"""Stacking classifier and regressor.""" # Authors: The scikit-learn developers # SPDX-License-Identifier: 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 ( ClassifierMixin, RegressorMixin, TransformerMixin, _fit_context, clone, is_classifier, is_regressor, ) from ..exceptions import NotFittedError from ..linear_model import LogisticRegression, RidgeCV from ..model_selection import check_cv, cross_val_predict from ..preprocessing import LabelEncoder from ..utils import Bunch from ..utils._estimator_html_repr import _VisualBlock from ..utils._param_validation import HasMethods, StrOptions from ..utils.metadata_routing import ( MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing, ) from ..utils.metaestimators import available_if from ..utils.multiclass import check_classification_targets, type_of_target from ..utils.parallel import Parallel, delayed from ..utils.validation import ( _check_feature_names_in, _check_response_method, _deprecate_positional_args, _estimator_has, check_is_fitted, column_or_1d, ) from ._base import _BaseHeterogeneousEnsemble, _fit_single_estimator 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": 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", delegates=("final_estimator_", "final_estimator")) ) 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 ` 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"), ) return router 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 `. .. 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 ` 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 for `fit` of all `estimators`. `None` means 1 unless in a `joblib.parallel_backend` context. -1 means using all processors. See :term:`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 estimator 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 fit on the output of `estimators_` and responsible for final predictions. 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 ` 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", delegates=("final_estimator_", "final_estimator")) ) 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 ` 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", delegates=("final_estimator_", "final_estimator") ) ) 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", delegates=("final_estimator_", "final_estimator") ) ) 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 `. .. 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. 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 ` 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 :term:`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 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 estimator 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 fit on the output of `estimators_` and responsible for final predictions. 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_ ) ) # 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. 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 ` for more details. Returns ------- self : object Returns a fitted instance. """ _raise_for_params(fit_params, self, "fit") y = column_or_1d(y, warn=True) if sample_weight is not None: fit_params["sample_weight"] = sample_weight return super().fit(X, y, **fit_params) 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) # 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_transform(self, X, y, *, sample_weight=None, **fit_params): """Fit the estimators and 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. 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. **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 ` for more details. Returns ------- y_preds : ndarray of shape (n_samples, n_estimators) Prediction outputs for each estimator. """ _raise_for_params(fit_params, self, "fit") if sample_weight is not None: fit_params["sample_weight"] = sample_weight return super().fit_transform(X, y, **fit_params) @available_if( _estimator_has("predict", delegates=("final_estimator_", "final_estimator")) ) 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 ` 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"]) return y_pred 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)