metrics.py 文件源码

def hierarchical_f_measure(tr, y_true, y_pred):
    """
    Calculate hierarchical f-measure. This is defined as the f-measure precision and recall
    calculated with the union of the ancestors
    of the given labels (including the labels themselves and excluding the root).

    Parameters
    ----------
    tr: ThesaursReader
        The thesaurus.
    y_true: {sparse matrix, array-like}
        The true labels
    y_pred: {sparse matrix, array-like}
        The predicited labels

    Returns
    -------
    float
        The hierarchical f_measure
    """
    graph = tr.nx_graph
    root = tr.nx_root
    if not sp.issparse(y_true):
        y_true = sp.coo_matrix(y_true)
        y_pred = sp.coo_matrix(y_pred)
    label_scores = []
    for i in range(0, y_true.shape[0]):
        row_true = y_true.getrow(i)
        row_pred = y_pred.getrow(i)
        true_ancestors = set.union(set(row_true.indices), *[nx.ancestors(graph, index) for index in row_true.indices])
        true_ancestors.discard(root)
        pred_ancestors = set.union(set(row_pred.indices), *[nx.ancestors(graph, index) for index in row_pred.indices])
        pred_ancestors.discard(root)
        intersection = len(pred_ancestors & true_ancestors)
        try:
            p = intersection / len(pred_ancestors)
            r = intersection / len(true_ancestors)
            label_scores.append(2 * p * r / (p + r))
        except ZeroDivisionError:
            warn('F_score is ill-defined and being set to 0.0 on samples with no predicted labels',
                 UndefinedMetricWarning, stacklevel=2)
            label_scores.append(0)
    return np.mean(label_scores)