Add support for continuous relaxation within optimize_acqf_mixed_alternating

botorch/optim/optimize_mixed.py

@@ -39,6 +39,10 @@
 MAX_ITER_ALTER = 64  # Maximum number of alternating iterations.
 MAX_ITER_DISCRETE = 4  # Maximum number of discrete iterations.
 MAX_ITER_CONT = 8  # Maximum number of continuous iterations.
+# Maximum number of discrete values for a discrete dimension.
+# If there are more values for a dimension, we will use continuous
+# relaxation to optimize it.
+MAX_DISCRETE_VALUES = 20
 # Maximum number of iterations for optimizing the continuous relaxation
 # during initialization
 MAX_ITER_INIT = 100
@@ -52,6 +56,7 @@
     "maxiter_discrete",
     "maxiter_continuous",
     "maxiter_init",
+    "max_discrete_values",
     "num_spray_points",
     "std_cont_perturbation",
     "batch_limit",
@@ -60,6 +65,40 @@
 SUPPORTED_INITIALIZATION = {"continuous_relaxation", "equally_spaced", "random"}
 
 
+def _setup_continuous_relaxation(
+    discrete_dims: list[int],
+    bounds: Tensor,
+    max_discrete_values: int,
+    post_processing_func: Callable[[Tensor], Tensor] | None,
+) -> tuple[list[int], Callable[[Tensor], Tensor] | None]:
+    r"""Update `discrete_dims` and `post_processing_func` to use
+    continuous relaxation for discrete dimensions that have more than
+    `max_discrete_values` values. These dimensions are removed from
+    `discrete_dims` and `post_processing_func` is updated to round
+    them to the nearest integer.
+    """
+    discrete_dims_t = torch.tensor(discrete_dims, dtype=torch.long)
+    num_discrete_values = (
+        bounds[1, discrete_dims_t] - bounds[0, discrete_dims_t]
+    ).cpu()
+    dims_to_relax = discrete_dims_t[num_discrete_values > max_discrete_values]
+    if dims_to_relax.numel() == 0:
+        # No dimension needs continuous relaxation.
+        return discrete_dims, post_processing_func
+    # Remove relaxed dims from `discrete_dims`.
+    discrete_dims = list(set(discrete_dims).difference(dims_to_relax.tolist()))
+
+    def new_post_processing_func(X: Tensor) -> Tensor:
+        r"""Round the relaxed dimensions to the nearest integer and apply the original
+        `post_processing_func`."""
+        X[..., dims_to_relax] = X[..., dims_to_relax].round()
+        if post_processing_func is not None:
+            X = post_processing_func(X)
+        return X
+
+    return discrete_dims, new_post_processing_func
+
+
 def _filter_infeasible(
     X: Tensor, inequality_constraints: list[tuple[Tensor, Tensor, float]] | None
 ) -> Tensor:
@@ -532,6 +571,9 @@ def optimize_acqf_mixed_alternating(
     iterations.
 
     NOTE: This method assumes that all discrete variables are integer valued.
+    The discrete dimensions that have more than
+    `options.get("max_discrete_values", MAX_DISCRETE_VALUES)` values will
+    be optimized using continuous relaxation.
 
     # TODO: Support categorical variables.
 
@@ -549,6 +591,9 @@ def optimize_acqf_mixed_alternating(
             Defaults to 4.
         - "maxiter_continuous": Maximum number of iterations in each continuous step.
             Defaults to 8.
+        - "max_discrete_values": Maximum number of values for a discrete dimension
+            to be optimized using discrete step / local search. The discrete dimensions
+            with more values will be optimized using continuous relaxation.
         - "num_spray_points": Number of spray points (around `X_baseline`) to add to
             the points generated by the initialization strategy. Defaults to 20 if
             all discrete variables are binary and to 0 otherwise.
@@ -598,6 +643,17 @@ def optimize_acqf_mixed_alternating(
             f"Received an unsupported option {unsupported_keys}. {SUPPORTED_OPTIONS=}."
         )
 
+    # Update discrete dims and post processing functions to account for any
+    # dimensions that should be using continuous relaxation.
+    discrete_dims, post_processing_func = _setup_continuous_relaxation(
+        discrete_dims=discrete_dims,
+        bounds=bounds,
+        max_discrete_values=assert_is_instance(
+            options.get("max_discrete_values", MAX_DISCRETE_VALUES), int
+        ),
+        post_processing_func=post_processing_func,
+    )
+
     opt_inputs = OptimizeAcqfInputs(
         acq_function=acq_function,
         bounds=bounds,
@@ -623,7 +679,7 @@ def optimize_acqf_mixed_alternating(
     # Remove fixed features from dims, so they don't get optimized.
     discrete_dims = [dim for dim in discrete_dims if dim not in fixed_features]
     if len(discrete_dims) == 0:
-        raise ValueError("There must be at least one discrete parameter.")
+        return _optimize_acqf(opt_inputs=opt_inputs)
     if not (
         isinstance(discrete_dims, list)
         and len(set(discrete_dims)) == len(discrete_dims)

