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Use int64 sampling for integer parameter bounds on Windows #624

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Use int64 sampling for integer parameter bounds on Windows #624

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075be2a
Use explicit int64 sampling for integer parameter bounds
AHMETHAKANBEZIR1 Oct 2, 2026
3abdd82
Merge master and preserve both parameter regressions
AHMETHAKANBEZIR1 Oct 2, 2026
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4 changes: 3 additions & 1 deletion bayes_opt/parameter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -278,7 +278,9 @@ def random_sample(
The samples.
"""
random_state = ensure_rng(random_state)
return random_state.randint(self.bounds[0], self.bounds[1] + 1, n_samples).astype(float)
return random_state.randint(self.bounds[0], self.bounds[1] + 1, n_samples, dtype=np.int64).astype(
float
)

def to_float(self, value: int | float) -> float:
"""Convert a parameter value to a float.
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14 changes: 14 additions & 0 deletions tests/test_parameter.py
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Expand Up @@ -87,6 +87,20 @@ def target_func(**kwargs):
assert p1.kernel_transform(np.array([1.3, 3.6, 7.2])) == pytest.approx(np.array([1, 4, 7]))


@pytest.mark.parametrize("bounds", [(3_000_000_000, 3_000_000_010), (-3_000_000_010, -3_000_000_000), (0, 5)])
def test_int_random_sample_large_bounds(bounds):
parameter = IntParameter("x", bounds)
samples = parameter.random_sample(100, random_state=np.random.RandomState(42))
repeated = parameter.random_sample(100, random_state=np.random.RandomState(42))
assert samples.dtype == np.dtype(float)
np.testing.assert_array_equal(samples, repeated)
assert np.all(samples >= bounds[0])
assert np.all(samples <= bounds[1])
assert np.all(samples == np.floor(samples))
assert bounds[0] in samples
assert bounds[1] in samples


def test_categorical_kernel_transform_preserves_batch_rows():
parameter = CategoricalParameter("x", ["a", "b", "c"])
values = np.array([[0.2, 0.8, 0.1], [0.9, 0.1, 0.2], [0.1, 0.2, 0.7], [0.2, 0.8, 0.1]])
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