def run_tests(wf, epos, epsilon):
_, epos = pyq.vmc(wf, epos, nblocks=1, nsteps=2, tstep=1) # move off node
for k, item in testwf.test_updateinternals(wf, epos).items():
print(k, item)
assert item < epsilon
testwf.test_mask(wf, 0, epos)
for fname, func in zip(
["gradient", "laplacian", "pgradient"],
[
testwf.test_wf_gradient,
testwf.test_wf_laplacian,
testwf.test_wf_pgradient,
],
):
err = [
func(wf, epos, delta) for delta in [1e-4, 1e-5, 1e-6, 1e-7, 1e-8]
]
assert min(err) < epsilon, "epsilon {0}".format(epsilon)
for fname, func in zip(
["gradient_value", "gradient_laplacian"],
[
testwf.test_wf_gradient_value,
testwf.test_wf_gradient_laplacian,
],
):
d = func(wf, epos)
for k, v in d.items():
assert v < 1e-10, (k, v)
def run_optimization_best_practice_2states(**kwargs):
"""
First optimize the ground state and then optimize the excited
states while fixing the
"""
mol, mf, mc = H2_casci()
import copy
mf.output = None
mol.output = None
mc.output = None
mc.stdout = None
mol.stdout = None
mc.stdout = None
nstates = 2
mcs = [copy.copy(mc) for i in range(nstates)]
for i in range(nstates):
mcs[i].ci = mc.ci[i]
wfs = []
to_opts = []
for i in range(nstates):
wf, to_opt = pyq.generate_wf(
mol, mf, mc=mcs[i], slater_kws=dict(optimize_determinants=True))
wfs.append(wf)
to_opts.append(to_opt)
configs = pyq.initial_guess(mol, 1000)
pgrad1 = pyq.gradient_generator(mol, wfs[0], to_opt=to_opts[0])
wfs[0], _ = pyq.line_minimization(wfs[0],
configs,
pgrad1,
verbose=True,
max_iterations=10)
for k in to_opts[0]:
to_opts[0][k] = np.zeros_like(to_opts[0][k])
to_opts[0]['wf1det_coeff'][0] = True #Bug workaround for linear transform
for to_opt in to_opts[1:]:
to_opt['wf1det_coeff'] = np.ones_like(to_opt['wf1det_coeff'])
transforms = [
pyqmc.accumulators.LinearTransform(wf.parameters, to_opt)
for wf, to_opt in zip(wfs, to_opts)
]
for wf in wfs[1:]:
for k in wf.parameters.keys():
if 'wf2' in k:
wf.parameters[k] = wfs[0].parameters[k].copy()
_, configs = pyq.vmc(wfs[0], configs)
energy = pyq.EnergyAccumulator(mol)
return optimize(wfs, configs, energy, transforms, **kwargs)
def test_sampler(H2_casci):
mol, mf, mc = H2_casci
ci_energies= mc.e_tot
mc1 = copy.copy(mc)
mc2 = copy.copy(mc)
mc1.ci = mc.ci[0]
mc2.ci = (mc.ci[0]+mc.ci[1])/np.sqrt(2)
wf1, to_opt1 = pyq.generate_slater(mol, mf,mc=mc1, optimize_determinants=True)
wf2, to_opt2 = pyq.generate_slater(mol, mf, mc=mc2, optimize_determinants=True)
for to_opt in [to_opt1, to_opt2]:
to_opt['det_coeff'] = np.ones_like(to_opt['det_coeff'],dtype=bool)
transform1 = pyqmc.accumulators.LinearTransform(wf1.parameters,to_opt1)
transform2 = pyqmc.accumulators.LinearTransform(wf2.parameters,to_opt2)
configs = pyq.initial_guess(mol, 2000)
_, configs = pyq.vmc(wf1, configs)
energy =pyq.EnergyAccumulator(mol)
data_weighted, data_unweighted, configs = sample_overlap_worker([wf1,wf2],configs, energy, [transform1,transform2], nsteps=40, nblocks=20)
avg, error = average(data_weighted, data_unweighted)
print(avg, error)
ref_energy1 = 0.5*(ci_energies[0] + ci_energies[1])
assert abs(avg['total'][1,1] - ref_energy1) < 3*error['total'][1][1]
ref_energy01 = ci_energies[0]/np.sqrt(2)
assert abs(avg['total'][0,1] - ref_energy01) < 3*error['total'][0,1]
overlap_tolerance = 0.2# magic number..be careful.
