Loading deepchem/utils/molecule_graph.py 0 → 100644 +186 −0 Original line number Diff line number Diff line from typing import Optional, Sequence import numpy as np class MoleculeGraphData: """MoleculeGraphData class This data class is almost same as `torch_geometric.data.Data <https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.Data>`_. Attributes ---------- node_features : np.ndarray Node feature matrix with shape [num_nodes, num_node_features] edge_index : np.ndarray, dtype int Graph connectivity in COO format with shape [2, num_edges] targets : np.ndarray Graph or node targets with arbitrary shape edge_features : np.ndarray, optional (default None) Edge feature matrix with shape [num_edges, num_edge_features] graph_features : np.ndarray, optional (default None) Graph feature vector with shape [num_graph_features,] num_nodes : int The number of nodes in the graph num_node_features : int The number of features per node in the graph num_edges : int The number of edges in the graph num_edges_features : int, , optional (default None) The number of features per edge in the graph """ def __init__( self, node_features: np.ndarray, edge_index: np.ndarray, targets: np.ndarray, edge_features: Optional[np.ndarray] = None, graph_features: Optional[np.ndarray] = None, ): """ Parameters ---------- node_features : np.ndarray Node feature matrix with shape [num_nodes, num_node_features] edge_index : np.ndarray, dtype int Graph connectivity in COO format with shape [2, num_edges] targets : np.ndarray Graph or node targets with arbitrary shape edge_features : np.ndarray, optional (default None) Edge feature matrix with shape [num_edges, num_edge_features] graph_features : np.ndarray, optional (default None) Graph feature vector with shape [num_graph_features,] """ # validate params if isinstance(node_features, np.ndarray) is False: raise ValueError('node_features must be np.ndarray.') if isinstance(edge_index, np.ndarray) is False: raise ValueError('edge_index must be np.ndarray.') elif edge_index.dtype != np.int: raise ValueError('edge_index.dtype must be np.int') elif edge_index.shape[0] != 2: raise ValueError('The shape of edge_index is [2, num_edges].') if isinstance(targets, np.ndarray) is False: raise ValueError('y must be np.ndarray.') if edge_features is not None: if isinstance(edge_features, np.ndarray) is False: raise ValueError('edge_features must be np.ndarray or None.') elif edge_index.shape[1] != edge_features.shape[0]: raise ValueError('The first dimension of edge_features must be the \ same as the second dimension of edge_index.') if graph_features is not None and isinstance(graph_features, np.ndarray) is False: raise ValueError('graph_features must be np.ndarray or None.') self.node_features = node_features self.edge_index = edge_index self.edge_features = edge_features self.graph_features = graph_features self.targets = targets self.num_nodes, self.num_node_features = self.node_features.shape self.num_edges = edge_index.shape[1] if self.node_features is not None: self.num_edge_features = self.edge_features.shape[1] def to_pyg_data(self): """Convert to PyTorch Geometric Data instance Returns ------- torch_geometric.data.Data Molecule graph data for PyTorch Geometric """ try: import torch from torch_geometric.data import Data except ModuleNotFoundError: raise ValueError("This class requires PyTorch Geometric to be installed.") return Data( x=torch.from_numpy(self.node_features), edge_index=torch.from_numpy(self.edge_index), edge_attr=None if self.edge_features is None \ else torch.from_numpy(self.edge_features), y=torch.from_numpy(self.targets), ) class BatchMoleculeGraphData(MoleculeGraphData): """Batch MoleculeGraphData class Attributes ---------- graph_index : np.ndarray, dtype int This vector indicates which graph the node belongs with shape [num_nodes,] """ def __init__(self, molecule_graphs: Sequence[MoleculeGraphData]): """ Parameters ---------- molecule_graphs : Iterable[MoleculeGraphData] List of MoleculeGraphData """ # stack features and targets