db = connect('reference_data.db')
primitive_structure = db.get(id=1).toatoms() # primitive structure
# step 2: Set up the basic structure and a cluster space
cs = ClusterSpace(structure=primitive_structure,
cutoffs=[13.5, 6.0, 5.5],
chemical_symbols=['Ag', 'Pd'])
print(cs)
# step 3: Parse the input structures and set up a structure container
sc = StructureContainer(cluster_space=cs)
for row in db.select('natoms<=8'):
sc.add_structure(structure=row.toatoms(),
user_tag=row.tag,
properties={'mixing_energy': row.mixing_energy})
print(sc)
# step 4: Train parameters
opt = CrossValidationEstimator(fit_data=sc.get_fit_data(key='mixing_energy'),
fit_method='lasso')
opt.validate()
opt.train()
print(opt)
# step 5: Construct cluster expansion and write it to file
ce = ClusterExpansion(cluster_space=cs,
parameters=opt.parameters,
metadata=opt.summary)
print(ce)
ce.write('mixing_energy.ce')
class TestClusterExpansion(unittest.TestCase):
"""Container for tests of the class functionality."""
def __init__(self, *args, **kwargs):
super(TestClusterExpansion, self).__init__(*args, **kwargs)
self.structure = bulk('Au')
self.cutoffs = [3.0] * 3
chemical_symbols = ['Au', 'Pd']
self.cs = ClusterSpace(self.structure, self.cutoffs, chemical_symbols)
def shortDescription(self):
"""Silences unittest from printing the docstrings in test cases."""
return None
def setUp(self):
"""Setup before each test."""
params_len = len(self.cs)
self.parameters = np.arange(params_len)
self.ce = ClusterExpansion(self.cs, self.parameters)
def test_init(self):
"""Tests that initialization works."""
self.assertIsInstance(self.ce, ClusterExpansion)
# test whether method raises Exception
with self.assertRaises(ValueError) as context:
ClusterExpansion(self.cs, [0.0])
self.assertTrue('cluster_space (5) and parameters (1) must have the'
' same length' in str(context.exception))
def test_predict(self):
"""Tests predict function."""
predicted_val = self.ce.predict(self.structure)
self.assertEqual(predicted_val, 10.0)
def test_property_orders(self):
"""Tests orders property."""
self.assertEqual(self.ce.orders, list(range(len(self.cutoffs) + 2)))
def test_property_to_dataframe(self):
"""Tests to_dataframe() property."""
df = self.ce.to_dataframe()
self.assertIn('radius', df.columns)
self.assertIn('order', df.columns)
self.assertIn('eci', df.columns)
self.assertEqual(len(df), len(self.parameters))
def test_get__clusterspace_copy(self):
"""Tests get cluster space copy."""
self.assertEqual(str(self.ce.get_cluster_space_copy()), str(self.cs))
def test_property_parameters(self):
"""Tests parameters properties."""
self.assertEqual(list(self.ce.parameters), list(self.parameters))
def test_len(self):
"""Tests len functionality."""
self.assertEqual(self.ce.__len__(), len(self.parameters))
def test_read_write(self):
"""Tests read and write functionalities."""
# save to file
temp_file = tempfile.NamedTemporaryFile()
self.ce.write(temp_file.name)
# read from file
temp_file.seek(0)
ce_read = ClusterExpansion.read(temp_file.name)
# check cluster space
self.assertEqual(self.cs._input_structure,
ce_read._cluster_space._input_structure)
self.assertEqual(self.cs._cutoffs, ce_read._cluster_space._cutoffs)
self.assertEqual(self.cs._input_chemical_symbols,
ce_read._cluster_space._input_chemical_symbols)
# check parameters
self.assertIsInstance(ce_read.parameters, np.ndarray)
self.assertEqual(list(ce_read.parameters), list(self.parameters))
# check metadata
self.assertEqual(len(self.ce.metadata), len(ce_read.metadata))
self.assertSequenceEqual(sorted(self.ce.metadata.keys()),
sorted(ce_read.metadata.keys()))
for key in self.ce.metadata.keys():
self.assertEqual(self.ce.metadata[key], ce_read.metadata[key])
def test_read_write_pruned(self):
"""Tests read and write functionalities."""
# save to file
temp_file = tempfile.NamedTemporaryFile()
self.ce.prune(indices=[2, 3])
self.ce.prune(tol=3)
pruned_params = self.ce.parameters
pruned_cs_len = len(self.ce._cluster_space)
self.ce.write(temp_file.name)
# read from file
temp_file.seek(0)
ce_read = ClusterExpansion.read(temp_file.name)
params_read = ce_read.parameters
cs_len_read = len(ce_read._cluster_space)
# check cluster space
self.assertEqual(cs_len_read, pruned_cs_len)
self.assertEqual(list(params_read), list(pruned_params))
def test_prune_cluster_expansion(self):
"""Tests pruning cluster expansion."""
len_before = len(self.ce)
self.ce.prune()
len_after = len(self.ce)
self.assertEqual(len_before, len_after)
# Set all parameters to zero except three
self.ce._parameters = np.array([0.0] * len_after)
self.ce._parameters[0] = 1.0
self.ce._parameters[1] = 2.0
self.ce._parameters[2] = 0.5
self.ce.prune()
self.assertEqual(len(self.ce), 3)
self.assertNotEqual(len(self.ce), len_after)
def test_prune_cluster_expansion_tol(self):
"""Tests pruning cluster expansion with tolerance."""
