reformat with black

Author: sfluegel05

chebai_graph/preprocessing/datasets/__init__.py

@@ -12,7 +12,7 @@
     ChEBI50_WFGE_WGN_GraphProp,
     ChEBI50GraphData,
     ChEBI50GraphProperties,
-    ChEBI100GraphProperties
+    ChEBI100GraphProperties,
 )
 from .pubchem import PubChemGraphProperties
 

chebai_graph/preprocessing/datasets/chebi.py

@@ -282,7 +282,9 @@ def _merge_props_into_base(self, row: pd.Series) -> GeomData:
             molecule_attr=molecule_attr,
         )
 
-    def load_processed_data(self, kind: Optional[str] = None, filename: Optional[str] = None) -> list[dict]:
+    def load_processed_data(
+        self, kind: Optional[str] = None, filename: Optional[str] = None
+    ) -> list[dict]:
         """
         Load dataset and merge cached properties into base features.
 
@@ -380,7 +382,9 @@ def __init__(self, properties=None, transform=None, **kwargs):
             f"Data module uses these properties (ordered): {', '.join([str(p) for p in self.properties])}",
         )
 
-    def load_processed_data(self, kind: Optional[str] = None, filename: Optional[str] = None) -> list[dict]:
+    def load_processed_data(
+        self, kind: Optional[str] = None, filename: Optional[str] = None
+    ) -> list[dict]:
         """
         Load dataset and merge cached properties into base features.
 

chebai_graph/preprocessing/property_encoder.py

@@ -19,7 +19,7 @@ class PropertyEncoder(abc.ABC):
     def __init__(self, property, eval=False, **kwargs) -> None:
         self.property = property
         self._encoding_length: int = 1
-        self.eval = eval # if True, do not update cache (for index encoder)
+        self.eval = eval  # if True, do not update cache (for index encoder)
 
     @property
     def name(self) -> str:
@@ -150,11 +150,11 @@ def encode(self, token: str | None) -> torch.Tensor:
         if token is None:
             self._count_for_unk_token += 1
             return torch.tensor([self._unk_token_idx])
-        
+
         if self.eval and str(token) not in self.cache:
             self._count_for_unk_token += 1
             return torch.tensor([self._unk_token_idx])
-        
+
         if str(token) not in self.cache:
             self.cache[str(token)] = len(self.cache)
         return torch.tensor([self.cache[str(token)] + self.offset])