fix pending candidates for universal constraint sampler

bofire/strategies/samplers/universal_constraint.py

@@ -30,12 +30,16 @@ def _ask(self, candidate_count: int) -> pd.DataFrame:
         samples = find_local_max_ipopt(
             domain=self.domain,
             model_type="linear",  # dummy model
-            n_experiments=int(candidate_count / self.sampling_fraction),
+            n_experiments=self.num_candidates
+            + int(candidate_count / self.sampling_fraction),
             ipopt_options=self.ipopt_options,
             objective=OptimalityCriterionEnum.SPACE_FILLING,
             fixed_experiments=self.candidates,
         )
 
+        samples = samples.iloc[
+            self.num_candidates :,
+        ]
         samples = samples.sample(
             n=candidate_count,
             replace=False,

tests/bofire/strategies/test_samplers.py

@@ -1,4 +1,5 @@
 import pytest
+from pandas import concat
 
 import bofire.data_models.strategies.api as data_models
 import bofire.strategies.api as strategies
@@ -51,6 +52,18 @@ def test_UniversalConstraintSampler(domain, num_samples):
     assert len(samples) == num_samples
 
 
+def test_UniversalConstraintSampler_pending_candidates():
+    data_model = data_models.UniversalConstraintSampler(domain=domains[0])
+    sampler = strategies.UniversalConstraintSampler(data_model=data_model)
+    pending_candidates = sampler.ask(2, add_pending=True)
+    samples = sampler.ask(1)
+    assert len(samples) == 1
+    all_samples = concat(
+        [samples, pending_candidates], axis=0, ignore_index=True
+    ).drop_duplicates()
+    assert len(all_samples) == 3
+
+
 inputs = Inputs(
     features=[ContinuousInput(key=f"if{i}", bounds=(0, 1)) for i in range(1, 4)]
 )