terms = collect_terms(avg,error)
norm = [np.sum(np.abs(m.ci)**2) for m in [mc1,mc2]]
norm_ref = norm
assert np.all( np.abs(norm_ref - terms['norm']) < overlap_tolerance)
norm_derivative_ref = 2*np.real(mc2.ci).flatten()
print(terms[('dp_norm',1)].shape, norm_derivative_ref.shape)
assert np.all(np.abs(norm_derivative_ref - terms[('dp_norm',1)])<overlap_tolerance)
overlap_ref = np.sum(mc1.ci*mc2.ci)
print('overlap test', overlap_ref, terms['overlap'][0,1])
assert abs(overlap_ref - terms['overlap'][0,1]) < overlap_tolerance
overlap_derivative_ref = (mc1.ci.flatten() - 0.5*overlap_ref * norm_derivative_ref)
assert np.all( np.abs(overlap_derivative_ref - terms[('dp_overlap',1)][:,0,1]) < overlap_tolerance)
en_derivative = take_derivative_casci_energy(mc, mc2.ci)
assert(np.all(abs(terms[('dp_energy',1)][:,1,1].reshape(mc2.ci.shape)-en_derivative) -overlap_tolerance) )
derivative = objective_function_derivative(terms,1.0, norm_relative_penalty=1.0, offdiagonal_energy_penalty=0.1)
def test_correlated_sampling(H2_casci):
mol, mf, mc = H2_casci
ci_energies= mc.e_tot
import copy
mc1 = copy.copy(mc)
mc2 = copy.copy(mc)
mc1.ci = mc.ci[0]
mc2.ci = mc.ci[1]
wf1, to_opt1 = pyq.generate_slater(mol, mf,mc=mc1, optimize_determinants=True)
wf2, to_opt2 = pyq.generate_slater(mol, mf, mc=mc2, optimize_determinants=True)
for to_opt in [to_opt1, to_opt2]:
to_opt['det_coeff'] = np.ones_like(to_opt['det_coeff'],dtype=bool)
transform1 = pyqmc.accumulators.LinearTransform(wf1.parameters,to_opt1)
transform2 = pyqmc.accumulators.LinearTransform(wf2.parameters,to_opt2)
configs = pyq.initial_guess(mol, 1000)
_, configs = pyq.vmc(wf1, configs)
energy =pyq.EnergyAccumulator(mol)
data_weighted, data_unweighted, configs = sample_overlap_worker([wf1,wf2],configs, energy, [transform1,transform2], nsteps=10, nblocks=10)
parameters1 = transform1.serialize_parameters(wf1.parameters)
parameters2 = transform1.serialize_parameters(wf2.parameters)
sample_parameters = []
energies_reference = []
overlap_reference = []
for theta in np.linspace(0,np.pi/2, 4):
a = np.cos(theta)
b = np.sin(theta)
sample_parameters.append([a*parameters1 + b*parameters2, a*parameters1 - b*parameters2])
energies_reference.append([a**2*ci_energies[0] + b**2*ci_energies[1]]*2)
overlap_reference.append([[1.0, a**2-b**2], [a**2-b**2,1.0]] )
energies_reference=np.asarray(energies_reference)
overlap_reference=np.asarray(overlap_reference)
correlated_results = correlated_sampling([wf1,wf2], configs,energy, [transform1,transform2], sample_parameters )
print(correlated_results)
energy_sample = correlated_results['energy']/correlated_results['overlap']
print('energy reference',energies_reference)
print('energy sample', energy_sample)
assert np.all(np.abs(energy_sample.diagonal(axis1=1,axis2=2) - energies_reference) < 0.1)
print('overlap sample', correlated_results['overlap'])
print('overlap reference', overlap_reference)
