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:
atom_array = construct_atomic_number_array(mol, out_size=self.out_size)
adj_array = construct_adj_matrix(mol, out_size=self.out_size)
"""
#mol_graph,featureでやってることを書く
type_check_num_atoms(mol, self.max_atoms)
smiles = Chem.MolToSmiles(mol)
ecfp_array = array_rep_from_smiles(smiles, False)
fcfp_array = array_rep_from_smiles(smiles, True)
atom_array1 = ecfp_array['atom_features'].astype(np.float32)
atom_array2 = fcfp_array['atom_features'].astype(np.float32)
degree = construct_adj_matrix(mol, out_size=self.out_size)
self.degree_num = [int(np.sum(x)) for x in degree]
adj_array = ecfp_array['bond_features']
#import pdb;pdb.set_trace()
return atom_array1, atom_array2, adj_array
def test_normal_padding(self, sample_molecule_2):
adj = common.construct_adj_matrix(sample_molecule_2, 8)
assert adj.shape == (8, 8)
expect = numpy.array([
[1., 1., 0., 0., 0., 0., 0., 0.], [1., 1., 1., 0., 0., 0., 1., 0.],
[0., 1., 1., 1., 0., 0., 0., 0.], [0., 0., 1., 1., 1., 0., 0., 0.],
[0., 0., 0., 1., 1., 1., 0., 0.], [0., 0., 0., 0., 1., 1., 1., 0.],
[0., 1., 0., 0., 0., 1., 1., 0.], [0., 0., 0., 0., 0., 0., 0., 0.]
],
dtype=numpy.float32)
numpy.testing.assert_equal(adj, expect)
def test_normal_no_self_connection(self, sample_molecule_2):
adj = common.construct_adj_matrix(sample_molecule_2,
self_connection=False)
assert adj.shape == (7, 7)
expect = numpy.array(
[[0., 1., 0., 0., 0., 0., 0.], [1., 0., 1., 0., 0., 0., 1.],
[0., 1., 0., 1., 0., 0., 0.], [0., 0., 1., 0., 1., 0., 0.],
[0., 0., 0., 1., 0., 1., 0.], [0., 0., 0., 0., 1., 0., 1.],
[0., 1., 0., 0., 0., 1., 0.]],
dtype=numpy.float32)
numpy.testing.assert_equal(adj, expect)
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)
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)
return atom_array, adj_array
def test_normal(self, sample_molecule_2):
adj = common.construct_adj_matrix(sample_molecule_2)
assert adj.shape == (7, 7)
expect = numpy.array(
[[1., 1., 0., 0., 0., 0., 0., ],
[1., 1., 1., 0., 0., 0., 1., ],
[0., 1., 1., 1., 0., 0., 0., ],
[0., 0., 1., 1., 1., 0., 0., ],
[0., 0., 0., 1., 1., 1., 0., ],
[0., 0., 0., 0., 1., 1., 1., ],
[0., 1., 0., 0., 0., 1., 1., ]],
dtype=numpy.float32)
numpy.testing.assert_equal(adj, expect)
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 test_normal_truncated(self, sample_molecule_2):
with pytest.raises(ValueError):
adj = common.construct_adj_matrix(sample_molecule_2, 6)
def test_normal_truncated(self, sample_molecule_2):
with pytest.raises(ValueError):
adj = common.construct_adj_matrix(sample_molecule_2, 6)
