stacking.py

"""Stacking classifier and regressor."""

# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: BSD 3 clause

from abc import ABCMeta, abstractmethod
from copy import deepcopy
from numbers import Integral

import numpy as np
import scipy.sparse as sparse

from ..base import clone
from ..base import ClassifierMixin, RegressorMixin, TransformerMixin
from ..base import is_classifier, is_regressor
from ..exceptions import NotFittedError
from ..utils._estimator_html_repr import _VisualBlock

from ._base import _fit_single_estimator
from ._base import _BaseHeterogeneousEnsemble

from ..linear_model import LogisticRegression
from ..linear_model import RidgeCV

from ..model_selection import cross_val_predict
from ..model_selection import check_cv

from ..preprocessing import LabelEncoder

from ..utils import Bunch
from ..utils.multiclass import check_classification_targets, type_of_target
from ..utils.metaestimators import available_if
from ..utils.validation import check_is_fitted
from ..utils.validation import column_or_1d
from ..utils.parallel import delayed, Parallel
from ..utils._param_validation import HasMethods, StrOptions
from ..utils.validation import _check_feature_names_in


def _estimator_has(attr):
    """Check if we can delegate a method to the underlying estimator.

    First, we check the first fitted final estimator if available, otherwise we
    check the unfitted final estimator.
    """
    return lambda self: (
        hasattr(self.final_estimator_, attr)
        if hasattr(self, "final_estimator_")
        else hasattr(self.final_estimator, attr)
    )


class _BaseStacking(TransformerMixin, _BaseHeterogeneousEnsemble, metaclass=ABCMeta):
    """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":
            if getattr(estimator, "predict_proba", None):
                return "predict_proba"
            elif getattr(estimator, "decision_function", None):
                return "decision_function"
            else:
                return "predict"
        else:
            if not hasattr(estimator, method):
                raise ValueError(
                    "Underlying estimator {} does not implement the method {}.".format(
                        name, method
                    )
                )
            return method

    def fit(self, X, y, sample_weight=None):
        """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.

        sample_weight : array-like of shape (n_samples,) or default=None
            Sample weights. If None, then samples are equally weighted.
            Note that this is supported only if all underlying estimators
            support sample weights.

            .. versionchanged:: 0.23
               when not None, `sample_weight` is passed to all underlying
               estimators

        Returns
        -------
        self : object
        """

        self._validate_params()

        # 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 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, sample_weight)
                for est in 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()

            fit_params = (
                {"sample_weight": sample_weight} if sample_weight is not None else None
            )
            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,
                    fit_params=fit_params,
                    verbose=self.verbose,
                )
                for est, meth in zip(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, sample_weight=sample_weight
        )

        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
            accounts 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)


class StackingClassifier(ClassifierMixin, _BaseStacking):
    """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

    def fit(self, X, y, sample_weight=None):
        """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.

        Returns
        -------
        self : object
            Returns a fitted instance of estimator.
        """
        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)
        return super().fit(X, y_encoded, sample_weight)

    @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
            accounts for uncertainty in the final estimator.

        Returns
        -------
        y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)
            Predicted targets.
        """
        y_pred = super().predict(X, **predict_params)
        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
        return super()._sk_visual_block_with_final_estimator(final_estimator)


class StackingRegressor(RegressorMixin, _BaseStacking):
    """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,
        *,
        cv=None,
        n_jobs=None,
        passthrough=False,
        verbose=0,
    ):
        super().__init__(
            estimators=estimators,
            final_estimator=final_estimator,
            cv=cv,
            stack_method="predict",
            n_jobs=n_jobs,
            passthrough=passthrough,
            verbose=verbose,
        )

    def _validate_final_estimator(self):
        self._clone_final_estimator(default=RidgeCV())
        if not is_regressor(self.final_estimator_):
            raise ValueError(
                "'final_estimator' parameter should be a regressor. Got {}".format(
                    self.final_estimator_
                )
            )

    def fit(self, X, y, sample_weight=None):
        """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.

        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.

        Returns
        -------
        self : object
            Returns a fitted instance.
        """
        y = column_or_1d(y, warn=True)
        return super().fit(X, y, sample_weight)

    def transform(self, X):
        """Return the predictions 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)
            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 = RidgeCV()
        else:
            final_estimator = self.final_estimator
        return super()._sk_visual_block_with_final_estimator(final_estimator)