Function ranked_batch

modAL/batch.py:121–184

Query our top :n_instances: to request 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. unlabeled:

(classifier: Union[BaseLearner, BaseCommittee],
                 unlabeled: modALinput,
                 uncertainty_scores: np.ndarray,
                 n_instances: int,
                 metric: Union[str, Callable],
                 n_jobs: Union[int, None])

def ranked_batch(classifier: Union[BaseLearner, BaseCommittee],
                 unlabeled: modALinput,
                 uncertainty_scores: np.ndarray,
                 n_instances: int,
                 metric: Union[str, Callable],
                 n_jobs: Union[int, None]) -> np.ndarray:
    """
    Query our top :n_instances: to request 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.
        unlabeled: Set of records to be considered for our active learning model.
        uncertainty_scores: Our classifier's predictions over the response variable.
        n_instances: Limit on the number of records to query from our unlabeled set.
        metric: This parameter is passed to :func:`~sklearn.metrics.pairwise.pairwise_distances`.
        n_jobs: This parameter is passed to :func:`~sklearn.metrics.pairwise.pairwise_distances`.

    Returns:
        The indices of the top n_instances ranked unlabelled samples.
        The uncertainty scores of the chosen instances. 

    """
    # Make a local copy of our classifier's training data.
    # Define our record container and record the best cold start instance in the case of cold start.

    # transform unlabeled data if needed
    if classifier.on_transformed:
        unlabeled = classifier.transform_without_estimating(unlabeled)

    if classifier.X_training is None:
        best_coldstart_instance_index, labeled = select_cold_start_instance(X=unlabeled, metric=metric, n_jobs=n_jobs)
        instance_index_ranking = [best_coldstart_instance_index]
    elif data_shape(classifier.X_training)[0] > 0:
        labeled = classifier.transform_without_estimating(
            classifier.X_training
        ) if classifier.on_transformed else classifier.X_training[:]
        instance_index_ranking = []
    
    # The maximum number of records to sample.
    ceiling = np.minimum(unlabeled.shape[0], n_instances) - len(instance_index_ranking)

    # mask for unlabeled initialized as transparent
    mask = np.ones(unlabeled.shape[0], bool)

    for _ in range(ceiling):

        # Receive the instance and corresponding index from our unlabeled copy that scores highest.
        instance_index, instance, mask = select_instance(X_training=labeled, X_pool=unlabeled,
                                                         X_uncertainty=uncertainty_scores, mask=mask,
                                                         metric=metric, n_jobs=n_jobs)

        # Add our instance we've considered for labeling to our labeled set. Although we don't
        # know it's label, we want further iterations to consider the newly-added instance so
        # that we don't query the same instance redundantly.
        labeled = data_vstack((labeled, instance))