Update docstrings

ecrl · Apr 7, 2023 · 0fbe482 · 0fbe482
1 parent c47cd93
commit 0fbe482
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Showing 2 changed files with 14 additions and 15 deletions.
diff --git a/graphchem/nn/gcn.py b/graphchem/nn/gcn.py
@@ -21,26 +21,26 @@ def __init__(self, atom_vocab_size: int, bond_vocab_size: int,
         Molecule graphs are first embedded (torch.nn.Embedding), then each
         message passing operation consists of:
 
-        bond_embedding -> EdgeConv -> updated bond_embedding
-        atom_embedding + bond_embedding -> GeneralConv -> updated
+        bond_embedding > EdgeConv > updated bond_embedding
+        atom_embedding + bond_embedding > GeneralConv > updated
             atom_embedding
 
         The sum of all atom states is then passed through a series of fully-
         connected readout layers to regress on a variable:
 
-        atom_embedding -> fully-connected readout layers -> target variable
+        atom_embedding > fully-connected readout layers > target variable
 
         Args:
             atom_vocab_size (int): num features (MoleculeEncoder.vocab_sizes)
             bond_vocab_size (int): num features (MoleculeEncoder.vocab_sizes)
             output_dim (int): number of target values per compound
-            embedding_dim (int, default=64): number of embedded features for
-                atoms and bonds
-            n_messages (int, default=2): number of message passes between atoms
-            n_readout (int, default=2): number of feed-forward post-readout
+            embedding_dim (int): number of embedded features for atoms and
+                bonds
+            n_messages (int): number of message passes between atoms
+            n_readout (int): number of feed-forward post-readout
                 layers (think standard NN/MLP)
-            readout_dim (int, default=64): number of neurons in readout layers
-            dropout (float, default=0.0): random neuron dropout during training
+            readout_dim (int): number of neurons in readout layers
+            dropout (float): random neuron dropout during training
         """
 
         super(MoleculeGCN, self).__init__()

diff --git a/graphchem/preprocessing/features.py b/graphchem/preprocessing/features.py
@@ -13,7 +13,7 @@ def get_ring_size(obj: Union['rdkit.Chem.Atom', 'rdkit.Chem.Bond'],
 
     Args:
         obj (Union[rdkit.Chem.Atom, rdkit.Chem.Bond]): atom or bond
-        max_size (int, default=12): maximum ring size to consider
+        max_size (int): maximum ring size to consider
     """
 
     if not obj.IsInRing():
@@ -172,9 +172,8 @@ def encode_many(self, smiles: List[str]) -> List[Tuple['torch.tensor']]:
             smiles (List[str]): list of SMILES strings
 
         Returns:
-            List[Tuple[torch.tensor, torch.tensor, torch.tensor]]: List of:
-                (atom encoding, bond encoding, connectivity matrix) for each
-                compound
+            List[Tuple[torch.tensor]]: List of: (atom encoding, bond encoding,
+                connectivity matrix) for each compound
         """
 
         encoded_compounds = []
@@ -189,8 +188,8 @@ def encode(self, smiles: str) -> Tuple['torch.tensor']:
             smiles (str): molecule's SMILES string
 
         Returns:
-            Tuple[torch.tensor, torch.tensor, torch.tensor]: (encoded atom
-            features, encoded bond features, molecule connectivity matrix)
+            Tuple[torch.tensor]: (encoded atom features, encoded bond features,
+                molecule connectivity matrix)
         """
 
         mol = rdkit.Chem.MolFromSmiles(smiles)