"""Bagging meta-estimator.""" # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import itertools import numbers from abc import ABCMeta, abstractmethod from functools import partial from numbers import Integral from warnings import warn import numpy as np from ..base import ClassifierMixin, RegressorMixin, _fit_context from ..metrics import accuracy_score, r2_score from ..tree import DecisionTreeClassifier, DecisionTreeRegressor from ..utils import ( Bunch, _safe_indexing, check_random_state, column_or_1d, ) from ..utils._mask import indices_to_mask from ..utils._param_validation import HasMethods, Interval, RealNotInt from ..utils._tags import get_tags from ..utils.metadata_routing import ( MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, get_routing_for_object, process_routing, ) from ..utils.metaestimators import available_if from ..utils.multiclass import check_classification_targets from ..utils.parallel import Parallel, delayed from ..utils.random import sample_without_replacement from ..utils.validation import ( _check_method_params, _check_sample_weight, _deprecate_positional_args, _estimator_has, check_is_fitted, has_fit_parameter, validate_data, ) from ._base import BaseEnsemble, _partition_estimators __all__ = ["BaggingClassifier", "BaggingRegressor"] MAX_INT = np.iinfo(np.int32).max def _generate_indices(random_state, bootstrap, n_population, n_samples): """Draw randomly sampled indices.""" # Draw sample indices if bootstrap: indices = random_state.randint(0, n_population, n_samples) else: indices = sample_without_replacement( n_population, n_samples, random_state=random_state ) return indices def _generate_bagging_indices( random_state, bootstrap_features, bootstrap_samples, n_features, n_samples, max_features, max_samples, ): """Randomly draw feature and sample indices.""" # Get valid random state random_state = check_random_state(random_state) # Draw indices feature_indices = _generate_indices( random_state, bootstrap_features, n_features, max_features ) sample_indices = _generate_indices( random_state, bootstrap_samples, n_samples, max_samples ) return feature_indices, sample_indices def _parallel_build_estimators( n_estimators, ensemble, X, y, seeds, total_n_estimators, verbose, check_input, fit_params, ): """Private function used to build a batch of estimators within a job.""" # Retrieve settings n_samples, n_features = X.shape max_features = ensemble._max_features max_samples = ensemble._max_samples bootstrap = ensemble.bootstrap bootstrap_features = ensemble.bootstrap_features has_check_input = has_fit_parameter(ensemble.estimator_, "check_input") requires_feature_indexing = bootstrap_features or max_features != n_features # Build estimators estimators = [] estimators_features = [] # TODO: (slep6) remove if condition for unrouted sample_weight when metadata # routing can't be disabled. support_sample_weight = has_fit_parameter(ensemble.estimator_, "sample_weight") if not _routing_enabled() and ( not support_sample_weight and fit_params.get("sample_weight") is not None ): raise ValueError( "The base estimator doesn't support sample weight, but sample_weight is " "passed to the fit method." ) for i in range(n_estimators): if verbose > 1: print( "Building estimator %d of %d for this parallel run (total %d)..." % (i + 1, n_estimators, total_n_estimators) ) random_state = seeds[i] estimator = ensemble._make_estimator(append=False, random_state=random_state) if has_check_input: estimator_fit = partial(estimator.fit, check_input=check_input) else: estimator_fit = estimator.fit # Draw random feature, sample indices features, indices = _generate_bagging_indices( random_state, bootstrap_features, bootstrap, n_features, n_samples, max_features, max_samples, ) fit_params_ = fit_params.copy() # TODO(SLEP6): remove if condition for unrouted sample_weight when metadata # routing can't be disabled. # 1. If routing is enabled, we will check if the routing supports sample # weight and use it if it does. # 2. If routing is not enabled, we will check if the base # estimator supports sample_weight and use it if it does. # Note: Row sampling can be achieved either through setting sample_weight or # by indexing. The former is more efficient. Therefore, use this method # if possible, otherwise use indexing. if _routing_enabled(): request_or_router = get_routing_for_object(ensemble.estimator_) consumes_sample_weight = request_or_router.consumes( "fit", ("sample_weight",) ) else: consumes_sample_weight = support_sample_weight if consumes_sample_weight: # Draw sub samples, using sample weights, and