Function max_loss

modAL/multilabel.py:75–108

Max Loss query strategy for SVM multilabel classification. For more details on this query strategy, see Li et al., Multilabel SVM active learning for image classification (http://dx.doi.org/10.1109/ICIP.2004.1421535) Args: classifier: The multilabel classifier for whic

(classifier: OneVsRestClassifier, X_pool: modALinput,
             n_instances: int = 1, random_tie_break: bool = False)

def max_loss(classifier: OneVsRestClassifier, X_pool: modALinput,
             n_instances: int = 1, random_tie_break: bool = False) -> np.ndarray:

    """
    Max Loss query strategy for SVM multilabel classification.

    For more details on this query strategy, see
    Li et al., Multilabel SVM active learning for image classification
    (http://dx.doi.org/10.1109/ICIP.2004.1421535)

    Args:
        classifier: The multilabel classifier for which the labels are to be queried. Should be an SVM model
            such as the ones from sklearn.svm. Although the function will execute for other models as well,
            the mathematical calculations in Li et al. work only for SVM-s.
        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 SVM-loss-max metric of the chosen instances; 

    """

    assert len(X_pool) >= n_instances, 'n_instances cannot be larger than len(X_pool)'

    most_certain_classes = classifier.predict_proba(X_pool).argmax(axis=1)
    loss = _SVM_loss(classifier, X_pool, most_certain_classes=most_certain_classes)

    if not random_tie_break:
        return multi_argmax(loss, n_instances)

    return shuffled_argmax(loss, n_instances)


def mean_max_loss(classifier: OneVsRestClassifier, X_pool: modALinput,