def get_input_features(self, mol):
"""get input features
Args:
mol (Mol):
Returns:
"""
type_check_num_atoms(mol, self.max_atoms)
num_atoms = mol.GetNumAtoms()
# Construct the atom array and adjacency matrix.
atom_array = construct_atomic_number_array(mol, out_size=self.out_size)
adj_array = construct_adj_matrix(mol, out_size=self.out_size)
# Adjust the adjacency matrix.
degree_vec = numpy.sum(adj_array[:num_atoms], axis=1)
degree_sqrt_inv = 1. / numpy.sqrt(degree_vec)
adj_array[:num_atoms, :num_atoms] *= numpy.broadcast_to(
degree_sqrt_inv[:, None], (num_atoms, num_atoms))
adj_array[:num_atoms, :num_atoms] *= numpy.broadcast_to(
degree_sqrt_inv[None, :], (num_atoms, num_atoms))
return atom_array, adj_array
def get_input_features(self, mol):
"""get input features
Args:
mol (Mol):
Returns:
"""
type_check_num_atoms(mol, self.max_atoms)
num_atoms = mol.GetNumAtoms()
# Construct the atom array and adjacency matrix.
atom_array = construct_atomic_number_array(mol, out_size=self.out_size)
adj_array = construct_adj_matrix(mol, out_size=self.out_size)
# Adjust the adjacency matrix.
degree_vec = numpy.sum(adj_array[:num_atoms], axis=1)
degree_sqrt_inv = 1. / numpy.sqrt(degree_vec)
adj_array[:num_atoms, :num_atoms] *= numpy.broadcast_to(
degree_sqrt_inv[:, None], (num_atoms, num_atoms))
adj_array[:num_atoms, :num_atoms] *= numpy.broadcast_to(
degree_sqrt_inv[None, :], (num_atoms, num_atoms))
super_node_x = construct_supernode_feature(mol, atom_array, adj_array, out_size=self.out_size_super)
return atom_array, adj_array, super_node_x
def get_input_features(self, mol):
"""get input features
Args:
mol (Mol):
Returns:
"""
type_check_num_atoms(mol, self.max_atoms)
atom_array = construct_atomic_number_array(mol, out_size=self.out_size)
adj_tensor = construct_discrete_edge_matrix(mol,
out_size=self.out_size)
return relGCN_pre((atom_array, adj_tensor), out_size=self.out_size)
def get_input_features(self, mol):
"""get input features
Args:
mol (Mol):
Returns:
"""
type_check_num_atoms(mol, self.max_atoms)
atom_array = construct_atomic_number_array(mol, out_size=self.out_size)
return atom_array
def get_input_features(self, mol):
"""get input features
Args:
mol (Mol): Molecule input
Returns:
"""
type_check_num_atoms(mol, self.max_atoms)
atom_array = construct_atomic_number_array(mol, out_size=self.out_size)
adj_array = construct_discrete_edge_matrix(mol, out_size=self.out_size)
return atom_array, adj_array
def get_input_features(self, mol):
"""get input features
Args:
mol (Mol):
Returns:
"""
type_check_num_atoms(mol, self.max_atoms)
atom_array = construct_atomic_number_array(mol, out_size=self.out_size)
adj_array = construct_adj_matrix(mol, out_size=self.out_size)
super_node_x = construct_supernode_feature(mol, atom_array, adj_array, out_size=self.out_size_super)
return atom_array, adj_array, super_node_x
def get_input_features(self, mol):
"""get input features
Args:
mol (Mol):
Returns:
"""
type_check_num_atoms(mol, self.max_atoms)
atom_array = construct_atomic_number_array(mol, out_size=self.out_size)
print ("atom_array : ", atom_array)
adj_array = construct_adj_matrix(mol, out_size=self.out_size)
print ("adj_array : ", adj_array)
return atom_array, adj_array
def get_input_features(self, mol):
"""get input features
Args:
mol (Mol):
Returns:
"""
type_check_num_atoms(mol, self.max_atoms)
atom_array = construct_atomic_number_array(mol, out_size=self.out_size)
dist_array = construct_distance_matrix(mol,
out_size=self.out_size,
contain_Hs=self.add_Hs)
return atom_array, dist_array
def test_construct_super_node_feature_adj_ndim2(sample_molecule):
adj = common.construct_adj_matrix(sample_molecule)
atom_array = common.construct_atomic_number_array(sample_molecule)
s = common.construct_supernode_feature(sample_molecule, atom_array, adj)
# print(s)
assert s.shape == (MAX_ATOMIC_NUM * 2 + 4, )
assert s[0] == len(atom_array)
assert s[1] == adj.sum()
assert s[2] == 1
assert s[3] == 1
assert s[3 + 6] == 1 # C
assert s[3 + 7] == 1 # N
assert s[3 + 8] == 1 # O
assert s[3 + MAX_ATOMIC_NUM] == 0 # other
assert s[3 + MAX_ATOMIC_NUM + 6] == 2 / len(atom_array)
