Code change

my_metric.py

@@ -94,51 +94,51 @@ class my_metric(evaluate.Metric):
         evaluator = Evaluator(name = 'Validity')
         Validity = evaluator(generated_smiles)
 
-        oracle = Oracle(name = 'QED')
-        QED = oracle(generated_smiles)
+        # oracle = Oracle(name = 'QED')
+        # QED = oracle(generated_smiles)
 
-        oracle = Oracle(name = 'SA')
-        SA = oracle(generated_smiles)
+        # oracle = Oracle(name = 'SA')
+        # SA = oracle(generated_smiles)
 
-        oracle = Oracle(name = 'MPO')
-        MPO = oracle(generated_smiles)
-        MPO = {key: sum(values)/len(values) for key, values in MPO.items()}
+        # oracle = Oracle(name = 'MPO')
+        # MPO = oracle(generated_smiles)
+        # MPO = {key: sum(values)/len(values) for key, values in MPO.items()}
 
         oracle_list = [
         'QED', 'SA', 'MPO', '3pbl_docking', 'GSK3B', 'JNK3',
         'DRD2', 'LogP', 'Rediscovery', 'Similarity',
         '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"PyTDC_{oracle_name}": score})
+        # 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"PyTDC_{oracle_name}": score})
     
         
-        Results.update({
-            "PyTDC_Diversity": Diversity,
-            "PyTDC_KL_Divergence": KL_Divergence,
-            "PyTDC_FCD_Distance": FCD_Distance,
-            "PyTDC_Novelty": Novelty,
-            "PyTDC_Validity": Validity,
-
-            "PyTDC_QED": sum(QED)/len(QED),
-            "PyTDC_SA": sum(SA)/len(SA),
-            "PyTDC_MPO": MPO
-        })
+            # Results.update({
+            #     "PyTDC_Diversity": Diversity,
+            #     "PyTDC_KL_Divergence": KL_Divergence,
+            #     "PyTDC_FCD_Distance": FCD_Distance,
+            #     "PyTDC_Novelty": Novelty,
+            #     "PyTDC_Validity": Validity,
+
+            #     "PyTDC_QED": sum(QED)/len(QED),
+            #     "PyTDC_SA": sum(SA)/len(SA),
+            #     "PyTDC_MPO": MPO
+            # })
 
         return {"results": Results}