def test_training_statistics():
"""
Ensure training statistics are being recorded correctly
:return:
"""
test_structure, forces = get_random_structure(np.eye(3),
['H', 'Be'],
10)
gp = GaussianProcess(kernel_name='2', cutoffs=[10])
data = gp.training_statistics
assert data['N'] == 0
assert len(data['species']) == 0
assert len(data['envs_by_species']) == 0
gp.update_db(test_structure, forces)
data = gp.training_statistics
assert data['N'] == 10
assert len(data['species']) == len(set(test_structure.coded_species))
assert len(data['envs_by_species']) == len(set(
test_structure.coded_species))
def two_body_gp() -> GaussianProcess:
"""Returns a GP instance with a two-body numba-based kernel"""
print("\nSetting up...\n")
# params
cell = np.eye(3)
unique_species = [2, 1]
cutoffs = np.array([0.8, 0.8])
noa = 5
# create test structure
test_structure, forces = get_random_structure(cell, unique_species, noa)
# test update_db
gaussian = \
GaussianProcess(kernel=en.three_body,
kernel_grad=en.three_body_grad,
hyps=np.array([1, 1, 1]),
hyp_labels=['Length', 'Signal Var.', 'Noise Var.'],
par=True, no_cpus=2,
cutoffs=cutoffs)
gaussian.update_db(test_structure, forces)
# return gaussian
yield gaussian
# code after yield will be executed once all tests are run
# this will not be run if an exception is raised in the setup
print("\n\nTearing down\n")
del gaussian
def two_plus_three_gp() -> GaussianProcess:
"""Returns a GP instance with a 2+3-body kernel."""
cutoffs = np.array([0.8, 0.8])
hyps = np.array([1.0, 1.0, 1.0, 1.0, 1.0])
# test update_db
gpname = "2+3_mc"
cutoffs = np.ones(2) * 0.8
gp_model = GaussianProcess(
kernel_name=gpname,
hyps=hyps,
cutoffs=cutoffs,
multihyps=False,
parallel=False,
n_cpus=1,
)
test_structure, forces = get_random_structure(np.eye(3), [1, 2], 3)
energy = 3.14
gp_model.update_db(test_structure, forces, energy=energy)
yield gp_model
del gp_model
def test_training_statistics():
"""
Ensure training statistics are being recorded correctly
:return:
"""
test_structure, forces = get_random_structure(np.eye(3), ["H", "Be"], 10)
energy = 3.14
gp = GaussianProcess(kernel_name="2", cutoffs=[10])
data = gp.training_statistics
assert data["N"] == 0
assert len(data["species"]) == 0
assert len(data["envs_by_species"]) == 0
gp.update_db(test_structure, forces, energy=energy)
data = gp.training_statistics
assert data["N"] == 10
assert len(data["species"]) == len(set(test_structure.coded_species))
assert len(data["envs_by_species"]) == len(
set(test_structure.coded_species))
def three_body_gp() -> GaussianProcess:
"""Returns a GP instance with a two-body numba-based kernel"""
print("\nSetting up...\n")
# params
cell = np.eye(3)
unique_species = [2, 1]
cutoffs = np.array([0.8, 0.8])
noa = 5
nbond = 0
ntriplet = 1
hyps, hm = generate_hm(nbond, ntriplet)
# create test structure
test_structure, forces = get_random_structure(cell, unique_species, noa)
# test update_db
gaussian = \
GaussianProcess(kernel=en.three_body_mc,
kernel_grad=en.three_body_mc_grad,
hyps=hyps,
hyp_labels=hm['hyps_label'],
cutoffs=cutoffs, multihyps=True, hyps_mask=hm)
gaussian.update_db(test_structure, forces)
# return gaussian
yield gaussian
# code after yield will be executed once all tests are run
# this will not be run if an exception is raised in the setup
print("\n\nTearing down\n")
del gaussian
def test_load_and_reload(self, all_gps, validation_env, multihyps):
test_gp = all_gps[multihyps]
test_gp.write_model('test_gp_write', 'pickle')
new_gp = GaussianProcess.from_file('test_gp_write.pickle')
for d in [0, 1, 2]:
assert np.all(
test_gp.predict(x_t=validation_env, d=d) == new_gp.predict(
x_t=validation_env, d=d))
os.remove('test_gp_write.pickle')
test_gp.write_model('test_gp_write', 'json')
with open('test_gp_write.json', 'r') as f:
new_gp = GaussianProcess.from_dict(json.loads(f.readline()))
for d in [0, 1, 2]:
assert np.all(
test_gp.predict(x_t=validation_env, d=d) == new_gp.predict(
x_t=validation_env, d=d))
os.remove('test_gp_write.json')
with raises(ValueError):
test_gp.write_model('test_gp_write', 'cucumber')
def predict_on_structure_en(
structure: Structure,
gp: GaussianProcess,
n_cpus: int = None) -> ('np.ndarray', 'np.ndarray', 'np.ndarray'):
"""
Return the forces/std. dev. uncertainty / local energy associated with each
individual atom in a structure. Forces are stored directly to the
structure and are also returned.
