Merge branch 'uri/split_initial_point_generation' of github.com:secon…

…dmind-labs/trieste into uri/split_initial_point_generation

tests/unit/models/gpflux/test_models.py

@@ -553,3 +553,26 @@ def test_deepgp_log(
 
     assert mocked_summary_scalar.call_count == num_scalars
     assert mocked_summary_histogram.call_count == num_histogram
+
+
+def test_deepgp_compile_args_specified() -> None:
+    x_observed = np.linspace(0, 10, 10).reshape((-1, 1))
+    model = single_layer_dgp_model(x_observed)
+    # If we get this error we know that the compile_args are being passed to the model
+    # because Keras will throw an error if it receives both of these arguments.
+    with pytest.raises(
+        ValueError, match="You cannot enable `run_eagerly` and `jit_compile` at the same time."
+    ):
+        DeepGaussianProcess(model, compile_args={"jit_compile": True, "run_eagerly": True})
+
+
+def test_deepgp_disallowed_compile_args_specified() -> None:
+    mock_model = unittest.mock.MagicMock(spec=DeepGP)
+    with pytest.raises(ValueError):
+        DeepGaussianProcess(
+            mock_model, compile_args={"run_eagerly": True, "optimizer": unittest.mock.MagicMock()}
+        )
+    with pytest.raises(ValueError):
+        DeepGaussianProcess(
+            mock_model, compile_args={"run_eagerly": True, "metrics": unittest.mock.MagicMock()}
+        )

tests/unit/models/keras/test_models.py

@@ -198,6 +198,36 @@ def test_deep_ensemble_is_compiled() -> None:
     assert model.model.optimizer is not None
 
 
+def test_deep_ensemble_compile_args_specified() -> None:
+    example_data = empty_dataset([1], [1])
+    # If we get this error we know that the compile_args are being passed to the model
+    # because Keras will throw an error if it receives both of these arguments.
+    with pytest.raises(
+        ValueError, match="You cannot enable `run_eagerly` and `jit_compile` at the same time."
+    ):
+        model, _, _ = trieste_deep_ensemble_model(
+            example_data, _ENSEMBLE_SIZE, compile_args={"run_eagerly": True, "jit_compile": True}
+        )
+
+
+def test_deep_ensemble_disallowed_compile_args_specified() -> None:
+    mock_ensemble = unittest.mock.MagicMock(spec=KerasEnsemble)
+    mock_ensemble.ensemble_size = _ENSEMBLE_SIZE
+    with pytest.raises(ValueError):
+        DeepEnsemble(
+            mock_ensemble,
+            compile_args={"run_eagerly": True, "optimizer": unittest.mock.MagicMock()},
+        )
+    with pytest.raises(ValueError):
+        DeepEnsemble(
+            mock_ensemble, compile_args={"run_eagerly": True, "loss": unittest.mock.MagicMock()}
+        )
+    with pytest.raises(ValueError):
+        DeepEnsemble(
+            mock_ensemble, compile_args={"run_eagerly": True, "metrics": unittest.mock.MagicMock()}
+        )
+
+
 def test_deep_ensemble_resets_lr_with_lr_schedule() -> None:
     example_data = _get_example_data([100, 1])
 

tests/util/models/keras/models.py

@@ -18,7 +18,7 @@
 
 from __future__ import annotations
 
-from typing import Optional, Tuple
+from typing import Any, Mapping, Optional, Tuple
 
 import tensorflow as tf
 from packaging.version import Version
@@ -63,6 +63,7 @@ def trieste_deep_ensemble_model(
     ensemble_size: int,
     bootstrap_data: bool = False,
     independent_normal: bool = False,
+    compile_args: Optional[Mapping[str, Any]] = None,
 ) -> Tuple[DeepEnsemble, KerasEnsemble, KerasOptimizer]:
     keras_ensemble = trieste_keras_ensemble_model(example_data, ensemble_size, independent_normal)
 
@@ -75,7 +76,9 @@ def trieste_deep_ensemble_model(
     }
     optimizer_wrapper = KerasOptimizer(optimizer, fit_args)
 
