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FIX improve error message for large sparse matrix input in LogisticRegression #21093

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Merged
merged 2 commits into from
Sep 24, 2021
Merged

FIX improve error message for large sparse matrix input in LogisticRegression #21093

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391742c
FIX improve error message for large sparse matrix input in LogisticRe…
TomDLT Sep 20, 2021
8463ab8
add whatsnew entry, add pull-request reference
TomDLT Sep 20, 2021
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7 changes: 7 additions & 0 deletions doc/whats_new/v1.1.rst
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Expand Up @@ -38,6 +38,13 @@ Changelog
:pr:`123456` by :user:`Joe Bloggs <joeongithub>`.
where 123456 is the *pull request* number, not the issue number.

:mod:`sklearn.linear_model`
...........................

- |Fix| :class:`linear_model.LogisticRegression` now raises a better error
message when the solver does not support sparse matrices with int64 indices.
:pr:`21093` by `Tom Dupre la Tour`_.

:mod:`sklearn.utils`
....................

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6 changes: 3 additions & 3 deletions sklearn/linear_model/_logistic.py
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Expand Up @@ -663,7 +663,7 @@ def _logistic_regression_path(
X,
accept_sparse="csr",
dtype=np.float64,
accept_large_sparse=solver != "liblinear",
accept_large_sparse=solver not in ["liblinear", "sag", "saga"],
)
y = check_array(y, ensure_2d=False, dtype=None)
check_consistent_length(X, y)
Expand Down Expand Up @@ -1511,7 +1511,7 @@ def fit(self, X, y, sample_weight=None):
accept_sparse="csr",
dtype=_dtype,
order="C",
accept_large_sparse=solver != "liblinear",
accept_large_sparse=solver not in ["liblinear", "sag", "saga"],
)
check_classification_targets(y)
self.classes_ = np.unique(y)
Expand Down Expand Up @@ -2080,7 +2080,7 @@ def fit(self, X, y, sample_weight=None):
accept_sparse="csr",
dtype=np.float64,
order="C",
accept_large_sparse=solver != "liblinear",
accept_large_sparse=solver not in ["liblinear", "sag", "saga"],
)
check_classification_targets(y)

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19 changes: 19 additions & 0 deletions sklearn/linear_model/tests/test_logistic.py
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Expand Up @@ -2237,3 +2237,22 @@ def test_sample_weight_not_modified(multi_class, class_weight):
)
clf.fit(X, y, sample_weight=W)
assert_allclose(expected, W)


@pytest.mark.parametrize("solver", ["liblinear", "lbfgs", "newton-cg", "sag", "saga"])
def test_large_sparse_matrix(solver):
# Solvers either accept large sparse matrices, or raise helpful error.
# Non-regression test for pull-request #21093.

# generate sparse matrix with int64 indices
X = sp.rand(20, 10, format="csr")
for attr in ["indices", "indptr"]:
setattr(X, attr, getattr(X, attr).astype("int64"))
y = np.random.randint(2, size=X.shape[0])

if solver in ["liblinear", "sag", "saga"]:
msg = "Only sparse matrices with 32-bit integer indices"
with pytest.raises(ValueError, match=msg):
LogisticRegression(solver=solver).fit(X, y)
else:
LogisticRegression(solver=solver).fit(X, y)