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"""
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 <se.raschka@gmail.com>,
# Gilles Louppe <g.louppe@gmail.com>,
# Ramil Nugmanov <stsouko@live.ru>
# Mohamed Ali Jamaoui <m.ali.jamaoui@gmail.com>
#
# License: BSD 3 clause
from abc import abstractmethod
import numpy as np
from joblib import Parallel, delayed
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.validation import check_is_fitted
from ..utils.multiclass import check_classification_targets
from ..utils.validation import column_or_1d
from ..utils.validation import _deprecate_positional_args
from ..exceptions import NotFittedError
from ..utils._estimator_html_repr import _VisualBlock
class _BaseVoting(TransformerMixin, _BaseHeterogeneousEnsemble):
"""Base class for voting.
Warning: This class should not be used directly. Use derived classes
instead.
"""
def _log_message(self, name, idx, total):
if not self.verbose:
return None
return '(%d of %d) Processing %s' % (idx, total, 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] not in (None, '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 %d weights, %d estimators'
% (len(self.weights), len(self.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 not in (None, 'drop')
)
self.named_estimators_ = Bunch()
# Uses None or 'drop' as placeholder for dropped estimators
est_iter = iter(self.estimators_)
for name, est in self.estimators:
current_est = est if est in (None, 'drop') else next(est_iter)
self.named_estimators_[name] = current_est
return self
@property
def n_features_in_(self):
# 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)
class VotingClassifier(ClassifierMixin, _BaseVoting):
"""Soft Voting/Majority Rule classifier for unfitted estimators.
.. versionadded:: 0.17
Read more in the :ref:`User Guide <voting_classifier>`.
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 ``set_params``.
.. versionchanged:: 0.21
``'drop'`` is accepted.
.. deprecated:: 0.22
Using ``None`` to drop an estimator is deprecated in 0.22 and
support will be dropped in 0.24. Use the string ``'drop'`` instead.
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 <n_jobs>`
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.
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
classes_ : array-like of shape (n_predictions,)
The classes labels.
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]
>>> 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)
"""
@_deprecate_positional_args
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
"""
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.')
if self.voting not in ('soft', 'hard'):
raise ValueError("Voting must be 'soft' or 'hard'; got (voting=%r)"
% self.voting)
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 _predict_proba(self, X):
"""Predict class probabilities for X in 'soft' voting."""
check_is_fitted(self)
avg = np.average(self._collect_probas(X), axis=0,
weights=self._weights_not_none)
return avg
@property
def predict_proba(self):
"""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.
"""
if self.voting == 'hard':
raise AttributeError("predict_proba is not available when"
" voting=%r" % self.voting)
return self._predict_proba
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_classifiers, n_samples *
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)
class VotingRegressor(RegressorMixin, _BaseVoting):
"""Prediction voting regressor for unfitted estimators.
.. versionadded:: 0.21
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 <voting_regressor>`.
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
``set_params``.
.. versionchanged:: 0.21
``'drop'`` is accepted.
.. deprecated:: 0.22
Using ``None`` to drop an estimator is deprecated in 0.22 and
support will be dropped in 0.24. Use the string ``'drop'`` instead.
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 <n_jobs>`
for more details.
verbose : bool, default=False
If True, the time elapsed while fitting will be printed as it
is completed.
Attributes
----------
estimators_ : list of regressors
The collection of fitted sub-estimators as defined in ``estimators``
that are not 'drop'.
named_estimators_ : Bunch
Attribute to access any fitted sub-estimators by name.
.. versionadded:: 0.20
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
>>> r1 = LinearRegression()
>>> r2 = RandomForestRegressor(n_estimators=10, random_state=1)
>>> 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)])
>>> print(er.fit(X, y).predict(X))
[ 3.3 5.7 11.8 19.7 28. 40.3]
"""
@_deprecate_positional_args
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.
"""
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)