diff --git a/src/learn_to_pick/base.py b/src/learn_to_pick/base.py
index a449815..e1a85aa 100644
--- a/src/learn_to_pick/base.py
+++ b/src/learn_to_pick/base.py
@@ -165,10 +165,9 @@ def __init__(
         featurizer: Featurizer,
         formatter: Callable,
         vw_logger: VwLogger,
-        *args: Any,
         **kwargs: Any,
     ):
-        super().__init__(*args, **kwargs)
+        super().__init__(**kwargs)
         self.model_repo = model_repo
         self.vw_cmd = vw_cmd
         self.workspace = self.model_repo.load(vw_cmd)
diff --git a/src/learn_to_pick/pick_best.py b/src/learn_to_pick/pick_best.py
index 1682010..abf70d6 100644
--- a/src/learn_to_pick/pick_best.py
+++ b/src/learn_to_pick/pick_best.py
@@ -143,17 +143,19 @@ def _dotproducts(self, context, actions):
                 for j in range(len(action_names))
             }
 
-    def _generic_namespace(self, featurized):
+    @staticmethod
+    def _generic_namespace(featurized):
         result = base.SparseFeatures()
         for ns in featurized.sparse.keys():
             if "default_ft" in featurized.sparse[ns]:
                 result[ns] = featurized.sparse[ns]["default_ft"]
         return result
 
-    def _generic_namespaces(self, context, actions):
-        context["@"] = self._generic_namespace(context)
+    @staticmethod
+    def _generic_namespaces(context, actions):
+        context["@"] = PickBestFeaturizer._generic_namespace(context)
         for a in actions:
-            a["#"] = self._generic_namespace(a)
+            a["#"] = PickBestFeaturizer._generic_namespace(a)
 
     def featurize(
         self, event: PickBestEvent
@@ -163,7 +165,7 @@ def featurize(
 
         if self.auto_embed:
             self._dotproducts(context, actions)
-            self._generic_namespaces(context, actions)
+            PickBestFeaturizer._generic_namespaces(context, actions)
 
         return context, actions, event.selected
 
@@ -183,9 +185,6 @@ def vw_cb_formatter(
 
 
 class PickBestRandomPolicy(base.Policy[PickBestEvent]):
-    def __init__(self):
-        ...
-
     def predict(self, event: PickBestEvent) -> List[Tuple[int, float]]:
         num_items = len(event.to_select_from)
         return [(i, 1.0 / num_items) for i in range(num_items)]
@@ -265,14 +264,6 @@ def _call_after_predict_before_scoring(
 
         next_inputs = inputs.copy()
 
-        # only one key, value pair in event.to_select_from
-        value = next(iter(event.to_select_from.values()))
-        v = (
-            value[event.selected.index]
-            if event.selected
-            else event.to_select_from.values()
-        )
-
         picked = {}
         for k, v in event.to_select_from.items():
             picked[k] = v[event.selected.index]