utils.py 文件源码

def compute_group_overlap_score(ref_labels, pred_labels,
                                threshold_overlap_pred=0.5,
                                threshold_overlap_ref=0.5):
    """How well do the pred_labels explain the ref_labels?

    A predicted cluster explains a reference cluster if it is contained within the reference
    cluster with at least 50% (threshold_overlap_pred) of its points and these correspond
    to at least 50% (threshold_overlap_ref) of the reference cluster.
    """
    ref_unique, ref_counts = np.unique(ref_labels, return_counts=True)
    ref_dict = dict(zip(ref_unique, ref_counts))
    pred_unique, pred_counts = np.unique(pred_labels, return_counts=True)
    pred_dict = dict(zip(pred_unique, pred_counts))
    summary = []
    for true in ref_unique:
        sub_pred_unique, sub_pred_counts = np.unique(pred_labels[true == ref_labels], return_counts=True)
        relative_overlaps_pred = [sub_pred_counts[i] / pred_dict[n] for i, n in enumerate(sub_pred_unique)]
        relative_overlaps_ref = [sub_pred_counts[i] / ref_dict[true] for i, n in enumerate(sub_pred_unique)]
        pred_best_index = np.argmax(relative_overlaps_pred)
        summary.append(1 if (relative_overlaps_pred[pred_best_index] >= threshold_overlap_pred and
                             relative_overlaps_ref[pred_best_index] >= threshold_overlap_ref)
                       else 0)
        # print(true, sub_pred_unique[pred_best_index], relative_overlaps_pred[pred_best_index],
        #       relative_overlaps_ref[pred_best_index], summary[-1])
    return sum(summary)/len(summary)