Function avg_confidence

modAL/multilabel.py:173–200

AvgConfidence query strategy for multilabel classification. For more details on this query strategy, see Esuli and Sebastiani., Active Learning Strategies for Multi-Label Text Classification (http://dx.doi.org/10.1007/978-3-642-00958-7_12) Args: classifier: The multila

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

def avg_confidence(classifier: OneVsRestClassifier, X_pool: modALinput,
                   n_instances: int = 1, random_tie_break: bool = False) -> np.ndarray:
    """
    AvgConfidence query strategy for multilabel classification.

    For more details on this query strategy, see
    Esuli and Sebastiani., Active Learning Strategies for Multi-Label Text Classification
    (http://dx.doi.org/10.1007/978-3-642-00958-7_12)

    Args:
        classifier: The multilabel classifier for which the labels are to be queried.
        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 average confidence metric of the chosen instances. 

    """

    classwise_confidence = classifier.predict_proba(X_pool)
    classwise_mean = np.mean(classwise_confidence, axis=1)

    if not random_tie_break:
        return multi_argmax(classwise_mean, n_instances)

    return shuffled_argmax(classwise_mean, n_instances)


def max_score(classifier: OneVsRestClassifier, X_pool: modALinput,