batch_node_features = np.vstack( [graph.node_features for graph in molecule_graphs]) batch_targets = np.vstack([graph.targets for graph in molecule_graphs]) # before stacking edge_features or graph_features, # we should check whether these are None or not if molecule_graphs[0].edge_features is not None: batch_edge_features = np.vstack( [graph.edge_features for graph in molecule_graphs]) else: batch_edge_features = None if molecule_graphs[0].graph_features is not None: batch_graph_features = np.vstack( [graph.graph_features for graph in molecule_graphs]) else: batch_graph_features = None # create new edge index num_nodes_list = [graph.num_nodes for graph in molecule_graphs] batch_edge_index = np.hstack( [graph.edge_index + prev_num_node for prev_num_node, graph \ in zip([0] + num_nodes_list[:-1], molecule_graphs)] ).astype(int) # graph_index indicates which nodes belong to which graph graph_index = [] for i, num_nodes in enumerate(num_nodes_list): graph_index.extend([i] * num_nodes) self.graph_index = np.array(graph_index, dtype=int) super().__init__( node_features=batch_node_features, edge_index=batch_edge_index, targets=batch_targets, edge_features=batch_edge_features, graph_features=batch_graph_features, ) @staticmethod # type: ignore def to_pyg_data(molecule_graphs: Sequence[MoleculeGraphData]): """Convert to PyTorch Geometric Batch instance Parameters ---------- molecule_graphs : Iterable[MoleculeGraphData] List of MoleculeGraphData Returns ------- torch_geometric.data.Batch Batch data of molecule graph for PyTorch Geometric """ try: from torch_geometric.data import Batch except ModuleNotFoundError: raise ValueError( "This class requires PyTorch Geometric to be installed.") data_list = [mol_graph.to_pyg_data() for mol_graph in molecule_graphs] return Batch.from_data_list(data_list=data_list) deepchem/utils/test/test_molecule_graph.py 0 → 100644 +93 −0 Original line number Diff line number Diff line import unittest import pytest import numpy as np from deepchem.utils.molecule_graph import MoleculeGraphData, BatchMoleculeGraphData class TestMoleculeGraph(unittest.TestCase): def test_molecule_graph_data(self): num_nodes, num_node_features = 4, 32 num_edges, num_edge_features = 6, 32 node_features = np.random.random_sample((num_nodes, num_node_features)) edge_features = np.random.random_sample((num_edges, num_edge_features)) targets = np.random.random_sample(5) edge_index = np.array([ [0, 1, 2, 2, 3, 4], [1, 2, 0, 3, 4, 0], ]) graph_features = None mol_graph = MoleculeGraphData( node_features=node_features, edge_index=edge_index, targets=targets, edge_features=edge_features, graph_features=graph_features) assert mol_graph.num_nodes == num_nodes assert mol_graph.num_node_features == num_node_features assert mol_graph.num_edges == num_edges assert mol_graph.num_edge_features == num_edge_features assert mol_graph.targets.shape == (5,) def test_invalid_molecule_graph_data(self): with pytest.raises(ValueError): invalid_node_features_type = list(np.random.random_sample((5, 5))) edge_index = np.array([ [0, 1, 2, 2, 3, 4], [1, 2, 0, 3, 4, 0], ]) targets = np.random.random_sample(5) mol_graph = MoleculeGraphData( node_features=invalid_node_features_type, edge_index=edge_index, targets=targets, ) with pytest.raises(ValueError): node_features = np.random.random_sample((5, 5)) invalid_edge_index_shape = np.array([ [0, 1, 2, 2, 3, 4], [1, 2, 0, 3, 4, 0], [2, 2, 1, 4, 0, 3], ]) targets = np.random.random_sample(5) mol_graph = MoleculeGraphData( node_features=node_features, edge_index=invalid_edge_index_shape, targets=targets, ) with pytest.raises(TypeError): node_features = np.random.random_sample((5, 5)) mol_graph = MoleculeGraphData(node_features=node_features) def test_batch_molecule_graph_data(self): num_nodes_list, num_edge_list = [3, 4, 5], [2, 4, 5] num_node_features, num_edge_features = 32, 32 edge_index_list = [ np.array([[0, 1], [1, 2]]), np.array([[0, 1, 2, 3], [1, 2, 0, 2]]), np.array([[0, 1, 2, 3, 4], [1, 2, 3, 4, 5]]) ] targets = np.random.random_sample(5) molecule_graphs = [ MoleculeGraphData( node_features=np.random.random_sample((num_nodes_list[i], num_node_features)), edge_index=edge_index_list[i], targets=targets, edge_features=np.random.random_sample((num_edge_list[i], num_edge_features)), graph_features=None) for i in range(len(num_edge_list)) ] batch = BatchMoleculeGraphData(molecule_graphs) assert batch.num_nodes == sum(num_nodes_list) assert batch.num_node_features == num_node_features assert batch.num_edges == sum(num_edge_list) assert batch.num_edge_features == num_edge_features assert batch.targets.shape == (3, 5) assert batch.graph_index.shape == (sum(num_nodes_list),) docs/dataclasses.rst +6 −0 Original line number Diff line number Diff line Loading @@ -18,3 +18,9 @@ These classes document the data classes for graph convolutions. We plan to simpl .. autoclass:: deepchem.feat.mol_graphs.WeaveMol :members: .. autoclass:: deepchem.utils.molecule_graph.MoleculeGraphData :members: .. autoclass:: deepchem.utils.molecule_graph.BatchMoleculeGraphData :members: docs/requirements.rst +5 −0 Original line number Diff line number Diff line Loading @@ -70,6 +70,10 @@ DeepChem has a number of "soft" requirements. | | | | | | | | +--------------------------------+---------------+---------------------------------------------------+ | `PyTorch Geometric`_ | Not Testing | :code:`dc.utils.molecule_graph` | | | | | | | | | +--------------------------------+---------------+---------------------------------------------------+ | `RDKit`_ | 2020.03.4 | Many modules | | | | (we recommend you to instal) | | | | | Loading Loading @@ -108,6 +112,7 @@ DeepChem has a number of "soft" requirements. .. _`pyGPGO`: https://pygpgo.readthedocs.io/en/latest/ .. _`Pymatgen`: https://pymatgen.org/ .. _`PyTorch`: https://pytorch.org/ .. _`PyTorch Geometric`: https://pytorch-geometric.readthedocs.io/en/latest/ .. _`RDKit`: http://www.rdkit.org/ocs/Install.html .. _`simdna`: https://github.com/kundajelab/simdna .. _`Tensorflow Probability`: https://www.tensorflow.org/probability Loading Loading
deepchem/utils/molecule_graph.py 0 → 100644 +186 −0 Original line number Diff line number Diff line from typing import Optional, Sequence import numpy as np class MoleculeGraphData: """MoleculeGraphData class This data class is almost same as `torch_geometric.data.Data <https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.Data>`_. Attributes ---------- node_features : np.ndarray Node feature matrix with shape [num_nodes, num_node_features] edge_index : np.ndarray, dtype int Graph connectivity in COO format with shape [2, num_edges] targets : np.ndarray Graph or node targets with arbitrary shape edge_features : np.ndarray, optional (default None) Edge feature matrix with shape [num_edges, num_edge_features] graph_features : np.ndarray, optional (default None) Graph feature vector with shape [num_graph_features,] num_nodes : int The number of nodes in the graph num_node_features : int The number of features per node in the graph num_edges : int The number of edges in the graph num_edges_features : int, , optional (default None) The number of features per edge in the graph """ def __init__( self, node_features: np.ndarray, edge_index: np.ndarray, targets: np.ndarray, edge_features: Optional[np.ndarray] = None, graph_features: Optional[np.ndarray] = None, ): """ Parameters ---------- node_features : np.ndarray Node feature matrix with shape [num_nodes, num_node_features] edge_index : np.ndarray, dtype int Graph connectivity in COO format with shape [2, num_edges] targets : np.ndarray Graph or node targets with arbitrary shape edge_features : np.ndarray, optional (default None) Edge feature matrix with shape [num_edges, num_edge_features] graph_features : np.ndarray, optional (default None) Graph feature vector with shape [num_graph_features,] """ # validate params if isinstance(node_features, np.ndarray) is False: raise ValueError('node_features must be np.ndarray.') if isinstance(edge_index, np.ndarray) is False: raise ValueError('edge_index must be np.ndarray.') elif