add option to train models for inactive objectives

src/blop/agent.py

@@ -72,6 +72,7 @@ def __init__(
         digestion_kwargs: dict = {},
         verbose: bool = False,
         enforce_all_objectives_valid: bool = True,
+        model_inactive_objectives: bool = False,
         tolerate_acquisition_errors: bool = False,
         sample_center_on_init: bool = False,
         trigger_delay: float = 0,
@@ -137,8 +138,8 @@ def __init__(
 
         self.verbose = verbose
 
+        self.model_inactive_objectives = model_inactive_objectives
         self.tolerate_acquisition_errors = tolerate_acquisition_errors
-
         self.enforce_all_objectives_valid = enforce_all_objectives_valid
 
         self.train_every = train_every
@@ -361,7 +362,8 @@ def tell(
         self._table.index = np.arange(len(self._table))
 
         if update_models:
-            for obj in self.objectives(active=True):
+            objectives_to_model = self.objectives if self.model_inactive_objectives else self.objectives(active=True)
+            for obj in objectives_to_model:
                 t0 = ttime.monotonic()
 
                 cached_hypers = obj.model.state_dict() if hasattr(obj, "model") else None
@@ -737,13 +739,15 @@ def _construct_model(self, obj, skew_dims=None):
 
     def _construct_all_models(self):
         """Construct a model for each objective."""
-        for obj in self.objectives(active=True):
+        objectives_to_construct = self.objectives if self.model_inactive_objectives else self.objectives(active=True)
+        for obj in objectives_to_construct:
             self._construct_model(obj)
 
     def _train_all_models(self, **kwargs):
         """Fit all of the agent's models. All kwargs are passed to `botorch.fit.fit_gpytorch_mll`."""
         t0 = ttime.monotonic()
-        for obj in self.objectives(active=True):
+        objectives_to_train = self.objectives if self.model_inactive_objectives else self.objectives(active=True)
+        for obj in objectives_to_train:
             self._train_model(obj.model)
             if obj.validity_conjugate_model is not None:
                 self._train_model(obj.validity_conjugate_model)