""" Soft Voting/Majority Rule classifier and Voting regressor. This module contains: - A Soft Voting/Majority Rule classifier for classification estimators. - A Voting regressor for regression estimators. """ # Authors: Sebastian Raschka , # Gilles Louppe , # Ramil Nugmanov # Mohamed Ali Jamaoui # # License: BSD 3 clause from abc import abstractmethod from numbers import Integral import numpy as np from ..base import ClassifierMixin from ..base import RegressorMixin from ..base import TransformerMixin from ..base import clone from ._base import _fit_single_estimator from ._base import _BaseHeterogeneousEnsemble from ..preprocessing import LabelEncoder from ..utils import Bunch from ..utils.metaestimators import available_if from ..utils.validation import check_is_fitted from ..utils.validation import _check_feature_names_in from ..utils.multiclass import check_classification_targets from ..utils.validation import column_or_1d from ..utils._param_validation import StrOptions from ..exceptions import NotFittedError from ..utils._estimator_html_repr import _VisualBlock from ..utils.parallel import delayed, Parallel class _BaseVoting(TransformerMixin, _BaseHeterogeneousEnsemble): """Base class for voting. Warning: This class should not be used directly. Use derived classes instead. """ _parameter_constraints: dict = { "estimators": [list], "weights": ["array-like", None], "n_jobs": [None, Integral], "verbose": ["verbose"], } def _log_message(self, name, idx, total): if not self.verbose: return None return f"({idx} of {total}) Processing {name}" @property def _weights_not_none(self): """Get the weights of not `None` estimators.""" if self.weights is None: return None return [w for est, w in zip(self.estimators, self.weights) if est[1] != "drop"] def _predict(self, X): """Collect results from clf.predict calls.""" return np.asarray([est.predict(X) for est in self.estimators_]).T @abstractmethod def fit(self, X, y, sample_weight=None): """Get common fit operations.""" names, clfs = self._validate_estimators() if self.weights is not None and len(self.weights) != len(self.estimators): raise ValueError( "Number of `estimators` and weights must be equal; got" f" {len(self.weights)} weights, {len(self.estimators)} estimators" ) self.estimators_ = Parallel(n_jobs=self.n_jobs)( delayed(_fit_single_estimator)( clone(clf), X, y, sample_weight=sample_weight, message_clsname="Voting", message=self._log_message(names[idx], idx + 1, len(clfs)), ) for idx, clf in enumerate(clfs) if clf != "drop" ) self.named_estimators_ = Bunch() # Uses 'drop' as placeholder for dropped estimators est_iter = iter(self.estimators_) for name, est in self.estimators: current_est = est if est == "drop" else next(est_iter) self.named_estimators_[name] = current_est if hasattr(current_est, "feature_names_in_"): self.feature_names_in_ = current_est.feature_names_in_ return self def fit_transform(self, X, y=None, **fit_params): """Return class labels or probabilities for each estimator. Return predictions for X for each estimator. Parameters ---------- X : {array-like, sparse matrix, dataframe} of shape \ (n_samples, n_features) Input samples. y : ndarray of shape (n_samples,), default=None Target values (None for unsupervised transformations). **fit_params : dict Additional fit parameters. Returns ------- X_new : ndarray array of shape (n_samples, n_features_new) Transformed array. """ return super().fit_transform(X, y, **fit_params) @property def n_features_in_(self): """Number of features seen during :term:`fit`.""" # For consistency with other estimators we raise a AttributeError so # that hasattr() fails if the estimator isn't fitted. try: check_is_fitted(self) except NotFittedError as nfe: raise AttributeError( "{} object has no n_features_in_ attribute.".format( self.__class__.__name__ ) ) from nfe return self.estimators_[0].n_features_in_ def _sk_visual_block_(self): names, estimators = zip(*self.estimators) return _VisualBlock("parallel", estimators, names=names) def _more_tags(self): return {"preserves_dtype": []} class VotingClassifier(ClassifierMixin, _BaseVoting): """Soft Voting/Majority Rule classifier for unfitted estimators. Read more in the :ref:`User Guide `. .. versionadded:: 0.17 Parameters ---------- estimators : list of (str, estimator) tuples Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones of those original estimators that will be stored in the class attribute ``self.estimators_``. An estimator can be set to ``'drop'`` using :meth:`set_params`. .. versionchanged:: 0.21 ``'drop'`` is accepted. Using None was deprecated in 0.22 and support was removed in 0.24. voting : {'hard', 'soft'}, default='hard' If 'hard', uses predicted class labels for majority rule voting. Else if 'soft', predicts the class label based on the argmax of the sums of the predicted probabilities, which is recommended for an ensemble of well-calibrated classifiers. weights : array-like of shape (n_classifiers,), default=None Sequence of weights (`float` or `int`) to weight the occurrences of predicted class labels (`hard` voting) or class probabilities before averaging (`soft` voting). Uses uniform weights if `None`. n_jobs : int, default=None The number of jobs to run in parallel for ``fit``. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. .. versionadded:: 0.18 flatten_transform : bool, default=True Affects shape of transform output only when voting='soft' If voting='soft' and flatten_transform=True, transform method returns matrix with shape (n_samples, n_classifiers * n_classes). If flatten_transform=False, it returns (n_classifiers, n_samples, n_classes). verbose : bool, default=False If True, the time elapsed while fitting will be printed as it is completed. .. versionadded:: 0.23 Attributes ---------- estimators_ : list of classifiers The collection of fitted sub-estimators as defined in ``estimators`` that are not 'drop'. named_estimators_ : :class:`~sklearn.utils.Bunch` Attribute to access any fitted sub-estimators by name. .. versionadded:: 0.20 le_ : :class:`~sklearn.preprocessing.LabelEncoder` Transformer used to encode the labels during fit and decode during prediction. classes_ : ndarray of shape (n_classes,) The classes labels. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying classifier exposes such an attribute when fit. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if the underlying estimators expose such an attribute when fit. .. versionadded:: 1.0 See Also -------- VotingRegressor : Prediction voting regressor. Examples -------- >>> import numpy as np >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.ensemble import RandomForestClassifier, VotingClassifier >>> clf1 = LogisticRegression(multi_class='multinomial', random_state=1) >>> clf2 = RandomForestClassifier(n_estimators=50, random_state=1) >>> clf3 = GaussianNB() >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> y = np.array([1, 1, 1, 2, 2, 2]) >>> eclf1 = VotingClassifier(estimators=[ ... ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard') >>> eclf1 = eclf1.fit(X, y) >>> print(eclf1.predict(X)) [1 1 1 2 2 2] >>> np.array_equal(eclf1.named_estimators_.lr.predict(X), ... eclf1.named_estimators_['lr'].predict(X)) True >>> eclf2 = VotingClassifier(estimators=[ ... ('lr', clf1), ('rf', clf2), ('gnb', clf3)], ... voting='soft') >>> eclf2 = eclf2.fit(X, y) >>> print(eclf2.predict(X)) [1 1 1 2 2 2] To drop an estimator, :meth:`set_params` can be used to remove it. Here we dropped one of the estimators, resulting in 2 fitted estimators: >>> eclf2 = eclf2.set_params(lr='drop') >>> eclf2 = eclf2.fit(X, y) >>> len(eclf2.estimators_) 2 Setting `flatten_transform=True` with `voting='soft'` flattens output shape of `transform`: >>> eclf3 = VotingClassifier(estimators=[ ... ('lr', clf1), ('rf', clf2), ('gnb', clf3)], ... voting='soft', weights=[2,1,1], ... flatten_transform=True) >>> eclf3 = eclf3.fit(X, y) >>> print(eclf3.predict(X)) [1 1 1 2 2 2] >>> print(eclf3.transform(X).shape) (6, 6) """ _parameter_constraints: dict = { **_BaseVoting._parameter_constraints, "voting": [StrOptions({"hard", "soft"})], "flatten_transform": ["boolean"], } def __init__( self, estimators, *, voting="hard", weights=None, n_jobs=None, flatten_transform=True, verbose=False, ): super().__init__(estimators=estimators) self.voting = voting self.weights = weights self.n_jobs = n_jobs self.flatten_transform = flatten_transform self.verbose = verbose 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. .. versionadded:: 0.18 Returns ------- self : object Returns the instance itself. """ self._validate_params() check_classification_targets(y) if isinstance(y, np.ndarray) and len(y.shape) > 1 and y.shape[1] > 1: raise NotImplementedError( "Multilabel and multi-output classification is not supported." ) self.le_ = LabelEncoder().fit(y) self.classes_ = self.le_.classes_ transformed_y = self.le_.transform(y) return super().fit(X, transformed_y, sample_weight) def predict(self, X): """Predict class labels for X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Returns ------- maj : array-like of shape (n_samples,) Predicted class labels. """ check_is_fitted(self) if self.voting == "soft": maj = np.argmax(self.predict_proba(X), axis=1) else: # 'hard' voting predictions = self._predict(X) maj = np.apply_along_axis( lambda x: np.argmax(np.bincount(x, weights=self._weights_not_none)), axis=1, arr=predictions, ) maj = self.le_.inverse_transform(maj) return maj def _collect_probas(self, X): """Collect results from clf.predict calls.""" return np.asarray([clf.predict_proba(X) for clf in self.estimators_]) def _check_voting(self): if self.voting == "hard": raise AttributeError( f"predict_proba is not available when voting={repr(self.voting)}" ) return True @available_if(_check_voting) def predict_proba(self, X): """Compute probabilities of possible outcomes for samples in X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Returns ------- avg : array-like of shape (n_samples, n_classes) Weighted average probability for each class per sample. """ check_is_fitted(self) avg = np.average( self._collect_probas(X), axis=0, weights=self._weights_not_none ) return avg 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 ------- probabilities_or_labels If `voting='soft'` and `flatten_transform=True`: returns ndarray of shape (n_samples, n_classifiers * n_classes), being class probabilities calculated by each classifier. If `voting='soft' and `flatten_transform=False`: ndarray of shape (n_classifiers, n_samples, n_classes) If `voting='hard'`: ndarray of shape (n_samples, n_classifiers), being class labels predicted by each classifier. """ check_is_fitted(self) if self.voting == "soft": probas = self._collect_probas(X) if not self.flatten_transform: return probas return np.hstack(probas) else: return self._predict(X) 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 Not used, present here for API consistency by convention. