"""Stacking classifier and regressor.""" # Authors: Guillaume Lemaitre # License: BSD 3 clause from abc import ABCMeta, abstractmethod from copy import deepcopy import numpy as np from joblib import Parallel, delayed 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.metaestimators import if_delegate_has_method from ..utils.multiclass import check_classification_targets from ..utils.validation import check_is_fitted from ..utils.validation import column_or_1d from ..utils.validation import _deprecate_positional_args class _BaseStacking(TransformerMixin, _BaseHeterogeneousEnsemble, metaclass=ABCMeta): """Base class for stacking method.""" @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) """ X_meta = [] for est_idx, preds in enumerate(predictions): # case where the the estimator returned a 1D array if preds.ndim == 1: X_meta.append(preds.reshape(-1, 1)) else: if (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 are perfectly # collinear. X_meta.append(preds[:, 1:]) else: X_meta.append(preds) 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 """ # 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) # 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': self.named_estimators_[name_est] = self.estimators_[ est_fitted_idx] est_fitted_idx += 1 else: self.named_estimators_[name_est] = 'drop' # 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() self.stack_method_ = [ self._method_name(name, est, meth) for name, est, meth in zip(names, all_estimators, stack_method) ] 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 " f"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) @if_delegate_has_method(delegate='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 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_(self, final_estimator): names, estimators = zip(*self.estimators) parallel = _VisualBlock('parallel', estimators, names=names, dash_wrapped=False) serial = _VisualBlock('serial', (parallel, final_estimator), dash_wrapped=False) return _VisualBlock('serial', [serial]) 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`. .. versionadded:: 0.22 Read more in the :ref:`User Guide `. 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 classifier which will be used to combine the base estimators. The default classifier is a `LogisticRegression`. cv : int, cross-validation generator or an iterable, 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. For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, `StratifiedKFold` is used. In all other cases, `KFold` is used. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. 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,) 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_`. named_estimators_ : :class:`~sklearn.utils.Bunch` Attribute to access any fitted sub-estimators by name. final_estimator_ : estimator The classifier which predicts given the output of `estimators_`. stack_method_ : list of str The method used by each base estimator. 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. 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... """ @_deprecate_positional_args 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 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 """ check_classification_targets(y) self._le = LabelEncoder().fit(y) self.classes_ = self._le.classes_ return super().fit(X, self._le.transform(y), sample_weight) @if_delegate_has_method(delegate='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 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) return self._le.inverse_transform(y_pred) @if_delegate_has_method(delegate='final_estimator_') def predict_proba(self, X): """Predict class probabilities for X using `final_estimator_.predict_proba`. 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) return self.final_estimator_.predict_proba(self.transform(X)) @if_delegate_has_method(delegate='final_estimator_') def decision_function(self, X): """Predict decision function for samples in X using `final_estimator_.decision_function`. 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_(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`. .. versionadded:: 0.22 Read more in the :ref:`User Guide `. 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 `RidgeCV`. cv : int, cross-validation generator or an iterable, 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. For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, `StratifiedKFold` is used. In all other cases, `KFold` is used. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. 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_`. named_estimators_ : :class:`~sklearn.utils.Bunch` Attribute to access any fitted sub-estimators by name. final_estimator_ : estimator The regressor to stacked the base estimators fitted. 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... """ @_deprecate_positional_args 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 """ 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_(final_estimator)