Function mixed_eval

project_utils/cluster_utils.py:75–115

Evaluate clustering metrics on two subsets of data, as defined by the mask 'mask' (Mask usually corresponding to `Old' and `New' classes in GCD setting) :param targets: All ground truth labels :param preds: All predictions :param mask: Mask defining two subsets :return:

(targets, preds, mask)

# Mixed Eval Function
# -------------------------------
def mixed_eval(targets, preds, mask):

    """
    Evaluate clustering metrics on two subsets of data, as defined by the mask 'mask'
    (Mask usually corresponding to `Old' and `New' classes in GCD setting)
    :param targets: All ground truth labels
    :param preds: All predictions
    :param mask: Mask defining two subsets
    :return:
    """

    mask = mask.astype(bool)

    # Labelled examples
    if mask.sum() == 0:  # All examples come from unlabelled classes

        unlabelled_acc, unlabelled_nmi, unlabelled_ari = cluster_acc(targets.astype(int), preds.astype(int)), \
                                                         nmi_score(targets, preds), \
                                                         ari_score(targets, preds)

        print('Unlabelled Classes Test acc {:.4f}, nmi {:.4f}, ari {:.4f}'
              .format(unlabelled_acc, unlabelled_nmi, unlabelled_ari))

        # Also return ratio between labelled and unlabelled examples
        return (unlabelled_acc, unlabelled_nmi, unlabelled_ari), mask.mean()

    else:

        labelled_acc, labelled_nmi, labelled_ari = cluster_acc(targets.astype(int)[mask],
                                                               preds.astype(int)[mask]), \
                                                   nmi_score(targets[mask], preds[mask]), \
                                                   ari_score(targets[mask], preds[mask])

        unlabelled_acc, unlabelled_nmi, unlabelled_ari = cluster_acc(targets.astype(int)[~mask],
                                                                     preds.astype(int)[~mask]), \
                                                         nmi_score(targets[~mask], preds[~mask]), \
                                                         ari_score(targets[~mask], preds[~mask])

        # Also return ratio between labelled and unlabelled examples
        return (labelled_acc, labelled_nmi, labelled_ari), (
            unlabelled_acc, unlabelled_nmi, unlabelled_ari), mask.mean()


class AverageMeter(object):