assert s[3 + MAX_ATOMIC_NUM + 7] == 1 / len(atom_array)
assert s[3 + MAX_ATOMIC_NUM + 8] == 1 / len(atom_array)
assert s[3 + MAX_ATOMIC_NUM * 2] == 0
def get_input_features(self, mol):
"""get input features
Args:
mol (Mol):
Returns:
"""
type_check_num_atoms(mol, self.max_atoms)
atom_array = construct_atomic_number_array(mol, out_size=self.out_size)
adj_array = construct_adj_matrix(mol, out_size=self.out_size)
# adjust adjacent matrix
degree_vec = numpy.sum(adj_array, axis=1)
degree_vec = numpy.sqrt(degree_vec)
adj_array *= numpy.broadcast_to(degree_vec[:, None], adj_array.shape)
adj_array *= numpy.broadcast_to(degree_vec[None, :], adj_array.shape)
return atom_array, adj_array
def get_input_features(self, mol):
"""get input features for WeaveNet
WeaveNetPreprocessor automatically add `H` to `mol`
Args:
mol (Mol):
"""
type_check_num_atoms(mol, self.max_atoms)
if self.use_fixed_atom_feature:
# original paper feature extraction
atom_array = construct_atom_feature(mol, self.add_Hs,
self.max_atoms, self.atom_list,
self.include_unknown_atom)
else:
# embed id of atomic numbers
atom_array = construct_atomic_number_array(mol, self.max_atoms)
pair_feature = construct_pair_feature(mol,
num_max_atoms=self.max_atoms)
return atom_array, pair_feature
def get_input_features(self, mol):
"""get input features for WeaveNet
WeaveNetPreprocessor automatically add `H` to `mol`
Args:
mol (Mol):
"""
type_check_num_atoms(mol, self.max_atoms)
if self.use_fixed_atom_feature:
# original paper feature extraction
atom_array = construct_atom_feature(mol, self.add_Hs,
self.max_atoms, self.atom_list,
self.include_unknown_atom)
else:
# embed id of atomic numbers
atom_array = construct_atomic_number_array(mol, self.max_atoms)
pair_feature = construct_pair_feature(mol,
num_max_atoms=self.max_atoms)
return atom_array, pair_feature
def test_construct_super_node_feature_adj_ndim3(sample_molecule):
adj = common.construct_discrete_edge_matrix(sample_molecule)
atom_array = common.construct_atomic_number_array(sample_molecule)
s = common.construct_supernode_feature(sample_molecule, atom_array, adj)
assert s.shape == (MAX_ATOMIC_NUM * 2 + 10, )
assert s[0] == len(atom_array)
assert s[1] == adj.sum()
assert s[2] == 1
assert s[3] == 1
assert s[4] == 0
assert s[5] == 0
assert pytest.approx(s[6], 1 * 2 / adj.sum()) # symmetric
assert pytest.approx(s[7], 2 * 2 / adj.sum()) # symmetric
assert s[8] == 0
assert s[9] == 0
assert s[9 + 6] == 1 # C
assert s[9 + 6] == 1 # N
assert s[9 + 7] == 1 # O
assert s[9 + MAX_ATOMIC_NUM] == 0 # other
assert s[9 + MAX_ATOMIC_NUM + 6] == 2 / len(atom_array)
assert s[9 + MAX_ATOMIC_NUM + 7] == 1 / len(atom_array)
assert s[9 + MAX_ATOMIC_NUM + 8] == 1 / len(atom_array)
assert s[9 + MAX_ATOMIC_NUM * 2] == 0
def test_padding(self, sample_molecule):
actual = common.construct_atomic_number_array(sample_molecule, 5)
assert actual.shape == (5,)
expect = numpy.array([6, 7, 6, 8, 0], dtype=numpy.int32)
numpy.testing.assert_equal(actual, expect)
def get_input_features(self, mol):
type_check_num_atoms(mol, self.max_atoms)
atom_array = construct_atomic_number_array(mol, out_size=self.out_size)
adj_array = construct_adj_matrix(mol, out_size=self.out_size)
return atom_array, adj_array
def construct_atom_id_array(mol, out_size=-1):
atomic_num_array = construct_atomic_number_array(mol, out_size)
atom_id_array = np.zeros(atomic_num_array.shape, dtype=np.float32)
for i in range(atomic_num_array.shape[0]):
atom_id_array[i] = id_to_atomic_num.index(atomic_num_array[i])
return atom_id_array
def test_padding(self, sample_molecule):
actual = common.construct_atomic_number_array(sample_molecule, 5)
assert actual.shape == (5, )
expect = numpy.array([6, 7, 6, 8, 0], dtype=numpy.int32)
numpy.testing.assert_equal(actual, expect)
def test_normal_truncated(self, sample_molecule):
with pytest.raises(ValueError):
adj = common.construct_atomic_number_array(sample_molecule,
3) # NOQA
def test_normal_truncated(self, sample_molecule):
with pytest.raises(ValueError):
adj = common.construct_atomic_number_array(sample_molecule, 3) # NOQA
