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Fix RIRPA docstring and reformat code. #27

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Jul 26, 2022
Merged

Fix RIRPA docstring and reformat code. #27

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9d02502
Fix RIRPA docstring and reformat code.
cjcscott Jul 26, 2022
3d7fd4d
Downgrade sphinx for now
obackhouse Jul 26, 2022
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2 changes: 1 addition & 1 deletion .github/workflows/ci.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@ jobs:
python .github/workflows/run_tests.py
- name: Build docs
run: |
python -m pip install sphinx sphinx_rtd_theme
python -m pip install sphinx==5.0.2 sphinx_rtd_theme
cd docs
bash make_apidoc.sh
make html
Expand Down
44 changes: 23 additions & 21 deletions vayesta/rpa/rirpa/RIRPA.py
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Expand Up @@ -16,18 +16,18 @@ class ssRIRPA:
----------
dfmf : pyscf.scf.SCF
PySCF density-fitted mean-field object.
rixc: tuple of tuples or arrays
rixc : tuple of tuples or arrays, optional
low-rank decomposition of exchange-correlation kernel. First tuple separates two different spin channels, and
the second the left- and right-sides of an asymmetric decomposition. Default value is None.
log: logging object
log : logging object, optional
Default value is None.
err_tol: float
err_tol : float, optional
Threshold defining estimated error at which to print various accuracy warnings.
Default value is 1e-6.
svd_tol: float
svd_tol : float, optional
Threshold defining negligible singular values when compressing various decompositions.
Default value is 1e-12.
Lpq:
Lpq : np.ndarray, optional
CDERIs in mo basis of provided mean field.
Default value is None.
"""
Expand Down Expand Up @@ -117,7 +117,7 @@ def kernel_moms(self, max_moment, target_rot=None, npoints=48, ainit=10, integra
ri_decomps = self.get_compressed_MP()
ri_mp, ri_apb, ri_amb = ri_decomps
# First need to calculate zeroth moment.
moments = np.zeros((max_moment+1,) + target_rot.shape)
moments = np.zeros((max_moment + 1,) + target_rot.shape)
moments[0], err0 = self._kernel_mom0(target_rot, npoints, ainit, integral_deduct, opt_quad, adaptive_quad,
alpha, ri_decomps=ri_decomps)

Expand All @@ -127,13 +127,13 @@ def kernel_moms(self, max_moment, target_rot=None, npoints=48, ainit=10, integra
moments[1] = einsum("pq,q->pq", target_rot, D) + dot(target_rot, ri_amb[0].T, ri_amb[1])

if max_moment > 1:
Dsq = D**2
for i in range(2, max_moment+1):
moments[i] = einsum("pq,q->pq", moments[i-2], Dsq) + dot(moments[i-2], ri_mp[1].T, ri_mp[0])
Dsq = D ** 2
for i in range(2, max_moment + 1):
moments[i] = einsum("pq,q->pq", moments[i - 2], Dsq) + dot(moments[i - 2], ri_mp[1].T, ri_mp[0])
return moments, err0

def _kernel_mom0(self, target_rot=None, npoints=48, ainit=10, integral_deduct="HO", opt_quad=True,
adaptive_quad=False, alpha=1.0, ri_decomps=None):
adaptive_quad=False, alpha=1.0, ri_decomps=None):

if target_rot is None:
self.log.warning("Warning; generating full moment rather than local component. Will scale as O(N^5).")
Expand Down Expand Up @@ -202,10 +202,10 @@ def test_eta0_error(self, mom0, target_rot, ri_apb, ri_amb):
"""
l1 = [dot(mom0, x.T) for x in ri_apb]
l2 = [dot(target_rot, x.T) for x in ri_amb]
#amb_exact = einsum("pq,q,rq->pr", target_rot, self.D, target_rot) + dot(l2[0], l2[1].T)
#print(amb_exact)
#amb_approx = einsum("pq,q,rq->pr", mom0, self.D, mom0) + dot(l1[0], l1[1].T)
#error = amb_approx - amb_exact
# amb_exact = einsum("pq,q,rq->pr", target_rot, self.D, target_rot) + dot(l2[0], l2[1].T)
# print(amb_exact)
# amb_approx = einsum("pq,q,rq->pr", mom0, self.D, mom0) + dot(l1[0], l1[1].T)
# error = amb_approx - amb_exact
amb = np.diag(self.D) + dot(ri_amb[0].T, ri_amb[1])
apb = np.diag(self.D) + dot(ri_apb[0].T, ri_apb[1])
amb_exact = dot(target_rot, amb, target_rot.T)
Expand All @@ -217,7 +217,7 @@ def test_eta0_error(self, mom0, target_rot, ri_apb, ri_amb):
peta_norm = np.linalg.norm(einsum("p,qp->pq", self.D, mom0) + dot(ri_apb[0].T, l1[1].T))

