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DOC Ensures that sklearn.utils.extmath.fast_logdet passes numpydoc validation #24605

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Merged
merged 5 commits into from
Oct 14, 2022
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DOC Ensures that sklearn.utils.extmath.fast_logdet passes numpydoc validation #24605

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4a9138e
Ensures that sklearn.utils.extmath.fast_logdet passes numpydoc valida…
awinml Oct 8, 2022
c30ef4f
Merge branch 'main' into fast_logdet-numpy-validation
awinml Oct 8, 2022
099a3f7
Update extmath.py
awinml Oct 8, 2022
6c2ad56
Merge remote-tracking branch 'upstream/main' into pr/awinml/24605
jeremiedbb Oct 14, 2022
2a113a7
simplify some parts, example in particular
jeremiedbb Oct 14, 2022
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10 changes: 0 additions & 10 deletions sklearn/tests/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,11 +11,6 @@

numpydoc_validation = pytest.importorskip("numpydoc.validate")

FUNCTION_DOCSTRING_IGNORE_LIST = [
"sklearn.utils.extmath.fast_logdet",
]
FUNCTION_DOCSTRING_IGNORE_LIST = set(FUNCTION_DOCSTRING_IGNORE_LIST)


def get_all_methods():
estimators = all_estimators()
Expand Down Expand Up @@ -142,11 +137,6 @@ def repr_errors(res, Klass=None, method: Optional[str] = None) -> str:
@pytest.mark.parametrize("function_name", get_all_functions_names())
def test_function_docstring(function_name, request):
"""Check function docstrings using numpydoc."""
if function_name in FUNCTION_DOCSTRING_IGNORE_LIST:
request.applymarker(
pytest.mark.xfail(run=False, reason="TODO pass numpydoc validation")
)

res = numpydoc_validation.validate(function_name)

res["errors"] = list(filter_errors(res["errors"], method="function"))
Expand Down
32 changes: 27 additions & 5 deletions sklearn/utils/extmath.py
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Original file line number Diff line number Diff line change
Expand Up @@ -82,15 +82,37 @@ def row_norms(X, squared=False):


def fast_logdet(A):
"""Compute log(det(A)) for A symmetric.
"""Compute logarithm of determinant of a square matrix.

Equivalent to : np.log(nl.det(A)) but more robust.
It returns -Inf if det(A) is non positive or is not defined.
The (natural) logarithm of the determinant of a square matrix
is returned if det(A) is non-negative and well defined.
If the determinant is zero or negative returns -Inf.

Equivalent to : np.log(np.det(A)) but more robust.

Parameters
----------
A : array-like
The matrix.
A : array_like of shape (n, n)
The square matrix.

Returns
-------
logdet : float
When det(A) is strictly positive, log(det(A)) is returned.
When det(A) is non-positive or not defined, then -inf is returned.

See Also
--------
numpy.linalg.slogdet : Compute the sign and (natural) logarithm of the determinant
of an array.

Examples
--------
>>> import numpy as np
>>> from sklearn.utils.extmath import fast_logdet
>>> a = np.array([[5, 1], [2, 8]])
>>> fast_logdet(a)
3.6375861597263857
"""
sign, ld = np.linalg.slogdet(A)
if not sign > 0:
Expand Down