Fixed error

molgenevalmetric.py

@@ -402,13 +402,13 @@ class molgenevalmetric(evaluate.Metric):
             inputs_description=_KWARGS_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "generated_smiles": datasets.Sequence(datasets.Value("string")),
-                    "train_smiles": datasets.Sequence(datasets.Value("string")),
+                    "gen": datasets.Sequence(datasets.Value("string")),
+                    "train": datasets.Sequence(datasets.Value("string")),
                 }
                 if self.config_name == "multilabel"
                 else {
-                    "generated_smiles": datasets.Value("string"),
-                    "train_smiles": datasets.Value("string"),
+                    "gen": datasets.Value("string"),
+                    "train": datasets.Value("string"),
                 }
             ),
                 

syba_test.py

@@ -1,34 +1,36 @@
-from syba.syba import SybaClassifier
-
-def SYBAscore(smiles_list):
-    """
-    Compute the average SYBA score for a list of SMILES strings.
-
-    Parameters:
-    - smiles_list (list of str): A list of SMILES strings representing molecules.
-
-    Returns:
-    - float: The average SYBA score for the list of molecules.
-    """
-    syba = SybaClassifier()
-    syba.fitDefaultScore()
-    scores = []
-
-    for smiles in smiles_list:
-        try:
-            score = syba.predict(smi=smiles)
-            scores.append(score)
-        except Exception as e:
-            print(f"Error processing SMILES '{smiles}': {e}")
-            continue
-
-    if scores:
-        return sum(scores) / len(scores)
-    else:
-        return None  # Or handle empty list or all failed predictions as needed
-
-
-syba = SybaClassifier()
-syba.fitDefaultScore()
-smi = "O=C(C)Oc1ccccc1C(=O)O"
-print(syba.predict(smi))
+# from syba.syba import SybaClassifier
+
+# def SYBAscore(smiles_list):
+#     """
+#     Compute the average SYBA score for a list of SMILES strings.
+
+#     Parameters:
+#     - smiles_list (list of str): A list of SMILES strings representing molecules.
+
+#     Returns:
+#     - float: The average SYBA score for the list of molecules.
+#     """
+#     syba = SybaClassifier()
+#     syba.fitDefaultScore()
+#     scores = []
+
+#     for smiles in smiles_list:
+#         try:
+#             score = syba.predict(smi=smiles)
+#             scores.append(score)
+#         except Exception as e:
+#             print(f"Error processing SMILES '{smiles}': {e}")
+#             continue
+
+#     if scores:
+#         return sum(scores) / len(scores)
+#     else:
+#         return None  # Or handle empty list or all failed predictions as needed
+
+
+# syba = SybaClassifier()
+# syba.fitDefaultScore()
+# smi = "O=C(C)Oc1ccccc1C(=O)O"
+# print(syba.predict(smi))
+
+import sascorer