view env/lib/python3.9/site-packages/networkx/algorithms/distance_measures.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

"""Graph diameter, radius, eccentricity and other properties."""

import networkx as nx
from networkx.utils import not_implemented_for

__all__ = [
    "extrema_bounding",
    "eccentricity",
    "diameter",
    "radius",
    "periphery",
    "center",
    "barycenter",
    "resistance_distance",
]


def extrema_bounding(G, compute="diameter"):
    """Compute requested extreme distance metric of undirected graph G

    Computation is based on smart lower and upper bounds, and in practice
    linear in the number of nodes, rather than quadratic (except for some
    border cases such as complete graphs or circle shaped graphs).

    Parameters
    ----------
    G : NetworkX graph
       An undirected graph

    compute : string denoting the requesting metric
       "diameter" for the maximal eccentricity value,
       "radius" for the minimal eccentricity value,
       "periphery" for the set of nodes with eccentricity equal to the diameter
       "center" for the set of nodes with eccentricity equal to the radius

    Returns
    -------
    value : value of the requested metric
       int for "diameter" and "radius" or
       list of nodes for "center" and "periphery"

    Raises
    ------
    NetworkXError
        If the graph consists of multiple components

    Notes
    -----
    This algorithm was proposed in the following papers:

    F.W. Takes and W.A. Kosters, Determining the Diameter of Small World
    Networks, in Proceedings of the 20th ACM International Conference on
    Information and Knowledge Management (CIKM 2011), pp. 1191-1196, 2011.
    doi: https://doi.org/10.1145/2063576.2063748

    F.W. Takes and W.A. Kosters, Computing the Eccentricity Distribution of
    Large Graphs, Algorithms 6(1): 100-118, 2013.
    doi: https://doi.org/10.3390/a6010100

    M. Borassi, P. Crescenzi, M. Habib, W.A. Kosters, A. Marino and F.W. Takes,
    Fast Graph Diameter and Radius BFS-Based Computation in (Weakly Connected)
    Real-World Graphs, Theoretical Computer Science 586: 59-80, 2015.
    doi: https://doi.org/10.1016/j.tcs.2015.02.033
    """

    # init variables
    degrees = dict(G.degree())  # start with the highest degree node
    minlowernode = max(degrees, key=degrees.get)
    N = len(degrees)  # number of nodes
    # alternate between smallest lower and largest upper bound
    high = False
    # status variables
    ecc_lower = dict.fromkeys(G, 0)
    ecc_upper = dict.fromkeys(G, N)
    candidates = set(G)

    # (re)set bound extremes
    minlower = N
    maxlower = 0
    minupper = N
    maxupper = 0

    # repeat the following until there are no more candidates
    while candidates:
        if high:
            current = maxuppernode  # select node with largest upper bound
        else:
            current = minlowernode  # select node with smallest lower bound
        high = not high

        # get distances from/to current node and derive eccentricity
        dist = dict(nx.single_source_shortest_path_length(G, current))
        if len(dist) != N:
            msg = "Cannot compute metric because graph is not connected."
            raise nx.NetworkXError(msg)
        current_ecc = max(dist.values())

        # print status update
        #        print ("ecc of " + str(current) + " (" + str(ecc_lower[current]) + "/"
        #        + str(ecc_upper[current]) + ", deg: " + str(dist[current]) + ") is "
        #        + str(current_ecc))
        #        print(ecc_upper)

        # (re)set bound extremes
        maxuppernode = None
        minlowernode = None

        # update node bounds
        for i in candidates:
            # update eccentricity bounds
            d = dist[i]
            ecc_lower[i] = low = max(ecc_lower[i], max(d, (current_ecc - d)))
            ecc_upper[i] = upp = min(ecc_upper[i], current_ecc + d)

            # update min/max values of lower and upper bounds
            minlower = min(ecc_lower[i], minlower)
            maxlower = max(ecc_lower[i], maxlower)
            minupper = min(ecc_upper[i], minupper)
            maxupper = max(ecc_upper[i], maxupper)

        # update candidate set
        if compute == "diameter":
            ruled_out = {
                i
                for i in candidates
                if ecc_upper[i] <= maxlower and 2 * ecc_lower[i] >= maxupper
            }

        elif compute == "radius":
            ruled_out = {
                i
                for i in candidates
                if ecc_lower[i] >= minupper and ecc_upper[i] + 1 <= 2 * minlower
            }

        elif compute == "periphery":
            ruled_out = {
                i
                for i in candidates
                if ecc_upper[i] < maxlower
                and (maxlower == maxupper or ecc_lower[i] > maxupper)
            }

        elif compute == "center":
            ruled_out = {
                i
                for i in candidates
                if ecc_lower[i] > minupper
                and (minlower == minupper or ecc_upper[i] + 1 < 2 * minlower)
            }

        elif compute == "eccentricities":
            ruled_out = {}

        ruled_out.update(i for i in candidates if ecc_lower[i] == ecc_upper[i])
        candidates -= ruled_out

