Function uncertainty_batch_sampling

modAL/batch.py:187–223

Batch sampling query strategy. Selects the least sure instances for labelling. This strategy differs from :func:`~modAL.uncertainty.uncertainty_sampling` because, although it is supported, traditional active learning query strategies suffer from sub-optimal record selection when passin

(classifier: Union[BaseLearner, BaseCommittee],
                               X: Union[np.ndarray, sp.csr_matrix],
                               n_instances: int = 20,
                               metric: Union[str, Callable] = 'euclidean',
                               n_jobs: Optional[int] = None,
                               **uncertainty_measure_kwargs
                               )

def uncertainty_batch_sampling(classifier: Union[BaseLearner, BaseCommittee],
                               X: Union[np.ndarray, sp.csr_matrix],
                               n_instances: int = 20,
                               metric: Union[str, Callable] = 'euclidean',
                               n_jobs: Optional[int] = None,
                               **uncertainty_measure_kwargs
                               ) -> np.ndarray:
    """
    Batch sampling query strategy. Selects the least sure instances for labelling.

    This strategy differs from :func:`~modAL.uncertainty.uncertainty_sampling` because, although it is supported,
    traditional active learning query strategies suffer from sub-optimal record selection when passing
    `n_instances` > 1. This sampling strategy extends the interactive uncertainty query sampling by allowing for
    batch-mode uncertainty query sampling. Furthermore, it also enforces a ranking -- that is, which records among the
    batch are most important for labeling?

    Refer to Cardoso et al.'s "Ranked batch-mode active learning":
        https://www.sciencedirect.com/science/article/pii/S0020025516313949

    Args:
        classifier: One of modAL's supported active learning models.
        X: Set of records to be considered for our active learning model.
        n_instances: Number of records to return for labeling from `X`.
        metric: This parameter is passed to :func:`~sklearn.metrics.pairwise.pairwise_distances`
        n_jobs: If not set, :func:`~sklearn.metrics.pairwise.pairwise_distances_argmin_min` is used for calculation of
            distances between samples. Otherwise it is passed to :func:`~sklearn.metrics.pairwise.pairwise_distances`.
        **uncertainty_measure_kwargs: Keyword arguments to be passed for the :meth:`predict_proba` of the classifier.

    Returns:
        Indices of the instances from `X` chosen to be labelled
        Records from `X` chosen to be labelled.
        The uncertainty scores of the chosen instances. 

    """
    uncertainty = classifier_uncertainty(classifier, X, **uncertainty_measure_kwargs)
    return ranked_batch(classifier, unlabeled=X, uncertainty_scores=uncertainty,
                                 n_instances=n_instances, metric=metric, n_jobs=n_jobs)