Use np.ndarray for constraint names

miplearn/features/extractor.py

@@ -200,11 +200,11 @@ class FeaturesExtractor:
     ) -> None:
         has_static_lazy = instance.has_static_lazy_constraints()
         user_features: List[Optional[List[float]]] = []
-        categories: List[Optional[str]] = []
+        categories: List[Optional[bytes]] = []
         lazy: List[bool] = []
         constr_categories_dict = instance.get_constraint_categories()
         constr_features_dict = instance.get_constraint_features()
-        constr_names = sample.get_vector("static_constr_names")
+        constr_names = sample.get_array("static_constr_names")
         assert constr_names is not None
 
         for (cidx, cname) in enumerate(constr_names):
@@ -215,8 +215,8 @@ class FeaturesExtractor:
                 user_features.append(None)
                 categories.append(None)
                 continue
-            assert isinstance(category, str), (
-                f"Constraint category must be a string. "
+            assert isinstance(category, bytes), (
+                f"Constraint category must be bytes. "
                 f"Found {type(category).__name__} instead for cname={cname}.",
             )
             categories.append(category)
@@ -242,7 +242,7 @@ class FeaturesExtractor:
                 lazy.append(False)
         sample.put_vector_list("static_constr_features", user_features)
         sample.put_vector("static_constr_lazy", lazy)
-        sample.put_vector("static_constr_categories", categories)
+        sample.put_array("static_constr_categories", np.array(categories, dtype="S"))
 
     def _extract_user_features_instance(
         self,