fix: fixed enzyme optmization with Kcat fitness function

Signed-off-by: yvesnana <[email protected]>

src/gt4sd/frameworks/enzeptional/core.py

@@ -347,24 +347,24 @@ def get_mutations(
 
 class Scorer:
     def __init__(self, scorer_filepath: str, scaler_filepath: Optional[str] = None):
-        '''Initialize the scorer.
+        """Initialize the scorer.
         Args:
             scorer_filepath (str): Pickled scorer filepath.
-            scaler_filepath (Optional[str], optional): Pickled scaler filepath. Defaults to None.   
-        '''
+            scaler_filepath (Optional[str], optional): Pickled scaler filepath. Defaults to None.
+        """
         self.scorer_filepath = scorer_filepath
         self.scorer = load(scorer_filepath)
         if scaler_filepath is not None:
             self.scaler = load(scaler_filepath)
 
     def predict_proba(self, feature_vector):
         return self.scorer.predict_proba(feature_vector)
-    
+
     def predict(self, feature_vector):
         if self.scaler is not None:
             feature_vector = self.scaler.transform(feature_vector)
         return self.scorer.predict(xgb.DMatrix(feature_vector))
-    
+
 
 class EnzymeOptimizer:
     """
@@ -623,16 +623,16 @@ def score_sequence(self, sequence: str) -> Dict[str, Any]:
         ]
         combined_embedding = np.concatenate(ordered_embeddings)
         combined_embedding = combined_embedding.reshape(1, -1)
-    
+
         if self.use_xgboost_scorer:
             if self.scaler is not None:
                 combined_embedding = self.scaler.transform(combined_embedding)
             score = self.scorer.predict(xgb.DMatrix(combined_embedding))[0]
         else:
             score = self.scorer.predict_proba(combined_embedding)[0][1]
-    
+
         return {"sequence": sequence, "score": score}
-    
+
     def score_sequences(self, sequences: List[str]) -> List[Dict[str, float]]:
         """
         Scores a list of protein sequences.
@@ -656,7 +656,7 @@ def score_sequences(self, sequences: List[str]) -> List[Dict[str, float]]:
             ]
             ordered_embeddings = [
                 embeddings[self.concat_order.index(item)] for item in self.concat_order
-                ]
+            ]
             combined_embedding = np.concatenate(ordered_embeddings)
             combined_embedding = combined_embedding.reshape(1, -1)