Make pending experiment candidate exclusion configurable

CHANGELOG.md

@@ -50,6 +50,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Discrete search space Cartesian product can be created lazily via Polars
 - `recommend` now accepts the `pending_experiments` argument, informing the algorithm
   about points that were already selected for evaluation
+- Pure recommenders now have the `allow_recommending_pending_experiments` flag,
+  controlling whether pending experiments are excluded from candidates in purely
+  discrete search spaces
 
 ### Changed
 - Passing an `Objective` to `Campaign` is now optional

baybe/recommenders/naive.py

@@ -6,7 +6,6 @@
 import pandas as pd
 from attrs import define, evolve, field, fields
 
-from baybe.exceptions import UnusedObjectWarning
 from baybe.objectives.base import Objective
 from baybe.recommenders.pure.base import PureRecommender
 from baybe.recommenders.pure.bayesian.base import BayesianRecommender
@@ -114,14 +113,6 @@ def recommend(  # noqa: D102
             )
 
         # We are in a hybrid setting now
-        if pending_experiments is not None:
-            raise UnusedObjectWarning(
-                f"Pending experiments were provided but the selected recommender "
-                f"'{self.__class__.__name__}' with discrete "
-                f"sub-recommender '{self.disc_recommender.__class__.__name__}' only "
-                f"utilizes this information for purely discrete spaces."
-            )
-
         # We will attach continuous parts to discrete parts and the other way round.
         # To make things simple, we sample a single point in the continuous space which
         # will then be attached to every discrete point when the acquisition function

baybe/recommenders/pure/base.py

@@ -32,6 +32,11 @@ class PureRecommender(ABC, RecommenderProtocol):
     """Allow to make recommendations that were measured previously.
     This only has an influence in discrete search spaces."""
 
+    allow_recommending_pending_experiments: bool = field(default=False, kw_only=True)
+    """Allow `pending_experiments` to be part of the recommendations. If set to `False`,
+    the corresponding points will be removed from the candidates. This only influences
+    recommendations for purely discrete search spaces."""
+
     def recommend(  # noqa: D102
         self,
         batch_size: int,
@@ -182,13 +187,17 @@ def _recommend_with_discrete_parts(
 
         # Get discrete candidates
         # Repeated recommendations are always allowed for hybrid spaces
-        # Pending experiments are excluded for discrete spaces
+        # Pending experiments are excluded for discrete spaces unless configured
+        # differently.
+        dont_exclude_pending = (
+            is_hybrid_space or self.allow_recommending_pending_experiments
+        )
         _, candidates_comp = searchspace.discrete.get_candidates(
             allow_repeated_recommendations=is_hybrid_space
             or self.allow_repeated_recommendations,
             allow_recommending_already_measured=is_hybrid_space
             or self.allow_recommending_already_measured,
-            exclude=None if is_hybrid_space else pending_experiments,
+            exclude=None if dont_exclude_pending else pending_experiments,
         )
 
         # Check if enough candidates are left

baybe/recommenders/pure/nonpredictive/base.py

@@ -4,6 +4,7 @@
 from abc import ABC
 
 import pandas as pd
+from attr import fields
 from attrs import define
 
 from baybe.exceptions import UnusedObjectWarning
@@ -40,14 +41,16 @@ def recommend(  # noqa: D102
                 f"consider any objectives, meaning that the argument is ignored.",
                 UnusedObjectWarning,
             )
-        if (
-            pending_experiments is not None
-            and searchspace.type is not SearchSpaceType.DISCRETE
+        if (pending_experiments is not None) and (
+            self.allow_recommending_pending_experiments
+            or searchspace.type is not SearchSpaceType.DISCRETE
         ):
             raise UnusedObjectWarning(
                 f"Pending experiments were provided but the selected recommender "
                 f"'{self.__class__.__name__}' only utilizes this information for "
-                f"purely discrete spaces."
+                f"purely discrete spaces and "
+                f"{fields(self.__class__).allow_recommending_pending_experiments.name}"
+                f"=False."
             )
         return super().recommend(
             batch_size=batch_size,

tests/test_pending_experiments.py

@@ -28,7 +28,9 @@
 # Repeated recommendations explicitly need to be allowed or the potential overlap will
 # be avoided trivially
 _flags = dict(
-    allow_repeated_recommendations=True, allow_recommending_already_measured=True
+    allow_repeated_recommendations=True,
+    allow_recommending_already_measured=True,
+    allow_recommending_pending_experiments=False,
 )