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BUG: fix check_array on pandas Series with custom dtype (eg categorical) #12706

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Merged
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Dec 3, 2018
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BUG: fix check_array on pandas Series with custom dtype (eg categorical) #12706

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7 changes: 7 additions & 0 deletions doc/whats_new/v0.20.rst
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Expand Up @@ -39,6 +39,13 @@ Changelog
function to return 0 when two all-zero vectors are compared.
:issue:`12685` by :user:`Thomas Fan <thomasjpfan>`.

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

- |Fix| Calling :func:`utils.check_array` on `pandas.Series` with categorical
data, which raised an error in 0.20.0, now returns the expected output again.
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categorical data -> something like pandas custom dtype? though I'm not able to come up with another application scenario.

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Sorry, I seem to have missed this comment.

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I think it's not important :)

:issue:`12699` by `Joris Van den Bossche`_.

.. _changes_0_20_1:

Version 0.20.1
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5 changes: 5 additions & 0 deletions sklearn/utils/tests/test_validation.py
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Expand Up @@ -701,6 +701,11 @@ def test_check_array_series():
warn_on_dtype=True)
assert_array_equal(res, np.array([1, 2, 3]))

# with categorical dtype (not a numpy dtype) (GH12699)
s = pd.Series(['a', 'b', 'c']).astype('category')
res = check_array(s, dtype=None, ensure_2d=False)
assert_array_equal(res, np.array(['a', 'b', 'c'], dtype=object))


def test_check_dataframe_warns_on_dtype():
# Check that warn_on_dtype also works for DataFrames.
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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 len(array.dtypes):
if hasattr(array, "dtypes") and hasattr(array.dtypes, '__array__'):
dtypes_orig = np.array(array.dtypes)

if dtype_numeric:
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