# Now to estimate resulting error estimate in eta0.
poly = np.polynomial.Polynomial([e_norm/p_norm, -2 * peta_norm / p_norm, 1])
poly = np.polynomial.Polynomial([e_norm / p_norm, -2 * peta_norm / p_norm, 1])
roots = poly.roots()
self.log.info("Proportional error in eta0 relation=%6.4e", e_norm / np.linalg.norm(amb_exact))
self.log.info("Resulting error lower bound: %6.4e", roots.min())
Expand Down Expand Up @@ -343,6 +343,7 @@ def get_unit_vec(pos):
x = np.zeros_like(self.D)
x[pos] = 1.0
return x

c0 = [get_unit_vec(pos) for pos in mininds]

def get_lowest_eigenvals(diag, ri_l, ri_r, x0, nroots=1, nosym=False):
Expand All @@ -353,6 +354,7 @@ def hop(x):

def precond(x, e, *args):
return x / (mdiag - e + 1e-4)

if nosym:
# Ensure left isn't in our kwargs.
kwargs.pop("left", None)
Expand All @@ -373,14 +375,14 @@ def precond(x, e, *args):
raise RuntimeError("RPA approximation broken down!")
# MP is asymmetric, so need to take care to obtain actual eigenvalues.
# Use Davidson to obtain accurate right eigenvectors...
e_mp_r, c_l_approx, c_r = get_lowest_eigenvals(self.D**2, *ri_mp, c0, nroots=nroots, nosym=True)
e_mp_r, c_l_approx, c_r = get_lowest_eigenvals(self.D ** 2, *ri_mp, c0, nroots=nroots, nosym=True)

if not calc_xy:
return e_mp_r ** (0.5)

# Then solve for accurate left eigenvectors, starting from subspace approximation from right eigenvectors. Take
# the real component since all solutions should be real.
e_mp_l, c_r_approx, c_l = get_lowest_eigenvals(self.D**2, ri_mp[1], ri_mp[0], c_l_approx.real,
e_mp_l, c_r_approx, c_l = get_lowest_eigenvals(self.D ** 2, ri_mp[1], ri_mp[0], c_l_approx.real,
nroots=nroots, nosym=True)
# We use c_r and c_l2, since these are likely the most accurate.
# Enforce correct RPA orthonormality.
Expand All @@ -389,7 +391,7 @@ def precond(x, e, *args):
if nroots > 1:
c_l = np.dot(np.linalg.inv(ovlp), c_l)
# Now diagonalise in corresponding subspace to get eigenvalues.
subspace = einsum("np,p,mp->nm", c_l, self.D**2, c_r) + einsum("np,yp,yq,mq->nm", c_l, *ri_mp, c_r)
subspace = einsum("np,p,mp->nm", c_l, self.D ** 2, c_r) + einsum("np,yp,yq,mq->nm", c_l, *ri_mp, c_r)
e, c_sub = np.linalg.eig(subspace)
# Now fold these eigenvectors into our definitions,
xpy = np.dot(c_sub.T, c_r)
Expand All @@ -400,11 +402,11 @@ def precond(x, e, *args):
xmy = xmy[sorted_args]
e = e[sorted_args]
else:
xpy = c_r / (ovlp**(0.5))
xpy = c_r / (ovlp ** (0.5))
xmy = c_l / (ovlp ** (0.5))
e = einsum("p,p,p->", xmy, self.D**2, xpy) + einsum("p,yp,yq,q->", xmy, *ri_mp, xpy)
e = einsum("p,p,p->", xmy, self.D ** 2, xpy) + einsum("p,yp,yq,q->", xmy, *ri_mp, xpy)

return e**(0.5), xpy, xmy
return e ** (0.5), xpy, xmy

def get_compressed_MP(self, alpha=1.0):
# AB corresponds to scaling RI components at this point.
Expand Down