Classifier Chains¶

class skmultilearn.problem_transform.ClassifierChain(classifier=None, require_dense=None, order=None)[source]¶

Bases: skmultilearn.base.problem_transformation.ProblemTransformationBase

Constructs a bayesian conditioned chain of per label classifiers

This class provides implementation of Jesse Read’s problem transformation method called Classifier Chains. For L labels it trains L classifiers ordered in a chain according to the Bayesian chain rule.

The first classifier is trained just on the input space, and then each next classifier is trained on the input space and all previous classifiers in the chain.

The default classifier chains follow the same ordering as provided in the training set, i.e. label in column 0, then 1, etc.

Parameters:

classifier (BaseEstimator) – scikit-learn compatible base classifier require_dense ([bool, bool], optional) – whether the base classifier requires dense representations for input features and classes/labels matrices in fit/predict. If value not provided, sparse representations are used if base classifier is an instance of MLClassifierBase and dense otherwise. order (List[int], permutation of range(n_labels), optional) – the order in which the chain should go through labels, the default is range(n_labels)

classifiers_¶

list of classifiers trained per partition, set in fit()

Type:	List[BaseEstimator] of shape n_labels

References

If used, please cite the scikit-multilearn library and the relevant paper:

@inproceedings{read2009classifier,
  title={Classifier chains for multi-label classification},
  author={Read, Jesse and Pfahringer, Bernhard and Holmes, Geoff and Frank, Eibe},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={254--269},
  year={2009},
  organization={Springer}
}

Examples

An example use case for Classifier Chains with an sklearn.svm.SVC base classifier which supports sparse input:

from skmultilearn.problem_transform import ClassifierChain
from sklearn.svm import SVC

# initialize Classifier Chain multi-label classifier
# with an SVM classifier
# SVM in scikit only supports the X matrix in sparse representation

classifier = ClassifierChain(
    classifier = SVC(),
    require_dense = [False, True]
)

# train
classifier.fit(X_train, y_train)

# predict
predictions = classifier.predict(X_test)

Another way to use this classifier is to select the best scenario from a set of single-label classifiers used with Classifier Chain, this can be done using cross validation grid search. In the example below, the model with highest accuracy results is selected from either a sklearn.naive_bayes.MultinomialNB or sklearn.svm.SVC base classifier, alongside with best parameters for that base classifier.

from skmultilearn.problem_transform import ClassifierChain
from sklearn.model_selection import GridSearchCV
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC

parameters = [
    {
        'classifier': [MultinomialNB()],
        'classifier__alpha': [0.7, 1.0],
    },
    {
        'classifier': [SVC()],
        'classifier__kernel': ['rbf', 'linear'],
    },
]

clf = GridSearchCV(ClassifierChain(), parameters, scoring='accuracy')
clf.fit(x, y)

print (clf.best_params_, clf.best_score_)

# result
# {'classifier': MultinomialNB(alpha=0.7, class_prior=None, fit_prior=True), 'classifier__alpha': 0.7} 0.16

fit(X, y, order=None)[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

Notes

Note

Input matrices are converted to sparse format internally if a numpy representation is passed

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)