then fit curr_sample_weight = _check_sample_weight( fit_params_.pop("sample_weight", None), X ).copy() if bootstrap: sample_counts = np.bincount(indices, minlength=n_samples) curr_sample_weight *= sample_counts else: not_indices_mask = ~indices_to_mask(indices, n_samples) curr_sample_weight[not_indices_mask] = 0 fit_params_["sample_weight"] = curr_sample_weight X_ = X[:, features] if requires_feature_indexing else X estimator_fit(X_, y, **fit_params_) else: # cannot use sample_weight, so use indexing y_ = _safe_indexing(y, indices) X_ = _safe_indexing(X, indices) fit_params_ = _check_method_params(X, params=fit_params_, indices=indices) if requires_feature_indexing: X_ = X_[:, features] estimator_fit(X_, y_, **fit_params_) estimators.append(estimator) estimators_features.append(features) return estimators, estimators_features def _parallel_predict_proba(estimators, estimators_features, X, n_classes): """Private function used to compute (proba-)predictions within a job.""" n_samples = X.shape[0] proba = np.zeros((n_samples, n_classes)) for estimator, features in zip(estimators, estimators_features): if hasattr(estimator, "predict_proba"): proba_estimator = estimator.predict_proba(X[:, features]) if n_classes == len(estimator.classes_): proba += proba_estimator else: proba[:, estimator.classes_] += proba_estimator[ :, range(len(estimator.classes_)) ] else: # Resort to voting predictions = estimator.predict(X[:, features]) for i in range(n_samples): proba[i, predictions[i]] += 1 return proba def _parallel_predict_log_proba(estimators, estimators_features, X, n_classes): """Private function used to compute log probabilities within a job.""" n_samples = X.shape[0] log_proba = np.empty((n_samples, n_classes)) log_proba.fill(-np.inf) all_classes = np.arange(n_classes, dtype=int) for estimator, features in zip(estimators, estimators_features): log_proba_estimator = estimator.predict_log_proba(X[:, features]) if n_classes == len(estimator.classes_): log_proba = np.logaddexp(log_proba, log_proba_estimator) else: log_proba[:, estimator.classes_] = np.logaddexp( log_proba[:, estimator.classes_], log_proba_estimator[:, range(len(estimator.classes_))], ) missing = np.setdiff1d(all_classes, estimator.classes_) log_proba[:, missing] = np.logaddexp(log_proba[:, missing], -np.inf) return log_proba def _parallel_decision_function(estimators, estimators_features, X): """Private function used to compute decisions within a job.""" return sum( estimator.decision_function(X[:, features]) for estimator, features in zip(estimators, estimators_features) ) def _parallel_predict_regression(estimators, estimators_features, X): """Private function used to compute predictions within a job.""" return sum( estimator.predict(X[:, features]) for estimator, features in zip(estimators, estimators_features) ) class BaseBagging(BaseEnsemble, metaclass=ABCMeta): """Base class for Bagging meta-estimator. Warning: This class should not be used directly. Use derived classes instead. """ _parameter_constraints: dict = { "estimator": [HasMethods(["fit", "predict"]), None], "n_estimators": [Interval(Integral, 1, None, closed="left")], "max_samples": [ Interval(Integral, 1, None, closed="left"), Interval(RealNotInt, 0, 1, closed="right"), ], "max_features": [ Interval(Integral, 1, None, closed="left"), Interval(RealNotInt, 0, 1, closed="right"), ], "bootstrap": ["boolean"], "bootstrap_features": ["boolean"], "oob_score": ["boolean"], "warm_start": ["boolean"], "n_jobs": [None, Integral], "random_state": ["random_state"], "verbose": ["verbose"], } @abstractmethod def __init__( self, estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0, ): super().__init__( estimator=estimator, n_estimators=n_estimators, ) self.max_samples = max_samples self.max_features = max_features self.bootstrap = bootstrap self.bootstrap_features = bootstrap_features self.oob_score = oob_score self.warm_start = warm_start self.n_jobs = n_jobs self.random_state = random_state self.verbose = verbose # 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 later, for backwards compatibility @_deprecate_positional_args(version="1.7") @_fit_context( # BaseBagging.estimator is not validated yet prefer_skip_nested_validation=False ) def fit(self, X, y, *, sample_weight=None, **fit_params): """Build a Bagging ensemble of estimators from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. y : array-like of shape (n_samples,) The target values (class labels in classification, real numbers in regression). 