def split(self, X, y=None, groups=None):
# Generate the composition vectors
frac_computer = ElementFraction()
elem_fracs = frac_computer.featurize_many(list(map(Composition, X)),
pbar=False)
# Generate the nearest-neighbor lookup tool
neigh = NearestNeighbors(**self.nn_kwargs)
neigh.fit(elem_fracs)
# Generate a list of all entries
all_inds = np.arange(0, len(X), 1)
# Loop through each entry in X
for i, x in enumerate(elem_fracs):
# Get all the entries within the threshold distance of the test point
too_close, = neigh.radius_neighbors([x],
self.dist_threshold,
return_distance=False)
# Get the training set as "not these points"
train_inds = np.setdiff1d(all_inds, too_close)
yield train_inds, [i]
def test_ape(self):
f = AtomicPackingEfficiency()
ef = ElementFraction()
ef.set_n_jobs(1)
# Test the APE calculation routines
self.assertAlmostEqual(1.11632, f.get_ideal_radius_ratio(15))
self.assertAlmostEqual(0.154701, f.get_ideal_radius_ratio(2))
self.assertAlmostEqual(1.65915, f.get_ideal_radius_ratio(27))
self.assertAlmostEqual(15, f.find_ideal_cluster_size(1.116)[0])
self.assertAlmostEqual(3, f.find_ideal_cluster_size(0.1)[0])
self.assertAlmostEqual(24, f.find_ideal_cluster_size(2)[0])
# Test the nearest neighbor lookup tool
nn_lookup = f.create_cluster_lookup_tool(
[Element('Cu'), Element('Zr')])
# Check that the table gets the correct structures
stable_clusters = [
Composition('CuZr10'),
Composition('Cu6Zr6'),
Composition('Cu8Zr5'),
Composition('Cu13Zr1'),
Composition('Cu3Zr12'),
Composition('Cu8Zr8'),
Composition('Cu12Zr5'),
Composition('Cu17Zr')
]
ds, _ = nn_lookup.kneighbors(ef.featurize_many(stable_clusters),
n_neighbors=1)
self.assertArrayAlmostEqual([[0]] * 8, ds)
self.assertEqual(8, nn_lookup._fit_X.shape[0])
# Swap the order of the clusters, make sure it gets the same list
nn_lookup_swapped = f.create_cluster_lookup_tool(
[Element('Zr'), Element('Cu')])
self.assertArrayAlmostEqual(sorted(nn_lookup._fit_X.tolist()),
sorted(nn_lookup_swapped._fit_X.tolist()))
# Make sure we had a cache hit
self.assertEqual(1, f._create_cluster_lookup_tool.cache_info().misses)
self.assertEqual(1, f._create_cluster_lookup_tool.cache_info().hits)
# Change the tolerance, see if it changes the results properly
f.threshold = 0.002
nn_lookup = f.create_cluster_lookup_tool(
[Element('Cu'), Element('Zr')])
self.assertEqual(2, nn_lookup._fit_X.shape[0])
ds, _ = nn_lookup.kneighbors(ef.featurize_many(
[Composition('CuZr10'),
Composition('Cu3Zr12')]),
n_neighbors=1)
self.assertArrayAlmostEqual([[0]] * 2, ds)
# Make sure we had a cache miss
self.assertEqual(2, f._create_cluster_lookup_tool.cache_info().misses)
self.assertEqual(1, f._create_cluster_lookup_tool.cache_info().hits)
# Compute the distances from Cu50Zr50
mean_dists = f.compute_nearest_cluster_distance(Composition('CuZr'))
self.assertArrayAlmostEqual([0.424264, 0.667602, 0.800561],
mean_dists,
decimal=6)
# Compute the optimal APE for Cu50Zr50
self.assertArrayAlmostEqual([0.000233857, 0.003508794],
f.compute_simultaneous_packing_efficiency(
Composition('Cu50Zr50')))
# Test the dataframe calculator
df = pd.DataFrame({'comp': [Composition('CuZr')]})
df = f.featurize_dataframe(df, 'comp')
