406 lines
13 KiB
Python
406 lines
13 KiB
Python
""" Test functions for the sparse.linalg.eigen.lobpcg module
|
|
"""
|
|
import itertools
|
|
import platform
|
|
|
|
import numpy as np
|
|
from numpy.testing import (assert_almost_equal, assert_equal,
|
|
assert_allclose, assert_array_less)
|
|
|
|
import pytest
|
|
|
|
from numpy import ones, r_, diag
|
|
from numpy.random import rand
|
|
from scipy.linalg import eig, eigh, toeplitz, orth
|
|
from scipy.sparse import spdiags, diags, eye
|
|
from scipy.sparse.linalg import eigs, LinearOperator
|
|
from scipy.sparse.linalg.eigen.lobpcg import lobpcg
|
|
|
|
def ElasticRod(n):
|
|
"""Build the matrices for the generalized eigenvalue problem of the
|
|
fixed-free elastic rod vibration model.
|
|
"""
|
|
L = 1.0
|
|
le = L/n
|
|
rho = 7.85e3
|
|
S = 1.e-4
|
|
E = 2.1e11
|
|
mass = rho*S*le/6.
|
|
k = E*S/le
|
|
A = k*(diag(r_[2.*ones(n-1), 1])-diag(ones(n-1), 1)-diag(ones(n-1), -1))
|
|
B = mass*(diag(r_[4.*ones(n-1), 2])+diag(ones(n-1), 1)+diag(ones(n-1), -1))
|
|
return A, B
|
|
|
|
|
|
def MikotaPair(n):
|
|
"""Build a pair of full diagonal matrices for the generalized eigenvalue
|
|
problem. The Mikota pair acts as a nice test since the eigenvalues are the
|
|
squares of the integers n, n=1,2,...
|
|
"""
|
|
x = np.arange(1, n+1)
|
|
B = diag(1./x)
|
|
y = np.arange(n-1, 0, -1)
|
|
z = np.arange(2*n-1, 0, -2)
|
|
A = diag(z)-diag(y, -1)-diag(y, 1)
|
|
return A, B
|
|
|
|
|
|
def compare_solutions(A, B, m):
|
|
"""Check eig vs. lobpcg consistency.
|
|
"""
|
|
n = A.shape[0]
|
|
np.random.seed(0)
|
|
V = rand(n, m)
|
|
X = orth(V)
|
|
eigvals, _ = lobpcg(A, X, B=B, tol=1e-5, maxiter=30, largest=False)
|
|
eigvals.sort()
|
|
w, _ = eig(A, b=B)
|
|
w.sort()
|
|
assert_almost_equal(w[:int(m/2)], eigvals[:int(m/2)], decimal=2)
|
|
|
|
|
|
def test_Small():
|
|
A, B = ElasticRod(10)
|
|
compare_solutions(A, B, 10)
|
|
A, B = MikotaPair(10)
|
|
compare_solutions(A, B, 10)
|
|
|
|
|
|
def test_ElasticRod():
|
|
A, B = ElasticRod(100)
|
|
compare_solutions(A, B, 20)
|
|
|
|
|
|
def test_MikotaPair():
|
|
A, B = MikotaPair(100)
|
|
compare_solutions(A, B, 20)
|
|
|
|
|
|
def test_regression():
|
|
"""Check the eigenvalue of the identity matrix is one.
|
|
"""
|
|
# https://mail.python.org/pipermail/scipy-user/2010-October/026944.html
|
|
n = 10
|
|
X = np.ones((n, 1))
|
|
A = np.identity(n)
|
|
w, _ = lobpcg(A, X)
|
|
assert_allclose(w, [1])
|
|
|
|
|
|
def test_diagonal():
|
|
"""Check for diagonal matrices.
|
|
"""
|
|
# This test was moved from '__main__' in lobpcg.py.
|
|
# Coincidentally or not, this is the same eigensystem
|
|
# required to reproduce arpack bug
|
|
# https://forge.scilab.org/p/arpack-ng/issues/1397/
|
|
# even using the same n=100.
|
|
|
|
np.random.seed(1234)
|
|
|
|
# The system of interest is of size n x n.
|
|
n = 100
|
|
|
|
# We care about only m eigenpairs.
|
|
m = 4
|
|
|
|
# Define the generalized eigenvalue problem Av = cBv
|
|
# where (c, v) is a generalized eigenpair,
|
|
# and where we choose A to be the diagonal matrix whose entries are 1..n
|
|
# and where B is chosen to be the identity matrix.
|
|
vals = np.arange(1, n+1, dtype=float)
|
|
A = diags([vals], [0], (n, n))
|
|
B = eye(n)
