def get_catlearn_fp(A, Name=False):
if not Name:
A = autogen_info([A])
gen = FeatureGenerator()
if isinstance(A, list):
gen._get_atom_types(A)
else:
gen._get_atom_types([A])
train_fpv = [gen.mean_chemisorbed_atoms,
gen.bulk,
gen.term,
gen.strain,
gen.mean_surf_ligands,
gen.mean_site,
gen.median_site,
gen.max_site,
gen.min_site,
gen.sum_site,
#gen.generalized_cn,
#gen.bag_cn,
#gen.en_difference_ads,
#gen.en_difference_chemi,
#gen.en_difference_active,
#gen.bag_atoms_ads,
#gen.bag_connections_ads,
gen.count_chemisorbed_fragment]
if not Name:
gen.normalize_features(A)
return gen.return_vec(A, train_fpv)
else:
return gen.return_names(train_fpv)
def test_recontruction(self):
images = database_to_list('data/ads_example.db')
slabs = []
for atoms in images:
slab = atoms.copy()
slab.pop(-1)
slabs.append(slab)
images = autogen_info(images)
slabs = images_connectivity(slabs)
image_pairs = zip(images, slabs)
r_index = check_reconstructions(image_pairs)
for i in range(len(images)):
species = images[i].info['key_value_pairs']['species']
connectivity2ads_index(images[i], species)
self.assertTrue(len(r_index) == 0)
def test_tags(self):
"""Test the feature generation."""
images = self.setup_metals()
images = autogen_info(images)
print(str(len(images)) + ' training examples.')
gen = FeatureGenerator(nprocs=1)
train_fpv = default_fingerprinters(gen, 'adsorbates')
train_fpv += [
gen.formal_charges, gen.bag_edges_ads, gen.ads_av, gen.ads_sum
]
matrix = gen.return_vec(images, train_fpv)
labels = gen.return_names(train_fpv)
print(np.shape(matrix), type(matrix))
if __name__ == '__main__':
for i, l in enumerate(labels):
print(i, l)
self.assertTrue(len(labels) == np.shape(matrix)[1])
def test_constrained_ads(self):
"""Test the feature generation."""
images = self.setup_metals()
[atoms.set_tags(np.zeros(len(atoms))) for atoms in images]
for atoms in images:
c_atoms = [a.index for a in atoms if
a.z < atoms.cell[2, 2] / 2. + 0.1]
atoms.set_constraint(FixAtoms(c_atoms))
images = autogen_info(images)
print(str(len(images)) + ' training examples.')
gen = FeatureGenerator(nprocs=1)
train_fpv = default_fingerprinters(gen, 'adsorbates')
matrix = gen.return_vec(images, train_fpv)
labels = gen.return_names(train_fpv)
print(np.shape(matrix), type(matrix))
if __name__ == '__main__':
for i, l in enumerate(labels):
print(i, l)
self.assertTrue(len(labels) == np.shape(matrix)[1])
def test_db_ads(self):
"""Test the feature generation."""
images = database_to_list('data/ads_example.db')
[atoms.set_tags(np.zeros(len(atoms))) for atoms in images]
images = autogen_info(images)
layers2ads_index(images[0],
images[0].info['key_value_pairs']['species'])
print(str(len(images)) + ' training examples.')
gen = FeatureGenerator(nprocs=1)
train_fpv = default_fingerprinters(gen, 'adsorbates')
# Test db specific functions.
train_fpv += [gen.db_size, gen.ctime, gen.dbid, gen.delta_energy]
# Old CatApp AxBy fingerprints.
train_fpv += [gen.catapp_AB]
matrix = gen.return_vec(images, train_fpv)
labels = gen.return_names(train_fpv)
print(np.shape(matrix), type(matrix))
if __name__ == '__main__':
for i, l in enumerate(labels):
print(i, l)
self.assertTrue(len(labels) == np.shape(matrix)[1])