Code change

my_metric.py

@@ -13,10 +13,8 @@ Moses and PyTDC metrics
 
 _KWARGS_DESCRIPTION = """
 Args:
-    predictions (`list` of `int`): Predicted labels.
-    references (`list` of `int`): Ground truth labels.
-    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
-    sample_weight (`list` of `float`): Sample weights Defaults to None.
+    list_of_generated_smiles (`list` of `string`): Predicted labels.
+    list_of_test_smiles (`list` of `string`): test.
 
 Returns:
     All moses metrics
@@ -64,38 +62,42 @@ class my_metric(evaluate.Metric):
             features=datasets.Features(
                 {
                     "list_of_generated_smiles": datasets.Sequence(datasets.Value("string")),
-                    # "list_of_test_smiles": datasets.Sequence(datasets.Value("string")),
+                    "list_of_test_smiles": datasets.Sequence(datasets.Value("string")),
                 }
                 if self.config_name == "multilabel"
                 else {
                     "list_of_generated_smiles": datasets.Value("string"),
-                    # "list_of_test_smiles": datasets.Value("string"),
+                    "list_of_test_smiles": datasets.Value("string"),
                 }
             ),
             reference_urls=["https://github.com/molecularsets/moses"],
         )
 
-    def _compute(self, list_of_generated_smiles):
+    def _compute(self, generated_smiles,train_smiles):
         
-        Results = metrics.get_all_metrics(list_of_generated_smiles)
+        Results = metrics.get_all_metrics(generated_smiles)
         
         evaluator = Evaluator(name = 'Diversity')
         Diversity = evaluator(list_of_generated_smiles)
         
         evaluator = Evaluator(name = 'KL_Divergence')
-        KL_Divergence = evaluator(list_of_generated_smiles, list_of_generated_smiles)
+        KL_Divergence = evaluator(generated_smiles, train_smiles)
         
         evaluator = Evaluator(name = 'FCD_Distance')
-        FCD_Distance = evaluator(list_of_generated_smiles, list_of_generated_smiles)
+        FCD_Distance = evaluator(generated_smiles, train_smiles)
         
         evaluator = Evaluator(name = 'Novelty')
-        Novelty = evaluator(list_of_generated_smiles, list_of_generated_smiles)
+        Novelty = evaluator(generated_smiles, train_smiles)
+        
+        evaluator = Evaluator(name = 'Validity')
+        Novelty = evaluator(generated_smiles)
         
         Results.update({
             "PyTDC_Diversity": Diversity,
             "PyTDC_KL_Divergence": KL_Divergence,
             "PyTDC_FCD_Distance": FCD_Distance,
-            "PyTDC_Novelty": Novelty
+            "PyTDC_Novelty": Novelty,
+            "PyTDC_Validity": Validity
         })
 
         return {"results": Results}