def test_lammps_cython_algorithms(algorithm):
base_directory = 'test_files/dftfit_calculators/'
training_schema = load_filename(base_directory + 'training.yaml')
potential_schema = load_filename(base_directory + 'potential.yaml')
configuration_schema = load_filename(base_directory + 'configuration.yaml')
configuration_schema['spec']['problem'].update({
'calculator': 'lammps_cython',
})
# do bare minimum calculations
configuration_schema['spec']['algorithm'].update({
'name': algorithm,
'steps': 1,
# Local optimization methods only opperate on one value
'population': 1 if 'nlopt' in algorithm else 8,
})
if algorithm in {'pygmo.xnes', 'pygmo.cmaes'}:
configuration_schema['spec']['algorithm']['force_bounds'] = True
num_features = len(configuration_schema['spec']['problem']['weights'])
with mock.patch('dftfit.io.lammps_cython.lammps.Lammps'):
with mock.patch('dftfit.problem.DFTFITProblemBase._fitness') as mock_fitness:
mock_fitness.return_value = tuple(np.random.random(num_features).tolist()), random.random()
run_id = dftfit(training_schema=training_schema,
potential_schema=potential_schema,
configuration_schema=configuration_schema)
def test_lammps_cython_algorithms(algorithm):
base_directory = 'test_files/dftfit_calculators/'
training_schema = load_filename(base_directory + 'training.yaml')
potential_schema = load_filename(base_directory + 'potential.yaml')
configuration_schema = load_filename(base_directory + 'configuration.yaml')
configuration_schema['spec']['problem'].update({
'calculator':
'lammps_cython',
})
# do bare minimum calculations
configuration_schema['spec']['algorithm'].update({
'name':
algorithm,
'steps':
1,
# Local optimization methods only opperate on one value
'population':
1 if 'nlopt' in algorithm else 8,
})
if algorithm in {'pygmo.xnes', 'pygmo.cmaes'}:
configuration_schema['spec']['algorithm']['force_bounds'] = True
num_features = len(configuration_schema['spec']['problem']['weights'])
with mock.patch('dftfit.io.lammps_cython.lammps.Lammps'):
with mock.patch(
'dftfit.problem.DFTFITProblemBase._fitness') as mock_fitness:
mock_fitness.return_value = tuple(
np.random.random(num_features).tolist()), random.random()
run_id = dftfit(training_schema=training_schema,
potential_schema=potential_schema,
configuration_schema=configuration_schema)
def test_lammps_cython_calculator_with_experimental():
# Read in configuration information
base_directory = 'test_files/dftfit_calculators/'
training_schema = load_filename('test_files/training/training-mattoolkit-mgo-properties.yaml')
potential_schema = load_filename(base_directory + 'potential.yaml')
configuration_schema = load_filename(base_directory + 'configuration.yaml')
configuration_schema['spec']['problem'].update({
'calculator': 'lammps_cython',
})
configuration_schema['spec']['problem']['weights'] = {
'forces': 0.7,
'stress': 0.1,
'lattice_constants': 0.1,
'elastic_constants': 0.1
}
# Run optimization
run_id = dftfit(training_schema=training_schema,
potential_schema=potential_schema,
configuration_schema=configuration_schema)
# Ensure that the calculation ran check database
configuration = Configuration(configuration_schema)
with configuration.dbm.connection:
query = configuration.dbm.connection.execute('''
SELECT count(*) FROM evaluation WHERE run_id = ?
''', (run_id,)).fetchone()
# because one initial run is done before calculation
assert query[0] == configuration.population * (configuration.steps + 1)
def test_pymatgen_lammps_calculator():
# Read in configuration information
base_directory = 'test_files/dftfit_calculators/'
training_schema = load_filename(base_directory + 'training.yaml')
potential_schema = load_filename(base_directory + 'potential.yaml')
configuration_schema = load_filename(base_directory + 'configuration.yaml')
configuration_schema['spec']['problem'].update({
'calculator': 'lammps',
'command': 'lammps',
'num_workers': 1
})
# Run optimization
run_id = dftfit(training_schema=training_schema,
potential_schema=potential_schema,
configuration_schema=configuration_schema)
# Ensure that the calculation ran check database
configuration = Configuration(configuration_schema)
with configuration.dbm.connection:
query = configuration.dbm.connection.execute(
'''
SELECT count(*) FROM evaluation WHERE run_id = ?
