f8df6021a1343d511d4c9b4c108ec5b683ce5487,modAL/batch.py,,ranked_batch,#,103

Before Change

    labeled = np.copy(classifier.X_training) if n_training_records > 0 else select_cold_start_instance(unlabeled)

    // Add uncertainty scores to our unlabeled data, and keep a copy of our unlabeled data.
    unlabeled_uncertainty = np.concatenate((unlabeled, np.expand_dims(uncertainty_scores, axis=1)), axis=1)
    unlabeled_uncertainty_copy = np.copy(unlabeled_uncertainty)

    // Define our record container and the maximum number of records to sample.
    instance_index_ranking = []
    ceiling = np.minimum(unlabeled.shape[0], n_instances)

    // TODO (dataframing) is there a better way to do this? Inherently sequential.
    for _ in range(ceiling):

        // Select the instance from our unlabeled copy that scores highest.
        raw_instance = select_instance(X_training=labeled, X_uncertainty=unlabeled_uncertainty_copy)
        instance = np.expand_dims(raw_instance, axis=1)

        // Find our record"s index in both the original unlabeled and our uncertainty copy.
        instance_index_original = np.where(np.all(unlabeled == raw_instance, axis=1))[0][0]
        instance_index_copy = np.where(np.all(unlabeled_uncertainty_copy[:, :-1] == instance.T, axis=1))[0][0]

        // 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

After Change

    labeled = np.copy(classifier.X_training) if n_training_records > 0 else select_cold_start_instance(unlabeled)

    // Add uncertainty scores to our unlabeled data, and keep a copy of our unlabeled data.
    expanded_uncertainty_scores = np.expand_dims(uncertainty_scores, axis=1)
    unlabeled_uncertainty = np.concatenate((unlabeled, expanded_uncertainty_scores), axis=1)

    // Define our null row, which will be filtered during the select_instance call.
    null_row = np.ones(shape=(unlabeled_uncertainty.shape[1],)) * np.nan

Instances