|
|
|
|
# Let the preconditioner M be the inverse of A.
|
|
M = diags([1./vals], [0], (n, n))
|
|
|
|
# Pick random initial vectors.
|
|
X = np.random.rand(n, m)
|
|
|
|
# Require that the returned eigenvectors be in the orthogonal complement
|
|
# of the first few standard basis vectors.
|
|
m_excluded = 3
|
|
Y = np.eye(n, m_excluded)
|
|
|
|
eigvals, vecs = lobpcg(A, X, B, M=M, Y=Y, tol=1e-4, maxiter=40, largest=False)
|
|
|
|
assert_allclose(eigvals, np.arange(1+m_excluded, 1+m_excluded+m))
|
|
_check_eigen(A, eigvals, vecs, rtol=1e-3, atol=1e-3)
|
|
|
|
|
|
def _check_eigen(M, w, V, rtol=1e-8, atol=1e-14):
|
|
"""Check if the eigenvalue residual is small.
|
|
"""
|
|
mult_wV = np.multiply(w, V)
|
|
dot_MV = M.dot(V)
|
|
assert_allclose(mult_wV, dot_MV, rtol=rtol, atol=atol)
|
|
|
|
|
|
def _check_fiedler(n, p):
|
|
"""Check the Fiedler vector computation.
|
|
"""
|
|
# This is not necessarily the recommended way to find the Fiedler vector.
|
|
np.random.seed(1234)
|
|
col = np.zeros(n)
|
|
col[1] = 1
|
|
A = toeplitz(col)
|
|
D = np.diag(A.sum(axis=1))
|
|
L = D - A
|
|
# Compute the full eigendecomposition using tricks, e.g.
|
|
# http://www.cs.yale.edu/homes/spielman/561/2009/lect02-09.pdf
|
|
tmp = np.pi * np.arange(n) / n
|
|
analytic_w = 2 * (1 - np.cos(tmp))
|
|
analytic_V = np.cos(np.outer(np.arange(n) + 1/2, tmp))
|
|
_check_eigen(L, analytic_w, analytic_V)
|
|
# Compute the full eigendecomposition using eigh.
|
|
eigh_w, eigh_V = eigh(L)
|
|
_check_eigen(L, eigh_w, eigh_V)
|
|
# Check that the first eigenvalue is near zero and that the rest agree.
|
|
assert_array_less(np.abs([eigh_w[0], analytic_w[0]]), 1e-14)
|
|
assert_allclose(eigh_w[1:], analytic_w[1:])
|
|
|
|
# Check small lobpcg eigenvalues.
|
|
X = analytic_V[:, :p]
|
|
lobpcg_w, lobpcg_V = lobpcg(L, X, largest=False)
|
|
assert_equal(lobpcg_w.shape, (p,))
|
|
assert_equal(lobpcg_V.shape, (n, p))
|
|
_check_eigen(L, lobpcg_w, lobpcg_V)
|
|
assert_array_less(np.abs(np.min(lobpcg_w)), 1e-14)
|
|
assert_allclose(np.sort(lobpcg_w)[1:], analytic_w[1:p])
|
|
|
|
# Check large lobpcg eigenvalues.
|
|
X = analytic_V[:, -p:]
|
|
lobpcg_w, lobpcg_V = lobpcg(L, X, largest=True)
|
|
assert_equal(lobpcg_w.shape, (p,))
|
|
assert_equal(lobpcg_V.shape, (n, p))
|
|
_check_eigen(L, lobpcg_w, lobpcg_V)
|
|
assert_allclose(np.sort(lobpcg_w), analytic_w[-p:])
|
|
|
|
# Look for the Fiedler vector using good but not exactly correct guesses.
|
|
fiedler_guess = np.concatenate((np.ones(n//2), -np.ones(n-n//2)))
|
|
X = np.vstack((np.ones(n), fiedler_guess)).T
|
|
lobpcg_w, _ = lobpcg(L, X, largest=False)
|
|
# Mathematically, the smaller eigenvalue should be zero
|
|
# and the larger should be the algebraic connectivity.
|
|
lobpcg_w = np.sort(lobpcg_w)
|
|
assert_allclose(lobpcg_w, analytic_w[:2], atol=1e-14)
|
|
|
|
|
|
def test_fiedler_small_8():
|
|
"""Check the dense workaround path for small matrices.
|
|
"""
|
|
# This triggers the dense path because 8 < 2*5.
|
|
_check_fiedler(8, 2)
|
|
|
|
|
|
def test_fiedler_large_12():
|
|
"""Check the dense workaround path avoided for non-small matrices.
|
|
"""
|
|
# This does not trigger the dense path, because 2*5 <= 12.
|
|
_check_fiedler(12, 2)
|
|
|
|
|
|
def test_hermitian():
|
|
"""Check complex-value Hermitian cases.
