S4VM

S4VM implements the S4VM algorithm in [1].

S4VM employs the Python version of libsvm [2] (available at https://www.csie.ntu.edu.tw/~cjlin/libsvm/).

References

[1]Yu-Feng Li and Zhi-Hua Zhou. Towards Making Unlabeled Data Never Hurt. In: Proceedings of the 28th International Conference on Machine Learning (ICML‘11), Bellevue, Washington, 2011.
[2]R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005.
class s3l.model_uncertainty.S4VM.S4VM(kernel='RBF', C1=100, C2=0.1, sample_time=100, gamma=0, n_clusters=10)[source]

Bases: s3l.base.InductiveEstimatorWOGraph

Base class for S4VM module.

Parameters:
  • kernel ('RBF' or 'Linear' (default='RBF')) – String identifier for kernel function to use or the kernel function itself. Only ‘RBF’ and ‘Linear’ strings are valid inputs.
  • gamma (float) – Parameter gamma is the width of RBF kernel. Default value is average distance between instances.
  • C1 (double (default=100)) – Weight for the hinge loss of labeled instance.
  • C2 (double (default=0.1)) – Weight for the hinge loss of unlabeled instance. If C2 is set as 0, our S4VM will degenerate to standard SVM.
  • sampleTime (integer (default=100)) – The sampling times for each sampleTime.
  • n_clusters (integer (default=10)) – The number of clusters to form as well as the number of centroids to generate for K-means.
fit(X, y, labeled_idx)[source]

Fit the model according to the given training data.

Parameters:
  • X ({array-like, sparse matrix}, shape = [n_samples, n_features]) – Training vector containing labeled and unlabeled instances, where n_samples in the number of samples and n_features is the number of features. All unlabeled samples will be transductively assigned labels.
  • y (array-like, shape = [n_labeled_samples]) – Target vector relative to labeled instances in X.
  • labeled_idx (array-like, shape = [n_labeled_samples]) – Index of labeled instances in X.
Returns:

self
Return type:

object
predict(u_ind)[source]

Predict method replace the unsafe prediction with the baseline_pred to improve the safeness.

Parameters:u_ind (array-like) – a row vector with length l, where l is the number of unlabeled instance. Each element is an index of a unlabeled instance.
Returns:pred – the label of the instance, including labeled and unlabeled instances, even though for labeled instances the prediction is consistent with the true label.
Return type:a column vector with length n. Each element is a prediction for
set_params(param)[source]

Parameter setting function.

Parameters:：dict (param) – Store parameter names and corresponding values {‘name’: value}.