change sparsity in gym (#1328)

* change sparsity in gym

* fix

nevergrad/functions/gym/multigym.py

@@ -120,7 +120,7 @@ class SmallActionSpaceLlvmEnv(gym.ActionWrapper):
     ]
 
     def __init__(self, env) -> None:
-        """Creating a counterpart of a compiler gym environement with a reduced action space."""
+        """Creating a counterpart of a compiler gym environment with a reduced action space."""
         super().__init__(env=env)
         # Array for translating from this tiny action space to the action space of
         # the wrapped environment.
@@ -643,12 +643,7 @@ def evaluation_function(self, *recommendations) -> float:
             for compiler_gym_pb_index in range(23)
         ]
         loss = -np.exp(sum(rewards) / len(rewards))
-        sparse_penalty = 0
-        if self.sparse_limit is not None:  # Then we penalize the weights above the threshold "sparse_limit".
-            sparse_penalty = (1 + np.abs(loss)) * max(
-                0, np.sum(recommendations[0].value["weights"]) - self.sparse_limit
-            )
-        return loss + sparse_penalty
+        return loss
 
     def forked_env(self):
         assert "compiler" in self.name
@@ -740,9 +735,13 @@ def sparse_gym_multi_function(
     ) -> float:
         assert all(x_ in [0, 1] for x_ in enablers)
         x = weights * enablers
-        return self.gym_multi_function(
+        loss = self.gym_multi_function(
             x, limited_fidelity=limited_fidelity, compiler_gym_pb_index=compiler_gym_pb_index
         )
+        sparse_penalty = 0
+        if self.sparse_limit is not None:  # Then we penalize the weights above the threshold "sparse_limit".
+            sparse_penalty = (1 + np.abs(loss)) * max(0, np.sum(enablers) - self.sparse_limit)
+        return loss + sparse_penalty
 
     def gym_multi_function(
         self, x: np.ndarray, limited_fidelity: bool = False, compiler_gym_pb_index: tp.Optional[int] = None