Function entropy_sampling

modAL/uncertainty.py:186–211

Entropy sampling query strategy. Selects the instances where the class probabilities have the largest entropy. Args: classifier: The classifier for which the labels are to be queried. X: The pool of samples to query from. n_instances: Number of samples to be que

(classifier: BaseEstimator, X: modALinput,
                     n_instances: int = 1, random_tie_break: bool = False,
                     **uncertainty_measure_kwargs)

def entropy_sampling(classifier: BaseEstimator, X: modALinput,
                     n_instances: int = 1, random_tie_break: bool = False,
                     **uncertainty_measure_kwargs) -> np.ndarray:
    """
    Entropy sampling query strategy. Selects the instances where the class probabilities
    have the largest entropy.

    Args:
        classifier: The classifier for which the labels are to be queried.
        X: The pool of samples to query from.
        n_instances: Number of samples to be queried.
        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.
        **uncertainty_measure_kwargs: Keyword arguments to be passed for the uncertainty
            measure function.

    Returns:
        The indices of the instances from X chosen to be labelled.
        The entropy metric of the chosen instances.
    """
    entropy = classifier_entropy(classifier, X, **uncertainty_measure_kwargs)

    if not random_tie_break:
        return multi_argmax(entropy, n_instances=n_instances)

    return shuffled_argmax(entropy, n_instances=n_instances)