assert np.all(np.abs(correlated_results['overlap']-overlap_reference)<0.1)
def test_shci_wf_is_better(H2_ccecp_hci):
mol, mf, cisolver = H2_ccecp_hci
configs = pyq.initial_guess(mol, 1000)
wf = Slater(mol, mf, cisolver, tol=0.0)
data, configs = pyq.vmc(
wf,
configs,
nblocks=40,
verbose=True,
accumulators={"energy": pyq.EnergyAccumulator(mol)},
)
en, err = avg(data["energytotal"][1:])
nsigma = 4
assert len(wf.parameters["det_coeff"]) == len(cisolver.ci)
assert en - nsigma * err < mf.e_tot
assert en + nsigma * err > cisolver.energy
def runtest(mol, mf, kind=0):
kpt = mf.kpts[kind]
dm = mf.make_rdm1()
print("original dm shape", dm.shape)
if len(dm.shape) == 4:
dm = np.sum(dm, axis=0)
dm = dm[kind]
#####################################
## evaluate KE in PySCF
#####################################
ke_mat = mol.pbc_intor("int1e_kin", hermi=1, kpts=np.array(kpt))
print("ke_mat", ke_mat.shape)
print("dm", dm.shape)
pyscfke = np.real(np.einsum("ij,ji->", ke_mat, dm))
print("PySCF kinetic energy: {0}".format(pyscfke))
#####################################
## evaluate KE integral with VMC
#####################################
wf = Slater(mol, mf)
coords = pyq.initial_guess(mol, 1200, 0.7)
warmup = 10
start = time.time()
df, coords = pyq.vmc(
wf,
coords,
nsteps=100 + warmup,
tstep=1,
accumulators={"energy": pyq.EnergyAccumulator(mol)},
verbose=False,
hdf_file=str(uuid.uuid4()),
)
print("VMC time", time.time() - start)
df = pd.DataFrame(df)
dfke = pyq.avg_reblock(df["energyke"][warmup:], 10)
vmcke, err = dfke.mean(), dfke.sem()
print("VMC kinetic energy: {0} +- {1}".format(vmcke, err))
assert (
np.abs(vmcke - pyscfke) < 5 * err
), "energy diff not within 5 sigma ({0:.6f}): energies \n{1} \n{2}".format(
5 * err, vmcke, pyscfke
)
def test_casci_energy(H2_ccecp_casci_s0):
"""
Checks that VMC energy matches energy calculated in PySCF
"""
nsteps = 200
warmup = 10
mol, mf, mc = H2_ccecp_casci_s0
wf = Slater(mol, mf, mc)
nconf = 1000
coords = pyq.initial_guess(mol, nconf)
df, coords = pyq.vmc(
wf, coords, nsteps=nsteps, accumulators={"energy": EnergyAccumulator(mol)}
)
df = pd.DataFrame(df)
df = pyq.avg_reblock(df["energytotal"][warmup:], 20)
en = df.mean()
err = df.sem()
assert en - mc.e_tot < 5 * err
def test_pbc_wfs(H_pbc_sto3g_krks, epsilon=1e-5, nconf=10):
"""
Ensure that the wave function objects are consistent in several situations.
"""
mol, mf = H_pbc_sto3g_krks
supercell = pyq.get_supercell(mol, S=(np.ones((3, 3)) - 2 * np.eye(3)))
epos = pyq.initial_guess(supercell, nconf)
for wf in [
MultiplyWF(Slater(supercell, mf),
generate_jastrow(supercell)[0]),
Slater(supercell, mf),
]:
for k in wf.parameters:
if "mo_coeff" not in k and k != "det_coeff":
wf.parameters[k] = cp.asarray(
np.random.rand(*wf.parameters[k].shape))
_, epos = pyq.vmc(wf, epos, nblocks=1, nsteps=2,
tstep=1) # move off node
run_tests(wf, epos, epsilon)