def variance_explained_score(clu, X):
    """Calculate the degree of variance explained by the latent 
    clustering, i.e. Calinski-Harabasz score.
    
    Parameters
    ----------
    clu : list of frozensets
        Resource groups in an organizational model.
    X : DataFrame
        The corresponding resource feature table used for deriving the 
        resource groups.

    Returns
    -------
    float
        The result score representing the degree of variance explained.

    Notes
    -----
    This is merely a wrap-up of function
    `sklearn.metrics.calinski_harabasz_score
    <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.calinski_harabasz_score.html>`_.
    """
    from sklearn.metrics import calinski_harabasz_score
    resources = list(X.index)
    labels = [-1] * len(resources)
    for ig, g in enumerate(clu):
        for r in g:
            labels[resources.index(r)] = ig
    return calinski_harabasz_score(X.values, labels)
