Harmonize allow_* flags

The original name allow_repeated_recommendations is not accurate since
it also suggests that repeated configurations within a batch would be
excluded, which is not the case.

baybe/campaign.py

@@ -84,18 +84,17 @@ class Campaign(SerialMixin):
     )
     """The employed recommender"""
 
-    allow_repeated_recommendations: bool = field(default=False, kw_only=True)
-    """Allow to make recommendations that were already recommended earlier.
+    allow_recommending_already_measured: bool = field(default=True, kw_only=True)
+    """Allow to recommend experiments that were already measured earlier.
     This only has an influence in discrete search spaces."""
 
-    allow_recommending_already_measured: bool = field(default=True, kw_only=True)
-    """Allow to make recommendations that were measured previously.
+    allow_recommending_already_recommended: bool = field(default=False, kw_only=True)
+    """Allow to recommend experiments that were already recommended earlier.
     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 has an
-    influence in discrete search spaces."""
+    """Allow pending experiments to be part of the recommendations.
+    This only has an influence in discrete search spaces."""
 
     # Metadata
     _searchspace_metadata: pd.DataFrame = field(init=False, eq=eq_dataframe)
@@ -384,7 +383,7 @@ def recommend(
 
         # Prepare the search space according to the current campaign state
         mask_todrop = self._searchspace_metadata[_EXCLUDED].copy()
-        if not self.allow_repeated_recommendations:
+        if not self.allow_recommending_already_recommended:
             mask_todrop |= self._searchspace_metadata[_RECOMMENDED]
         if not self.allow_recommending_already_measured:
             mask_todrop |= self._searchspace_metadata[_MEASURED]

docs/userguide/campaigns.md

@@ -119,7 +119,7 @@ set of recommendable points is built based on the trajectory the campaign has ta
 far. This is done by setting the following Boolean flags:
 - `allow_recommending_already_measured`:  Controls whether points that have already been
   measured points can be recommended.
-- `allow_repeated_recommendations`: Controls whether previously recommended points can
+- `allow_recommending_already_recommended`: Controls whether previously recommended points can
   be recommended again.
 - `allow_recommending_pending_experiments`: Controls whether points marked as
   `pending_experiments` can be recommended (see [asynchronous

examples/Multi_Armed_Bandit/bernoulli_multi_armed_bandit.py

@@ -142,7 +142,7 @@ def simulate(acqf: AcquisitionFunction) -> SimulationResult:
             objective,
             recommender,
             # The same arm can be pulled several times:
-            allow_repeated_recommendations=True,
+            allow_recommending_already_recommended=True,
             allow_recommending_already_measured=True,
         )
 

examples/Serialization/create_from_config.py

@@ -81,7 +81,7 @@
         },
         "switch_after": 1
     },
-    "allow_repeated_recommendations": false,
+    "allow_recommending_already_recommended": false,
     "allow_recommending_already_measured": false
 }
 """

examples/Serialization/validate_config.py

@@ -80,7 +80,7 @@
         },
         "switch_after": 1
     },
-    "allow_repeated_recommendations": false,
+    "allow_recommending_already_recommended": false,
     "allow_recommending_already_measured": false
 }
 """
@@ -147,7 +147,7 @@
                 "type": "GaussianProcessSurrogate"
             },
             "acquisition_function": "qEI",
-            "allow_repeated_recommendations": false,
+            "allow_recommending_already_recommended": false,
             "allow_recommending_already_measured": false
         }
     }

tests/conftest.py

@@ -585,7 +585,7 @@ def fixture_campaign(
     constraints,
     recommender,
     objective,
-    allow_repeated_recommendations,
+    allow_recommending_already_recommended,
     allow_recommending_already_measured,
 ):
     """Returns a campaign."""
@@ -595,7 +595,7 @@ def fixture_campaign(
         ),
         recommender=recommender,
         objective=objective,
-        allow_repeated_recommendations=allow_repeated_recommendations,
+        allow_recommending_already_recommended=allow_recommending_already_recommended,
         allow_recommending_already_measured=allow_recommending_already_measured,
     )
 
@@ -657,8 +657,8 @@ def fixture_default_surrogate_model(request, kernel):
     return GaussianProcessSurrogate(kernel_or_factory=kernel)
 
 
-@pytest.fixture(name="allow_repeated_recommendations")
-def fixture_allow_repeated_recommendations():
+@pytest.fixture(name="allow_recommending_already_recommended")
+def fixture_allow_recommending_already_recommended():
     return False
 
 

tests/test_pending_experiments.py

@@ -28,7 +28,7 @@
 
 # Repeated recommendations explicitly need to be allowed or the potential overlap will
 # be avoided trivially
-@pytest.mark.parametrize("allow_repeated_recommendations", [True])
+@pytest.mark.parametrize("allow_recommending_already_recommended", [True])
 @pytest.mark.parametrize("allow_recommending_already_measured", [True])
 @pytest.mark.parametrize(
     "parameter_names, recommender",