Multilabel k Nearest Neighbours¶

class skmultilearn.adapt.MLTSVM(c_k=0, sor_omega=1.0, threshold=1e-06, lambda_param=1.0, max_iteration=500)[source]¶

Twin multi-Label Support Vector Machines

Parameters:

c_k (int) – the empirical risk penalty parameter that determines the trade-off between the loss terms sor_omega (float (default is 1.0)) – the smoothing parameter threshold (int (default is 1e-6)) – threshold above which a label should be assigned lambda_param (float (default is 1.0)) – the regularization parameter max_iteration (int (default is 500)) – maximum number of iterations to use in successive overrelaxation

References

If you use this classifier please cite the original paper introducing the method:

@article{chen2016mltsvm,
  title={MLTSVM: a novel twin support vector machine to multi-label learning},
  author={Chen, Wei-Jie and Shao, Yuan-Hai and Li, Chun-Na and Deng, Nai-Yang},
  journal={Pattern Recognition},
  volume={52},
  pages={61--74},
  year={2016},
  publisher={Elsevier}
}

Examples

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

from skmultilearn.adapt import MLTSVM

classifier = MLTSVM(c_k = 2**-1)

# 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 MLTSVM
from sklearn.model_selection import GridSearchCV

parameters = {'c_k': [2**i for i in range(-5, 5, 2)]}
score = 'f1-macro

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

print (clf.best_params_, clf.best_score_)

# output
{'c_k': 0.03125} 0.347518217573

fit(X, Y)[source]¶

Abstract method to fit classifier with training data

It must return a fitted instance of self.

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:	object
Raises:	NotImplementedError – this is just an abstract method

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)[source]¶

Abstract method to predict labels

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
Raises:	NotImplementedError – this is just an abstract method

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.