len_before = len(self.ce)
self.ce.prune()
len_after = len(self.ce)
self.assertEqual(len_before, len_after)
# Set all parameters to zero except two, one of which is
# non-zero but below the tolerance
self.ce._parameters = np.array([0.0] * len_after)
self.ce._parameters[0] = 1.0
self.ce._parameters[1] = 0.01
self.ce.prune(tol=0.02)
self.assertEqual(len(self.ce), 1)
self.assertNotEqual(len(self.ce), len_after)
def test_prune_pairs(self):
"""Tests pruning pairs only."""
df = self.ce.to_dataframe()
pair_indices = df.index[df['order'] == 2].tolist()
self.ce.prune(indices=pair_indices)
df_new = self.ce.to_dataframe()
pair_indices_new = df_new.index[df_new['order'] == 2].tolist()
self.assertEqual(pair_indices_new, [])
def test_prune_zerolet(self):
"""Tests pruning zerolet."""
with self.assertRaises(ValueError) as context:
self.ce.prune(indices=[0])
self.assertTrue('zerolet may not be pruned' in str(context.exception))
def test_plot_ecis(self):
"""Tests plot_ecis."""
self.ce.plot_ecis()
def test_repr(self):
"""Tests repr functionality."""
retval = self.ce.__repr__()
target = """
================================================ Cluster Expansion =================================================
space group : Fm-3m (225)
chemical species : ['Au', 'Pd'] (sublattice A)
cutoffs : 3.0000 3.0000 3.0000
total number of parameters : 5
number of parameters by order : 0= 1 1= 1 2= 1 3= 1 4= 1
fractional_position_tolerance : 2e-06
position_tolerance : 1e-05
symprec : 1e-05
total number of nonzero parameters : 4
number of nonzero parameters by order : 0= 0 1= 1 2= 1 3= 1 4= 1
--------------------------------------------------------------------------------------------------------------------
index | order | radius | multiplicity | orbit_index | multi_component_vector | sublattices | parameter | ECI
--------------------------------------------------------------------------------------------------------------------
0 | 0 | 0.0000 | 1 | -1 | . | . | 0 | 0
1 | 1 | 0.0000 | 1 | 0 | [0] | A | 1 | 1
2 | 2 | 1.4425 | 6 | 1 | [0, 0] | A-A | 2 | 0.333
3 | 3 | 1.6657 | 8 | 2 | [0, 0, 0] | A-A-A | 3 | 0.375
4 | 4 | 1.7667 | 2 | 3 | [0, 0, 0, 0] | A-A-A-A | 4 | 2
====================================================================================================================
""" # noqa
self.assertEqual(strip_surrounding_spaces(target),
strip_surrounding_spaces(retval))
def test_get_string_representation(self):
"""Tests _get_string_representation functionality."""
retval = self.ce._get_string_representation(print_threshold=2,
print_minimum=1)
target = """
================================================ Cluster Expansion =================================================
space group : Fm-3m (225)
chemical species : ['Au', 'Pd'] (sublattice A)
cutoffs : 3.0000 3.0000 3.0000
total number of parameters : 5
number of parameters by order : 0= 1 1= 1 2= 1 3= 1 4= 1
fractional_position_tolerance : 2e-06
position_tolerance : 1e-05
symprec : 1e-05
total number of nonzero parameters : 4
number of nonzero parameters by order : 0= 0 1= 1 2= 1 3= 1 4= 1
--------------------------------------------------------------------------------------------------------------------
index | order | radius | multiplicity | orbit_index | multi_component_vector | sublattices | parameter | ECI
--------------------------------------------------------------------------------------------------------------------
0 | 0 | 0.0000 | 1 | -1 | . | . | 0 | 0
...
4 | 4 | 1.7667 | 2 | 3 | [0, 0, 0, 0] | A-A-A-A | 4 | 2
====================================================================================================================
""" # noqa
self.assertEqual(strip_surrounding_spaces(target),
strip_surrounding_spaces(retval))
def test_print_overview(self):
"""Tests print_overview functionality."""
with StringIO() as capturedOutput:
sys.stdout = capturedOutput # redirect stdout
self.ce.print_overview()
sys.stdout = sys.__stdout__ # reset redirect
self.assertTrue('Cluster Expansion' in capturedOutput.getvalue())
with open(os.path.join(cwd, 'cells.raw'), 'rb') as f:
cells = numpy.loadtxt(f)
with open(os.path.join(cwd, 'coordinates.raw'), 'rb') as f:
positions = numpy.loadtxt(f)
with open(os.path.join(cwd, 'atomic_numbers.raw'), 'rb') as f:
atomic_numbers = numpy.loadtxt(f, dtype=int)
with open(os.path.join(cwd, 'natoms.raw'), 'rb') as f:
natoms = numpy.loadtxt(f, dtype=int)
with open(os.path.join(cwd, 'energies.raw'), 'rb') as f:
energies = numpy.loadtxt(f)
structurelist = create_structurelist(cells, positions, atomic_numbers,
natoms)
cv_list = numpy.array([cs.get_cluster_vector(s) for s in structurelist])
fit_method = data.get('fit_method', 'lasso')
start_time = time.time()
opt = CrossValidationEstimator(fit_data=(cv_list, energies),
fit_method='lasso')
opt.validate()
opt.train()
ce = ClusterExpansion(cluster_space=cs,
parameters=opt.parameters,
metadata=opt.summary)
ce.write('model.ce')
run_time = time.time() - start_time
print_runing_info(run_time)
