Binary Relevance kNN

Binary Relevance kNN¶

class skmultilearn.adapt.BRkNNaClassifier(k=10)[source]¶

Binary Relevance multi-label classifier based on k-Nearest Neighbors method.

This version of the classifier assigns the labels that are assigned to at least half of the neighbors.

Parameters:	k (int) – number of neighbours

knn_¶: an instance of sklearn.NearestNeighbors – the nearest neighbors single-label classifier used underneath

neighbors_¶: array of arrays of int, shape = (n_samples, k) – k neighbors of each sample

confidences_¶: matrix of int, shape = (n_samples, n_labels) – label assignment confidences

References

If you use this method please cite the relevant paper:

@inproceedings{EleftheriosSpyromitros2008,
   author = {Eleftherios Spyromitros, Grigorios Tsoumakas, Ioannis Vlahavas},
   booktitle = {Proc. 5th Hellenic Conference on Artificial Intelligence (SETN 2008)},
   title = {An Empirical Study of Lazy Multilabel Classification Algorithms},
   year = {2008},
   location = {Syros, Greece}
}

Examples

Here’s a very simple example of using BRkNNaClassifier with a fixed number of neighbors:

from skmultilearn.adapt import BRkNNaClassifier

classifier = BRkNNaClassifier(k=3)

# train
classifier.fit(X_train, y_train)

# predict
predictions = classifier.predict(X_test)

You can also use GridSearchCV to find an optimal set of parameters:

from skmultilearn.adapt import BRkNNaClassifier
from sklearn.model_selection import GridSearchCV

parameters = {'k': range(1,3)}
score = 'f1-macro

clf = GridSearchCV(BRkNNaClassifier(), parameters, scoring=score)
clf.fit(X, y)

fit(X, y)¶

Fit classifier with training data

Internally this method uses a sparse CSC representation for y (scipy.sparse.csc_matrix).

Parameters:	X (numpy.ndarray or scipy.sparse) – input features, can be a dense or sparse matrix of size `(n_samples, n_features)` y (numpy.ndaarray or scipy.sparse {0,1}) – binary indicator matrix with label assignments.
Returns:	fitted instance of self
Return type:	self

get_params(deep=True)¶

Get parameters to sub-objects

Introspection of classifier for search models like cross-validation and grid search.

Parameters:	deep (bool) – if `True` all params will be introspected also and appended to the output dictionary.
Returns:	out – dictionary of all parameters and their values. If `deep=True` the dictionary also holds the parameters of the parameters.
Return type:	dict

predict(X)¶

Predict labels for X

Parameters:	X (numpy.ndarray or scipy.sparse.csc_matrix) – input features of shape `(n_samples, n_features)`
Returns:	binary indicator matrix with label assignments with shape `(n_samples, n_labels)`
Return type:	scipy.sparse of int

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], optional) – Sample weights.
Returns:	score – Mean accuracy of self.predict(X) wrt. y.
Return type:	float

set_params(**parameters)¶

Propagate parameters to sub-objects

Set parameters as returned by get_params. Please see this link.

class skmultilearn.adapt.BRkNNbClassifier(k=10)[source]¶