@experimental(as_of="0.4.0")
def lladser_pe(counts, r=10):
    """Calculate single point estimate of conditional uncovered probability.

    Parameters
    ----------
    counts : 1-D array_like, int
        Vector of counts.
    r : int, optional
        Number of new colors that are required for the next prediction.

    Returns
    -------
    double
        Single point estimate of the conditional uncovered probability. May be
        ``np.nan`` if a point estimate could not be computed.

    See Also
    --------
    lladser_ci

    Notes
    -----
    This function is just a wrapper around the full point estimator described
    in Theorem 2 (i) in [1]_, intended to be called for a single best estimate
    on a complete sample. This function is not guaranteed to return estimated
    uncovered probabilities less than 1 if the coverage is too low.

    References
    ----------
    .. [1] Lladser, Gouet, and Reeder, "Extrapolation of Urn Models via
       Poissonization: Accurate Measurements of the Microbial Unknown" PLoS
       2011.

    """
    counts = _validate_counts_vector(counts)
    sample = _expand_counts(counts)
    np.random.shuffle(sample)

    try:
        pe = list(_lladser_point_estimates(sample, r))[-1][0]
    except __HOLE__:
        pe = np.nan

    return pe
dataset/ETHPy150Open biocore/scikit-bio/skbio/diversity/alpha/_lladser.py/lladser_pe