        #        for i in ruled_out:
        #            print("removing %g: ecc_u: %g maxl: %g ecc_l: %g maxu: %g"%
        #                    (i,ecc_upper[i],maxlower,ecc_lower[i],maxupper))
        #        print("node %g: ecc_u: %g maxl: %g ecc_l: %g maxu: %g"%
        #                    (4,ecc_upper[4],maxlower,ecc_lower[4],maxupper))
        #        print("NODE 4: %g"%(ecc_upper[4] <= maxlower))
        #        print("NODE 4: %g"%(2 * ecc_lower[4] >= maxupper))
        #        print("NODE 4: %g"%(ecc_upper[4] <= maxlower
        #                            and 2 * ecc_lower[4] >= maxupper))

        # updating maxuppernode and minlowernode for selection in next round
        for i in candidates:
            if (
                minlowernode is None
                or (
                    ecc_lower[i] == ecc_lower[minlowernode]
                    and degrees[i] > degrees[minlowernode]
                )
                or (ecc_lower[i] < ecc_lower[minlowernode])
            ):
                minlowernode = i

            if (
                maxuppernode is None
                or (
                    ecc_upper[i] == ecc_upper[maxuppernode]
                    and degrees[i] > degrees[maxuppernode]
                )
                or (ecc_upper[i] > ecc_upper[maxuppernode])
            ):
                maxuppernode = i

        # print status update
    #        print (" min=" + str(minlower) + "/" + str(minupper) +
    #        " max=" + str(maxlower) + "/" + str(maxupper) +
    #        " candidates: " + str(len(candidates)))
    #        print("cand:",candidates)
    #        print("ecc_l",ecc_lower)
    #        print("ecc_u",ecc_upper)
    #        wait = input("press Enter to continue")

    # return the correct value of the requested metric
    if compute == "diameter":
        return maxlower
    elif compute == "radius":
        return minupper
    elif compute == "periphery":
        p = [v for v in G if ecc_lower[v] == maxlower]
        return p
    elif compute == "center":
        c = [v for v in G if ecc_upper[v] == minupper]
        return c
    elif compute == "eccentricities":
        return ecc_lower
    return None


def eccentricity(G, v=None, sp=None):
    """Returns the eccentricity of nodes in G.

    The eccentricity of a node v is the maximum distance from v to
    all other nodes in G.

    Parameters
    ----------
    G : NetworkX graph
       A graph

    v : node, optional
       Return value of specified node

    sp : dict of dicts, optional
       All pairs shortest path lengths as a dictionary of dictionaries

    Returns
    -------
    ecc : dictionary
       A dictionary of eccentricity values keyed by node.
    """
    #    if v is None:                # none, use entire graph
    #        nodes=G.nodes()
    #    elif v in G:               # is v a single node
    #        nodes=[v]
    #    else:                      # assume v is a container of nodes
    #        nodes=v
    order = G.order()

    e = {}
    for n in G.nbunch_iter(v):
        if sp is None:
            length = nx.single_source_shortest_path_length(G, n)
            L = len(length)
        else:
            try:
                length = sp[n]
                L = len(length)
            except TypeError as e:
                raise nx.NetworkXError('Format of "sp" is invalid.') from e
        if L != order:
            if G.is_directed():
                msg = (
                    "Found infinite path length because the digraph is not"
                    " strongly connected"
                )
            else:
                msg = "Found infinite path length because the graph is not" " connected"
            raise nx.NetworkXError(msg)

        e[n] = max(length.values())

    if v in G:
        return e[v]  # return single value
    else:
        return e


def diameter(G, e=None, usebounds=False):
    """Returns the diameter of the graph G.

    The diameter is the maximum eccentricity.

    Parameters
    ----------
    G : NetworkX graph
       A graph

    e : eccentricity dictionary, optional
      A precomputed dictionary of eccentricities.

    Returns
    -------
    d : integer
       Diameter of graph

    See Also
    --------
    eccentricity
    """
    if usebounds is True and e is None and not G.is_directed():
        return extrema_bounding(G, compute="diameter")
    if e is None:
        e = eccentricity(G)
    return max(e.values())


def periphery(G, e=None, usebounds=False):
    """Returns the periphery of the graph G.

    The periphery is the set of nodes with eccentricity equal to the diameter.

