593 lines
19 KiB
Python
593 lines
19 KiB
Python
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# -*- coding: utf-8 -*-
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# Copyright (C) 2014 ysitu <ysitu@users.noreply.github.com>
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# All rights reserved.
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# BSD license.
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#
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# Author: ysitu <ysitu@users.noreply.github.com>
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"""
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Algebraic connectivity and Fiedler vectors of undirected graphs.
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"""
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from functools import partial
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import networkx as nx
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from networkx.utils import not_implemented_for
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from networkx.utils import reverse_cuthill_mckee_ordering
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from networkx.utils import random_state
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try:
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from numpy import array, asmatrix, asarray, dot, ndarray, ones, sqrt, zeros
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from numpy.linalg import norm, qr
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from numpy.random import normal
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from scipy.linalg import eigh, inv
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from scipy.sparse import csc_matrix, spdiags
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from scipy.sparse.linalg import eigsh, lobpcg
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__all__ = ['algebraic_connectivity', 'fiedler_vector', 'spectral_ordering']
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except ImportError:
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__all__ = []
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try:
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from scipy.linalg.blas import dasum, daxpy, ddot
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except ImportError:
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if __all__:
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# Make sure the imports succeeded.
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# Use minimal replacements if BLAS is unavailable from SciPy.
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dasum = partial(norm, ord=1)
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ddot = dot
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def daxpy(x, y, a):
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y += a * x
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return y
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class _PCGSolver(object):
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"""Preconditioned conjugate gradient method.
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To solve Ax = b:
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M = A.diagonal() # or some other preconditioner
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solver = _PCGSolver(lambda x: A * x, lambda x: M * x)
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x = solver.solve(b)
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The inputs A and M are functions which compute
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matrix multiplication on the argument.
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A - multiply by the matrix A in Ax=b
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M - multiply by M, the preconditioner surragate for A
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Warning: There is no limit on number of iterations.
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"""
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def __init__(self, A, M):
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self._A = A
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self._M = M or (lambda x: x.copy())
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def solve(self, B, tol):
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B = asarray(B)
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X = ndarray(B.shape, order='F')
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for j in range(B.shape[1]):
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X[:, j] = self._solve(B[:, j], tol)
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return X
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def _solve(self, b, tol):
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A = self._A
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M = self._M
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tol *= dasum(b)
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# Initialize.
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x = zeros(b.shape)
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r = b.copy()
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z = M(r)
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rz = ddot(r, z)
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p = z.copy()
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# Iterate.
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while True:
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Ap = A(p)
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alpha = rz / ddot(p, Ap)
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x = daxpy(p, x, a=alpha)
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r = daxpy(Ap, r, a=-alpha)
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if dasum(r) < tol:
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return x
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z = M(r)
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beta = ddot(r, z)
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beta, rz = beta / rz, beta
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p = daxpy(p, z, a=beta)
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class _CholeskySolver(object):
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"""Cholesky factorization.
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To solve Ax = b:
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solver = _CholeskySolver(A)
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x = solver.solve(b)
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optional argument `tol` on solve method is ignored but included
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to match _PCGsolver API.
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"""
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def __init__(self, A):
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if not self._cholesky:
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raise nx.NetworkXError('Cholesky solver unavailable.')
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self._chol = self._cholesky(A)
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def solve(self, B, tol=None):
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return self._chol(B)
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try:
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from scikits.sparse.cholmod import cholesky
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_cholesky = cholesky
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except ImportError:
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_cholesky = None
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class _LUSolver(object):
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"""LU factorization.
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To solve Ax = b:
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solver = _LUSolver(A)
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x = solver.solve(b)
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optional argument `tol` on solve method is ignored but included
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to match _PCGsolver API.
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"""
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def __init__(self, A):
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if not self._splu:
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raise nx.NetworkXError('LU solver unavailable.')
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self._LU = self._splu(A)
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def solve(self, B, tol=None):
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B = asarray(B)
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X = ndarray(B.shape, order='F')
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for j in range(B.shape[1]):
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X[:, j] = self._LU.solve(B[:, j])
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return X
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try:
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from scipy.sparse.linalg import splu
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_splu = partial(splu, permc_spec='MMD_AT_PLUS_A', diag_pivot_thresh=0.,
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options={'Equil': True, 'SymmetricMode': True})
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except ImportError:
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_splu = None
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def _preprocess_graph(G, weight):
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"""Compute edge weights and eliminate zero-weight edges.