-    model = DeepEnsemble(keras_ensemble, optimizer_wrapper, bootstrap_data)
+    model = DeepEnsemble(
+        keras_ensemble, optimizer_wrapper, bootstrap_data, compile_args=compile_args
+    )
 
     return model, keras_ensemble, optimizer_wrapper
 

trieste/models/gpflux/models.py

@@ -14,7 +14,7 @@
 
 from __future__ import annotations
 
-from typing import Any, Callable, Optional
+from typing import Any, Callable, Mapping, Optional
 
 import dill
 import gpflow
@@ -64,6 +64,7 @@ def __init__(
         optimizer: KerasOptimizer | None = None,
         num_rff_features: int = 1000,
         continuous_optimisation: bool = True,
+        compile_args: Optional[Mapping[str, Any]] = None,
     ):
         """
         :param model: The underlying GPflux deep Gaussian process model. Passing in a named closure
@@ -82,9 +83,23 @@ def __init__(
         :param continuous_optimisation: if True (default), the optimizer will keep track of the
             number of epochs across BO iterations and use this number as initial_epoch. This is
             essential to allow monitoring of model training across BO iterations.
+        :param compile_args: Keyword arguments to pass to the ``compile`` method of the
+            Keras model (:class:`~tf.keras.Model`).
+            See https://keras.io/api/models/model_training_apis/#compile-method for a
+            list of possible arguments. The ``optimizer`` and ``metrics`` arguments
+            must not be included.
         :raise ValueError: If ``model`` has unsupported layers, ``num_rff_features`` is less than 0,
-            or if the ``optimizer`` is not of a supported type.
+            if the ``optimizer`` is not of a supported type, or `compile_args` contains
+            disallowed arguments.
         """
+        if compile_args is None:
+            compile_args = {}
+
+        if not {"optimizer", "metrics"}.isdisjoint(compile_args):
+            raise ValueError(
+                "optimizer and metrics arguments must not be included in compile_args."
+            )
+
         if isinstance(model, DeepGP):
             self._model_closure = None
         else:
@@ -152,7 +167,9 @@ def scheduler(epoch: int, lr: float) -> float:
             dtype=tf.float64,
         )
         self._model_keras = model.as_training_model()
-        self._model_keras.compile(self.optimizer.optimizer, metrics=self.optimizer.metrics)
+        self._model_keras.compile(
+            optimizer=self.optimizer.optimizer, metrics=self.optimizer.metrics, **compile_args
+        )
         self._absolute_epochs = 0
         self._continuous_optimisation = continuous_optimisation
 

trieste/models/keras/models.py

@@ -15,7 +15,7 @@
 from __future__ import annotations
 
 import re
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Mapping, Optional
 
 import dill
 import keras.callbacks
@@ -85,6 +85,7 @@ def __init__(
         bootstrap: bool = False,
         diversify: bool = False,
         continuous_optimisation: bool = True,
+        compile_args: Optional[Mapping[str, Any]] = None,
     ) -> None:
         """
         :param model: A Keras ensemble model with probabilistic networks as ensemble members. The
@@ -106,15 +107,28 @@ def __init__(
         :param continuous_optimisation: If True (default), the optimizer will keep track of the
             number of epochs across BO iterations and use this number as initial_epoch. This is
             essential to allow monitoring of model training across BO iterations.
+        :param compile_args: Keyword arguments to pass to the ``compile`` method of the
+            Keras model (:class:`~tf.keras.Model`).
+            See https://keras.io/api/models/model_training_apis/#compile-method for a
+            list of possible arguments. The ``optimizer``, ``loss`` and ``metrics`` arguments
+            must not be included.
         :raise ValueError: If ``model`` is not an instance of
-            :class:`~trieste.models.keras.KerasEnsemble` or ensemble has less than two base
-            learners (networks).
+            :class:`~trieste.models.keras.KerasEnsemble`, or ensemble has less than two base
+            learners (networks), or `compile_args` contains disallowed arguments.
         """
         if model.ensemble_size < 2:
             raise ValueError(f"Ensemble size must be greater than 1 but got {model.ensemble_size}.")
 
         super().__init__(optimizer)
 
+        if compile_args is None:
+            compile_args = {}
+
+        if not {"optimizer", "loss", "metrics"}.isdisjoint(compile_args):
+            raise ValueError(
+                "optimizer, loss and metrics arguments must not be included in compile_args."
+            )
+
         if not self.optimizer.fit_args:
             self.optimizer.fit_args = {
                 "verbose": 0,
@@ -134,9 +148,10 @@ def __init__(
             self.optimizer.metrics = ["mse"]
 
         model.model.compile(
-            self.optimizer.optimizer,
+            optimizer=self.optimizer.optimizer,
             loss=[self.optimizer.loss] * model.ensemble_size,
             metrics=[self.optimizer.metrics] * model.ensemble_size,
+            **compile_args,
         )
 
         if not isinstance(