    Parameters
    ----------
    G : NetworkX graph
       A graph

    e : eccentricity dictionary, optional
      A precomputed dictionary of eccentricities.

    Returns
    -------
    p : list
       List of nodes in periphery

    See Also
    --------
    barycenter
    center
    """
    if usebounds is True and e is None and not G.is_directed():
        return extrema_bounding(G, compute="periphery")
    if e is None:
        e = eccentricity(G)
    diameter = max(e.values())
    p = [v for v in e if e[v] == diameter]
    return p


def radius(G, e=None, usebounds=False):
    """Returns the radius of the graph G.

    The radius is the minimum eccentricity.

    Parameters
    ----------
    G : NetworkX graph
       A graph

    e : eccentricity dictionary, optional
      A precomputed dictionary of eccentricities.

    Returns
    -------
    r : integer
       Radius of graph
    """
    if usebounds is True and e is None and not G.is_directed():
        return extrema_bounding(G, compute="radius")
    if e is None:
        e = eccentricity(G)
    return min(e.values())


def center(G, e=None, usebounds=False):
    """Returns the center of the graph G.

    The center is the set of nodes with eccentricity equal to radius.

    Parameters
    ----------
    G : NetworkX graph
       A graph

    e : eccentricity dictionary, optional
      A precomputed dictionary of eccentricities.

    Returns
    -------
    c : list
       List of nodes in center

    See Also
    --------
    barycenter
    periphery
    """
    if usebounds is True and e is None and not G.is_directed():
        return extrema_bounding(G, compute="center")
    if e is None:
        e = eccentricity(G)
    radius = min(e.values())
    p = [v for v in e if e[v] == radius]
    return p


def barycenter(G, weight=None, attr=None, sp=None):
    r"""Calculate barycenter of a connected graph, optionally with edge weights.

    The :dfn:`barycenter` a
    :func:`connected <networkx.algorithms.components.is_connected>` graph
    :math:`G` is the subgraph induced by the set of its nodes :math:`v`
    minimizing the objective function

    .. math::

        \sum_{u \in V(G)} d_G(u, v),

    where :math:`d_G` is the (possibly weighted) :func:`path length
    <networkx.algorithms.shortest_paths.generic.shortest_path_length>`.
    The barycenter is also called the :dfn:`median`. See [West01]_, p. 78.

    Parameters
    ----------
    G : :class:`networkx.Graph`
        The connected graph :math:`G`.
    weight : :class:`str`, optional
        Passed through to
        :func:`~networkx.algorithms.shortest_paths.generic.shortest_path_length`.
    attr : :class:`str`, optional
        If given, write the value of the objective function to each node's
        `attr` attribute. Otherwise do not store the value.
    sp : dict of dicts, optional
       All pairs shortest path lengths as a dictionary of dictionaries

    Returns
    -------
    list
        Nodes of `G` that induce the barycenter of `G`.

    Raises
    ------
    NetworkXNoPath
        If `G` is disconnected. `G` may appear disconnected to
        :func:`barycenter` if `sp` is given but is missing shortest path
        lengths for any pairs.
    ValueError
        If `sp` and `weight` are both given.

    See Also
    --------
    center
    periphery
    """
    if sp is None:
        sp = nx.shortest_path_length(G, weight=weight)
    else:
        sp = sp.items()
        if weight is not None:
            raise ValueError("Cannot use both sp, weight arguments together")
    smallest, barycenter_vertices, n = float("inf"), [], len(G)
    for v, dists in sp:
        if len(dists) < n:
            raise nx.NetworkXNoPath(
                f"Input graph {G} is disconnected, so every induced subgraph "
                "has infinite barycentricity."
            )
        barycentricity = sum(dists.values())
        if attr is not None:
            G.nodes[v][attr] = barycentricity
        if barycentricity < smallest:
            smallest = barycentricity
            barycenter_vertices = [v]
        elif barycentricity == smallest:
            barycenter_vertices.append(v)
    return barycenter_vertices


def _laplacian_submatrix(node, mat, node_list):
    """Removes row/col from a sparse matrix and returns the submatrix
    """
    j = node_list.index(node)
    n = list(range(len(node_list)))
    n.pop(j)

    if mat.shape[0] != mat.shape[1]:
        raise nx.NetworkXError("Matrix must be square")
    elif len(node_list) != mat.shape[0]:
        msg = "Node list length does not match matrix dimentions"
        raise nx.NetworkXError(msg)

    mat = mat.tocsr()
    mat = mat[n, :]

    mat = mat.tocsc()
    mat = mat[:, n]

    node_list.pop(j)

    return mat, node_list


def _count_lu_permutations(perm_array):
    """Counts the number of permutations in SuperLU perm_c or perm_r
    """
    perm_cnt = 0
    arr = perm_array.tolist()
    for i in range(len(arr)):
        if i != arr[i]:
            perm_cnt += 1
            n = arr.index(i)
            arr[n] = arr[i]
            arr[i] = i

    return perm_cnt


@not_implemented_for("directed")
def resistance_distance(G, nodeA, nodeB, weight=None, invert_weight=True):
    """Returns the resistance distance between node A and node B on graph G.