:param structure: FLARE structure to obtain forces for, with N atoms
:param gp: Gaussian Process model
:param n_cpus: Dummy parameter passed as an argument to allow for
flexibility when the callable may or may not be parallelized
:return: N x 3 array of forces, N x 3 array of uncertainties,
N-length array of energies
:rtype: (np.ndarray, np.ndarray, np.ndarray)
"""
# Set up local energy array
local_energies = np.array([0 for _ in range(structure.nat)])
# Loop through atoms in structure and predict forces, uncertainties,
# and energies
for n in range(structure.nat):
chemenv = AtomicEnvironment(structure, n, gp.cutoffs)
for i in range(3):
force, var = gp.predict(chemenv, i + 1)
structure.forces[n][i] = float(force)
structure.stds[n][i] = np.sqrt(np.abs(var))
local_energies[n] = gp.predict_local_energy(chemenv)
forces = np.array(structure.forces)
stds = np.array(structure.stds)
return forces, stds, local_energies
def predict_on_structure_en(
structure: Structure,
gp: GaussianProcess,
n_cpus: int = None,
write_to_structure: bool = True,
selective_atoms: List[int] = None,
skipped_atom_value=0) -> ('np.ndarray', 'np.ndarray', 'np.ndarray'):
"""
Return the forces/std. dev. uncertainty / local energy associated with each
individual atom in a structure. Forces are stored directly to the
structure and are also returned.
:param structure: FLARE structure to obtain forces for, with N atoms
:param gp: Gaussian Process model
:param n_cpus: Dummy parameter passed as an argument to allow for
flexibility when the callable may or may not be parallelized
:return: N x 3 array of forces, N x 3 array of uncertainties,
N-length array of energies
:rtype: (np.ndarray, np.ndarray, np.ndarray)
"""
# Set up local energy array
forces = np.zeros((structure.nat, 3))
stds = np.zeros((structure.nat, 3))
local_energies = np.zeros(structure.nat)
forces = np.zeros(shape=(structure.nat, 3))
stds = np.zeros(shape=(structure.nat, 3))
if selective_atoms:
forces.fill(skipped_atom_value)
stds.fill(skipped_atom_value)
local_energies.fill(skipped_atom_value)
else:
selective_atoms = []
# Loop through atoms in structure and predict forces, uncertainties,
# and energies
for n in range(structure.nat):
if selective_atoms and n not in selective_atoms:
continue
chemenv = AtomicEnvironment(structure,
n,
gp.cutoffs,
cutoffs_mask=gp.hyps_mask)
for i in range(3):
force, var = gp.predict(chemenv, i + 1)
forces[n][i] = float(force)
stds[n][i] = np.sqrt(np.abs(var))
if write_to_structure and structure.forces is not None:
structure.forces[n][i] = float(force)
structure.stds[n][i] = np.sqrt(np.abs(var))
local_energies[n] = gp.predict_local_energy(chemenv)
return forces, stds, local_energies
def test_seed_and_run():
the_gp = GaussianProcess(
kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06,
1.53990678e-02,
2.50624782e-05,
5.07884426e-01,
1.70172923e-03,
]),
cutoffs=np.array([5, 3]),
hyp_labels=["l2", "s2", "l3", "s3", "n0"],
maxiter=1,
opt_algorithm="L-BFGS-B",
)
with open(path.join(TEST_FILE_DIR, "methanol_frames.json"), "r") as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
with open(path.join(TEST_FILE_DIR, "methanol_envs.json"), "r") as f:
data_dicts = [loads(s) for s in f.readlines()[:6]]
