Fixed error

molgenevalmetric.py

@@ -3,8 +3,8 @@ import datasets
 # import moses
 # from moses import metrics
 import pandas as pd
-# from tdc import Evaluator
-# from tdc import Oracle
+from tdc import Evaluator
+from tdc import Oracle
 # from metrics import novelty, fraction_valid, fraction_unique, SAscore, internal_diversity,fcd_metric, SYBAscore, oracles
 
 import os
@@ -314,34 +314,34 @@ def fcd_metric(gen, train, n_jobs = 8, device = 'cuda:0'):
 #     else:
 #         return None  # Or handle empty list or all failed predictions as needed
 
-# def oracles(gen, train):
-#         Result = {}
-#         # evaluator = Evaluator(name = 'KL_Divergence')
-#         # KL_Divergence = evaluator(gen, train)
+def oracles(gen, train):
+        Result = {}
+        # evaluator = Evaluator(name = 'KL_Divergence')
+        # KL_Divergence = evaluator(gen, train)
                 
-#         # Result["KL_Divergence"]: KL_Divergence
+        # Result["KL_Divergence"]: KL_Divergence
 
 
-#         oracle_list = [
-#         'QED', 'SA', 'MPO', 'GSK3B', 'JNK3',
-#         'DRD2', 'LogP', 'Rediscovery', 'Similarity',
-#         'Median', 'Isomers', 'Valsartan_SMARTS', 'Hop'
-#         ]
+        oracle_list = [
+        'QED', 'SA', 'MPO', 'GSK3B', 'JNK3',
+        'DRD2', 'LogP', 'Rediscovery', 'Similarity',
+        'Median', 'Isomers', 'Valsartan_SMARTS', 'Hop'
+        ]
     
-#         for oracle_name in oracle_list:
-#             oracle = Oracle(name=oracle_name)
-#             if oracle_name in ['Rediscovery', 'MPO', 'Similarity', 'Median', 'Isomers', 'Hop']:
-#                 score = oracle(gen)
-#                 if isinstance(score, dict):
-#                     score = {key: sum(values)/len(values) for key, values in score.items()}
-#             else:
-#                 score = oracle(gen)
-#                 if isinstance(score, list):
-#                     score = sum(score) / len(score)
+        for oracle_name in oracle_list:
+            oracle = Oracle(name=oracle_name)
+            if oracle_name in ['Rediscovery', 'MPO', 'Similarity', 'Median', 'Isomers', 'Hop']:
+                score = oracle(gen)
+                if isinstance(score, dict):
+                    score = {key: sum(values)/len(values) for key, values in score.items()}
+            else:
+                score = oracle(gen)
+                if isinstance(score, list):
+                    score = sum(score) / len(score)
             
-#             Result[f"{oracle_name}"] = score
+            Result[f"{oracle_name}"] = score
         
-#         return Result
+        return Result
 
 
 
@@ -423,6 +423,8 @@ class molgenevalmetric(evaluate.Metric):
         metrics['unique'] = fraction_unique(gen=gensmi)
         metrics['IntDiv'] = internal_diversity(gen=gensmi)
         metrics['FCD'] = fcd_metric(gen = gensmi, train = trainsmi)
+        metrics['Oracles'] = oracles(gen = gensmi, train = trainsmi)
+        
         # metrics['SA'] = SAscore(gen=gensmi)
         # metrics['SCS'] = SAscore(gen=trainsmi)
 

requirements.txt

@@ -7,3 +7,4 @@ torch
 rdkit
 pyarrow
 fcd-torch
+PyTDC