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 the base estimator supports sample weighting. **fit_params : dict Parameters to pass to the underlying estimators. .. versionadded:: 1.5 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 Fitted estimator. """ _raise_for_params(fit_params, self, "fit") # Convert data (X is required to be 2d and indexable) X, y = validate_data( self, X, y, accept_sparse=["csr", "csc"], dtype=None, ensure_all_finite=False, multi_output=True, ) if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X, dtype=None) fit_params["sample_weight"] = sample_weight return self._fit(X, y, max_samples=self.max_samples, **fit_params) def _parallel_args(self): return {} def _fit( self, X, y, max_samples=None, max_depth=None, check_input=True, **fit_params, ): """Build a Bagging ensemble of estimators from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. y : array-like of shape (n_samples,) The target values (class labels in classification, real numbers in regression). max_samples : int or float, default=None Argument to use instead of self.max_samples. max_depth : int, default=None Override value used when constructing base estimator. Only supported if the base estimator has a max_depth parameter. check_input : bool, default=True Override value used when fitting base estimator. Only supported if the base estimator has a check_input parameter for fit function. If the meta-estimator already checks the input, set this value to False to prevent redundant input validation. **fit_params : dict, default=None Parameters to pass to the :term:`fit` method of the underlying estimator. Returns ------- self : object Fitted estimator. """ random_state = check_random_state(self.random_state) # Remap output n_samples = X.shape[0] self._n_samples = n_samples y = self._validate_y(y) # Check parameters self._validate_estimator(self._get_estimator()) if _routing_enabled(): routed_params = process_routing(self, "fit", **fit_params) else: routed_params = Bunch() routed_params.estimator = Bunch(fit=fit_params) if "sample_weight" in fit_params: routed_params.estimator.fit["sample_weight"] = fit_params[ "sample_weight" ] if max_depth is not None: self.estimator_.max_depth = max_depth # Validate max_samples if max_samples is None: max_samples = self.max_samples elif not isinstance(max_samples, numbers.Integral): max_samples = int(max_samples * X.shape[0]) if max_samples > X.shape[0]: raise ValueError("max_samples must be <= n_samples") # Store validated integer row sampling value self._max_samples = max_samples # Validate max_features if isinstance(self.max_features, numbers.Integral): max_features = self.max_features elif isinstance(self.max_features, float): max_features = int(self.max_features * self.n_features_in_) if max_features > self.n_features_in_: raise ValueError("max_features must be <= n_features") max_features = max(1, int(max_features)) # Store validated integer feature sampling value self._max_features = max_features # Other checks if not self.bootstrap and self.oob_score: raise ValueError("Out of bag estimation only available if bootstrap=True") if self.warm_start and self.oob_score: raise ValueError("Out of bag estimate only available if warm_start=False") if hasattr(self, "oob_score_") and self.warm_start: del self.oob_score_ if not self.warm_start or not hasattr(self, "estimators_"): # Free allocated memory, if any self.estimators_ = [] self.estimators_features_ = [] n_more_estimators = self.n_estimators - len(self.estimators_) if n_more_estimators < 0: raise ValueError( "n_estimators=%d must be larger or equal to " "len(estimators_)=%d when warm_start==True" % (self.n_estimators, len(self.estimators_)) ) elif n_more_estimators == 0: warn( "Warm-start fitting without increasing n_estimators does not " "fit new trees." ) return self # Parallel loop n_jobs, n_estimators, starts = _partition_estimators( n_more_estimators, self.n_jobs ) total_n_estimators = sum(n_estimators) # Advance random state to state after training # the first n_estimators if self.warm_start and len(self.estimators_) > 0: random_state.randint(MAX_INT, size=len(self.estimators_)) seeds = random_state.randint(MAX_INT, size=n_more_estimators) self._seeds = seeds all_results = Parallel( n_jobs=n_jobs, verbose=self.verbose, **self._parallel_args() )( delayed(_parallel_build_estimators)( n_estimators[i], self, X, y, seeds[starts[i] : starts[i + 1]], total_n_estimators, verbose=self.verbose, check_input=check_input, fit_params=routed_params.estimator.fit, ) for i in range(n_jobs) ) # Reduce self.estimators_ += list( itertools.chain.from_iterable(t[0] for t in