<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def choicerank( digraph, traffic_in, traffic_out, weight=None, initial_params=None, alpha=1.0, max_iter=10000, tol=1e-8):
 """Compute the MAP estimate of a network choice model's parameters. This function computes the maximum-a-posteriori (MAP) estimate of model parameters given a network structure and node-level traffic data (see :ref:`data-network`), using the ChoiceRank algorithm [MG17]_, [KTVV15]_. The nodes are assumed to be labeled using consecutive integers starting from 0. Parameters digraph : networkx.DiGraph Directed graph representing the network. traffic_in : array_like Number of arrivals at each node. traffic_out : array_like Number of departures at each node. weight : str, optional The edge attribute that holds the numerical value used for the edge weight. If None (default) then all edge weights are 1. initial_params : array_like, optional Parameters used to initialize the iterative procedure. alpha : float, optional Regularization parameter. 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 MAP estimate of model parameters. Raises ------ ImportError If the NetworkX library cannot be imported. """

    import networkx as nx
    # Compute the (sparse) adjacency matrix.
    n_items = len(digraph)
    nodes = np.arange(n_items)
    adj = nx.to_scipy_sparse_matrix(digraph, nodelist=nodes, weight=weight)
    adj_t = adj.T.tocsr()
    # Process the data into a standard form.
    traffic_in = np.asarray(traffic_in)
    traffic_out = np.asarray(traffic_out)
    data = (adj, adj_t, traffic_in, traffic_out)
    return _mm(
            n_items, data, initial_params, alpha, max_iter, tol, _choicerank)