def test_caching(self):
"""Test whether MultiFeaturizer properly caches """
# have to iterate over entries to enable caching
feat = MultipleFeaturizer([
SiteStatsFingerprint.from_preset("LocalPropertyDifference_ward-prb-2017"),
SiteStatsFingerprint.from_preset("CoordinationNumber_ward-prb-2017")
], iterate_over_entries=True)
# Reset the cache before tests
_get_all_nearest_neighbors.cache_clear()
# Create a dataframe with two SC structures in it
data = pd.DataFrame({'strcs': [
Structure([[3.52, 0, 0], [0, 3.52, 0], [0, 0, 3.52]], ["Al"], [[0, 0, 0]]),
Structure([[3.52, 0, 0], [0, 3.52, 0], [0, 0, 3.52]], ["Ni"], [[0, 0, 0]]),
]})
# Call featurize on both, check the number of cache misses/hits
feat.featurize(data['strcs'][0])
feat.featurize(data['strcs'][1])
self.assertEqual(2, _get_all_nearest_neighbors.cache_info().hits)
self.assertEqual(2, _get_all_nearest_neighbors.cache_info().misses)
# Verify the number of cache misses, it should be the same as before
feat.set_n_jobs(1)
_get_all_nearest_neighbors.cache_clear()
feat.featurize_dataframe(data, 'strcs')
self.assertEqual(2, _get_all_nearest_neighbors.cache_info().hits)
self.assertEqual(2, _get_all_nearest_neighbors.cache_info().misses)
def test_caching(self):
"""Test whether MultiFeaturizer properly caches """
# have to iterate over entries to enable caching
feat = MultipleFeaturizer([
SiteStatsFingerprint.from_preset("LocalPropertyDifference_ward-prb-2017"),
SiteStatsFingerprint.from_preset("CoordinationNumber_ward-prb-2017")
], iterate_over_entries=True)
# Create a dataframe with two SC structures in it
data = pd.DataFrame({'strcs': [
Structure([[3.52, 0, 0], [0, 3.52, 0], [0, 0, 3.52]], ["Al"], [[0, 0, 0]]),
Structure([[3.52, 0, 0], [0, 3.52, 0], [0, 0, 3.52]], ["Ni"], [[0, 0, 0]]),
]})
# Call featurize on both, check the number of cache misses/hits
feat.featurize(data['strcs'][0])
feat.featurize(data['strcs'][1])
self.assertEqual(2, _get_all_nearest_neighbors.cache_info().hits)
self.assertEqual(2, _get_all_nearest_neighbors.cache_info().misses)
# Verify the number of cache misses, it should be the same as before
feat.set_n_jobs(1)
_get_all_nearest_neighbors.cache_clear()
feat.featurize_dataframe(data, 'strcs')
self.assertEqual(2, _get_all_nearest_neighbors.cache_info().hits)
self.assertEqual(2, _get_all_nearest_neighbors.cache_info().misses)
def test_cache(self):
x = Structure(Lattice([[2.189, 0, 1.264], [0.73, 2.064, 1.264],
[0, 0, 2.528]]), ["C0+", "C0+"],
[[2.554, 1.806, 4.423], [0.365, 0.258, 0.632]],
validate_proximity=False,
to_unit_cell=False,
coords_are_cartesian=True)
# Reset the cache
_get_all_nearest_neighbors.cache_clear()
# Compute the nearest neighbors
method = VoronoiNN()
nn_1 = get_nearest_neighbors(method, x, 0)
# Compute it again and make sure the cache hits
nn_2 = get_nearest_neighbors(method, x, 0)
self.assertAlmostEqual(nn_1[0]['weight'], nn_2[0]['weight'])
self.assertEqual(1, _get_all_nearest_neighbors.cache_info().misses)
self.assertEqual(1, _get_all_nearest_neighbors.cache_info().hits)
# Reinstantiate the VoronoiNN class, should not cause a miss
method = VoronoiNN()
nn_2 = get_nearest_neighbors(method, x, 0)
self.assertAlmostEqual(nn_1[0]['weight'], nn_2[0]['weight'])
self.assertEqual(1, _get_all_nearest_neighbors.cache_info().misses)
self.assertEqual(2, _get_all_nearest_neighbors.cache_info().hits)
# Change the NN method, should induce a miss
method = VoronoiNN(weight='volume')
get_nearest_neighbors(method, x, 0)
self.assertEqual(2, _get_all_nearest_neighbors.cache_info().misses)
self.assertEqual(2, _get_all_nearest_neighbors.cache_info().hits)
# Perturb the structure, make sure it induces a miss and
# a change in the NN weights
x.perturb(0.1)
nn_2 = get_nearest_neighbors(method, x, 0)
self.assertNotAlmostEqual(nn_1[0]['weight'], nn_2[0]['weight'])
self.assertEqual(3, _get_all_nearest_neighbors.cache_info().misses)
self.assertEqual(2, _get_all_nearest_neighbors.cache_info().hits)