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"""
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if G.is_directed():
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H = nx.MultiGraph()
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H.add_nodes_from(G)
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H.add_weighted_edges_from(((u, v, e.get(weight, 1.))
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for u, v, e in G.edges(data=True)
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if u != v), weight=weight)
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G = H
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if not G.is_multigraph():
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edges = ((u, v, abs(e.get(weight, 1.)))
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for u, v, e in G.edges(data=True) if u != v)
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else:
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edges = ((u, v, sum(abs(e.get(weight, 1.)) for e in G[u][v].values()))
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for u, v in G.edges() if u != v)
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H = nx.Graph()
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H.add_nodes_from(G)
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H.add_weighted_edges_from((u, v, e) for u, v, e in edges if e != 0)
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return H
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def _rcm_estimate(G, nodelist):
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"""Estimate the Fiedler vector using the reverse Cuthill-McKee ordering.
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"""
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G = G.subgraph(nodelist)
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order = reverse_cuthill_mckee_ordering(G)
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n = len(nodelist)
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index = dict(zip(nodelist, range(n)))
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x = ndarray(n, dtype=float)
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for i, u in enumerate(order):
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x[index[u]] = i
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x -= (n - 1) / 2.
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return x
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def _tracemin_fiedler(L, X, normalized, tol, method):
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"""Compute the Fiedler vector of L using the TraceMIN-Fiedler algorithm.
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The Fiedler vector of a connected undirected graph is the eigenvector
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corresponding to the second smallest eigenvalue of the Laplacian matrix of
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of the graph. This function starts with the Laplacian L, not the Graph.
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Parameters
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----------
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L : Laplacian of a possibly weighted or normalized, but undirected graph
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X : Initial guess for a solution. Usually a matrix of random numbers.
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This function allows more than one column in X to identify more than
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one eigenvector if desired.
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normalized : bool
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Whether the normalized Laplacian matrix is used.
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tol : float
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Tolerance of relative residual in eigenvalue computation.
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Warning: There is no limit on number of iterations.
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method : string
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Should be 'tracemin_pcg', 'tracemin_chol' or 'tracemin_lu'.
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Otherwise exception is raised.
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Returns
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-------
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sigma, X : Two NumPy arrays of floats.
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The lowest eigenvalues and corresponding eigenvectors of L.
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The size of input X determines the size of these outputs.
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As this is for Fiedler vectors, the zero eigenvalue (and
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constant eigenvector) are avoided.
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"""
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n = X.shape[0]
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if normalized:
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# Form the normalized Laplacian matrix and determine the eigenvector of
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# its nullspace.
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e = sqrt(L.diagonal())
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D = spdiags(1. / e, [0], n, n, format='csr')
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L = D * L * D
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e *= 1. / norm(e, 2)
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if normalized:
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def project(X):
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"""Make X orthogonal to the nullspace of L.
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"""
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X = asarray(X)
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for j in range(X.shape[1]):
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X[:, j] -= dot(X[:, j], e) * e
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else:
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def project(X):
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"""Make X orthogonal to the nullspace of L.
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"""
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X = asarray(X)
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for j in range(X.shape[1]):
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X[:, j] -= X[:, j].sum() / n
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if method == 'tracemin_pcg':
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D = L.diagonal().astype(float)
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solver = _PCGSolver(lambda x: L * x, lambda x: D * x)
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elif method == 'tracemin_chol' or method == 'tracemin_lu':
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# Convert A to CSC to suppress SparseEfficiencyWarning.
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A = csc_matrix(L, dtype=float, copy=True)
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# Force A to be nonsingular. Since A is the Laplacian matrix of a
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# connected graph, its rank deficiency is one, and thus one diagonal
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# element needs to modified. Changing to infinity forces a zero in the
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# corresponding element in the solution.
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i = (A.indptr[1:] - A.indptr[:-1]).argmax()
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A[i, i] = float('inf')
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if method == 'tracemin_chol':
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solver = _CholeskySolver(A)
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else:
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solver = _LUSolver(A)
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else:
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raise nx.NetworkXError('Unknown linear system solver: ' + method)
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# Initialize.
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Lnorm = abs(L).sum(axis=1).flatten().max()
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project(X)
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W = asmatrix(ndarray(X.shape, order='F'))
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while True:
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# Orthonormalize X.
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X = qr(X)[0]
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# Compute iteration matrix H.