envs = [AtomicEnvironment.from_dict(d) for d in data_dicts]
forces = [np.array(d["forces"]) for d in data_dicts]
seeds = list(zip(envs, forces))
tt = TrajectoryTrainer(
frames,
gp=the_gp,
shuffle_frames=True,
rel_std_tolerance=0,
abs_std_tolerance=0,
skip=10,
pre_train_seed_envs=seeds,
pre_train_seed_frames=[frames[-1]],
max_atoms_from_frame=4,
output_name="meth_test",
model_format="pickle",
train_checkpoint_interval=1,
pre_train_atoms_per_element={"H": 1},
)
tt.run()
with open("meth_test_model.pickle", "rb") as f:
new_gp = pickle.load(f)
test_env = envs[0]
for d in [1, 2, 3]:
assert np.all(
the_gp.predict(x_t=test_env, d=d) == new_gp.predict(x_t=test_env,
d=d))
for f in glob(f"meth_test*"):
remove(f)
def predict_on_atom_en(structure: Structure, atom: int, gp: GaussianProcess):
chemenv = AtomicEnvironment(structure, atom, gp.cutoffs)
comps = []
stds = []
# predict force components and standard deviations
for i in range(3):
force, var = gp.predict(chemenv, i + 1)
comps.append(float(force))
stds.append(np.sqrt(np.abs(var)))
# predict local energy
local_energy = gp.predict_local_energy(chemenv)
return comps, stds, local_energy
def test_seed_and_run():
the_gp = GaussianProcess(kernel=two_plus_three_body_mc,
kernel_grad=two_plus_three_body_mc_grad,
hyps=np.array([
3.75996759e-06, 1.53990678e-02,
2.50624782e-05, 5.07884426e-01, 1.70172923e-03
]),
cutoffs=np.array([7, 7]),
hyp_labels=['l2', 's2', 'l3', 's3', 'n0'],
maxiter=1,
opt_algorithm='L-BFGS-B')
with open('./test_files/methanol_frames.json', 'r') as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
with open('./test_files/methanol_envs.json', 'r') as f:
data_dicts = [loads(s) for s in f.readlines()[:6]]
envs = [AtomicEnvironment.from_dict(d) for d in data_dicts]
forces = [np.array(d['forces']) for d in data_dicts]
seeds = list(zip(envs, forces))
tt = TrajectoryTrainer(frames,
gp=the_gp,
shuffle_frames=True,
rel_std_tolerance=0,
abs_std_tolerance=0,
skip=15,
pre_train_seed_envs=seeds,
pre_train_seed_frames=[frames[-1]],
max_atoms_from_frame=4,
model_write='meth_test.pickle',
model_format='pickle',
checkpoint_interval=1,
pre_train_atoms_per_element={'H': 1})
tt.run()
with open('meth_test.pickle', 'rb') as f:
new_gp = pickle.load(f)
test_env = envs[0]
for d in [0, 1, 2]:
assert np.all(
the_gp.predict(x_t=test_env, d=d) == new_gp.predict(x_t=test_env,
d=d))
os.system('rm ./gp_from_aimd.out')
os.system('rm ./gp_from_aimd.xyz')
os.system('rm ./gp_from_aimd-f.xyz')
os.system('rm ./meth_test.pickle')
def predict_on_structure_en(structure: Structure, gp: GaussianProcess):
local_energies = [0 for _ in range(structure.nat)]
for n in range(structure.nat):
chemenv = AtomicEnvironment(structure, n, gp.cutoffs)
for i in range(3):
force, var = gp.predict(chemenv, i + 1)
structure.forces[n][i] = float(force)
structure.stds[n][i] = np.sqrt(np.abs(var))
local_energies[n] = gp.predict_local_energy(chemenv)
forces = np.array(structure.forces)
stds = np.array(structure.stds)
return forces, stds, local_energies
def restart(self):
# Recover atomic configuration: positions, velocities, forces
positions, self.nsteps = self.read_frame('positions.xyz', -1)
self.atoms.set_positions(positions)
self.atoms.set_velocities(self.read_frame('velocities.dat', -1)[0])
