Loading deepchem/feat/molecule_graph.py 0 → 100644 +109 −0 Original line number Diff line number Diff line from typing import Optional, List import numpy as np class MoleculeGraphData(object): """Molecule Graph Data class for sparse pattern""" 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 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.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, self.num_edge_features = None, None if self.node_features is not None: self.num_edges, self.num_edge_features = self.edge_features.shape class BatchMoleculeGraphData(MoleculeGraphData): """Batch Data class for sparse pattern""" def __init__(self, molecule_graph_list: List[MoleculeGraphData]): """ Parameters ---------- molecule_graph_list : List[MoleculeGraphData] List of MoleculeGraphData """ # stack features and targets batch_node_features = np.vstack( [graph.node_features for graph in molecule_graph_list]) batch_targets = np.vstack([graph.targets for graph in molecule_graph_list]) # before stacking edge_features or graph_features, # we should check whether these are None or not if molecule_graph_list[0].edge_features is not None: batch_edge_features = np.vstack( [graph.edge_features for graph in molecule_graph_list]) else: batch_edge_features = None if molecule_graph_list[0].graph_features is not None: batch_graph_features = np.vstack( [graph.graph_features for graph in molecule_graph_list]) else: batch_graph_features = None # create new edge index num_nodes_list = [graph.num_nodes for graph in molecule_graph_list] batch_edge_index = np.hstack( [graph.edge_index + prev_num_node for prev_num_node, graph \ in zip([0] + num_nodes_list[:-1], molecule_graph_list)] ).astype(int) # graph idx indicates which nodes belong to which graph graph_idx = [] for i, num_nodes in enumerate(num_nodes_list): graph_idx.extend([i] * num_nodes) self.graph_idx = np.array(graph_idx, 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, ) deepchem/utils/molecule_graph.pydeleted 100644 → 0 +0 −59 Original line number Diff line number Diff line from typing import Optional import numpy as np class MoleculeGraphData(object): """Molecule Graph Data class for sparse pattern""" def __init__(self, node_features: Optional[np.ndarray] = None, edge_index: Optional[np.ndarray] = None, edge_features: Optional[np.ndarray] = None, graph_features: Optional[np.ndarray] = None, targets : Optional[np.ndarray] = None,): """ Parameters ---------- node_features : np.ndarray, optional (default None) Node feature matrix with shape [num_nodes, num_node_features] edge_index : np.ndarray, optional (default None) Graph connectivity in COO format with shape [2, num_edges] 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,] targets : np.ndarray, optional (default None) Graph or node targets with arbitrary shape """ super(MoleculeGraphData, self).__init__() # validate params if node_features is not None and isinstance(node_features, np.ndarray) is False: raise ValueError('node_features must be np.ndarray or None.') if edge_index is not None: if isinstance(edge_index, np.ndarray) is False: raise ValueError('edge_index must be np.ndarray or None.') elif edge_index.shape[0] != 2: raise ValueError('The shape of edge_index is [2, num_edges].') if edge_features is not None: if instance(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.') if targets is not None and isinstance(targets, np.ndarray) is False: raise ValueError('y 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 = None, None self.num_edges, self.num_edge_features = None, None if self.node_features is not None: self.num_nodes, self.num_node_features = self.node_features.shape if self.node_features is not None: self.num_edges, self.num_edge_features = self.edge_features.shape Loading
deepchem/feat/molecule_graph.py 0 → 100644 +109 −0 Original line number Diff line number Diff line from typing import Optional, List import numpy as np class MoleculeGraphData(object): """Molecule Graph Data class for sparse pattern""" 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 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.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, self.num_edge_features = None, None if self.node_features is not None: self.num_edges, self.num_edge_features = self.edge_features.shape class BatchMoleculeGraphData(MoleculeGraphData): """Batch Data class for sparse pattern""" def __init__(self, molecule_graph_list: List[MoleculeGraphData]): """ Parameters ---------- molecule_graph_list : List[MoleculeGraphData] List of MoleculeGraphData """ # stack features and targets batch_node_features = np.vstack( [graph.node_features for graph in molecule_graph_list]) batch_targets = np.vstack([graph.targets for graph in molecule_graph_list]) # before stacking edge_features or graph_features, # we should check whether these are None or not if molecule_graph_list[0].edge_features is not None: batch_edge_features = np.vstack( [graph.edge_features for graph in molecule_graph_list]) else: batch_edge_features = None if molecule_graph_list[0].graph_features is not None: batch_graph_features = np.vstack( [graph.graph_features for graph in molecule_graph_list]) else: batch_graph_features = None # create new edge index num_nodes_list = [graph.num_nodes for graph in molecule_graph_list] batch_edge_index = np.hstack( [graph.edge_index + prev_num_node for prev_num_node, graph \ in zip([0] + num_nodes_list[:-1], molecule_graph_list)] ).astype(int) # graph idx indicates which nodes belong to which graph graph_idx = [] for i, num_nodes in enumerate(num_nodes_list): graph_idx.extend([i] * num_nodes) self.graph_idx = np.array(graph_idx, 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, )
deepchem/utils/molecule_graph.pydeleted 100644 → 0 +0 −59 Original line number Diff line number Diff line from typing import Optional import numpy as np class MoleculeGraphData(object): """Molecule Graph Data class for sparse pattern""" def __init__(self, node_features: Optional[np.ndarray] = None, edge_index: Optional[np.ndarray] = None, edge_features: Optional[np.ndarray] = None, graph_features: Optional[np.ndarray] = None, targets : Optional[np.ndarray] = None,): """ Parameters ---------- node_features : np.ndarray, optional (default None) Node feature matrix with shape [num_nodes, num_node_features] edge_index : np.ndarray, optional (default None) Graph connectivity in COO format with shape [2, num_edges] 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,] targets : np.ndarray, optional (default None) Graph or node targets with arbitrary shape """ super(MoleculeGraphData, self).__init__() # validate params if node_features is not None and isinstance(node_features, np.ndarray) is False: raise ValueError('node_features must be np.ndarray or None.') if edge_index is not None: if isinstance(edge_index, np.ndarray) is False: raise ValueError('edge_index must be np.ndarray or None.') elif edge_index.shape[0] != 2: raise ValueError('The shape of edge_index is [2, num_edges].') if edge_features is not None: if instance(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.') if targets is not None and isinstance(targets, np.ndarray) is False: raise ValueError('y 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 = None, None self.num_edges, self.num_edge_features = None, None if self.node_features is not None: self.num_nodes, self.num_node_features = self.node_features.shape if self.node_features is not None: self.num_edges, self.num_edge_features = self.edge_features.shape