all_results) ) self.estimators_features_ += list( itertools.chain.from_iterable(t[1] for t in all_results) ) if self.oob_score: self._set_oob_score(X, y) return self @abstractmethod def _set_oob_score(self, X, y): """Calculate out of bag predictions and score.""" def _validate_y(self, y): if len(y.shape) == 1 or y.shape[1] == 1: return column_or_1d(y, warn=True) return y def _get_estimators_indices(self): # Get drawn indices along both sample and feature axes for seed in self._seeds: # Operations accessing random_state must be performed identically # to those in `_parallel_build_estimators()` feature_indices, sample_indices = _generate_bagging_indices( seed, self.bootstrap_features, self.bootstrap, self.n_features_in_, self._n_samples, self._max_features, self._max_samples, ) yield feature_indices, sample_indices @property def estimators_samples_(self): """ The subset of drawn samples for each base estimator. Returns a dynamically generated list of indices identifying the samples used for fitting each member of the ensemble, i.e., the in-bag samples. Note: the list is re-created at each call to the property in order to reduce the object memory footprint by not storing the sampling data. Thus fetching the property may be slower than expected. """ return [sample_indices for _, sample_indices in self._get_estimators_indices()] def get_metadata_routing(self): """Get metadata routing of this object. Please check :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.5 Returns ------- routing : MetadataRouter A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. """ router = MetadataRouter(owner=self.__class__.__name__) router.add( estimator=self._get_estimator(), method_mapping=MethodMapping().add(callee="fit", caller="fit"), ) return router @abstractmethod def _get_estimator(self): """Resolve which estimator to return.""" def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.sparse = get_tags(self._get_estimator()).input_tags.sparse tags.input_tags.allow_nan = get_tags(self._get_estimator()).input_tags.allow_nan return tags class BaggingClassifier(ClassifierMixin, BaseBagging): """A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it. This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]_. If samples are drawn with replacement, then the method is known as Bagging [2]_. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]_. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4]_. Read more in the :ref:`User Guide `. .. versionadded:: 0.15 Parameters ---------- estimator : object, default=None The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a :class:`~sklearn.tree.DecisionTreeClassifier`. .. versionadded:: 1.2 `base_estimator` was renamed to `estimator`. n_estimators : int, default=10 The number of base estimators in the ensemble. max_samples : int or float, default=1.0 The number of samples to draw from X to train each base estimator (with replacement by default, see `bootstrap` for more details). - If int, then draw `max_samples` samples. - If float, then draw `max_samples * X.shape[0]` samples. max_features : int or float, default=1.0 The number of features to draw from X to train each base estimator ( without replacement by default, see `bootstrap_features` for more details). - If int, then draw `max_features` features. - If float, then draw `max(1, int(max_features * n_features_in_))` features. bootstrap : bool, default=True Whether samples are drawn with replacement. If False, sampling without replacement is performed. bootstrap_features : bool, default=False Whether features are drawn with replacement. oob_score : bool, default=False Whether to use out-of-bag samples to estimate the generalization error. Only available if bootstrap=True. warm_start : bool, default=False When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble. See :term:`the Glossary `. .. versionadded:: 0.17 *warm_start* constructor parameter. n_jobs : int, default=None The number of jobs to run in parallel for both :meth:`fit` and :meth:`predict`. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. random_state : int, RandomState instance or None, default=None Controls the random resampling of the original dataset (sample wise and feature wise). If the base estimator accepts a `random_state` attribute, a different seed is generated for each instance in the ensemble. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. verbose : int, default=0 Controls the verbosity when fitting and predicting. Attributes ---------- estimator_ : estimator The base estimator from which the ensemble is grown. .. versionadded:: 1.2 `base_estimator_` was renamed to `estimator_`. