8000 [MRG + 1] Fix ValueError in LabelEncoder when using inverse_transform on unseen labels · Pull Request #9816 · scikit-learn/scikit-learn · GitHub
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[MRG + 1] Fix ValueError in LabelEncoder when using inverse_transform on unseen labels #9816

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Merged
merged 2 commits into from Sep 21, 2017
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[MRG + 1] Fix ValueError in LabelEncoder when using inverse_transform on unseen labels #9816

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397a8a2
Fix incorrect ValueError, add extra test case
charlienewey Sep 21, 2017
effbd45
Wrap error message to <79 chars
charlienewey Sep 21, 2017
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8 changes: 5 additions & 3 deletions sklearn/preprocessing/label.py
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Original file line number Diff line number Diff line change
Expand Up @@ -130,7 +130,8 @@ def transform(self, y):
classes = np.unique(y)
if len(np.intersect1d(classes, self.classes_)) < len(classes):
diff = np.setdiff1d(classes, self.classes_)
raise ValueError("y contains new labels: %s" % str(diff))
raise ValueError(
"y contains previously unseen labels: %s" % str(diff))
return np.searchsorted(self.classes_, y)

def inverse_transform(self, y):
Expand All @@ -148,8 +149,9 @@ def inverse_transform(self, y):
check_is_fitted(self, 'classes_')

diff = np.setdiff1d(y, np.arange(len(self.classes_)))
if diff:
raise ValueError("y contains new labels: %s" % str(diff))
if len(diff):
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This len is the fix, right?

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Correct - that's all that was needed to produce a sensible error 😄

raise ValueError(
"y contains previously unseen labels: %s" % str(diff))
y = np.asarray(y)
return self.classes_[y]

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6 changes: 4 additions & 2 deletions sklearn/preprocessing/tests/test_label.py
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Expand Up @@ -203,8 +203,10 @@ def test_label_encoder_errors():

# Fail on unseen labels
le = LabelEncoder()
le.fit([1, 2, 3, 1, -1])
assert_raises(ValueError, le.inverse_transform, [-1])
le.fit([1, 2, 3, -1, 1])
msg = "contains previously unseen labels"
assert_raise_message(ValueError, msg, le.inverse_transform, [-2])
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I find the organization of the tests a bit weird but not your fault. The test that it actually works if they are present is way at the top of the file.

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Happy to reorganise tomorrow if you are able to give me some pointers - I'm not very familiar with the testing structure of sklearn as this is my first issue.

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it's fine, I think.

assert_raise_message(ValueError, msg, le.inverse_transform, [-2, -3, -4])


def test_sparse_output_multilabel_binarizer():
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