8000 fix check_array dtype check for pandas series by amueller · Pull Request #12625 · scikit-learn/scikit-learn · GitHub
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fix check_array dtype check for pandas series #12625

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Merged
merged 7 commits into from
Nov 20, 2018
Merged

fix check_array dtype check for pandas series #12625

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0d85e3e
fix check_array dtype check for pandas series
amueller Nov 20, 2018
2e35165
add comment explaining fix
amueller Nov 20, 2018
75ed85b
don't define dtypes for series
amueller Nov 20, 2018
87cd3fa
check output of check_array on series
amueller Nov 20, 2018
7bfaf37
add whatsnew for pandas series fix, fix link to my website
amueller Nov 20, 2018
6734c93
fix flake8
amueller Nov 20, 2018
b2e0849
Update v0.20.rst
jnothman Nov 20, 2018
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2 changes: 1 addition & 1 deletion doc/whats_new/_contributors.rst
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Expand Up @@ -48,7 +48,7 @@

.. _Bertrand Thirion: https://team.inria.fr/parietal/bertrand-thirions-page

.. _Andreas Müller: https://peekaboo-vision.blogspot.com/
.. _Andreas Müller: https://amueller.github.io/

.. _Matthieu Perrot: http://brainvisa.info/biblio/lnao/en/Author/PERROT-M.html

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4 changes: 4 additions & 0 deletions doc/whats_new/v0.20.rst
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Expand Up @@ -176,6 +176,10 @@ Changelog
precision issues in :class:`preprocessing.StandardScaler` and
:class:`decomposition.IncrementalPCA` when using float32 datasets.
:issue:`12338` by :user:`bauks <bauks>`.

- |Fix| Calling :func:`utils.check_array` on `pandas.Series`, which
raised an error in 0.20.0, now returns the expected output again.
:issue:`12625` by `Andreas Müller`_

Miscellaneous
.............
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8 changes: 8 additions & 0 deletions sklearn/utils/tests/test_validation.py
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Expand Up @@ -694,6 +694,14 @@ def test_suppress_validation():
assert_raises(ValueError, assert_all_finite, X)


def test_check_array_series():
# regression test that check_array works on pandas Series
pd = importorskip("pandas")
res = check_array(pd.Series([1, 2, 3]), ensure_2d=False,
warn_on_dtype=True)
assert_array_equal(res, np.array([1, 2, 3]))


def test_check_dataframe_warns_on_dtype():
# Check that warn_on_dtype also works for DataFrames.
# https://github.com/scikit-learn/scikit-learn/issues/10948
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2 changes: 1 addition & 1 deletion sklearn/utils/validation.py
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Expand Up @@ -477,7 +477,7 @@ def check_array(array, accept_sparse=False, accept_large_sparse=True,
# check if the object contains several dtypes (typically a pandas
# DataFrame), and store them. If not, store None.
dtypes_orig = None
if hasattr(array, "dtypes") and hasattr(array, "__array__"):
if hasattr(array, "dtypes") and len(array.dtypes):
dtypes_orig = np.array(array.dtypes)

if dtype_numeric:
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