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 estimators_ : list of estimators The collection of fitted base estimators. estimators_samples_ : list of arrays The subset of drawn samples (i.e., the in-bag samples) for each base estimator. Each subset is defined by an array of the indices selected. estimators_features_ : list of arrays The subset of drawn features for each base estimator. classes_ : ndarray of shape (n_classes,) The classes labels. n_classes_ : int or list The number of classes. oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when ``oob_score`` is True. oob_decision_function_ : ndarray of shape (n_samples, n_classes) Decision function computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, `oob_decision_function_` might contain NaN. This attribute exists only when ``oob_score`` is True. See Also -------- BaggingRegressor : A Bagging regressor. References ---------- .. [1] L. Breiman, "Pasting small votes for classification in large databases and on-line", Machine Learning, 36(1), 85-103, 1999. .. [2] L. Breiman, "Bagging predictors", Machine Learning, 24(2), 123-140, 1996. .. [3] T. Ho, "The random subspace method for constructing decision forests", Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998. .. [4] G. Louppe and P. Geurts, "Ensembles on Random Patches", Machine Learning and Knowledge Discovery in Databases, 346-361, 2012. Examples -------- >>> from sklearn.svm import SVC >>> from sklearn.ensemble import BaggingClassifier >>> from sklearn.datasets import make_classification >>> X, y = make_classification(n_samples=100, n_features=4, ... n_informative=2, n_redundant=0, ... random_state=0, shuffle=False) >>> clf = BaggingClassifier(estimator=SVC(), ... n_estimators=10, random_state=0).fit(X, y) >>> clf.predict([[0, 0, 0, 0]]) array([1]) """ def __init__( self, estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0, ): super().__init__( estimator=estimator, n_estimators=n_estimators, max_samples=max_samples, max_features=max_features, bootstrap=bootstrap, bootstrap_features=bootstrap_features, oob_score=oob_score, warm_start=warm_start, n_jobs=n_jobs, random_state=random_state, verbose=verbose, ) def _get_estimator(self): """Resolve which estimator to return (default is DecisionTreeClassifier)""" if self.estimator is None: return DecisionTreeClassifier() return self.estimator def _set_oob_score(self, X, y): n_samples = y.shape[0] n_classes_ = self.n_classes_ predictions = np.zeros((n_samples, n_classes_)) for estimator, samples, features in zip( self.estimators_, self.estimators_samples_, self.estimators_features_ ): # Create mask for OOB samples mask = ~indices_to_mask(samples, n_samples) if hasattr(estimator, "predict_proba"): predictions[mask, :] += estimator.predict_proba( (X[mask, :])[:, features] ) else: p = estimator.predict((X[mask, :])[:, features]) j = 0 for i in range(n_samples): if mask[i]: predictions[i, p[j]] += 1 j += 1 if (predictions.sum(axis=1) == 0).any(): warn( "Some inputs do not have OOB scores. " "This probably means too few estimators were used " "to compute any reliable oob estimates." ) oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] oob_score = accuracy_score(y, np.argmax(predictions, axis=1)) self.oob_decision_function_ = oob_decision_function self.oob_score_ = oob_score def _validate_y(self, y): y = column_or_1d(y, warn=True) check_classification_targets(y) self.classes_, y = np.unique(y, return_inverse=True) self.n_classes_ = len(self.classes_) return y def predict(self, X): """Predict class for X. The predicted class of an input sample is computed as the class with the highest mean predicted probability. If base estimators do not implement a ``predict_proba`` method, then it resorts to voting. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- y : ndarray of shape (n_samples,) The predicted classes. """ predicted_probabilitiy = self.predict_proba(X) return self.classes_.take((np.argmax(predicted_probabilitiy, axis=1)), axis=0) def predict_proba(self, X): """Predict class probabilities for X. The predicted class probabilities of an input sample is computed as the mean predicted class probabilities of the base estimators in the ensemble. If base estimators do not implement a ``predict_proba`` method, then it resorts to voting and the predicted class probabilities of an input sample represents the proportion of estimators predicting each class. