VotingClassifier

class ibex.sklearn.ensemble.VotingClassifier(estimators, voting='hard', weights=None, n_jobs=1, flatten_transform=None)

Bases: sklearn.ensemble.voting_classifier.VotingClassifier, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Soft Voting/Majority Rule classifier for unfitted estimators.

New in version 0.17.

Read more in the User Guide.

estimators : list of (string, 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 None using set_params.
voting : str, {‘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, shape = [n_classifiers], optional (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, optional (default=1)
The number of jobs to run in parallel for fit. If -1, then the number of jobs is set to the number of cores.
flatten_transform : bool, optional (default=None)
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).
estimators_ : list of classifiers
The collection of fitted sub-estimators as defined in estimators that are not None.
classes_ : array-like, shape = [n_predictions]
The classes labels.
>>> import numpy as np
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.ensemble import RandomForestClassifier, VotingClassifier
>>> clf1 = LogisticRegression(random_state=1)
>>> clf2 = RandomForestClassifier(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]
>>> 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)
>>>
fit(X, y, sample_weight=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit the estimators.

X : {array-like, sparse matrix}, 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, shape = [n_samples]
Target values.
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted. Note that this is supported only if all underlying estimators support sample weights.

self : object

fit_transform(X, y=None, **fit_params)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
predict(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Predict class labels for X.

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of features.
maj : array-like, shape = [n_samples]
Predicted class labels.
predict_proba()

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Compute probabilities of possible outcomes for samples in X.

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of features.
avg : array-like, shape = [n_samples, n_classes]
Weighted average probability for each class per sample.
score(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
score : float
Mean accuracy of self.predict(X) wrt. y.
transform(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Return class labels or probabilities for X for each estimator.

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of features.
If voting=’soft’ and flatten_transform=True:

array-like = (n_classifiers, n_samples * n_classes) otherwise array-like = (n_classifiers, n_samples, n_classes)

Class probabilities calculated by each classifier.
If voting=’hard’:
array-like = [n_samples, n_classifiers]
Class labels predicted by each classifier.