def test_parallel_sparse(self):
"""Tests creating sparse output parallelly.
"""
# Test indices
samples = [molecule("CO"), molecule("N2O")]
desc = LMBTR(species=[6, 7, 8],
k=[2],
grid={"k2": {
"n": 100,
"min": 0,
"max": 2,
"sigma": 0.1
}},
virtual_positions=False,
periodic=False,
flatten=True,
sparse=True)
n_features = desc.get_number_of_features()
# Multiple systems, serial job
output = desc.create(
system=samples,
positions=[[0], [0, 1]],
n_jobs=1,
).toarray()
assumed = np.empty((3, n_features))
assumed[0, :] = desc.create(samples[0], [0]).toarray()
assumed[1, :] = desc.create(samples[1], [0]).toarray()
assumed[2, :] = desc.create(samples[1], [1]).toarray()
self.assertTrue(np.allclose(output, assumed))
# Test when position given as indices
output = desc.create(
system=samples,
positions=[[0], [0, 1]],
n_jobs=2,
).toarray()
assumed = np.empty((3, n_features))
assumed[0, :] = desc.create(samples[0], [0]).toarray()
assumed[1, :] = desc.create(samples[1], [0]).toarray()
assumed[2, :] = desc.create(samples[1], [1]).toarray()
self.assertTrue(np.allclose(output, assumed))
# Test with cartesian positions. In this case virtual positions have to
# be enabled
desc = LMBTR(species=[6, 7, 8],
k=[2],
grid={"k2": {
"n": 100,
"min": 0,
"max": 2,
"sigma": 0.1
}},
virtual_positions=True,
periodic=False,
flatten=True,
sparse=True)
output = desc.create(
system=samples,
positions=[[[0, 0, 0], [1, 2, 0]], [[1, 2, 0]]],
n_jobs=2,
).toarray()
assumed = np.empty((2 + 1, n_features))
assumed[0, :] = desc.create(samples[0], [[0, 0, 0]]).toarray()
assumed[1, :] = desc.create(samples[0], [[1, 2, 0]]).toarray()
assumed[2, :] = desc.create(samples[1], [[1, 2, 0]]).toarray()
self.assertTrue(np.allclose(output, assumed))
def test_number_of_features(self):
"""LMBTR: Tests that the reported number of features is correct.
"""
# K = 1
n = 100
atomic_numbers = [1, 8]
n_elem = len(atomic_numbers) + 1 # Including ghost atom
lmbtr = LMBTR(
species=atomic_numbers,
k=[1],
grid={"k1": {
"min": 1,
"max": 8,
"sigma": 0.1,
"n": 100,
}},
virtual_positions=False,
periodic=False,
flatten=True)
n_features = lmbtr.get_number_of_features()
expected = n_elem * n
self.assertEqual(n_features, expected)
# K = 2
lmbtr = LMBTR(species=atomic_numbers,
k={1, 2},
grid={
"k1": {
"min": 1,
"max": 8,
"sigma": 0.1,
"n": 100,
},
"k2": {
"min": 0,
"max": 1 / 0.7,
"sigma": 0.1,
"n": n,
}
},
virtual_positions=False,
periodic=False,
flatten=True)
n_features = lmbtr.get_number_of_features()
expected = n_elem * n + 1 / 2 * (n_elem) * (n_elem + 1) * n
self.assertEqual(n_features, expected)
# K = 3
lmbtr = LMBTR(species=atomic_numbers,
k={3},
grid={
"k1": {
"min": 1,
"max": 8,
"sigma": 0.1,
"n": 100,
},
"k2": {
"min": 0,
"max": 1 / 0.7,
"sigma": 0.1,
"n": n,
},
"k3": {
"min": -1,
"max": 1,
"sigma": 0.1,
"n": n,
}
},
virtual_positions=False,
periodic=False,
flatten=True)
n_features = lmbtr.get_number_of_features()
expected = n_elem * 1 / 2 * (n_elem) * (n_elem + 1) * n
self.assertEqual(n_features, expected)