|
|
"""
|
|
np.random.seed(1234)
|
|
|
|
sizes = [3, 10, 50]
|
|
ks = [1, 3, 10, 50]
|
|
gens = [True, False]
|
|
|
|
for size, k, gen in itertools.product(sizes, ks, gens):
|
|
if k > size:
|
|
continue
|
|
|
|
H = np.random.rand(size, size) + 1.j * np.random.rand(size, size)
|
|
H = 10 * np.eye(size) + H + H.T.conj()
|
|
|
|
X = np.random.rand(size, k)
|
|
|
|
if not gen:
|
|
B = np.eye(size)
|
|
w, v = lobpcg(H, X, maxiter=5000)
|
|
w0, _ = eigh(H)
|
|
else:
|
|
B = np.random.rand(size, size) + 1.j * np.random.rand(size, size)
|
|
B = 10 * np.eye(size) + B.dot(B.T.conj())
|
|
w, v = lobpcg(H, X, B, maxiter=5000, largest=False)
|
|
w0, _ = eigh(H, B)
|
|
|
|
for wx, vx in zip(w, v.T):
|
|
# Check eigenvector
|
|
assert_allclose(np.linalg.norm(H.dot(vx) - B.dot(vx) * wx)
|
|
/ np.linalg.norm(H.dot(vx)),
|
|
0, atol=5e-4, rtol=0)
|
|
|
|
# Compare eigenvalues
|
|
j = np.argmin(abs(w0 - wx))
|
|
assert_allclose(wx, w0[j], rtol=1e-4)
|
|
|
|
|
|
# The n=5 case tests the alternative small matrix code path that uses eigh().
|
|
@pytest.mark.parametrize('n, atol', [(20, 1e-3), (5, 1e-8)])
|
|
def test_eigs_consistency(n, atol):
|
|
"""Check eigs vs. lobpcg consistency.
|
|
"""
|
|
vals = np.arange(1, n+1, dtype=np.float64)
|
|
A = spdiags(vals, 0, n, n)
|
|
np.random.seed(345678)
|
|
X = np.random.rand(n, 2)
|
|
lvals, lvecs = lobpcg(A, X, largest=True, maxiter=100)
|
|
vals, _ = eigs(A, k=2)
|
|
|
|
_check_eigen(A, lvals, lvecs, atol=atol, rtol=0)
|
|
assert_allclose(np.sort(vals), np.sort(lvals), atol=1e-14)
|
|
|
|
|
|
def test_verbosity(tmpdir):
|
|
"""Check that nonzero verbosity level code runs.
|
|
"""
|
|
A, B = ElasticRod(100)
|
|
n = A.shape[0]
|
|
m = 20
|
|
np.random.seed(0)
|
|
V = rand(n, m)
|
|
X = orth(V)
|
|
_, _ = lobpcg(A, X, B=B, tol=1e-5, maxiter=30, largest=False,
|
|
verbosityLevel=9)
|
|
|
|
|
|
@pytest.mark.xfail(platform.machine() == 'ppc64le',
|
|
reason="fails on ppc64le")
|
|
def test_tolerance_float32():
|
|
"""Check lobpcg for attainable tolerance in float32.
|
|
"""
|
|
np.random.seed(1234)
|
|
n = 50
|
|
m = 3
|
|
vals = -np.arange(1, n + 1)
|
|
A = diags([vals], [0], (n, n))
|
|
A = A.astype(np.float32)
|
|
X = np.random.randn(n, m)
|
|
X = X.astype(np.float32)
|
|
eigvals, _ = lobpcg(A, X, tol=1e-9, maxiter=50, verbosityLevel=0)
|
|
assert_allclose(eigvals, -np.arange(1, 1 + m), atol=1e-5)
|
|
|
|
|
|
def test_random_initial_float32():
|
|
"""Check lobpcg in float32 for specific initial.
|
|
"""
|
|
np.random.seed(3)
|
|
n = 50
|
|
m = 4
|
|
vals = -np.arange(1, n + 1)
|
|
A = diags([vals], [0], (n, n))
|
|
A = A.astype(np.float32)
|
|
X = np.random.rand(n, m)
|
|
X = X.astype(np.float32)
|
|
eigvals, _ = lobpcg(A, X, tol=1e-3, maxiter=50, verbosityLevel=1)
|
|
assert_allclose(eigvals, -np.arange(1, 1 + m), atol=1e-2)
|
|
|
|
|
|
def test_maxit_None():
|
|
"""Check lobpcg if maxit=None runs 20 iterations (the default)
|
|
by checking the size of the iteration history output, which should
|
|
be the number of iterations plus 2 (initial and final values).
