Commit c94fa0b1 authored by nd-02110114's avatar nd-02110114
Browse files

🐛 go back to original implementaion

parent 88dd25b5

deepchem/feat/base_classes.py

+11 −13
Original line number Diff line number Diff line
@@ -144,10 +144,10 @@ class MolecularFeaturizer(Featurizer): 
  molecule.



  The defining feature of a `MolecularFeaturizer` is that it

  uses SMILES strings and RDKIT molecule objects to represent

  uses SMILES strings and RDKit molecule objects to represent

  small molecules. All other featurizers which are subclasses of

  this class should plan to process input which comes as smiles

  strings or RDKIT molecules.

  strings or RDKit molecules.



  Child classes need to implement the _featurize method for

  calculating features for a single molecule.
@@ -157,7 +157,7 @@ class MolecularFeaturizer(Featurizer): 
  The subclasses of this class require RDKit to be installed.

  """



  def featurize(self, molecules, log_every_n=1000, canonical=True):

  def featurize(self, molecules, log_every_n=1000):

    """Calculate features for molecules.



    Parameters
@@ -167,8 +167,6 @@ class MolecularFeaturizer(Featurizer): 
      strings.

    log_every_n: int, default 1000

      Logging messages reported every `log_every_n` samples.

    canonical: bool, default True

      Whether to use a canonical order of atoms returned by RDKit



    Returns

    -------
@@ -177,6 +175,8 @@ class MolecularFeaturizer(Featurizer): 
    """

    try:

      from rdkit import Chem

      from rdkit.Chem import rdmolfiles

      from rdkit.Chem import rdmolops

      from rdkit.Chem.rdchem import Mol

    except ModuleNotFoundError:

      raise ValueError("This class requires RDKit to be installed.")
@@ -194,13 +194,11 @@ class MolecularFeaturizer(Featurizer): 
        logger.info("Featurizing datapoint %i" % i)

      try:

        if isinstance(mol, str):

          # mol must be a SMILES string so parse

          # mol must be a RDKit Mol object, so parse a SMILES

          mol = Chem.MolFromSmiles(mol)

        # canonicalize

        if canonical:

          canonical_smiles = Chem.MolToSmiles(mol)

          mol = Chem.MolFromSmiles(canonical_smiles)



          # SMILES is unique, so set a canonical order of atoms

          new_order = rdmolfiles.CanonicalRankAtoms(mol)

          mol = rdmolops.RenumberAtoms(mol, new_order)

        features.append(self._featurize(mol))

      except:

        logger.warning(

deepchem/feat/graph_data.py

+56 −23
Original line number Diff line number Diff line
@@ -16,8 +16,8 @@ class GraphData: 
    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,]

  node_pos_features: np.ndarray, optional (default None)

    Node position matrix with shape [num_nodes, num_dimensions].

  num_nodes: int

    The number of nodes in the graph

  num_node_features: int
@@ -40,7 +40,7 @@ class GraphData: 
      node_features: np.ndarray,

      edge_index: np.ndarray,

      edge_features: Optional[np.ndarray] = None,

      graph_features: Optional[np.ndarray] = None,

      node_pos_features: Optional[np.ndarray] = None,

  ):

    """

    Parameters
@@ -51,8 +51,8 @@ class GraphData: 
      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,]

    node_pos_features: np.ndarray, optional (default None)

      Node position matrix with shape [num_nodes, num_dimensions].

    """

    # validate params

    if isinstance(node_features, np.ndarray) is False:
@@ -74,14 +74,18 @@ class GraphData: 
        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 node_pos_features is not None:

      if isinstance(node_pos_features, np.ndarray) is False:

        raise ValueError('node_pos_features must be np.ndarray or None.')

      elif node_pos_features.shape[0] != node_features.shape[0]:

        raise ValueError(

            'The length of node_pos_features must be the same as the \

                          length of node_features.')



    self.node_features = node_features

    self.edge_index = edge_index

    self.edge_features = edge_features

    self.graph_features = graph_features

    self.node_pos_features = node_pos_features

    self.num_nodes, self.num_node_features = self.node_features.shape

    self.num_edges = edge_index.shape[1]

    if self.edge_features is not None:
@@ -106,12 +110,18 @@ class GraphData: 
      raise ValueError(

          "This function requires PyTorch Geometric to be installed.")