self.assertEqual(6, len(df.columns))
self.assertIn('dist from 5 clusters |APE| < 0.002', df.columns)
self.assertAlmostEqual(0.003508794,
df['mean abs simul. packing efficiency'][0])
# Make sure it works with composition that do not match any efficient clusters
feat = f.compute_nearest_cluster_distance(Composition('Al'))
self.assertArrayAlmostEqual([1] * 3, feat)
def test_ape(self):
f = AtomicPackingEfficiency()
ef = ElementFraction()
ef.set_n_jobs(1)
# Test the APE calculation routines
self.assertAlmostEqual(1.11632, f.get_ideal_radius_ratio(15))
self.assertAlmostEqual(0.154701, f.get_ideal_radius_ratio(2))
self.assertAlmostEqual(1.65915, f.get_ideal_radius_ratio(27))
self.assertAlmostEqual(15, f.find_ideal_cluster_size(1.116)[0])
self.assertAlmostEqual(3, f.find_ideal_cluster_size(0.1)[0])
self.assertAlmostEqual(24, f.find_ideal_cluster_size(2)[0])
# Test the nearest neighbor lookup tool
nn_lookup = f.create_cluster_lookup_tool([Element('Cu'),
Element('Zr')])
# Check that the table gets the correct structures
stable_clusters = [Composition('CuZr10'), Composition('Cu6Zr6'),
Composition('Cu8Zr5'), Composition('Cu13Zr1'),
Composition('Cu3Zr12'), Composition('Cu8Zr8'),
Composition('Cu12Zr5'), Composition('Cu17Zr')]
ds, _ = nn_lookup.kneighbors(
ef.featurize_many(stable_clusters),
n_neighbors=1)
self.assertArrayAlmostEqual([[0]]*8, ds)
self.assertEqual(8, nn_lookup._fit_X.shape[0])
# Swap the order of the clusters, make sure it gets the same list
nn_lookup_swapped = f.create_cluster_lookup_tool([Element('Zr'),
Element('Cu')])
self.assertArrayAlmostEqual(sorted(nn_lookup._fit_X.tolist()),
sorted(nn_lookup_swapped._fit_X.tolist()))
# Make sure we had a cache hit
self.assertEqual(1, f._create_cluster_lookup_tool.cache_info().misses)
self.assertEqual(1, f._create_cluster_lookup_tool.cache_info().hits)
# Change the tolerance, see if it changes the results properly
f.threshold = 0.002
nn_lookup = f.create_cluster_lookup_tool([Element('Cu'),
Element('Zr')])
self.assertEqual(2, nn_lookup._fit_X.shape[0])
ds, _ = nn_lookup.kneighbors(
ef.featurize_many([Composition('CuZr10'), Composition('Cu3Zr12')]),
n_neighbors=1)
self.assertArrayAlmostEqual([[0]]*2, ds)
# Make sure we had a cache miss
self.assertEqual(2, f._create_cluster_lookup_tool.cache_info().misses)
self.assertEqual(1, f._create_cluster_lookup_tool.cache_info().hits)
# Compute the distances from Cu50Zr50
mean_dists = f.compute_nearest_cluster_distance(Composition('CuZr'))
self.assertArrayAlmostEqual([0.424264, 0.667602, 0.800561], mean_dists,
decimal=6)
# Compute the optimal APE for Cu50Zr50
self.assertArrayAlmostEqual([0.000233857, 0.003508794],
f.compute_simultaneous_packing_efficiency(
Composition('Cu50Zr50')
))
# Test the dataframe calculator
df = pd.DataFrame({'comp': [Composition('CuZr')]})
f.featurize_dataframe(df, 'comp')
self.assertEqual(6, len(df.columns))
self.assertIn('dist from 5 clusters |APE| < 0.002', df.columns)
self.assertAlmostEqual(0.003508794,
df['mean abs simul. packing efficiency'][0])
# Make sure it works with composition that do not match any efficient clusters
feat = f.compute_nearest_cluster_distance(Composition('Al'))
self.assertArrayAlmostEqual([1]*3, feat)