    The resistance distance between two nodes of a graph is akin to treating
    the graph as a grid of resistorses with a resistance equal to the provided
    weight.

    If weight is not provided, then a weight of 1 is used for all edges.

    Parameters
    ----------
    G : NetworkX graph
       A graph

    nodeA : node
      A node within graph G.

    nodeB : node
      A node within graph G, exclusive of Node A.

    weight : string or None, optional (default=None)
       The edge data key used to compute the resistance distance.
       If None, then each edge has weight 1.

    invert_weight : boolean (default=True)
        Proper calculation of resistance distance requires building the
        Laplacian matrix with the reciprocal of the weight. Not required
        if the weight is already inverted. Weight cannot be zero.

    Returns
    -------
    rd : float
       Value of effective resistance distance

    Notes
    -----
    Overview discussion:
    * https://en.wikipedia.org/wiki/Resistance_distance
    * http://mathworld.wolfram.com/ResistanceDistance.html

    Additional details:
    Vaya Sapobi Samui Vos, “Methods for determining the effective resistance,” M.S.,
    Mathematisch Instituut, Universiteit Leiden, Leiden, Netherlands, 2016
    Available: `Link to thesis <https://www.universiteitleiden.nl/binaries/content/assets/science/mi/scripties/master/vos_vaya_master.pdf>`_
    """
    import numpy as np
    import scipy.sparse

    if not nx.is_connected(G):
        msg = "Graph G must be strongly connected."
        raise nx.NetworkXError(msg)
    elif nodeA not in G:
        msg = "Node A is not in graph G."
        raise nx.NetworkXError(msg)
    elif nodeB not in G:
        msg = "Node B is not in graph G."
        raise nx.NetworkXError(msg)
    elif nodeA == nodeB:
        msg = "Node A and Node B cannot be the same."
        raise nx.NetworkXError(msg)

    G = G.copy()
    node_list = list(G)

    if invert_weight and weight is not None:
        if G.is_multigraph():
            for (u, v, k, d) in G.edges(keys=True, data=True):
                d[weight] = 1 / d[weight]
        else:
            for (u, v, d) in G.edges(data=True):
                d[weight] = 1 / d[weight]
    # Replace with collapsing topology or approximated zero?

    # Using determinants to compute the effective resistance is more memory
    # efficent than directly calculating the psuedo-inverse
    L = nx.laplacian_matrix(G, node_list, weight=weight)

    Lsub_a, node_list_a = _laplacian_submatrix(nodeA, L.copy(), node_list[:])
    Lsub_ab, node_list_ab = _laplacian_submatrix(nodeB, Lsub_a.copy(), node_list_a[:])

    # Factorize Laplacian submatrixes and extract diagonals
    # Order the diagonals to minimize the likelihood over overflows
    # during computing the determinant
    lu_a = scipy.sparse.linalg.splu(Lsub_a, options=dict(SymmetricMode=True))
    LdiagA = lu_a.U.diagonal()
    LdiagA_s = np.product(np.sign(LdiagA)) * np.product(lu_a.L.diagonal())
    LdiagA_s *= (-1) ** _count_lu_permutations(lu_a.perm_r)
    LdiagA_s *= (-1) ** _count_lu_permutations(lu_a.perm_c)
    LdiagA = np.absolute(LdiagA)
    LdiagA = np.sort(LdiagA)

    lu_ab = scipy.sparse.linalg.splu(Lsub_ab, options=dict(SymmetricMode=True))
    LdiagAB = lu_ab.U.diagonal()
    LdiagAB_s = np.product(np.sign(LdiagAB)) * np.product(lu_ab.L.diagonal())
    LdiagAB_s *= (-1) ** _count_lu_permutations(lu_ab.perm_r)
    LdiagAB_s *= (-1) ** _count_lu_permutations(lu_ab.perm_c)
    LdiagAB = np.absolute(LdiagAB)
    LdiagAB = np.sort(LdiagAB)

    # Calculate the ratio of determinant, rd = det(Lsub_ab)/det(Lsub_a)
    Ldet = np.product(np.divide(np.append(LdiagAB, [1]), LdiagA))
    rd = Ldet * LdiagAB_s / LdiagA_s

    return rd