RAkELo: random overlapping label space division with Label Powerset¶

class skmultilearn.ensemble.RakelO(base_classifier=None, model_count=None, labelset_size=3, base_classifier_require_dense=None)[source]¶

Bases: skmultilearn.base.base.MLClassifierBase

Overlapping RAndom k-labELsets multi-label classifier

Divides the label space in to m subsets of size k, trains a Label Powerset classifier for each subset and assign a label to an instance if more than half of all classifiers (majority) from clusters that contain the label assigned the label to the instance.

Parameters:

base_classifier (BaseEstimator) – scikit-learn compatible base classifier, will be set under self.classifier.classifier. base_classifier_require_dense ([bool, bool]) – whether the base classifier requires [input, output] matrices in dense representation. Will be automatically set under self.classifier.require_dense labelset_size (int) – the desired size of each of the partitions, parameter k according to paper. According to paper, the best parameter is 3, so it’s set as default Will be automatically set under self.labelset_size model_count (int) – the desired number of classifiers, parameter m according to paper. According to paper, the best value for this parameter is 2M (being M the number of labels) Will be automatically set under self.model_count_.

classifier¶

the voting classifier initialized with LabelPowerset multi-label classifier with base_classifier and RandomLabelSpaceClusterer

Type:	MajorityVotingClassifier

References

If you use this class please cite the paper introducing the method:

@ARTICLE{5567103,
    author={G. Tsoumakas and I. Katakis and I. Vlahavas},
    journal={IEEE Transactions on Knowledge and Data Engineering},
    title={Random k-Labelsets for Multilabel Classification},
    year={2011},
    volume={23},
    number={7},
    pages={1079-1089},
    doi={10.1109/TKDE.2010.164},
    ISSN={1041-4347},
    month={July},
}

Examples

Here’s a simple example of how to use this class with a base classifier from scikit-learn to teach 6 classifiers each trained on a quarter of labels, which is sure to overlap:

from sklearn.naive_bayes import GaussianNB
from skmultilearn.ensemble import RakelO

classifier = RakelO(
    base_classifier=GaussianNB(),
    base_classifier_require_dense=[True, True],
    labelset_size=y_train.shape[1] // 4,
    model_count_=6
)

classifier.fit(X_train, y_train)
prediction = classifier.predict(X_train, y_train)

fit(X, y)[source]¶

Fits classifier to training data

Parameters:	X (array_like, numpy.matrix or scipy.sparse matrix, shape=(n_samples, n_features)) – input feature matrix y (array_like, numpy.matrix or scipy.sparse matrix of {0, 1}, shape=(n_samples, n_labels)) – binary indicator matrix with label assignments
Returns:	fitted instance of self
Return type:	self

predict(X)[source]¶

Predict labels for X

Parameters:	X (array_like, numpy.matrix or scipy.sparse matrix, shape=(n_samples, n_features)) – input feature matrix
Returns:	binary indicator matrix with label assignments
Return type:	scipy.sparse matrix of {0, 1}, shape=(n_samples, n_labels)

predict_proba(X)[source]¶

Predict probabilities of label assignments for X

Parameters:	X (array_like, numpy.matrix or scipy.sparse matrix, shape=(n_samples, n_features)) – input feature matrix
Returns:	matrix with label assignment probabilities
Return type:	scipy.sparse matrix of float in [0.0, 1.0], shape=(n_samples, n_labels)