def test_efs_kern_vec(params, ihyps):
name, cutoffs, hyps_mask_list, _ = params
np.random.seed(10)
test_point = get_tstp()
size1 = len(flare.gp_algebra._global_training_data[name])
size2 = len(flare.gp_algebra._global_training_structures[name])
hyps_mask = hyps_mask_list[ihyps]
hyps = hyps_mask["hyps"]
kernel = str_to_kernel_set(hyps_mask["kernels"], "mc", hyps_mask)
test_point = get_tstp()
energy_vector, force_array, stress_array = efs_kern_vec(
name, kernel[5], kernel[4], test_point, hyps, cutoffs, hyps_mask)
energy_vector_par, force_array_par, stress_array_par = efs_kern_vec(
name,
kernel[5],
kernel[4],
test_point,
hyps,
cutoffs,
hyps_mask,
n_cpus=2,
n_sample=100,
)
assert np.equal(energy_vector, energy_vector_par).all()
assert np.equal(force_array, force_array_par).all()
assert np.equal(stress_array, stress_array_par).all()
def predict_efs(self, x_t: AtomicEnvironment):
"""Predict the local energy, forces, and partial stresses of an
atomic environment and their predictive variances."""
# Kernel vector allows for evaluation of atomic environments.
if self.parallel and not self.per_atom_par:
n_cpus = self.n_cpus
else:
n_cpus = 1
_global_training_data[self.name] = self.training_data
_global_training_labels[self.name] = self.training_labels_np
energy_vector, force_array, stress_array = efs_kern_vec(
self.name,
self.efs_force_kernel,
self.efs_energy_kernel,
x_t,
self.hyps,
cutoffs=self.cutoffs,
hyps_mask=self.hyps_mask,
n_cpus=n_cpus,
n_sample=self.n_sample,
)
# Check that alpha is up to date with training set.
self.check_L_alpha()
# Compute mean predictions.
en_pred = np.matmul(energy_vector, self.alpha)
force_pred = np.matmul(force_array, self.alpha)
stress_pred = np.matmul(stress_array, self.alpha)
# Compute uncertainties.
args = from_mask_to_args(self.hyps, self.cutoffs, self.hyps_mask)
self_en, self_force, self_stress = self.efs_self_kernel(x_t, *args)
en_var = self_en - np.matmul(np.matmul(energy_vector, self.ky_mat_inv),
energy_vector)
force_var = self_force - np.diag(
np.matmul(np.matmul(force_array, self.ky_mat_inv),
force_array.transpose()))
stress_var = self_stress - np.diag(
np.matmul(np.matmul(stress_array, self.ky_mat_inv),
stress_array.transpose()))
return en_pred, force_pred, stress_pred, en_var, force_var, stress_var