|
|
"""
|
|
np.random.seed(1566950023)
|
|
n = 50
|
|
m = 4
|
|
vals = -np.arange(1, n + 1)
|
|
A = diags([vals], [0], (n, n))
|
|
A = A.astype(np.float32)
|
|
X = np.random.randn(n, m)
|
|
X = X.astype(np.float32)
|
|
_, _, l_h = lobpcg(A, X, tol=1e-8, maxiter=20, retLambdaHistory=True)
|
|
assert_allclose(np.shape(l_h)[0], 20+2)
|
|
|
|
|
|
@pytest.mark.slow
|
|
def test_diagonal_data_types():
|
|
"""Check lobpcg for diagonal matrices for all matrix types.
|
|
"""
|
|
np.random.seed(1234)
|
|
n = 40
|
|
m = 4
|
|
# Define the generalized eigenvalue problem Av = cBv
|
|
# where (c, v) is a generalized eigenpair,
|
|
# and where we choose A and B to be diagonal.
|
|
vals = np.arange(1, n + 1)
|
|
|
|
list_sparse_format = ['bsr', 'coo', 'csc', 'csr', 'dia', 'dok', 'lil']
|
|
sparse_formats = len(list_sparse_format)
|
|
for s_f_i, s_f in enumerate(list_sparse_format):
|
|
|
|
As64 = diags([vals * vals], [0], (n, n), format=s_f)
|
|
As32 = As64.astype(np.float32)
|
|
Af64 = As64.toarray()
|
|
Af32 = Af64.astype(np.float32)
|
|
listA = [Af64, As64, Af32, As32]
|
|
|
|
Bs64 = diags([vals], [0], (n, n), format=s_f)
|
|
Bf64 = Bs64.toarray()
|
|
listB = [Bf64, Bs64]
|
|
|
|
# Define the preconditioner function as LinearOperator.
|
|
Ms64 = diags([1./vals], [0], (n, n), format=s_f)
|
|
|
|
def Ms64precond(x):
|
|
return Ms64 @ x
|
|
Ms64precondLO = LinearOperator(matvec=Ms64precond,
|
|
matmat=Ms64precond,
|
|
shape=(n, n), dtype=float)
|
|
Mf64 = Ms64.toarray()
|
|
|
|
def Mf64precond(x):
|
|
return Mf64 @ x
|
|
Mf64precondLO = LinearOperator(matvec=Mf64precond,
|
|
matmat=Mf64precond,
|
|
shape=(n, n), dtype=float)
|
|
Ms32 = Ms64.astype(np.float32)
|
|
|
|
def Ms32precond(x):
|
|
return Ms32 @ x
|
|
Ms32precondLO = LinearOperator(matvec=Ms32precond,
|
|
matmat=Ms32precond,
|
|
shape=(n, n), dtype=np.float32)
|
|
Mf32 = Ms32.toarray()
|
|
|
|
def Mf32precond(x):
|
|
return Mf32 @ x
|
|
Mf32precondLO = LinearOperator(matvec=Mf32precond,
|
|
matmat=Mf32precond,
|
|
shape=(n, n), dtype=np.float32)
|
|
listM = [None, Ms64precondLO, Mf64precondLO,
|
|
Ms32precondLO, Mf32precondLO]
|
|
|
|
# Setup matrix of the initial approximation to the eigenvectors
|
|
# (cannot be sparse array).
|
|
Xf64 = np.random.rand(n, m)
|
|
Xf32 = Xf64.astype(np.float32)
|
|
listX = [Xf64, Xf32]
|
|
|
|
# Require that the returned eigenvectors be in the orthogonal complement
|
|
# of the first few standard basis vectors (cannot be sparse array).
|
|
m_excluded = 3
|
|
Yf64 = np.eye(n, m_excluded, dtype=float)
|
|
Yf32 = np.eye(n, m_excluded, dtype=np.float32)
|
|
listY = [Yf64, Yf32]
|
|
|
|
tests = list(itertools.product(listA, listB, listM, listX, listY))
|
|
# This is one of the slower tests because there are >1,000 configs
|
|
# to test here, instead of checking product of all input, output types
|
|
# test each configuration for the first sparse format, and then
|
|
# for one additional sparse format. this takes 2/7=30% as long as
|
|
# testing all configurations for all sparse formats.
|
|
if s_f_i > 0:
|
|
tests = tests[s_f_i - 1::sparse_formats-1]
|
|
|
|
for A, B, M, X, Y in tests:
|
|
eigvals, _ = lobpcg(A, X, B=B, M=M, Y=Y, tol=1e-4,
|
|
maxiter=100, largest=False)
|
|
assert_allclose(eigvals,
|
|
np.arange(1 + m_excluded, 1 + m_excluded + m))
|