cb347641837feb9f651038c2f88317563a347627,libact/models/svm.py,SVM,score,#SVM#Any#,78

Before Change

        if self.m == None:
            raise RuntimeError("Model not trained")
        X, y = zip(*testing_dataset.get_labeled_entries())
        p_label, p_acc, p_val = svmutil.svm_predict(y, [x.tolist() for x in X],
                self.m)
        return p_acc[0] / 100. //ACC

After Change

        return self.model.predict(feature, *args, **kwargs)

    def score(self, testing_dataset, *args, **kwargs):
        return self.model.score(*(testing_dataset.format_sklearn() + args), **kwargs)

Instances