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[MRG] Fixes get_feature_names results when using drop functionality #13894

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Merged
merged 6 commits into from
May 23, 2019
Merged

[MRG] Fixes get_feature_names results when using drop functionality #13894

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d08efd1
Fix get_feature_names results when using drop functionality
jamesmyatt May 16, 2019
15d1eaf
Test names for all drop methods
jamesmyatt May 16, 2019
fadc4de
TST Make expected_names a parameter
jnothman May 23, 2019
f92cf9e
Merge branch 'master' into HEAD
jnothman May 23, 2019
72f7369
DOC what's new
jnothman May 23, 2019
8d9eb61
[ci skip] fix formatting
ogrisel May 23, 2019
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15 changes: 11 additions & 4 deletions doc/whats_new/v0.21.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -15,10 +15,17 @@ Changelog
:mod:`sklearn.metrics`
......................

- |Fix| Fixed a bug in :func:`euclidean_distances` where a part of the distance
matrix was left un-instanciated for suffiently large float32 datasets
(regression introduced in 0.21) :issue:`13910`
by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| Fixed a bug in :func:`metrics.euclidean_distances` where a part of the
distance matrix was left un-instanciated for suffiently large float32
datasets (regression introduced in 0.21). :pr:`13910` by :user:`Jérémie du
Boisberranger <jeremiedbb>`.

:mod:`sklearn.preprocessing`
............................

- |Fix| Fixed a bug in :class:`preprocessing.OneHotEncoder` where the new
`drop` parameter was not reflected in `get_feature_names`. :pr:`13894`
by :user:`James Myatt <jamesmyatt>`.

.. _changes_0_21_1:

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2 changes: 2 additions & 0 deletions sklearn/preprocessing/_encoders.py
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Expand Up @@ -848,6 +848,8 @@ def get_feature_names(self, input_features=None):
for i in range(len(cats)):
names = [
input_features[i] + '_' + str(t) for t in cats[i]]
if self.drop is not None:
names.pop(self.drop_idx_[i])
feature_names.extend(names)

return np.array(feature_names, dtype=object)
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15 changes: 15 additions & 0 deletions sklearn/preprocessing/tests/test_encoders.py
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Expand Up @@ -590,6 +590,21 @@ def test_one_hot_encoder_feature_names_unicode():
assert_array_equal(['n👍me_c❤t1', 'n👍me_dat2'], feature_names)


@pytest.mark.parametrize("drop, expected_names",
[('first', ['x0_c', 'x2_b']),
(['c', 2, 'b'], ['x0_b', 'x2_a'])],
ids=['first', 'manual'])
def test_one_hot_encoder_feature_names_drop(drop, expected_names):
X = [['c', 2, 'a'],
['b', 2, 'b']]

ohe = OneHotEncoder(drop=drop)
ohe.fit(X)
feature_names = ohe.get_feature_names()
assert isinstance(feature_names, np.ndarray)
assert_array_equal(expected_names, feature_names)


@pytest.mark.parametrize("X", [np.array([[1, np.nan]]).T,
np.array([['a', np.nan]], dtype=object).T],
ids=['numeric', 'object'])
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