def test_parallel_sparse(self):
"""Tests creating sparse output parallelly.
"""
# Test indices
samples = [bulk("NaCl", "rocksalt", a=5.64), bulk('Cu', 'fcc', a=3.6)]
desc = SineMatrix(n_atoms_max=5,
permutation="none",
flatten=True,
sparse=True)
n_features = desc.get_number_of_features()
# Multiple systems, serial job
output = desc.create(
system=samples,
n_jobs=1,
).toarray()
assumed = np.empty((2, n_features))
assumed[0, :] = desc.create(samples[0]).toarray()
assumed[1, :] = desc.create(samples[1]).toarray()
self.assertTrue(np.allclose(output, assumed))
# Multiple systems, parallel job
output = desc.create(
system=samples,
n_jobs=2,
).toarray()
assumed = np.empty((2, n_features))
assumed[0, :] = desc.create(samples[0]).toarray()
assumed[1, :] = desc.create(samples[1]).toarray()
self.assertTrue(np.allclose(output, assumed))
# Non-flattened output
desc = SineMatrix(n_atoms_max=5,
permutation="none",
flatten=False,
sparse=True)
output = [
x.toarray() for x in desc.create(
system=samples,
n_jobs=2,
)
]
assumed = np.empty((2, 5, 5))
assumed[0] = desc.create(samples[0]).toarray()
assumed[1] = desc.create(samples[1]).toarray()
self.assertTrue(np.allclose(np.array(output), assumed))
def test_parallel_dense(self):
"""Tests creating dense output parallelly."""
samples = [bulk("NaCl", "rocksalt", a=5.64), bulk("Cu", "fcc", a=3.6)]
desc = SineMatrix(n_atoms_max=5,
permutation="none",
flatten=True,
sparse=False)
n_features = desc.get_number_of_features()
# Determining number of jobs based on the amount of CPUs
desc.create(system=samples, n_jobs=-1, only_physical_cores=False)
desc.create(system=samples, n_jobs=-1, only_physical_cores=True)
# Multiple systems, serial job
output = desc.create(
system=samples,
n_jobs=1,
)
assumed = np.empty((2, n_features))
assumed[0, :] = desc.create(samples[0])
assumed[1, :] = desc.create(samples[1])
self.assertTrue(np.allclose(output, assumed))
# Multiple systems, parallel job
output = desc.create(
system=samples,
n_jobs=2,
)
assumed = np.empty((2, n_features))
assumed[0, :] = desc.create(samples[0])
assumed[1, :] = desc.create(samples[1])
self.assertTrue(np.allclose(output, assumed))
# Non-flattened output
desc = SineMatrix(n_atoms_max=5,
permutation="none",
flatten=False,
sparse=False)
output = desc.create(
system=samples,
n_jobs=2,
)
assumed = np.empty((2, 5, 5))
assumed[0] = desc.create(samples[0])
assumed[1] = desc.create(samples[1])
self.assertTrue(np.allclose(np.array(output), assumed))
def test_number_of_features(self):
"""Tests that the reported number of features is correct.
"""
desc = SineMatrix(n_atoms_max=5, permutation="none", flatten=False)
n_features = desc.get_number_of_features()
self.assertEqual(n_features, 25)