Copy dataset in optimizers to avoid changing it

tests/unit/test_ask_tell_optimization.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 from __future__ import annotations
 
-import copy
 from typing import Mapping, Optional
 
 import numpy.testing as npt
@@ -503,7 +502,7 @@ def update(self, dataset: Dataset) -> None:
 
     observer = mk_batch_observer(lambda x: Dataset(x, x))
     rule = FixedAcquisitionRule(query_points)
-    ask_tell = AskTellOptimizer(search_space, copy.deepcopy(init_data), models, rule)
+    ask_tell = AskTellOptimizer(search_space, init_data, models, rule)
 
     points = ask_tell.ask()
     new_data = observer(points)

tests/unit/test_bayesian_optimizer.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 from __future__ import annotations
 
-import copy
 import tempfile
 from collections.abc import Mapping
 from pathlib import Path
@@ -303,7 +302,7 @@ def update(self, dataset: Dataset) -> None:
 
     optimizer = BayesianOptimizer(lambda x: Dataset(x, x), search_space)
     rule = FixedAcquisitionRule(query_points)
-    optimizer.optimize(1, copy.deepcopy(init_data), models, rule).final_result.unwrap()
+    optimizer.optimize(1, init_data, models, rule).final_result.unwrap()
 
 
 @pytest.mark.parametrize("mode", ["early", "fail", "full"])

trieste/ask_tell_optimization.py

@@ -187,6 +187,9 @@ def __init__(
         if not datasets or not models:
             raise ValueError("dicts of datasets and models must be populated.")
 
+        # Copy the dataset so we don't change the one provided by the user.
+        datasets = deepcopy(datasets)
+
         if isinstance(datasets, Dataset):
             datasets = {OBJECTIVE: datasets}
         if not isinstance(models, Mapping):

trieste/bayesian_optimizer.py

@@ -635,6 +635,9 @@ def optimize(
             - ``datasets`` or ``models`` are empty
             - the default `acquisition_rule` is used and the tags are not `OBJECTIVE`.
         """
+        # Copy the dataset so we don't change the one provided by the user.
+        datasets = copy.deepcopy(datasets)
+
         if isinstance(datasets, Dataset):
             datasets = {OBJECTIVE: datasets}
         if not isinstance(models, Mapping):