test/optim/test_optimize_mixed.py

@@ -19,12 +19,14 @@
 from botorch.models.gp_regression import SingleTaskGP
 from botorch.optim.optimize import _optimize_acqf, OptimizeAcqfInputs
 from botorch.optim.optimize_mixed import (
+    _setup_continuous_relaxation,
     complement_indices,
     continuous_step,
     discrete_step,
     generate_starting_points,
     get_nearest_neighbors,
     get_spray_points,
+    MAX_DISCRETE_VALUES,
     optimize_acqf_mixed_alternating,
     sample_feasible_points,
 )
@@ -544,20 +546,29 @@ def test_optimize_acqf_mixed_binary_only(self) -> None:
         self.assertEqual(candidates.shape[-1], dim)
         c_binary = candidates[:, binary_dims + [2]]
         self.assertTrue(((c_binary == 0) | (c_binary == 1)).all())
-        # Only continuous parameters will raise an error.
-        with self.assertRaisesRegex(
-            ValueError,
-            "There must be at least one discrete parameter",
-        ):
+        # Only continuous parameters should fallback to optimize_acqf.
+        with mock.patch(
+            f"{OPT_MODULE}._optimize_acqf", wraps=_optimize_acqf
+        ) as wrapped_optimize:
             optimize_acqf_mixed_alternating(
                 acq_function=acqf,
                 bounds=bounds,
                 discrete_dims=[],
                 options=options,
                 q=1,
                 raw_samples=20,
-                num_restarts=20,
+                num_restarts=2,
+            )
+        wrapped_optimize.assert_called_once_with(
+            opt_inputs=_make_opt_inputs(
+                acq_function=acqf,
+                bounds=bounds,
+                options=options,
+                q=1,
+                raw_samples=20,
+                num_restarts=2,
             )
+        )
         # Only discrete works fine.
         candidates, _ = optimize_acqf_mixed_alternating(
             acq_function=acqf,
@@ -720,3 +731,71 @@ def test_optimize_acqf_mixed_integer(self) -> None:
         wrapped_sample_feasible.assert_called_once()
         # Should request 4 candidates, since all 4 are infeasible.
         self.assertEqual(wrapped_sample_feasible.call_args.kwargs["num_points"], 4)
+
+    def test_optimize_acqf_mixed_continuous_relaxation(self) -> None:
+        # Testing with integer variables.
+        train_X, train_Y, binary_dims, cont_dims = self._get_data()
+        # Update the data to introduce integer dimensions.
+        binary_dims = [0]
+        integer_dims = [3, 4]
+        discrete_dims = binary_dims + integer_dims
+        bounds = torch.tensor(
+            [[0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 40.0, 15.0]],
+            dtype=torch.double,
+            device=self.device,
+        )
+        # Update the model to have a different optimizer.
+        root = torch.tensor([0.0, 0.0, 0.0, 25.0, 10.0], device=self.device)
+        model = QuadraticDeterministicModel(root)
+        acqf = qLogNoisyExpectedImprovement(model=model, X_baseline=train_X)
+
+        for max_discrete_values, post_processing_func in (
+            (None, None),
+            (5, lambda X: X + 10),
+        ):
+            options = {
+                "batch_limit": 5,
+                "init_batch_limit": 20,
+                "maxiter_alternating": 1,
+            }
+            if max_discrete_values is not None:
+                options["max_discrete_values"] = max_discrete_values
+            with mock.patch(
+                f"{OPT_MODULE}._setup_continuous_relaxation",
+                wraps=_setup_continuous_relaxation,
+            ) as wrapped_setup, mock.patch(
+                f"{OPT_MODULE}.discrete_step", wraps=discrete_step
+            ) as wrapped_discrete:
+                candidates, _ = optimize_acqf_mixed_alternating(
+                    acq_function=acqf,
+                    bounds=bounds,
+                    discrete_dims=discrete_dims,
+                    q=3,
+                    raw_samples=32,
+                    num_restarts=4,
+                    options=options,
+                    post_processing_func=post_processing_func,
+                )
+            wrapped_setup.assert_called_once_with(
+                discrete_dims=discrete_dims,
+                bounds=bounds,
+                max_discrete_values=max_discrete_values or MAX_DISCRETE_VALUES,
+                post_processing_func=post_processing_func,
+            )
+            discrete_call_args = wrapped_discrete.call_args.kwargs
+            expected_dims = [0, 4] if max_discrete_values is None else [0]
+            self.assertAllClose(
+                discrete_call_args["discrete_dims"],
+                torch.tensor(expected_dims, device=self.device),
+            )
+            # Check that dim 3 is rounded.
+            X = torch.ones(1, 5, device=self.device) * 0.6
+            X_expected = X.clone()
+            X_expected[0, 3] = 1.0
+            if max_discrete_values is not None:
+                X_expected[0, 4] = 1.0
+            if post_processing_func is not None:
+                X_expected = post_processing_func(X_expected)
+            self.assertAllClose(
+                discrete_call_args["opt_inputs"].post_processing_func(X), X_expected
+            )

test/test_utils/test_mock.py

@@ -98,7 +98,7 @@ def test_mock_optimize_mixed_alternating(self) -> None:
         ) as mock_neighbors:
             optimize_acqf_mixed_alternating(
                 acq_function=SinAcqusitionFunction(),
-                bounds=torch.tensor([[-2.0, 0.0], [2.0, 200.0]]),
+                bounds=torch.tensor([[-2.0, 0.0], [2.0, 20.0]]),
                 discrete_dims=[1],
                 num_restarts=1,
             )