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- p : ndarray of shape (n_samples, n_classes) The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. """ check_is_fitted(self) # Check data X = validate_data( self, X, accept_sparse=["csr", "csc"], dtype=None, ensure_all_finite=False, reset=False, ) # Parallel loop n_jobs, _, starts = _partition_estimators(self.n_estimators, self.n_jobs) all_proba = Parallel( n_jobs=n_jobs, verbose=self.verbose, **self._parallel_args() )( delayed(_parallel_predict_proba)( self.estimators_[starts[i] : starts[i + 1]], self.estimators_features_[starts[i] : starts[i + 1]], X, self.n_classes_, ) for i in range(n_jobs) ) # Reduce proba = sum(all_proba) / self.n_estimators return proba def predict_log_proba(self, X): """Predict class log-probabilities for X. The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the base estimators in the ensemble. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- p : ndarray of shape (n_samples, n_classes) The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. """ check_is_fitted(self) if hasattr(self.estimator_, "predict_log_proba"): # Check data X = validate_data( self, X, accept_sparse=["csr", "csc"], dtype=None, ensure_all_finite=False, reset=False, ) # Parallel loop n_jobs, _, starts = _partition_estimators(self.n_estimators, self.n_jobs) all_log_proba = Parallel(n_jobs=n_jobs, verbose=self.verbose)( delayed(_parallel_predict_log_proba)( self.estimators_[starts[i] : starts[i + 1]], self.estimators_features_[starts[i] : starts[i + 1]], X, self.n_classes_, ) for i in range(n_jobs) ) # Reduce log_proba = all_log_proba[0] for j in range(1, len(all_log_proba)): log_proba = np.logaddexp(log_proba, all_log_proba[j]) log_proba -= np.log(self.n_estimators) else: log_proba = np.log(self.predict_proba(X)) return log_proba @available_if( _estimator_has("decision_function", delegates=("estimators_", "estimator")) ) def decision_function(self, X): """Average of the decision functions of the base classifiers. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- score : ndarray of shape (n_samples, k) The decision function of the input samples. The columns correspond to the classes in sorted order, as they appear in the attribute ``classes_``. Regression and binary classification are special cases with ``k == 1``, otherwise ``k==n_classes``. """ check_is_fitted(self) # Check data X = validate_data( self, X, accept_sparse=["csr", "csc"], dtype=None, ensure_all_finite=False, reset=False, ) # Parallel loop n_jobs, _, starts = _partition_estimators(self.n_estimators, self.n_jobs) all_decisions = Parallel(n_jobs=n_jobs, verbose=self.verbose)( delayed(_parallel_decision_function)( self.estimators_[starts[i] : starts[i + 1]], self.estimators_features_[starts[i] : starts[i + 1]], X, ) for i in range(n_jobs) ) # Reduce decisions = sum(all_decisions) / self.n_estimators return decisions class BaggingRegressor(RegressorMixin, BaseBagging): """A Bagging regressor. A Bagging regressor is an ensemble meta-estimator that fits base regressors each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it. This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]_. If samples are drawn with replacement, then the method is known as Bagging [2]_. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]_. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4]_. Read more in the :ref:`User Guide `. .. versionadded:: 0.15 Parameters ---------- estimator : object, default=None The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a :class:`~sklearn.tree.DecisionTreeRegressor`. .. versionadded:: 1.2 `base_estimator` was renamed to `estimator`. n_estimators : int, default=10 The number of base estimators in the ensemble. max_samples : int or float, default=1.0 The number of samples to draw from X to train each base estimator (with replacement by default, see `bootstrap` for more details). - If int, then draw `max_samples` samples. - If float, then draw `max_samples * X.shape[0]` samples. max_features : int or float, default=1.0 The number of features to draw from X to train each base estimator ( without replacement by default, see `bootstrap_features` for more details). - If int, then draw `max_features` features. - If float, then draw `max(1, int(max_features * n_features_in_))` features. bootstrap : bool, default=True Whether samples are drawn with replacement. If False, sampling without replacement is performed. bootstrap_features : bool, default=False Whether features are drawn with replacement. oob_score : bool, default=False Whether to use out-of-bag samples to estimate the generalization error. Only available if bootstrap=True. warm_start : bool, default=False When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble. See :term:`the Glossary `. n_jobs : int, default=None The number of jobs to run in parallel for both :meth:`fit` and :meth:`predict`. