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FIX Raises error in PLSRegression for invalid n_components #29710

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Aug 29, 2024
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FIX Raises error in PLSRegression for invalid n_components #29710

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5be4d28
FIX Raises error in PLSRegression for invalid n_components
thomasjpfan Aug 24, 2024
c913e2b
DOC Adds PR number to whats new
thomasjpfan Aug 24, 2024
59dbd78
Merge remote-tracking branch 'upstream/main' into thomasjpfan/fix_pls_pr
thomasjpfan Aug 28, 2024
c614872
CLN Remove unneeded change
thomasjpfan Aug 28, 2024
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6 changes: 6 additions & 0 deletions doc/whats_new/v1.6.rst
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Expand Up @@ -172,6 +172,12 @@ Changelog
now accepts string format or callable to generate feature names. :pr:`28934` by
:user:`Marc Bresson <MarcBresson>`.

:mod:`sklearn.cross_decomposition`
..................................

- |Fix| :class:`cross_decomposition.PLSRegression` properly raises an error when
`n_components` is larger than `n_samples`. :pr:`29710` by `Thomas Fan`_.

:mod:`sklearn.datasets`
.......................

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4 changes: 3 additions & 1 deletion sklearn/cross_decomposition/_pls.py
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Expand Up @@ -291,7 +291,9 @@ def fit(self, X, y=None, Y=None):
# With PLSRegression n_components is bounded by the rank of (X.T X) see
# Wegelin page 25. With CCA and PLSCanonical, n_components is bounded
# by the rank of X and the rank of Y: see Wegelin page 12
rank_upper_bound = p if self.deflation_mode == "regression" else min(n, p, q)
rank_upper_bound = (
min(n, p) if self.deflation_mode == "regression" else min(n, p, q)
)
if n_components > rank_upper_bound:
raise ValueError(
f"`n_components` upper bound is {rank_upper_bound}. "
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11 changes: 11 additions & 0 deletions sklearn/cross_decomposition/tests/test_pls.py
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Expand Up @@ -480,6 +480,17 @@ def test_n_components_upper_bounds(Estimator):
est.fit(X, Y)


def test_n_components_upper_PLSRegression():
"""Check the validation of `n_components` upper bounds for PLSRegression."""
rng = np.random.RandomState(0)
X = rng.randn(20, 64)
Y = rng.randn(20, 3)
est = PLSRegression(n_components=30)
err_msg = "`n_components` upper bound is 20. Got 30 instead. Reduce `n_components`."
with pytest.raises(ValueError, match=err_msg):
est.fit(X, Y)


@pytest.mark.parametrize("n_samples, n_features", [(100, 10), (100, 200)])
def test_singular_value_helpers(n_samples, n_features, global_random_seed):
# Make sure SVD and power method give approximately the same results
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