self.atoms.calc.results['forces'] = self.read_frame('forces.dat', -1)[0]
print('Last frame recovered')
# # Recover training data set
# gp_model = self.atoms.calc.gp_model
# atoms = deepcopy(self.atoms)
# nat = len(self.atoms.positions)
# dft_positions = self.read_all_frames('dft_positions.xyz', nat)
# dft_forces = self.read_all_frames('dft_forces.dat', nat)
# added_atoms = self.read_all_frames('added_atoms.dat', 1, 1, 'int')
# for i, frame in enumerate(dft_positions):
# atoms.set_positions(frame)
# curr_struc = Structure.from_ase_atoms(atoms)
# gp_model.update_db(curr_struc, dft_forces[i], added_atoms[i].tolist())
# gp_model.set_L_alpha()
# print('GP training set ready')
# Recover FLARE calculator
self.atoms.calc.gp_model = GaussianProcess.from_file(self.restart_from+'/gp_model.pickle')
# gp_model.ky_mat_inv = np.load(self.restart_from+'/ky_mat_inv.npy')
# gp_model.alpha = np.load(self.restart_from+'/alpha.npy')
if self.atoms.calc.use_mapping:
for map_3 in self.atoms.calc.mgp_model.maps_3:
map_3.load_grid = self.restart_from + '/'
self.atoms.calc.build_mgp(skip=False)
self.atoms.calc.mgp_updated = True
print('GP and MGP ready')
self.l_bound = 10
def test_load_one_frame_and_run():
the_gp = GaussianProcess(
kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06,
1.53990678e-02,
2.50624782e-05,
5.07884426e-01,
1.70172923e-03,
]),
cutoffs=np.array([5, 3]),
hyp_labels=["l2", "s2", "l3", "s3", "n0"],
maxiter=1,
opt_algorithm="L-BFGS-B",
)
with open(path.join(TEST_FILE_DIR, "methanol_frames.json"), "r") as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
tt = TrajectoryTrainer(
frames,
gp=the_gp,
shuffle_frames=True,
print_as_xyz=True,
rel_std_tolerance=0,
abs_std_tolerance=0,
skip=15,
)
tt.run()
for f in glob(f"gp_from_aimd*"):
remove(f)
def test_load_reload_huge(self, all_gps):
"""
Unit tests that loading and reloading a huge GP works.
:param all_gps:
:return:
"""
test_gp = deepcopy(all_gps[False])
test_gp.set_L_alpha()
dummy_gp = deepcopy(test_gp)
N_data = len(dummy_gp.training_data)
prev_ky_mat = deepcopy(dummy_gp.ky_mat)
prev_l_mat = deepcopy(dummy_gp.l_mat)
for model_format in ["pickle", "json"]:
dummy_gp.write_model("test_gp_write", model_format, N_data - 1)
new_gp = GaussianProcess.from_file(f"test_gp_write.{model_format}")
assert np.allclose(prev_ky_mat, new_gp.ky_mat)
assert np.allclose(prev_l_mat, new_gp.l_mat)
assert new_gp.training_data is not test_gp.training_data
os.remove(f"test_gp_write.{model_format}")
dummy_gp = deepcopy(test_gp)
os.remove(f"test_gp_write_ky_mat.npy")
def all_gps() -> GaussianProcess:
"""Returns a GP instance with a two-body numba-based kernel"""
gp_dict = {True: None, False: None}
for multihyps in multihyps_list:
hyps, hm, cutoffs = generate_hm(1, 1, multihyps=multihyps)
hl = hm["hyp_labels"]
# test update_db
gp_dict[multihyps] = GaussianProcess(
kernels=hm["kernels"],
hyps=hyps,
hyp_labels=hl,
cutoffs=cutoffs,
hyps_mask=hm,
parallel=False,
n_cpus=1,
)
test_structure, forces = get_random_structure(np.eye(3), [1, 2], 3)
energy = 3.14
gp_dict[multihyps].update_db(test_structure, forces, energy=energy)
yield gp_dict
del gp_dict
def test_serialization_method(two_body_gp, test_point):
"""
Serialize and then un-serialize a GP and ensure that no info was lost.