''', (run_id, )).fetchone()
# because one initial run is done before calculation
assert query[0] == configuration.population * (configuration.steps + 1)
def test_lammps_cython_calculator_with_experimental():
# Read in configuration information
base_directory = 'test_files/dftfit_calculators/'
training_schema = load_filename(
'test_files/training/training-mattoolkit-mgo-properties.yaml')
potential_schema = load_filename(base_directory + 'potential.yaml')
configuration_schema = load_filename(base_directory + 'configuration.yaml')
configuration_schema['spec']['problem'].update({
'calculator':
'lammps_cython',
})
configuration_schema['spec']['problem']['weights'] = {
'forces': 0.7,
'stress': 0.1,
'lattice_constants': 0.1,
'elastic_constants': 0.1
}
# Run optimization
run_id = dftfit(training_schema=training_schema,
potential_schema=potential_schema,
configuration_schema=configuration_schema)
# Ensure that the calculation ran check database
configuration = Configuration(configuration_schema)
with configuration.dbm.connection:
query = configuration.dbm.connection.execute(
'''
SELECT count(*) FROM evaluation WHERE run_id = ?
''', (run_id, )).fetchone()
# because one initial run is done before calculation
assert query[0] == configuration.population * (configuration.steps + 1)
def test_lammps_cython_calculator_benchmark(benchmark, num_workers):
# Read in configuration information
base_directory = 'test_files/dftfit_calculators/'
training_schema = load_filename(base_directory + 'training.yaml')
potential_schema = load_filename(base_directory + 'potential.yaml')
configuration_schema = load_filename(base_directory + 'configuration.yaml')
configuration_schema['spec']['problem'].update({
'calculator': 'lammps_cython',
'num_workers': num_workers
})
configuration_schema['spec']['steps'] = 3
@benchmark
def test_speed():
dftfit(training_schema=training_schema,
potential_schema=potential_schema,
configuration_schema=configuration_schema)
def test_lammps_cython_calculator_benchmark(benchmark, num_workers):
# Read in configuration information
base_directory = 'test_files/dftfit_calculators/'
training_schema = load_filename(base_directory + 'training.yaml')
potential_schema = load_filename(base_directory + 'potential.yaml')
configuration_schema = load_filename(base_directory + 'configuration.yaml')
configuration_schema['spec']['problem'].update({
'calculator': 'lammps_cython',
'num_workers': num_workers
})
configuration_schema['spec']['steps'] = 3
@benchmark
def test_speed():
dftfit(training_schema=training_schema,
potential_schema=potential_schema,
configuration_schema=configuration_schema)
def test_lammps_cython_algorithms():
base_directory = 'test_files/dftfit_calculators/'
training_schema = load_filename(base_directory + 'training.yaml')
potential_schema = load_filename(base_directory + 'potential.yaml')
configuration_schema = load_filename(base_directory + 'configuration.yaml')
batch_schema = load_filename('test_files/batch/example.yaml')
configuration_schema['spec']['problem'].update({
'calculator': 'lammps_cython',
})
num_features = len(configuration_schema['spec']['problem']['weights'])
with mock.patch('dftfit.io.lammps_cython.lammps.Lammps'):
with mock.patch('dftfit.problem.DFTFITProblemBase._fitness') as mock_fitness:
mock_fitness.return_value = tuple(np.random.random(num_features).tolist()), random.random()
num_jobs = dftfit_batch(training_schema=training_schema,
potential_schema=potential_schema,
configuration_schema=configuration_schema,
batch_schema=batch_schema)
assert len(num_jobs) == 4
def test_lammps_cython_algorithms():
base_directory = 'test_files/dftfit_calculators/'
training_schema = load_filename(base_directory + 'training.yaml')
potential_schema = load_filename(base_directory + 'potential.yaml')
configuration_schema = load_filename(base_directory + 'configuration.yaml')
batch_schema = load_filename('test_files/batch/example.yaml')
configuration_schema['spec']['problem'].update({
'calculator':
'lammps_cython',
})
num_features = len(configuration_schema['spec']['problem']['weights'])
with mock.patch('dftfit.io.lammps_cython.lammps.Lammps'):
with mock.patch(
'dftfit.problem.DFTFITProblemBase._fitness') as mock_fitness:
mock_fitness.return_value = tuple(
np.random.random(num_features).tolist()), random.random()
num_jobs = dftfit_batch(training_schema=training_schema,
potential_schema=potential_schema,
configuration_schema=configuration_schema,
batch_schema=batch_schema)
assert len(num_jobs) == 4
def test_lammps_cython_calculator():
# Read in configuration information
base_directory = 'test_files/dftfit_calculators/'
training_schema = load_filename(base_directory + 'training.yaml')
potential_schema = load_filename(base_directory + 'potential.yaml')
configuration_schema = load_filename(base_directory + 'configuration.yaml')
configuration_schema['spec']['problem'].update({
'calculator': 'lammps_cython',
})
# Run optimization
run_id = dftfit(training_schema=training_schema,
potential_schema=potential_schema,
configuration_schema=configuration_schema)
# Ensure that the calculation ran check database
configuration = Configuration(configuration_schema)
with configuration.dbm.connection:
query = configuration.dbm.connection.execute('''
SELECT count(*) FROM evaluation WHERE run_id = ?