edge_index.dtype != np.int: raise ValueError('edge_index.dtype must be np.int') elif edge_index.shape[0] != 2: raise ValueError('The shape of edge_index is [2, num_edges].') if isinstance(targets, np.ndarray) is False: raise ValueError('y must be np.ndarray.') if edge_features is not None: if isinstance(edge_features, np.ndarray) is False: raise ValueError('edge_features must be np.ndarray or None.') elif edge_index.shape[1] != edge_features.shape[0]: raise ValueError('The first dimension of edge_features must be the \ same as the second dimension of edge_index.') if graph_features is not None and isinstance(graph_features, np.ndarray) is False: raise ValueError('graph_features must be np.ndarray or None.') self.node_features = node_features self.edge_index = edge_index self.edge_features = edge_features self.graph_features = graph_features self.targets = targets self.num_nodes, self.num_node_features = self.node_features.shape self.num_edges = edge_index.shape[1] if self.node_features is not None: self.num_edge_features = self.edge_features.shape[1] def to_pyg_data(self): """Convert to PyTorch Geometric Data instance Returns ------- torch_geometric.data.Data Molecule graph data for PyTorch Geometric """ try: import torch from torch_geometric.data import Data except ModuleNotFoundError: raise ValueError("This class requires PyTorch Geometric to be installed.") return Data( x=torch.from_numpy(self.node_features), edge_index=torch.from_numpy(self.edge_index), edge_attr=None if self.edge_features is None \ else torch.from_numpy(self.edge_features), y=torch.from_numpy(self.targets), ) class BatchMoleculeGraphData(MoleculeGraphData): """Batch MoleculeGraphData class Attributes ---------- graph_index : np.ndarray, dtype int This vector indicates which graph the node belongs with shape [num_nodes,] """ def __init__(self, molecule_graphs: Sequence[MoleculeGraphData]): """ Parameters ---------- molecule_graphs : Iterable[MoleculeGraphData] List of MoleculeGraphData """ # stack features and targets batch_node_features = np.vstack( [graph.node_features for graph in molecule_graphs]) batch_targets = np.vstack([graph.targets for graph in molecule_graphs]) # before stacking edge_features or graph_features, # we should check whether these are None or not if molecule_graphs[0].edge_features is not None: batch_edge_features = np.vstack( [graph.edge_features for graph in molecule_graphs]) else: batch_edge_features = None if molecule_graphs[0].graph_features is not None: batch_graph_features = np.vstack( [graph.graph_features for graph in molecule_graphs]) else: batch_graph_features = None # create new edge index num_nodes_list = [graph.num_nodes for graph in molecule_graphs] batch_edge_index = np.hstack( [graph.edge_index + prev_num_node for prev_num_node, graph \ in zip([0] + num_nodes_list[:-1], molecule_graphs)] ).astype(int) # graph_index indicates which nodes belong to which graph graph_index = [] for i, num_nodes in enumerate(num_nodes_list): graph_index.extend([i] * num_nodes) self.graph_index = np.array(graph_index, dtype=int) super().__init__( node_features=batch_node_features, edge_index=batch_edge_index, targets=batch_targets, edge_features=batch_edge_features, graph_features=batch_graph_features, ) @staticmethod # type: ignore def to_pyg_data(molecule_graphs: Sequence[MoleculeGraphData]): """Convert to PyTorch Geometric Batch instance Parameters ---------- molecule_graphs : Iterable[MoleculeGraphData] List of MoleculeGraphData Returns ------- torch_geometric.data.Batch Batch data of molecule graph for PyTorch Geometric """ try: from torch_geometric.data import Batch except ModuleNotFoundError: raise ValueError( "This class requires PyTorch Geometric to be installed.") data_list = [mol_graph.to_pyg_data() for mol_graph in molecule_graphs] return Batch.from_data_list(data_list=data_list)