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W[:, :] = L * X
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H = X.T * W
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sigma, Y = eigh(H, overwrite_a=True)
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# Compute the Ritz vectors.
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X *= Y
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# Test for convergence exploiting the fact that L * X == W * Y.
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res = dasum(W * asmatrix(Y)[:, 0] - sigma[0] * X[:, 0]) / Lnorm
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if res < tol:
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break
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# Compute X = L \ X / (X' * (L \ X)).
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# L \ X can have an arbitrary projection on the nullspace of L,
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# which will be eliminated.
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W[:, :] = solver.solve(X, tol)
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X = (inv(W.T * X) * W.T).T # Preserves Fortran storage order.
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project(X)
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return sigma, asarray(X)
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def _get_fiedler_func(method):
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"""Returns a function that solves the Fiedler eigenvalue problem.
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"""
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if method == "tracemin": # old style keyword <v2.1
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method = "tracemin_pcg"
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if method in ("tracemin_pcg", "tracemin_chol", "tracemin_lu"):
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def find_fiedler(L, x, normalized, tol, seed):
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q = 1 if method == 'tracemin_pcg' else min(4, L.shape[0] - 1)
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X = asmatrix(seed.normal(size=(q, L.shape[0]))).T
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sigma, X = _tracemin_fiedler(L, X, normalized, tol, method)
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return sigma[0], X[:, 0]
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elif method == 'lanczos' or method == 'lobpcg':
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def find_fiedler(L, x, normalized, tol, seed):
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L = csc_matrix(L, dtype=float)
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n = L.shape[0]
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if normalized:
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D = spdiags(1. / sqrt(L.diagonal()), [0], n, n, format='csc')
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L = D * L * D
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if method == 'lanczos' or n < 10:
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# Avoid LOBPCG when n < 10 due to
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# https://github.com/scipy/scipy/issues/3592
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# https://github.com/scipy/scipy/pull/3594
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sigma, X = eigsh(L, 2, which='SM', tol=tol,
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return_eigenvectors=True)
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return sigma[1], X[:, 1]
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else:
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X = asarray(asmatrix(x).T)
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M = spdiags(1. / L.diagonal(), [0], n, n)
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Y = ones(n)
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if normalized:
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Y /= D.diagonal()
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sigma, X = lobpcg(L, X, M=M, Y=asmatrix(Y).T, tol=tol,
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maxiter=n, largest=False)
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return sigma[0], X[:, 0]
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else:
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raise nx.NetworkXError("unknown method '%s'." % method)
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return find_fiedler
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@random_state(5)
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@not_implemented_for('directed')
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def algebraic_connectivity(G, weight='weight', normalized=False, tol=1e-8,
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method='tracemin_pcg', seed=None):
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"""Returns the algebraic connectivity of an undirected graph.
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The algebraic connectivity of a connected undirected graph is the second
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smallest eigenvalue of its Laplacian matrix.
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Parameters
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----------
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G : NetworkX graph
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An undirected graph.
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weight : object, optional (default: None)
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The data key used to determine the weight of each edge. If None, then
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each edge has unit weight.
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normalized : bool, optional (default: False)
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Whether the normalized Laplacian matrix is used.
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tol : float, optional (default: 1e-8)
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Tolerance of relative residual in eigenvalue computation.
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method : string, optional (default: 'tracemin_pcg')
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Method of eigenvalue computation. It must be one of the tracemin
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options shown below (TraceMIN), 'lanczos' (Lanczos iteration)
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or 'lobpcg' (LOBPCG).
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The TraceMIN algorithm uses a linear system solver. The following
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values allow specifying the solver to be used.
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=============== ========================================
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Value Solver
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=============== ========================================
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|
|
'tracemin_pcg' Preconditioned conjugate gradient method
|
||
|
|
'tracemin_chol' Cholesky factorization
|
||
|
|
'tracemin_lu' LU factorization
|
||
|
|
=============== ========================================
|
||
|
|
|
||
|
|
seed : integer, random_state, or None (default)
|
||
|
|
Indicator of random number generation state.
|
||
|
|
See :ref:`Randomness<randomness>`.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
algebraic_connectivity : float
|
||
|
|
Algebraic connectivity.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
NetworkXNotImplemented
|
||
|
|
If G is directed.
|
||
|
|
|
||
|
|
NetworkXError
|
||
|
|
If G has less than two nodes.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Edge weights are interpreted by their absolute values. For MultiGraph's,
|
||
|
|
weights of parallel edges are summed. Zero-weighted edges are ignored.