    edge_features = self.edge_features

    if edge_features is not None:

      edge_features = torch.from_numpy(self.edge_features).float()

    node_pos_features = self.node_pos_features

    if node_pos_features is not None:

      node_pos_features = torch.from_numpy(self.node_pos_features).float()



    return Data(

        x=torch.from_numpy(self.node_features),

        x=torch.from_numpy(self.node_features).float(),

        edge_index=torch.from_numpy(self.edge_index).long(),

        edge_attr=None

        if self.edge_features is None else torch.from_numpy(self.edge_features),

    )

        edge_attr=edge_features,

        pos=node_pos_features)



  def to_dgl_graph(self):

    """Convert to DGL graph data instance
@@ -136,10 +146,13 @@ class GraphData: 
    g.add_edges(

        torch.from_numpy(self.edge_index[0]).long(),

        torch.from_numpy(self.edge_index[1]).long())

    g.ndata['x'] = torch.from_numpy(self.node_features)

    g.ndata['x'] = torch.from_numpy(self.node_features).float()



    if self.node_pos_features is not None:

      g.ndata['pos'] = torch.from_numpy(self.node_pos_features).float()



    if self.edge_features is not None:

      g.edata['edge_attr'] = torch.from_numpy(self.edge_features)

      g.edata['edge_attr'] = torch.from_numpy(self.edge_features).float()



    return g
@@ -149,8 +162,28 @@ class BatchGraphData(GraphData): 


  Attributes

  ----------

  node_features: np.ndarray

    Concatenated node feature matrix with shape [num_nodes, num_node_features].

    `num_nodes` is total number of nodes in the batch graph.

  edge_index: np.ndarray, dtype int

    Concatenated graph connectivity in COO format with shape [2, num_edges].

    `num_edges` is total number of edges in the batch graph.

  edge_features: np.ndarray, optional (default None)

    Concatenated edge feature matrix with shape [num_edges, num_edge_features].

    `num_edges` is total number of edges in the batch graph.

  node_pos_features: np.ndarray, optional (default None)

    Concatenated node position matrix with shape [num_nodes, num_dimensions].

    `num_nodes` is total number of edges in the batch graph.

  num_nodes: int

    The number of nodes in the batch graph.

  num_node_features: int

    The number of features per node in the graph.

  num_edges: int

    The number of edges in the batch graph.

  num_edges_features: int, optional (default None)

    The number of features per edge in the graph.

  graph_index: np.ndarray, dtype int

    This vector indicates which graph the node belongs with shape [num_nodes,]

    This vector indicates which graph the node belongs with shape [num_nodes,].



  Examples

  --------
@@ -177,7 +210,7 @@ class BatchGraphData(GraphData): 
    batch_node_features = np.vstack(

        [graph.node_features for graph in graph_list])



    # before stacking edge_features or graph_features,

    # before stacking edge_features or node_pos_features,

    # we should check whether these are None or not

    if graph_list[0].edge_features is not None:

      batch_edge_features = np.vstack(
@@ -185,11 +218,11 @@ class BatchGraphData(GraphData): 
    else:

      batch_edge_features = None



    if graph_list[0].graph_features is not None:

      batch_graph_features = np.vstack(

          [graph.graph_features for graph in graph_list])

    if graph_list[0].node_pos_features is not None:

      batch_node_pos_features = np.vstack(

          [graph.node_pos_features for graph in graph_list])

    else:

      batch_graph_features = None

      batch_node_pos_features = None



    # create new edge index

    num_nodes_list = [graph.num_nodes for graph in graph_list]
@@ -208,5 +241,5 @@ class BatchGraphData(GraphData): 
        node_features=batch_node_features,

        edge_index=batch_edge_index,

        edge_features=batch_edge_features,

        graph_features=batch_graph_features,

        node_pos_features=batch_node_pos_features,

    )

deepchem/feat/tests/test_graph_data.py

+3 −3
Original line number Diff line number Diff line
@@ -15,13 +15,13 @@ class TestGraph(unittest.TestCase): 
        [0, 1, 2, 2, 3, 4],

        [1, 2, 0, 3, 4, 0],

    ])

    graph_features = None

    node_pos_features = None



    graph = GraphData(

        node_features=node_features,

        edge_index=edge_index,

        edge_features=edge_features,

        graph_features=graph_features)

        node_pos_features=node_pos_features)



    assert graph.num_nodes == num_nodes

    assert graph.num_node_features == num_node_features
@@ -92,7 +92,7 @@ class TestGraph(unittest.TestCase): 
            edge_index=edge_index_list[i],

            edge_features=np.random.random_sample((num_edge_list[i],

                                                   num_edge_features)),

            graph_features=None) for i in range(len(num_edge_list))

            node_pos_features=None) for i in range(len(num_edge_list))

    ]

    batch = BatchGraphData(graph_list)

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