deepchem/utils/test/test_molecule_graph.py 0 → 100644 +93 −0 Original line number Diff line number Diff line import unittest import pytest import numpy as np from deepchem.utils.molecule_graph import MoleculeGraphData, BatchMoleculeGraphData class TestMoleculeGraph(unittest.TestCase): def test_molecule_graph_data(self): num_nodes, num_node_features = 4, 32 num_edges, num_edge_features = 6, 32 node_features = np.random.random_sample((num_nodes, num_node_features)) edge_features = np.random.random_sample((num_edges, num_edge_features)) targets = np.random.random_sample(5) edge_index = np.array([ [0, 1, 2, 2, 3, 4], [1, 2, 0, 3, 4, 0], ]) graph_features = None mol_graph = MoleculeGraphData( node_features=node_features, edge_index=edge_index, targets=targets, edge_features=edge_features, graph_features=graph_features) assert mol_graph.num_nodes == num_nodes assert mol_graph.num_node_features == num_node_features assert mol_graph.num_edges == num_edges assert mol_graph.num_edge_features == num_edge_features assert mol_graph.targets.shape == (5,) def test_invalid_molecule_graph_data(self): with pytest.raises(ValueError): invalid_node_features_type = list(np.random.random_sample((5, 5))) edge_index = np.array([ [0, 1, 2, 2, 3, 4], [1, 2, 0, 3, 4, 0], ]) targets = np.random.random_sample(5) mol_graph = MoleculeGraphData( node_features=invalid_node_features_type, edge_index=edge_index, targets=targets, ) with pytest.raises(ValueError): node_features = np.random.random_sample((5, 5)) invalid_edge_index_shape = np.array([ [0, 1, 2, 2, 3, 4], [1, 2, 0, 3, 4, 0], [2, 2, 1, 4, 0, 3], ]) targets = np.random.random_sample(5) mol_graph = MoleculeGraphData( node_features=node_features, edge_index=invalid_edge_index_shape, targets=targets, ) with pytest.raises(TypeError): node_features = np.random.random_sample((5, 5)) mol_graph = MoleculeGraphData(node_features=node_features) def test_batch_molecule_graph_data(self): num_nodes_list, num_edge_list = [3, 4, 5], [2, 4, 5] num_node_features, num_edge_features = 32, 32 edge_index_list = [ np.array([[0, 1], [1, 2]]), np.array([[0, 1, 2, 3], [1, 2, 0, 2]]), np.array([[0, 1, 2, 3, 4], [1, 2, 3, 4, 5]]) ] targets = np.random.random_sample(5) molecule_graphs = [ MoleculeGraphData( node_features=np.random.random_sample((num_nodes_list[i], num_node_features)), edge_index=edge_index_list[i], targets=targets, edge_features=np.random.random_sample((num_edge_list[i], num_edge_features)), graph_features=None) for i in range(len(num_edge_list)) ] batch = BatchMoleculeGraphData(molecule_graphs) assert batch.num_nodes == sum(num_nodes_list) assert batch.num_node_features == num_node_features assert batch.num_edges == sum(num_edge_list) assert batch.num_edge_features == num_edge_features assert batch.targets.shape == (3, 5) assert batch.graph_index.shape == (sum(num_nodes_list),)
docs/dataclasses.rst +6 −0 Original line number Diff line number Diff line Loading @@ -18,3 +18,9 @@ These classes document the data classes for graph convolutions. We plan to simpl .. autoclass:: deepchem.feat.mol_graphs.WeaveMol :members: .. autoclass:: deepchem.utils.molecule_graph.MoleculeGraphData :members: .. autoclass:: deepchem.utils.molecule_graph.BatchMoleculeGraphData :members:
docs/requirements.rst +5 −0 Original line number Diff line number Diff line Loading @@ -70,6 +70,10 @@ DeepChem has a number of "soft" requirements. | | | | | | | | +--------------------------------+---------------+---------------------------------------------------+ | `PyTorch Geometric`_ | Not Testing | :code:`dc.utils.molecule_graph` | | | | | | | | | +--------------------------------+---------------+---------------------------------------------------+ | `RDKit`_ | 2020.03.4 | Many modules | | | | (we recommend you to instal) | | | | | Loading Loading @@ -108,6 +112,7 @@ DeepChem has a number of "soft" requirements. .. _`pyGPGO`: https://pygpgo.readthedocs.io/en/latest/ .. _`Pymatgen`: https://pymatgen.org/ .. _`PyTorch`: https://pytorch.org/ .. _`PyTorch Geometric`: https://pytorch-geometric.readthedocs.io/en/latest/ .. _`RDKit`: http://www.rdkit.org/ocs/Install.html .. _`simdna`: https://github.com/kundajelab/simdna .. _`Tensorflow Probability`: https://www.tensorflow.org/probability Loading
