def ilsr_rankings(<EOL>n_items, data, alpha=<NUM_LIT:0.0>, initial_params=None, max_iter=<NUM_LIT:100>, tol=<NUM_LIT>):
fun = functools.partial(<EOL>lsr_rankings, n_items=n_items, data=data, alpha=alpha)<EOL>return _ilsr(fun, initial_params, max_iter, tol)<EOL>
Compute the ML estimate of model parameters using I-LSR.

    This function computes the maximum-likelihood (ML) estimate of model
    parameters given ranking data (see :ref:`data-rankings`), using the
    iterative Luce Spectral Ranking algorithm [MG15]_.

    The transition rates of the LSR Markov chain are initialized with
    ``alpha``. When ``alpha > 0``, this corresponds to a form of regularization
    (see :ref:`regularization` for details).

    Parameters
    ----------
    n_items : int
        Number of distinct items.
    data : list of lists
        Ranking data.
    alpha : float, optional
        Regularization parameter.
    initial_params : array_like, optional
        Parameters used to initialize the iterative procedure.
    max_iter : int, optional
        Maximum number of iterations allowed.
    tol : float, optional
        Maximum L1-norm of the difference between successive iterates to
        declare convergence.

    Returns
    -------
    params : numpy.ndarray
        The ML estimate of model parameters.