Multi trust regions

tests/integration/test_bayesian_optimization.py

@@ -52,7 +52,9 @@
     BatchHypervolumeSharpeRatioIndicator,
     DiscreteThompsonSampling,
     EfficientGlobalOptimization,
+    MultiTrustRegionBox,
     TrustRegion,
+    TrustRegionBox,
 )
 from trieste.acquisition.sampler import ThompsonSamplerFromTrajectory
 from trieste.bayesian_optimizer import (
@@ -197,6 +199,17 @@ def GPR_OPTIMIZER_PARAMS() -> Tuple[str, List[ParameterSet]]:
                 TURBO(ScaledBranin.search_space, rule=DiscreteThompsonSampling(500, 3)),
                 id="Turbo",
             ),
+            pytest.param(
+                10,
+                MultiTrustRegionBox(
+                    [TrustRegionBox(ScaledBranin.search_space) for _ in range(3)],
+                    EfficientGlobalOptimization(
+                        ParallelContinuousThompsonSampling(),
+                        num_query_points=3,
+                    ),
+                ),
+                id="MultiTrustRegionBox",
+            ),
             pytest.param(15, DiscreteThompsonSampling(500, 5), id="DiscreteThompsonSampling"),
             pytest.param(
                 15,

tests/unit/acquisition/test_optimizer.py

@@ -45,6 +45,7 @@
     DiscreteSearchSpace,
     LinearConstraint,
     SearchSpace,
+    TaggedMultiSearchSpace,
     TaggedProductSearchSpace,
 )
 from trieste.types import TensorType
@@ -203,6 +204,15 @@ def test_optimize_continuous_raises_with_invalid_vectorized_batch_size(batch_siz
         generate_continuous_optimizer()(search_space, (acq_fn, batch_size))
 
 
+def test_optimize_continuous_raises_with_mismatch_multi_search_space() -> None:
+    space_A = Box([-1], [2])
+    space_B = Box([3], [4])
+    multi_space = TaggedMultiSearchSpace(spaces=[space_A, space_B])
+    acq_fn = _quadratic_sum([1.0])
+    with pytest.raises(TF_DEBUGGING_ERROR_TYPES, match="The batch shape of initial samples 2 must"):
+        generate_continuous_optimizer()(multi_space, acq_fn)
+
+
 @pytest.mark.parametrize("num_optimization_runs", [1, 10])
 @pytest.mark.parametrize("num_initial_samples", [1000, 5000])
 def test_optimize_continuous_correctly_uses_init_params(