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Note non-determinism in random projection transforms #7129

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rapids-bot merged 1 commit into from
Aug 22, 2025
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Note non-determinism in random projection transforms #7129

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6 changes: 6 additions & 0 deletions python/cuml/cuml/random_projection/random_projection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -246,6 +246,9 @@ class GaussianRandomProjection(_BaseRandomProjection):
-----
Inspired by Scikit-learn's implementation:
https://scikit-learn.org/stable/modules/random_projection.html

Currently passing a sparse array to `transform` may result in close (but
not exactly identical) results due to https://github.com/cupy/cupy/issues/9323.
"""

def _gen_random_matrix(self, n_components, n_features, dtype):
Expand Down Expand Up @@ -362,6 +365,9 @@ class SparseRandomProjection(_BaseRandomProjection):
-----
Inspired by Scikit-learn's implementation:
https://scikit-learn.org/stable/modules/random_projection.html

Currently passing a dense array to `transform` may result in close (but
not exactly identical) results due to https://github.com/cupy/cupy/issues/9323.
"""

def __init__(
Expand Down
13 changes: 11 additions & 2 deletions python/cuml/tests/test_random_projection.py
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Expand Up @@ -163,10 +163,19 @@ def test_random_seed_consistency(cls, sparse):
t1 = model1.transform(X)
model2 = cls(n_components=5, random_state=42).fit(X)
t2 = model2.transform(X)
np.testing.assert_allclose(
np.testing.assert_array_equal(
asdense(model1.components_), asdense(model2.components_)
)
np.testing.assert_allclose(asdense(t1), asdense(t2))
# Due to https://github.com/cupy/cupy/issues/9323 only sparse @ sparse or
# dense @ dense outputs are exactly reproducible. All other combinations
# result in close but not identical outputs. For now we document this and
# relax the test constraint.
if (cls is SparseRandomProjection) != sparse:
# Mix of sparse and dense, check outputs are close
np.testing.assert_allclose(asdense(t1), asdense(t2), rtol=1e-4)
else:
# Both dense or sparse, can check exactly
np.testing.assert_array_equal(asdense(t1), asdense(t2))


@pytest.mark.parametrize("cls", classes)
Expand Down