Function SVM_binary_minimum

modAL/multilabel.py:44–72

SVM binary minimum multilabel active learning strategy. For details see the paper Klaus Brinker, On Active Learning in Multi-label Classification (https://link.springer.com/chapter/10.1007%2F3-540-31314-1_24) Args: classifier: The multilabel classifier for which the labels

(classifier: ActiveLearner, X_pool: modALinput,
                       random_tie_break: bool = False)

def SVM_binary_minimum(classifier: ActiveLearner, X_pool: modALinput,
                       random_tie_break: bool = False) -> np.ndarray:
    """
    SVM binary minimum multilabel active learning strategy. For details see the paper
    Klaus Brinker, On Active Learning in Multi-label Classification
    (https://link.springer.com/chapter/10.1007%2F3-540-31314-1_24)

    Args:
        classifier: The multilabel classifier for which the labels are to be queried. Must be an SVM model
            such as the ones from sklearn.svm.
        X_pool: The pool of samples to query from.
        random_tie_break: If True, shuffles utility scores to randomize the order. This
            can be used to break the tie when the highest utility score is not unique.

    Returns:
        The index of the instance from X_pool chosen to be labelled;
        The instance from X_pool chosen to be labelled.
        The Minimum absolute distance metric of the chosen instance; 
    """

    decision_function = np.array([svm.decision_function(X_pool)
                                  for svm in classifier.estimator.estimators_]).T

    min_abs_dist = np.min(np.abs(decision_function), axis=1)

    if not random_tie_break:
        return np.argmin(min_abs_dist)

    return shuffled_argmax(min_abs_dist)


def max_loss(classifier: OneVsRestClassifier, X_pool: modALinput,