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