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Make optimizers raise an error when provided negative fixed features #2603

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Make optimizers raise an error when provided negative fixed features #2603

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19 changes: 14 additions & 5 deletions botorch/optim/optimize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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:
Expand Down Expand Up @@ -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).
Expand Down Expand Up @@ -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).
Expand Down Expand Up @@ -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).
Expand Down Expand Up @@ -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
Expand Down
42 changes: 41 additions & 1 deletion test/optim/test_optimize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@

import itertools
import warnings
from functools import partial
from itertools import product
from typing import Any
from unittest import mock
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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,
Expand Down
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