Compare one calculation to ensure predictions work correctly.
:param two_body_gp:
:return:
"""
old_gp_dict = two_body_gp.as_dict()
new_gp = GaussianProcess.from_dict(old_gp_dict)
new_gp_dict = new_gp.as_dict()
assert len(new_gp_dict) == len(old_gp_dict)
for k1, k2 in zip(sorted(new_gp_dict.keys()), sorted(old_gp_dict.keys())):
x = new_gp_dict[k1]
y = new_gp_dict[k2]
if isinstance(x, np.ndarray):
assert np.equal(x, y).all()
elif hasattr(x, '__len__'):
if isinstance(x[0], np.ndarray):
assert np.equal(x, y).all()
else:
for xx, yy in zip(x, y):
assert xx == yy
else:
assert x == y
for d in [0, 1, 2]:
assert np.all(
two_body_gp.predict(x_t=test_point, d=d) == new_gp.predict(
x_t=test_point, d=d))
def test_load_one_frame_and_run():
the_gp = GaussianProcess(kernel=two_plus_three_body_mc,
kernel_grad=two_plus_three_body_mc_grad,
hyps=np.array([
3.75996759e-06, 1.53990678e-02,
2.50624782e-05, 5.07884426e-01, 1.70172923e-03
]),
cutoffs=np.array([7, 7]),
hyp_labels=['l2', 's2', 'l3', 's3', 'n0'],
maxiter=1,
opt_algorithm='L-BFGS-B')
with open('./test_files/methanol_frames.json', 'r') as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
tt = TrajectoryTrainer(frames,
gp=the_gp,
shuffle_frames=True,
rel_std_tolerance=0,
abs_std_tolerance=0,
skip=15)
tt.run()
os.system('rm ./gp_from_aimd.gp')
os.system('rm ./gp_from_aimd.out')
os.system('rm ./gp_from_aimd.xyz')
os.system('rm ./gp_from_aimd-f.xyz')
def predict_on_structure(structure: Structure,
gp: GaussianProcess,
n_cpus: int = None) -> ('np.ndarray', 'np.ndarray'):
"""
Return the forces/std. dev. uncertainty associated with each
individual atom in a structure. Forces are stored directly to the
structure and are also returned.
:param structure: FLARE structure to obtain forces for, with N atoms
:param gp: Gaussian Process model
:return: N x 3 numpy array of foces, Nx3 numpy array of uncertainties
:rtype: (np.ndarray, np.ndarray)
"""
# Loop through individual atoms, cast to atomic environments,
# make predictions
for n in range(structure.nat):
chemenv = AtomicEnvironment(structure, n, gp.cutoffs)
for i in range(3):
force, var = gp.predict(chemenv, i + 1)
structure.forces[n][i] = float(force)
structure.stds[n][i] = np.sqrt(np.abs(var))
forces = np.array(structure.forces)
stds = np.array(structure.stds)
return forces, stds
def test_permuted_initalization(self):
"""
Run through some common permutations of input sequences
to ensure that the GP correctly initializes.
