Function margin_sampling

modAL/uncertainty.py:160–183

Margin sampling query strategy. Selects the instances where the difference between the first most likely and second most likely classes are the smallest. Args: classifier: The classifier for which the labels are to be queried. X: The pool of samples to query from.

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

def margin_sampling(classifier: BaseEstimator, X: modALinput,
                    n_instances: int = 1, random_tie_break: bool = False,
                    **uncertainty_measure_kwargs) -> np.ndarray:
    """
    Margin sampling query strategy. Selects the instances where the difference between
    the first most likely and second most likely classes are the smallest.
    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 margin metric of the chosen instances.
    """
    margin = classifier_margin(classifier, X, **uncertainty_measure_kwargs)

    if not random_tie_break:
        return multi_argmin(margin, n_instances=n_instances)

    return shuffled_argmin(margin, n_instances=n_instances)


def entropy_sampling(classifier: BaseEstimator, X: modALinput,