EnsembleVoteClassifier
EnsembleVoteClassifier(clfs, voting='hard', weights=None, verbose=0, refit=True)
Soft Voting/Majority Rule classifier for scikit-learn estimators.
Parameters
-
clfs
: array-like, shape = [n_classifiers]A list of classifiers. Invoking the
fit
method on theVotingClassifier
will fit clones of those original classifiers that will be stored in the class attributeself.clfs_
ifrefit=True
(default). -
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 probalities, which is recommended for an ensemble of well-calibrated classifiers.
-
weights
: array-like, shape = [n_classifiers], optional (default=None
)Sequence of weights (
float
orint
) to weight the occurances of predicted class labels (hard
voting) or class probabilities before averaging (soft
voting). Uses uniform weights ifNone
. -
verbose
: int, optional (default=0)Controls the verbosity of the building process. -
verbose=0
(default): Prints nothing -verbose=1
: Prints the number & name of the clf being fitted -verbose=2
: Prints info about the parameters of the clf being fitted -verbose>2
: Changesverbose
param of the underlying clf to self.verbose - 2 -
refit
: bool (default: True)Refits classifiers in
clfs
if True; uses references to theclfs
, otherwise (assumes that the classifiers were already fit). Note: refit=False is incompatible to mist scikit-learn wrappers! For instance, if any form of cross-validation is performed this would require the re-fitting classifiers to training folds, which would raise a NotFitterError if refit=False. (New in mlxtend v0.6.)
Attributes
-
classes_
: array-like, shape = [n_predictions] -
clf
: array-like, shape = [n_predictions]The unmodified input classifiers
-
clf_
: array-like, shape = [n_predictions]Fitted clones of the input classifiers
Examples
>>> import numpy as np
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.ensemble import RandomForestClassifier
>>> from mlxtend.sklearn import EnsembleVoteClassifier
>>> clf1 = LogisticRegression(random_seed=1)
>>> clf2 = RandomForestClassifier(random_seed=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 = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3],
... voting='hard', verbose=1)
>>> eclf1 = eclf1.fit(X, y)
>>> print(eclf1.predict(X))
[1 1 1 2 2 2]
>>> eclf2 = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting='soft')
>>> eclf2 = eclf2.fit(X, y)
>>> print(eclf2.predict(X))
[1 1 1 2 2 2]
>>> eclf3 = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3],
... voting='soft', weights=[2,1,1])
>>> eclf3 = eclf3.fit(X, y)
>>> print(eclf3.predict(X))
[1 1 1 2 2 2]
>>>
For more usage examples, please see http://rasbt.github.io/mlxtend/user_guide/classifier/EnsembleVoteClassifier/
Methods
fit(X, y, sample_weight=None)
Learn weight coefficients from training data for each classifier.
Parameters
-
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], optionalSample weights passed as sample_weights to each regressor in the regressors list as well as the meta_regressor. Raises error if some regressor does not support sample_weight in the fit() method.
Returns
self
: object
fit_transform(X, y=None, fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters
-
X
: numpy array of shape [n_samples, n_features]Training set.
-
y
: numpy array of shape [n_samples]Target values.
Returns
-
X_new
: numpy array of shape [n_samples, n_features_new]Transformed array.
get_params(deep=True)
Return estimator parameter names for GridSearch support.
predict(X)
Predict class labels for X.
Parameters
-
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.
Returns
-
maj
: array-like, shape = [n_samples]Predicted class labels.
predict_proba(X)
Predict class probabilities for X.
Parameters
-
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.
Returns
-
avg
: array-like, shape = [n_samples, n_classes]Weighted average probability for each class per sample.
score(X, y, sample_weight=None)
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.
Parameters
-
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], optionalSample weights.
Returns
-
score
: floatMean accuracy of self.predict(X) wrt. y.
set_params(params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it's possible to update each
component of a nested object.
Returns
self
transform(X)
Return class labels or probabilities for X for each estimator.
Parameters
-
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.
Returns
-
If
voting='soft'`` : array-like = [n_classifiers, n_samples, n_classes]Class probabilties calculated by each classifier.
-
If
voting='hard'`` : array-like = [n_classifiers, n_samples]Class labels predicted by each classifier.