"""
for kernel_list in [["2"], ["3"], ["2", "3"]]:
GaussianProcess(kernels=kernel_list)
with raises(ValueError):
GaussianProcess(kernels=["2", "3", "mb"])
full_kernel_list = ["2", "3"]
for component in ["sc", "mc"]:
GaussianProcess(kernels=full_kernel_list, component=component)
for parallel in [True, False]:
GaussianProcess(parallel=parallel)
for per_atom_par in [True, False]:
GaussianProcess(per_atom_par=per_atom_par)
def all_gps() -> GaussianProcess:
"""Returns a GP instance with a two-body numba-based kernel"""
gp_dict = {True: None, False: None}
for multihyps in multihyps_list:
cutoffs = np.ones(2)*0.8
hyps, hm, _ = generate_hm(1, 1, multihyps=multihyps)
hl = hm['hyps_label']
if (multihyps is False):
hm = None
# test update_db
gpname = '2+3+mb_mc'
hyps = np.hstack([hyps, [1, 1]])
hl = np.hstack([hl[:-1], ['sigm', 'lsm'], hl[-1]])
cutoffs = np.ones(3)*0.8
gp_dict[multihyps] = \
GaussianProcess(kernel_name=gpname,
hyps=hyps,
hyp_labels=hl,
cutoffs=cutoffs,
multihyps=multihyps, hyps_mask=hm,
parallel=False, n_cpus=1)
test_structure, forces = get_random_structure(np.eye(3),
[1, 2],
3)
gp_dict[multihyps].update_db(test_structure, forces)
yield gp_dict
del gp_dict
def test_load_and_reload(two_body_gp, test_point):
two_body_gp.write_model('two_body', 'pickle')
with open('two_body.pickle', 'rb') as f:
new_gp = pickle.load(f)
for d in [0, 1, 2]:
assert np.all(
two_body_gp.predict(x_t=test_point, d=d) == new_gp.predict(
x_t=test_point, d=d))
os.remove('two_body.pickle')
two_body_gp.write_model('two_body', 'json')
with open('two_body.json', 'r') as f:
new_gp = GaussianProcess.from_dict(json.loads(f.readline()))
for d in [0, 1, 2]:
assert np.all(
two_body_gp.predict(x_t=test_point, d=d) == new_gp.predict(
x_t=test_point, d=d))
os.remove('two_body.json')
with raises(ValueError):
two_body_gp.write_model('two_body', 'cucumber')
def test_otf_parser_from_checkpt(software):
if not os.environ.get(cmd[software], False):
pytest.skip(f"{cmd[software]} not found in environment:"
" Please install the code "
f" and set the {cmd[software]} env. "
"variable to point to the executable.")
if software == "cp2k":
pytest.skip()
example = 1
casename = name_list[example]
log_name = f"{casename}_otf_{software}"
output_name = f"{log_name}.out"
otf_traj = OtfAnalysis(output_name)
try:
replicated_gp = otf_traj.make_gp()
except:
init_gp = GaussianProcess.from_file(log_name + "_gp.json")
replicated_gp = otf_traj.make_gp(init_gp=init_gp)
outdir = f"test_outputs_{software}"
if not os.path.isdir(outdir):
os.mkdir(outdir)
for f in os.listdir("./"):
if f"{casename}_otf_{software}" in f:
shutil.move(f, outdir)
cleanup(software, f"{casename}_otf_{software}")
def get_gp(
bodies,
kernel_type="mc",
multihyps=True,
cellabc=[1, 1, 1.5],
force_only=False,
noa=5,
) -> GaussianProcess:
"""Returns a GP instance with a two-body numba-based kernel"""
print("\nSetting up...\n")
# params
cell = np.diag(cellabc)
unique_species = [1, 2]
ntwobody = 0
nthreebody = 0
prefix = bodies
if "2" in bodies or "two" in bodies:
ntwobody = 1
if "3" in bodies or "three" in bodies:
nthreebody = 1
hyps, hm, _ = generate_hm(ntwobody,
nthreebody,
