make objective models read-only

src/blop/agent.py

@@ -248,7 +248,7 @@ def ask(self, acqf="qei", n=1, route=True, sequential=True, upsample=1, **acqf_k
 
         else:
             # check that all the objectives have models
-            if not all(hasattr(obj, "model") for obj in active_objs):
+            if not all(hasattr(obj, "_model") for obj in active_objs):
                 raise RuntimeError(
                     f"Can't construct non-trivial acquisition function '{acqf}' as the agent is not initialized."
                 )
@@ -367,7 +367,7 @@ def tell(
             for obj in objectives_to_model:
                 t0 = ttime.monotonic()
 
-                cached_hypers = obj.model.state_dict() if hasattr(obj, "model") else None
+                cached_hypers = obj.model.state_dict() if hasattr(obj, "_model") else None
                 n_before_tell = obj.n_valid
                 self._construct_model(obj)
                 n_after_tell = obj.n_valid
@@ -538,8 +538,8 @@ def reset(self):
         self._table = pd.DataFrame()
 
         for obj in self.objectives(active=True):
-            if hasattr(obj, "model"):
-                del obj.model
+            if hasattr(obj, "_model"):
+                del obj._model
 
         self.n_last_trained = 0
 
@@ -573,7 +573,7 @@ def benchmark(
     def model(self):
         """A model encompassing all the fitnesses and constraints."""
         active_objs = self.objectives(active=True)
-        if all(hasattr(obj, "model") for obj in active_objs):
+        if all(hasattr(obj, "_model") for obj in active_objs):
             return ModelListGP(*[obj.model for obj in active_objs]) if len(active_objs) > 1 else active_objs[0].model
         raise ValueError("Not all active objectives have models.")
 
@@ -689,7 +689,7 @@ def _construct_model(self, obj, skew_dims=None):
 
         trusted = inputs_are_trusted & targets_are_trusted
 
-        obj.model = construct_single_task_model(
+        obj._model = construct_single_task_model(
             X=train_inputs[trusted],
             y=train_targets[trusted],
             min_noise=obj.min_noise,
@@ -731,7 +731,7 @@ def _train_all_models(self, **kwargs):
         t0 = ttime.monotonic()
         objectives_to_train = self.objectives if self.model_inactive_objectives else self.objectives(active=True)
         for obj in objectives_to_train:
-            train_model(obj.model)
+            train_model(obj._model)
             if obj.validity_conjugate_model is not None:
                 train_model(obj.validity_conjugate_model)
 

src/blop/objectives.py

@@ -254,6 +254,10 @@ def fitness_prediction(self, X):
         if isinstance(self.target, tuple):
             return self.targeting_constraint(X).log().clamp(min=-16)
 
+    @property
+    def model(self):
+        return self._model.eval()
+
 
 class ObjectiveList(Sequence):
     def __init__(self, objectives: list = []):