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. """ if self.voting == "soft" and not self.flatten_transform: raise ValueError( "get_feature_names_out is not supported when `voting='soft'` and " "`flatten_transform=False`" ) _check_feature_names_in(self, input_features, generate_names=False) class_name = self.__class__.__name__.lower() active_names = [name for name, est in self.estimators if est != "drop"] if self.voting == "hard": return np.asarray( [f"{class_name}_{name}" for name in active_names], dtype=object ) # voting == "soft" n_classes = len(self.classes_) names_out = [ f"{class_name}_{name}{i}" for name in active_names for i in range(n_classes) ] return np.asarray(names_out, dtype=object) class VotingRegressor(RegressorMixin, _BaseVoting): """Prediction voting regressor for unfitted estimators. A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averages the individual predictions to form a final prediction. Read more in the :ref:`User Guide `. .. versionadded:: 0.21 Parameters ---------- estimators : list of (str, estimator) tuples Invoking the ``fit`` method on the ``VotingRegressor`` will fit clones of those original estimators that will be stored in the class attribute ``self.estimators_``. An estimator can be set to ``'drop'`` using :meth:`set_params`. .. versionchanged:: 0.21 ``'drop'`` is accepted. Using None was deprecated in 0.22 and support was removed in 0.24. weights : array-like of shape (n_regressors,), default=None Sequence of weights (`float` or `int`) to weight the occurrences of predicted values before averaging. Uses uniform weights if `None`. n_jobs : int, default=None The number of jobs to run in parallel for ``fit``. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. verbose : bool, default=False If True, the time elapsed while fitting will be printed as it is completed. .. versionadded:: 0.23 Attributes ---------- estimators_ : list of regressors The collection of fitted sub-estimators as defined in ``estimators`` that are not 'drop'. named_estimators_ : :class:`~sklearn.utils.Bunch` Attribute to access any fitted sub-estimators by name. .. versionadded:: 0.20 n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying regressor exposes such an attribute when fit. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if the underlying estimators expose such an attribute when fit. .. versionadded:: 1.0 See Also -------- VotingClassifier : Soft Voting/Majority Rule classifier. Examples -------- >>> import numpy as np >>> from sklearn.linear_model import LinearRegression >>> from sklearn.ensemble import RandomForestRegressor >>> from sklearn.ensemble import VotingRegressor >>> from sklearn.neighbors import KNeighborsRegressor >>> r1 = LinearRegression() >>> r2 = RandomForestRegressor(n_estimators=10, random_state=1) >>> r3 = KNeighborsRegressor() >>> X = np.array([[1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36]]) >>> y = np.array([2, 6, 12, 20, 30, 42]) >>> er = VotingRegressor([('lr', r1), ('rf', r2), ('r3', r3)]) >>> print(er.fit(X, y).predict(X)) [ 6.8... 8.4... 12.5... 17.8... 26... 34...] In the following example, we drop the `'lr'` estimator with :meth:`~VotingRegressor.set_params` and fit the remaining two estimators: >>> er = er.set_params(lr='drop') >>> er = er.fit(X, y) >>> len(er.estimators_) 2 """ def __init__(self, estimators, *, weights=None, n_jobs=None, verbose=False): super().__init__(estimators=estimators) self.weights = weights self.n_jobs = n_jobs self.verbose = verbose 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 Fitted estimator. """ self._validate_params() y = column_or_1d(y, warn=True) return super().fit(X, y, sample_weight) 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 input samples. Returns ------- y : ndarray of shape (n_samples,) The predicted values. """ check_is_fitted(self) return np.average(self._predict(X), axis=1, weights=self._weights_not_none) def transform(self, X): """Return predictions for X for each estimator. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Returns ------- predictions : ndarray of shape (n_samples, n_classifiers) Values predicted by each regressor. """ check_is_fitted(self) return self._predict(X) 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 Not used, present here for API consistency by convention. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. """ _check_feature_names_in(self, input_features, generate_names=False) class_name = self.__class__.__name__.lower() return np.asarray( [f"{class_name}_{name}" for name, est in self.estimators if est != "drop"], dtype=object, )