nmanybody=0,
multihyps=multihyps)
cutoffs = hm["cutoffs"]
kernels = hm["kernels"]
hl = hm["hyp_labels"]
# create test structure
test_structure, forces = get_random_structure(cell, unique_species, noa)
energy = 3.14
# test update_db
gaussian = GaussianProcess(
kernels=kernels,
component=kernel_type,
hyps=hyps,
hyp_labels=hl,
cutoffs=cutoffs,
hyps_mask=hm,
parallel=False,
n_cpus=1,
)
if force_only:
gaussian.update_db(test_structure, forces)
else:
gaussian.update_db(test_structure, forces, energy=energy)
gaussian.check_L_alpha()
# print(gaussian.alpha)
return gaussian
def test_pred_on_elements():
the_gp = GaussianProcess(kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06, 1.53990678e-02,
2.50624782e-05, 5.07884426e-01, 1.70172923e-03
]),
cutoffs=np.array([7, 3]),
hyp_labels=['l2', 's2', 'l3', 's3', 'n0'],
maxiter=1,
opt_algorithm='L-BFGS-B')
with open('./test_files/methanol_frames.json', 'r') as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
with open('./test_files/methanol_envs.json', 'r') as f:
data_dicts = [loads(s) for s in f.readlines()[:6]]
envs = [AtomicEnvironment.from_dict(d) for d in data_dicts]
forces = [np.array(d['forces']) for d in data_dicts]
seeds = list(zip(envs, forces))
all_frames = deepcopy(frames)
tt = TrajectoryTrainer(frames,
gp=the_gp,
shuffle_frames=False,
rel_std_tolerance=0,
abs_std_tolerance=0,
abs_force_tolerance=.001,
skip=5,
min_atoms_per_train=100,
pre_train_seed_envs=seeds,
pre_train_seed_frames=[frames[-1]],
max_atoms_from_frame=4,
output_name='meth_test',
model_format='json',
atom_checkpoint_interval=50,
pre_train_atoms_per_element={'H': 1},
predict_atoms_per_element={
'H': 0,
'C': 1,
'O': 0
})
# Set to predict only on Carbon after training on H to ensure errors are
# high and that they get added to the gp
tt.run()
# Ensure forces weren't written directly to structure
for i in range(len(all_frames)):
assert np.array_equal(all_frames[i].forces, frames[i].forces)
# Assert that Carbon atoms were correctly added
assert the_gp.training_statistics['envs_by_species']['C'] > 2
for f in glob(f"meth_test*"):
remove(f)
for f in glob(f"gp_from_aimd*"):
remove(f)
def two_plus_three_gp() -> GaussianProcess:
"""Returns a GP instance with a 2+3-body kernel."""
cutoffs = {'twobody': 0.8, 'threebody': 0.8}
hyps = np.array([1., 1., 1., 1., 1.])
gp_model = \
GaussianProcess(kernels=['twobody', 'threebody'],
hyps=hyps, cutoffs=cutoffs,
multihyps=False, parallel=False, n_cpus=1)
test_structure, forces = \
get_random_structure(np.eye(3), [1, 2], 3)
energy = 3.14
gp_model.update_db(test_structure, forces, energy=energy)
yield gp_model
del gp_model
def test_constrained_optimization_simple():
"""
Test constrained optimization with a standard
number of hyperparameters (3 for a 3-body)
:return:
"""
# params
cell = np.eye(3)
species = [1, 1, 2, 2, 2]
positions = np.random.uniform(0, 1, (5, 3))
forces = np.random.uniform(0, 1, (5, 3))
two_species_structure = Structure(cell=cell,
species=species,
positions=positions,
forces=forces)
hyp_labels = [
'2-Body_sig2,', '2-Body_l2', '3-Body_sig2', '3-Body_l2', 'noise'
]
hyps = np.array([1.2, 2.2, 3.2, 4.2, 12.])
