Removed some metrics

molgenevalmetric.py

@@ -81,30 +81,10 @@ class molgenevalmetric(evaluate.Metric):
         
         Results = metrics.get_all_metrics(gen = generated_smiles, train= train_smiles)
         
-        # evaluator = Evaluator(name = 'Diversity')
-        # Diversity = evaluator(generated_smiles)
-
-        # Results = {}
-        
         evaluator = Evaluator(name = 'KL_Divergence')
         KL_Divergence = evaluator(generated_smiles, train_smiles)
-        
-        # evaluator = Evaluator(name = 'FCD_Distance')
-        # FCD_Distance = evaluator(generated_smiles, train_smiles)
-        
-        # evaluator = Evaluator(name = 'Novelty')
-        # Novelty = evaluator(generated_smiles, train_smiles)
-        
-        # evaluator = Evaluator(name = 'Validity')
-        # Validity = evaluator(generated_smiles)
-
-                
         Results.update({
-            # "PyTDC_Diversity": Diversity,
             "KL_Divergence": KL_Divergence,
-            # "PyTDC_Validity": Validity,FCD_Distance": FCD_Distance,
-            # "PyTDC_Novelty": Novelty,
-            # "PyTDC_
         })
 
 
@@ -114,26 +94,21 @@ class molgenevalmetric(evaluate.Metric):
         'Median', 'Isomers', 'Valsartan_SMARTS', 'Hop'
         ]
     
-        # Iterate through each oracle and compute its score
         for oracle_name in oracle_list:
             oracle = Oracle(name=oracle_name)
             if oracle_name in ['Rediscovery', 'MPO', 'Similarity', 'Median', 'Isomers', 'Hop']:
-                # Assuming these oracles return a dictionary where values are lists of scores
                 score = oracle(generated_smiles)
                 if isinstance(score, dict):
-                    # Convert lists of scores to average score for these specific metrics
                     score = {key: sum(values)/len(values) for key, values in score.items()}
             else:
-                # Assuming other oracles return a list of scores
                 score = oracle(generated_smiles)
                 if isinstance(score, list):
-                    # Convert list of scores to average score
                     score = sum(score) / len(score)
             
             Results.update({f"{oracle_name}": score})
 
         keys_to_remove = ["FCD/TestSF", "SNN/TestSF", "Frag/TestSF", "Scaf/TestSF"]
         for key in keys_to_remove:
-            result['results'].pop(key, None)    
+            Results.pop(key, None)    
 
         return {"results": Results}