''', (run_id,)).fetchone()
# because one initial run is done before calculation
assert query[0] == configuration.population * (configuration.steps + 1)
def test_calculator_equivalency(structure):
target_a = 4.1990858
s = structure('test_files/structure/MgO.cif')
lattice = pmg.Lattice.from_parameters(target_a, target_a, target_a, 90, 90,
90)
s.modify_lattice(lattice)
assert np.all(np.isclose(s.lattice.abc, (target_a, target_a, target_a)))
s = s * (2, 2, 2)
assert len(s) == 64
base_directory = 'test_files/dftfit_calculators/'
potential_schema = load_filename(base_directory + 'potential.yaml')
potential_schema['spec']['charge']['Mg']['initial'] = 1.4
potential_schema['spec']['charge']['O']['initial'] = -1.4
potential = Potential(potential_schema)
command = None
if shutil.which('lammps'): command = 'lammps'
elif shutil.which('lmp_serial'): command = 'lmp_serial'
calculators = [
LammpsLocalDFTFITCalculator(structures=[s],
potential=potential,
command=command,
num_workers=1),
LammpsCythonDFTFITCalculator(structures=[s], potential=potential)
]
loop = asyncio.get_event_loop()
results = []
async def run(calc, potential):
await calc.create()
return await calc.submit(potential)
for calc in calculators:
results.append(loop.run_until_complete(run(calc, potential)))
assert len(results) == 2
assert len(results[0]) == 1
assert len(results[1]) == 1
for r1, r2 in zip(*results):
assert r1.structure == r2.structure
assert abs(r1.energy - r2.energy) < 1e-4
assert np.all(np.isclose(r1.forces, r2.forces, atol=1e-8))
assert np.all(np.isclose(r1.stress, r2.stress, atol=1e-8))
def test_calculator_equivalency(structure):
target_a = 4.1990858
s = structure('test_files/structure/MgO.cif')
lattice = pmg.Lattice.from_parameters(target_a, target_a, target_a, 90, 90, 90)
s.modify_lattice(lattice)
assert np.all(np.isclose(s.lattice.abc, (target_a, target_a, target_a)))
s = s * (2, 2, 2)
assert len(s) == 64
base_directory = 'test_files/dftfit_calculators/'
potential_schema = load_filename(base_directory + 'potential.yaml')
potential_schema['spec']['charge']['Mg']['initial'] = 1.4
potential_schema['spec']['charge']['O']['initial'] = -1.4
potential = Potential(potential_schema)
command = None
if shutil.which('lammps'): command = 'lammps'
elif shutil.which('lmp_serial'): command = 'lmp_serial'
calculators = [
LammpsLocalDFTFITCalculator(structures=[s], potential=potential, command=command, num_workers=1),
LammpsCythonDFTFITCalculator(structures=[s], potential=potential)
]
loop = asyncio.get_event_loop()
results = []
async def run(calc, potential):
await calc.create()
return await calc.submit(potential)
for calc in calculators:
results.append(loop.run_until_complete(run(calc, potential)))
assert len(results) == 2
assert len(results[0]) == 1
assert len(results[1]) == 1
for r1, r2 in zip(*results):
assert r1.structure == r2.structure
assert abs(r1.energy - r2.energy) < 1e-4
assert np.all(np.isclose(r1.forces, r2.forces, atol=1e-8))
assert np.all(np.isclose(r1.stress, r2.stress, atol=1e-8))
from dftfit.cli.utils import load_filename
from dftfit.dftfit import dftfit
base_directory = 'test_files/dftfit_calculators/'
training_schema = load_filename(base_directory + 'training.yaml')
potential_schema = load_filename(base_directory + 'potential.yaml')
configuration_schema = load_filename(base_directory + 'configuration.yaml')
configuration_schema['spec']['problem'].update({
'calculator': 'lammps',
'command': 'lammps',
'num_workers': 2
})
configuration_schema['spec']['steps'] = 10
run_id = dftfit(training_schema=training_schema,
potential_schema=potential_schema,
configuration_schema=configuration_schema)
