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Co-authored-by: AdrianSosic <adrian.sosic@merckgroup.com>

CHANGELOG.md

@@ -49,7 +49,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   `DiscreteNoLabelDuplicatesConstraint`
 - Discrete search space Cartesian product can be created lazily via Polars
 - `recommend` now accepts the `pending_measurements` argument, informing the algorithm
-  that these points were already selected for evaluation
+  about points that were already selected for evaluation
 
 ### Changed
 - Passing an `Objective` to `Campaign` is now optional

README.md

@@ -36,7 +36,8 @@ Besides functionality to perform a typical recommend-measure loop, BayBE's highl
 - 🎭 Hybrid (mixed continuous and discrete) spaces
 - 🚀 Transfer learning: Mix data from multiple campaigns and accelerate optimization
 - 🌎 Distributed workflows: Run campaigns asynchronously with pending measurements
-- 🎓 Active Learning: Perform smart data acquisition campaigns
+- 🎓 Active learning: Perform smart data acquisition campaigns
+
 - ⚙️ Custom surrogate models: For specialized problems
 - 📈 Comprehensive backtest, simulation and imputation utilities: Benchmark and find your best settings
 - 📝 Fully typed and hypothesis-tested: Robust code base

baybe/acquisition/base.py

@@ -78,9 +78,8 @@ def to_botorch(
                 additional_params["X_pending"] = to_tensor(pending_x)
             else:
                 raise IncompatibleAcquisitionFunctionError(
-                    f"Non-empty pending measurements were provided, but the "
-                    f"chosen acquisition function {self.__class__.__name__} does not "
-                    f"support this."
+                    f"Pending measurements were provided but the chosen acquisition "
+                    f"function '{self.__class__.__name__}' does not support this."
                 )
 
         params_dict.update(additional_params)

baybe/recommenders/naive.py

@@ -116,9 +116,9 @@ def recommend(  # noqa: D102
         # We are in a hybrid setting now
         if pending_measurements is not None:
             raise UnusedObjectWarning(
-                f"A non-empty set of pending measurements was provided, but the "
-                f"selected recommender {self.__class__.__name__} with discrete "
-                f"sub-recommender {self.disc_recommender.__class__.__name__} only "
+                f"Pending measurements 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."
             )
 

baybe/recommenders/pure/nonpredictive/base.py

@@ -45,9 +45,9 @@ def recommend(  # noqa: D102
             and searchspace.type is not SearchSpaceType.DISCRETE
         ):
             raise UnusedObjectWarning(
-                f"A non-empty set of pending measurements was provided, but the "
-                f"selected recommender {self.__class__.__name__} only utilizes this "
-                f"information for purely discrete spaces."
+                f"Pending measurements were provided but the selected recommender "
+                f"'{self.__class__.__name__}' only utilizes this information for "
+                f"purely discrete spaces."
             )
         return super().recommend(
             batch_size=batch_size,