|
||
|
|
|
||
|
|
To use Cholesky factorization in the TraceMIN algorithm, the
|
||
|
|
:samp:`scikits.sparse` package must be installed.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
laplacian_matrix
|
||
|
|
"""
|
||
|
|
if len(G) < 2:
|
||
|
|
raise nx.NetworkXError('graph has less than two nodes.')
|
||
|
|
G = _preprocess_graph(G, weight)
|
||
|
|
if not nx.is_connected(G):
|
||
|
|
return 0.
|
||
|
|
|
||
|
|
L = nx.laplacian_matrix(G)
|
||
|
|
if L.shape[0] == 2:
|
||
|
|
return 2. * L[0, 0] if not normalized else 2.
|
||
|
|
|
||
|
|
find_fiedler = _get_fiedler_func(method)
|
||
|
|
x = None if method != 'lobpcg' else _rcm_estimate(G, G)
|
||
|
|
sigma, fiedler = find_fiedler(L, x, normalized, tol, seed)
|
||
|
|
return sigma
|
||
|
|
|
||
|
|
|
||
|
|
@random_state(5)
|
||
|
|
@not_implemented_for('directed')
|
||
|
|
def fiedler_vector(G, weight='weight', normalized=False, tol=1e-8,
|
||
|
|
method='tracemin_pcg', seed=None):
|
||
|
|
"""Returns the Fiedler vector of a connected undirected graph.
|
||
|
|
|
||
|
|
The Fiedler vector of a connected undirected graph is the eigenvector
|
||
|
|
corresponding to the second smallest eigenvalue of the Laplacian matrix of
|
||
|
|
of the graph.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : NetworkX graph
|
||
|
|
An undirected graph.
|
||
|
|
|
||
|
|
weight : object, optional (default: None)
|
||
|
|
The data key used to determine the weight of each edge. If None, then
|
||
|
|
each edge has unit weight.
|
||
|
|
|
||
|
|
normalized : bool, optional (default: False)
|
||
|
|
Whether the normalized Laplacian matrix is used.
|
||
|
|
|
||
|
|
tol : float, optional (default: 1e-8)
|
||
|
|
Tolerance of relative residual in eigenvalue computation.
|
||
|
|
|
||
|
|
method : string, optional (default: 'tracemin_pcg')
|
||
|
|
Method of eigenvalue computation. It must be one of the tracemin
|
||
|
|
options shown below (TraceMIN), 'lanczos' (Lanczos iteration)
|
||
|
|
or 'lobpcg' (LOBPCG).
|
||
|
|
|
||
|
|
The TraceMIN algorithm uses a linear system solver. The following
|
||
|
|
values allow specifying the solver to be used.
|
||
|
|
|
||
|
|
=============== ========================================
|
||
|
|
Value Solver
|
||
|
|
=============== ========================================
|
||
|
|
'tracemin_pcg' Preconditioned conjugate gradient method
|
||
|
|
'tracemin_chol' Cholesky factorization
|
||
|
|
'tracemin_lu' LU factorization
|
||
|
|
=============== ========================================
|
||
|
|
|
||
|
|
seed : integer, random_state, or None (default)
|
||
|
|
Indicator of random number generation state.
|
||
|
|
See :ref:`Randomness<randomness>`.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
fiedler_vector : NumPy array of floats.
|
||
|
|
Fiedler vector.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
NetworkXNotImplemented
|
||
|
|
If G is directed.
|
||
|
|
|
||
|
|
NetworkXError
|
||
|
|
If G has less than two nodes or is not connected.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Edge weights are interpreted by their absolute values. For MultiGraph's,
|
||
|
|
weights of parallel edges are summed. Zero-weighted edges are ignored.
|
||
|
|
|
||
|
|
To use Cholesky factorization in the TraceMIN algorithm, the
|
||
|
|
:samp:`scikits.sparse` package must be installed.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
laplacian_matrix
|
||
|
|
"""
|
||
|
|
if len(G) < 2:
|
||
|
|
raise nx.NetworkXError('graph has less than two nodes.')
|
||
|
|
G = _preprocess_graph(G, weight)
|
||
|
|
if not nx.is_connected(G):
|
||
|
|
raise nx.NetworkXError('graph is not connected.')
|
||
|
|
|
||
|
|
if len(G) == 2:
|
||
|
|
return array([1., -1.])