cutoffs = np.array((.8, .8))
# Define hyp masks
spec_mask = np.zeros(118, dtype=int)
spec_mask[1] = 1
hyps_mask = {
'nspec': 2,
'spec_mask': spec_mask,
'nbond': 2,
'bond_mask': [0, 1, 1, 1],
'ntriplet': 2,
'triplet_mask': [0, 1, 1, 1, 1, 1, 1, 1],
'original': np.array([1.1, 1.2, 2.1, 2.2, 3.1, 3.2, 4.1, 4.2, 12.]),
'train_noise': True,
'map': [1, 3, 5, 7, 8]
}
gp = GaussianProcess(kernel=en.two_plus_three_body_mc,
kernel_grad=en.two_plus_three_body_mc_grad,
hyps=hyps,
hyp_labels=hyp_labels,
cutoffs=cutoffs,
par=False,
n_cpus=1,
hyps_mask=hyps_mask,
maxiter=1,
multihyps=True)
gp.update_db(two_species_structure, two_species_structure.forces)
# Check that the hyperparameters were updated
results = gp.train()
assert not np.equal(results.x, hyps).all()
def __init__(
self,
n_experts: int = 1,
ndata_per_expert: int = 200,
prior_variance: float = 0.5,
per_expert_parallel: bool = True,
**kwargs,
):
self.n_experts = n_experts
self.prior_variance = prior_variance
self.log_prior_var = np.log(prior_variance)
self.ndata_per_expert = ndata_per_expert
self.per_expert_parallel = per_expert_parallel
GaussianProcess.__init__(self, **kwargs)
# Index of which expert is currently addressed
self.current_expert = 0
self.reset_container()
def test_otf_al():
"""
Test that an otf run can survive going for more steps
:return:
"""
os.system('cp ./test_files/qe_input_2.in ./pwscf.in')
# make gp model
kernel = en.three_body
kernel_grad = en.three_body_grad
hyps = np.array([0.1, 1, 0.01])
hyp_labels = ['Signal Std', 'Length Scale', 'Noise Std']
cutoffs = np.array([3.9, 3.9])
energy_force_kernel = en.three_body_force_en
gp = \
GaussianProcess(kernel=kernel,
kernel_grad=kernel_grad,
hyps=hyps,
cutoffs=cutoffs,
hyp_labels=hyp_labels,
energy_force_kernel=energy_force_kernel,
maxiter=50)
# set up DFT calculator
qe_input = './pwscf.in' # quantum espresso input file
dft_loc = os.environ.get('PWSCF_COMMAND')
# set up OTF parameters
dt = 0.001 # timestep (ps)
number_of_steps = 100 # number of steps
std_tolerance_factor = 1
max_atoms_added = 2
freeze_hyps = 3
otf = OTF(qe_input,
dt,
number_of_steps,
gp,
dft_loc,
std_tolerance_factor,
init_atoms=[0],
calculate_energy=True,
output_name='al_otf_qe',
freeze_hyps=freeze_hyps,
skip=5,
max_atoms_added=max_atoms_added)
# run OTF MD
otf.run()
os.system('mkdir test_outputs')
os.system('mv al_otf_qe* test_outputs')
cleanup_espresso_run()
def predict_on_structure(
structure: Structure,
gp: GaussianProcess,
n_cpus: int = None,
write_to_structure: bool = True,
selective_atoms: List[int] = None,
skipped_atom_value=0,
) -> ("np.ndarray", "np.ndarray"):
"""
Return the forces/std. dev. uncertainty associated with each
individual atom in a structure. Forces are stored directly to the
structure and are also returned.
:param structure: FLARE structure to obtain forces for, with N atoms
:param gp: Gaussian Process model
:param write_to_structure: Write results to structure's forces,
std attributes
:param selective_atoms: Only predict on these atoms; e.g. [0,1,2] will
only predict and return for those atoms
:param skipped_atom_value: What value to use for atoms that are skipped.
Defaults to 0 but other options could be e.g. NaN. Will NOT
write this to the structure if write_to_structure is True.
:return: N x 3 numpy array of foces, Nx3 numpy array of uncertainties
:rtype: (np.ndarray, np.ndarray)
"""
forces = np.zeros((structure.nat, 3))
stds = np.zeros((structure.nat, 3))
if selective_atoms:
forces.fill(skipped_atom_value)
stds.fill(skipped_atom_value)
else:
selective_atoms = []
for n in range(structure.nat):
# Skip the atoms which we aren't predicting on if
# selective atoms is on.
if n not in selective_atoms and selective_atoms:
continue
chemenv = AtomicEnvironment(structure, n, gp.cutoffs, cutoffs_mask=gp.hyps_mask)
force, var = gp.predict_force_xyz(chemenv)
std = np.sqrt(np.abs(var))
forces[n] = force
stds[n] = std
if write_to_structure:
structure.forces[n] = force
structure.stds[n] = std
return forces, stds