Make optimizers raise an error when provided negative fixed features

Summary:
Context: See #2602

This PR:
* Adds a check for negative fixed_features keys to input validation for optimizers. This applies to all of the optimizers that take fixed_features.
* Updates docstrings

Differential Revision: D65272024

Author: esantorella

botorch/optim/optimize.py

@@ -125,6 +125,10 @@ def __post_init__(self) -> None:
                     "Must specify `raw_samples` when "
                     "`batch_initial_conditions` is None`."
                 )
+        if self.fixed_features is not None and any(
+            (k < 0 for k in self.fixed_features)
+        ):
+            raise ValueError("All indices (keys) in `fixed_features` must be >= 0.")
 
     def get_ic_generator(self) -> TGenInitialConditions:
         if self.ic_generator is not None:
@@ -467,7 +471,8 @@ def optimize_acqf(
             is set to 1, which will be done automatically if not specified in
             `options`.
         fixed_features: A map `{feature_index: value}` for features that
-            should be fixed to a particular value during generation.
+            should be fixed to a particular value during generation. All indices
+            should be non-negative.
         post_processing_func: A function that post-processes an optimization
             result appropriately (i.e., according to `round-trip`
             transformations).
@@ -610,7 +615,8 @@ def optimize_acqf_cyclic(
             with each tuple encoding an inequality constraint of the form
             `\sum_i (X[indices[i]] * coefficients[i]) = rhs`
         fixed_features: A map `{feature_index: value}` for features that
-            should be fixed to a particular value during generation.
+            should be fixed to a particular value during generation. All indices
+            should be non-negative.
         post_processing_func: A function that post-processes an optimization
             result appropriately (i.e., according to `round-trip`
             transformations).
@@ -758,11 +764,13 @@ def optimize_acqf_list(
             Using non-linear inequality constraints also requires that `batch_limit`
             is set to 1, which will be done automatically if not specified in
             `options`.
-        fixed_features: A map `{feature_index: value}` for features that
-            should be fixed to a particular value during generation.
+        fixed_features: A map `{feature_index: value}` for features that should
+            be fixed to a particular value during generation. All indices
+            (`feature_index`) should be non-negative.
         fixed_features_list: A list of maps `{feature_index: value}`. The i-th
             item represents the fixed_feature for the i-th optimization. If
             `fixed_features_list` is provided, `optimize_acqf_mixed` is invoked.
+            All indices (`feature_index`) should be non-negative.
         post_processing_func: A function that post-processes an optimization
             result appropriately (i.e., according to `round-trip`
             transformations).
@@ -872,7 +880,8 @@ def optimize_acqf_mixed(
         raw_samples: Number of samples for initialization. This is required
             if `batch_initial_conditions` is not specified.
         fixed_features_list: A list of maps `{feature_index: value}`. The i-th
-            item represents the fixed_feature for the i-th optimization.
+            item represents the fixed_feature for the i-th optimization. All
+            indices (`feature_index`) should be non-negative.
         options: Options for candidate generation.
         inequality constraints: A list of tuples (indices, coefficients, rhs),
             with each tuple encoding an inequality constraint of the form

test/optim/test_optimize.py

@@ -6,6 +6,7 @@
 
 import itertools
 import warnings
+from functools import partial
 from itertools import product
 from typing import Any
 from unittest import mock
@@ -1119,6 +1120,45 @@ def __call__(self, x, f):
         self.assertEqual(f_np_wrapper.call_count, 2)
 
 
+class TestAllOptimizers(BotorchTestCase):
+    def test_raises_with_negative_fixed_features(self) -> None:
+        cases = {
+            "optimize_acqf": partial(
+                optimize_acqf,
+                acq_function=MockAcquisitionFunction(),
+                fixed_features={-1: 0.0},
+                q=1,
+            ),
+            "optimize_acqf_cyclic": partial(
+                optimize_acqf_cyclic,
+                acq_function=MockAcquisitionFunction(),
+                fixed_features={-1: 0.0},
+                q=1,
+            ),
+            "optimize_acqf_mixed": partial(
+                optimize_acqf_mixed,
+                acq_function=MockAcquisitionFunction(),
+                fixed_features_list=[{-1: 0.0}],
+                q=1,
+            ),
+            "optimize_acqf_list": partial(
+                optimize_acqf_list,
+                acq_function_list=[MockAcquisitionFunction()],
+                fixed_features={-1: 0.0},
+            ),
+        }
+
+        for name, func in cases.items():
+            with self.subTest(name), self.assertRaisesRegex(
+                ValueError, "must be >= 0."
+            ):
+                func(
+                    bounds=torch.tensor([[0.0, 0.0], [1.0, 1.0]], device=self.device),
+                    num_restarts=4,
+                    raw_samples=16,
+                )
+
+
 class TestOptimizeAcqfCyclic(BotorchTestCase):
     @mock.patch("botorch.optim.optimize._optimize_acqf")  # noqa: C901
     # TODO: make sure this runs without mock
@@ -1171,7 +1211,7 @@ def test_optimize_acqf_cyclic(self, mock_optimize_acqf):
                 "set_X_pending",
                 wraps=mock_acq_function.set_X_pending,
             ) as mock_set_X_pending:
-                candidates, acq_value = optimize_acqf_cyclic(
+                candidates, _ = optimize_acqf_cyclic(
                     acq_function=mock_acq_function,
                     bounds=bounds,
                     q=q,