|
||
|
|
|
||
|
|
find_fiedler = _get_fiedler_func(method)
|
||
|
|
L = nx.laplacian_matrix(G)
|
||
|
|
x = None if method != 'lobpcg' else _rcm_estimate(G, G)
|
||
|
|
sigma, fiedler = find_fiedler(L, x, normalized, tol, seed)
|
||
|
|
return fiedler
|
||
|
|
|
||
|
|
|
||
|
|
@random_state(5)
|
||
|
|
def spectral_ordering(G, weight='weight', normalized=False, tol=1e-8,
|
||
|
|
method='tracemin_pcg', seed=None):
|
||
|
|
"""Compute the spectral_ordering of a graph.
|
||
|
|
|
||
|
|
The spectral ordering of a graph is an ordering of its nodes where nodes
|
||
|
|
in the same weakly connected components appear contiguous and ordered by
|
||
|
|
their corresponding elements in the Fiedler vector of the component.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : NetworkX graph
|
||
|
|
A graph.
|
||
|
|
|
||
|
|
weight : object, optional (default: None)
|
||
|
|
The data key used to determine the weight of each edge. If None, then
|
||
|
|
each edge has unit weight.
|
||
|
|
|
||
|
|
normalized : bool, optional (default: False)
|
||
|
|
Whether the normalized Laplacian matrix is used.
|
||
|
|
|
||
|
|
tol : float, optional (default: 1e-8)
|
||
|
|
Tolerance of relative residual in eigenvalue computation.
|
||
|
|
|
||
|
|
method : string, optional (default: 'tracemin_pcg')
|
||
|
|
Method of eigenvalue computation. It must be one of the tracemin
|
||
|
|
options shown below (TraceMIN), 'lanczos' (Lanczos iteration)
|
||
|
|
or 'lobpcg' (LOBPCG).
|
||
|
|
|
||
|
|
The TraceMIN algorithm uses a linear system solver. The following
|
||
|
|
values allow specifying the solver to be used.
|
||
|
|
|
||
|
|
=============== ========================================
|
||
|
|
Value Solver
|
||
|
|
=============== ========================================
|
||
|
|
'tracemin_pcg' Preconditioned conjugate gradient method
|
||
|
|
'tracemin_chol' Cholesky factorization
|
||
|
|
'tracemin_lu' LU factorization
|
||
|
|
=============== ========================================
|
||
|
|
|
||
|
|
seed : integer, random_state, or None (default)
|
||
|
|
Indicator of random number generation state.
|
||
|
|
See :ref:`Randomness<randomness>`.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
spectral_ordering : NumPy array of floats.
|
||
|
|
Spectral ordering of nodes.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
NetworkXError
|
||
|
|
If G is empty.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Edge weights are interpreted by their absolute values. For MultiGraph's,
|
||
|
|
weights of parallel edges are summed. Zero-weighted edges are ignored.
|
||
|
|
|
||
|
|
To use Cholesky factorization in the TraceMIN algorithm, the
|
||
|
|
:samp:`scikits.sparse` package must be installed.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
laplacian_matrix
|
||
|
|
"""
|
||
|
|
if len(G) == 0:
|
||
|
|
raise nx.NetworkXError('graph is empty.')
|
||
|
|
G = _preprocess_graph(G, weight)
|
||
|
|
|
||
|
|
find_fiedler = _get_fiedler_func(method)
|
||
|
|
order = []
|
||
|
|
for component in nx.connected_components(G):
|
||
|
|
size = len(component)
|
||
|
|
if size > 2:
|
||
|
|
L = nx.laplacian_matrix(G, component)
|
||
|
|
x = None if method != 'lobpcg' else _rcm_estimate(G, component)
|
||
|
|
sigma, fiedler = find_fiedler(L, x, normalized, tol, seed)
|
||
|
|
sort_info = zip(fiedler, range(size), component)
|
||
|
|
order.extend(u for x, c, u in sorted(sort_info))
|
||
|
|
else:
|
||
|
|
order.extend(component)
|
||
|
|
|
||
|
|
return order
|
||
|
|
|
||
|
|
|
||
|
|
# fixture for pytest
|
||
|
|
def setup_module(module):
|
||
|
|
import pytest
|
||
|
|
numpy = pytest.importorskip('numpy')
|
||
|
|
scipy.sparse = pytest.importorskip('scipy.sparse')
|