def batch_graph(self) -> BatchMolGraph:
r"""
Constructs a :class:`~chemprop.features.BatchMolGraph` with the graph featurization of all the molecules.
.. note::
The :class:`~chemprop.features.BatchMolGraph` is cached in after the first time it is computed
and is simply accessed upon subsequent calls to :meth:`batch_graph`. This means that if the underlying
set of :class:`MoleculeDatapoint`\ s changes, then the returned :class:`~chemprop.features.BatchMolGraph`
will be incorrect for the underlying data.
:return: A :class:`~chemprop.features.BatchMolGraph` containing the graph featurization of all the molecules.
"""
if self._batch_graph is None:
mol_graphs = []
for d in self._data:
if d.smiles in SMILES_TO_GRAPH:
mol_graph = SMILES_TO_GRAPH[d.smiles]
else:
mol_graph = MolGraph(d.mol, d.atom_features)
if cache_graph():
SMILES_TO_GRAPH[d.smiles] = mol_graph
mol_graphs.append(mol_graph)
self._batch_graph = BatchMolGraph(mol_graphs)
return self._batch_graph
def batch_graph(self) -> List[BatchMolGraph]:
r"""
Constructs a :class:`~chemprop.features.BatchMolGraph` with the graph featurization of all the molecules.
.. note::
The :class:`~chemprop.features.BatchMolGraph` is cached in after the first time it is computed
and is simply accessed upon subsequent calls to :meth:`batch_graph`. This means that if the underlying
set of :class:`MoleculeDatapoint`\ s changes, then the returned :class:`~chemprop.features.BatchMolGraph`
will be incorrect for the underlying data.
:return: A list of :class:`~chemprop.features.BatchMolGraph` containing the graph featurization of all the
molecules in each :class:`MoleculeDatapoint`.
"""
if self._batch_graph is None:
self._batch_graph = []
mol_graphs = []
for d in self._data:
mol_graphs_list = []
for s, m in zip(d.smiles, d.mol):
if s in SMILES_TO_GRAPH:
mol_graph = SMILES_TO_GRAPH[s]
else:
if len(d.smiles) > 1 and (d.atom_features is not None
or d.bond_features
is not None):
raise NotImplementedError(
"Atom descriptors are currently only supported with one molecule "
"per input (i.e., number_of_molecules = 1).")
mol_graph = MolGraph(
m,
d.atom_features,
d.bond_features,
overwrite_default_atom_features=d.
overwrite_default_atom_features,
overwrite_default_bond_features=d.
overwrite_default_bond_features,
)
if cache_graph():
SMILES_TO_GRAPH[s] = mol_graph
mol_graphs_list.append(mol_graph)
mol_graphs.append(mol_graphs_list)
self._batch_graph = [
BatchMolGraph([g[i] for g in mol_graphs])
for i in range(len(mol_graphs[0]))
]
return self._batch_graph
def batch_graph(self, cache: bool = False) -> BatchMolGraph:
"""
Returns a BatchMolGraph with the graph featurization of the molecules.
:param cache: Whether to store the graph featurizations in the global cache.
:return: A BatchMolGraph.
"""
if self._batch_graph is None:
mol_graphs = []
for d in self._data:
if d.smiles in SMILES_TO_GRAPH:
mol_graph = SMILES_TO_GRAPH[d.smiles]
else:
mol_graph = MolGraph(d.mol)
if cache:
SMILES_TO_GRAPH[d.smiles] = mol_graph
mol_graphs.append(mol_graph)
self._batch_graph = BatchMolGraph(mol_graphs)
return self._batch_graph