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. random_state : int, RandomState instance or None, default=None Controls the random resampling of the original dataset (sample wise and feature wise). If the base estimator accepts a `random_state` attribute, a different seed is generated for each instance in the ensemble. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. verbose : int, default=0 Controls the verbosity when fitting and predicting. Attributes ---------- estimator_ : estimator The base estimator from which the ensemble is grown. .. versionadded:: 1.2 `base_estimator_` was renamed to `estimator_`. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 estimators_ : list of estimators The collection of fitted sub-estimators. estimators_samples_ : list of arrays The subset of drawn samples (i.e., the in-bag samples) for each base estimator. Each subset is defined by an array of the indices selected. estimators_features_ : list of arrays The subset of drawn features for each base estimator. oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when ``oob_score`` is True. oob_prediction_ : ndarray of shape (n_samples,) Prediction computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, `oob_prediction_` might contain NaN. This attribute exists only when ``oob_score`` is True. See Also -------- BaggingClassifier : A Bagging classifier. References ---------- .. [1] L. Breiman, "Pasting small votes for classification in large databases and on-line", Machine Learning, 36(1), 85-103, 1999. .. [2] L. Breiman, "Bagging predictors", Machine Learning, 24(2), 123-140, 1996. .. [3] T. Ho, "The random subspace method for constructing decision forests", Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998. .. [4] G. Louppe and P. Geurts, "Ensembles on Random Patches", Machine Learning and Knowledge Discovery in Databases, 346-361, 2012. Examples -------- >>> from sklearn.svm import SVR >>> from sklearn.ensemble import BaggingRegressor >>> from sklearn.datasets import make_regression >>> X, y = make_regression(n_samples=100, n_features=4, ... n_informative=2, n_targets=1, ... random_state=0, shuffle=False) >>> regr = BaggingRegressor(estimator=SVR(), ... n_estimators=10, random_state=0).fit(X, y) >>> regr.predict([[0, 0, 0, 0]]) array([-2.8720...]) """ def __init__( self, estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0, ): super().__init__( estimator=estimator, n_estimators=n_estimators, max_samples=max_samples, max_features=max_features, bootstrap=bootstrap, bootstrap_features=bootstrap_features, oob_score=oob_score, warm_start=warm_start, n_jobs=n_jobs, random_state=random_state, verbose=verbose, ) def predict(self, X): """Predict regression target for X. The predicted regression target of an input sample is computed as the mean predicted regression targets of the estimators in the ensemble. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- y : ndarray of shape (n_samples,) The predicted values. """ check_is_fitted(self) # Check data X = validate_data( self, X, accept_sparse=["csr", "csc"], dtype=None, ensure_all_finite=False, reset=False, ) # Parallel loop n_jobs, _, starts = _partition_estimators(self.n_estimators, self.n_jobs) all_y_hat = Parallel(n_jobs=n_jobs, verbose=self.verbose)( delayed(_parallel_predict_regression)( self.estimators_[starts[i] : starts[i + 1]], self.estimators_features_[starts[i] : starts[i + 1]], X, ) for i in range(n_jobs) ) # Reduce y_hat = sum(all_y_hat) / self.n_estimators return y_hat def _set_oob_score(self, X, y): n_samples = y.shape[0] predictions = np.zeros((n_samples,)) n_predictions = np.zeros((n_samples,)) for estimator, samples, features in zip( self.estimators_, self.estimators_samples_, self.estimators_features_ ): # Create mask for OOB samples mask = ~indices_to_mask(samples, n_samples) predictions[mask] += estimator.predict((X[mask, :])[:, features]) n_predictions[mask] += 1 if (n_predictions == 0).any(): warn( "Some inputs do not have OOB scores. " "This probably means too few estimators were used " "to compute any reliable oob estimates." ) n_predictions[n_predictions == 0] = 1 predictions /= n_predictions self.oob_prediction_ = predictions self.oob_score_ = r2_score(y, predictions) def _get_estimator(self): """Resolve which estimator to return (default is DecisionTreeClassifier)""" if self.estimator is None: return DecisionTreeRegressor() return self.estimator