8000 DOC Ensures that MultiLabelBinarizer passes numpydoc validation by jmloyola · Pull Request #21315 · scikit-learn/scikit-learn · GitHub
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DOC Ensures that MultiLabelBinarizer passes numpydoc validation #21315

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Merged
merged 3 commits into from
Oct 20, 2021
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DOC Ensures that MultiLabelBinarizer passes numpydoc validation #21315

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0a8b70a
Remove MultiLabelBinarizer from DOCSTRING_IGNORE_LIST
jmloyola Oct 12, 2021
35a96d8
Fix numpydocs from MultiLabelBinarizer
jmloyola Oct 12, 2021
93a503b
Change docstrings to maintain consistency
jmloyola Oct 12, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,6 @@
"LabelPropagation",
"LabelSpreading",
"LocallyLinearEmbedding",
"MultiLabelBinarizer",
"MultiTaskElasticNet",
"MultiTaskElasticNetCV",
"MultiTaskLasso",
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19 changes: 12 additions & 7 deletions sklearn/preprocessing/_label.py
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Expand Up @@ -692,6 +692,11 @@ class MultiLabelBinarizer(TransformerMixin, BaseEstimator):
Otherwise it corresponds to the sorted set of classes found
when fitting.

See Also
--------
OneHotEncoder : Encode categorical features using a one-hot aka one-of-K
scheme.

Examples
--------
>>> from sklearn.preprocessing import MultiLabelBinarizer
Expand Down Expand Up @@ -724,11 +729,6 @@ class MultiLabelBinarizer(TransformerMixin, BaseEstimator):
MultiLabelBinarizer()
>>> mlb.classes_
array(['comedy', 'sci-fi', 'thriller'], dtype=object)

See Also
--------
OneHotEncoder : Encode categorical features using a one-hot aka one-of-K
scheme.
"""

def __init__(self, *, classes=None, sparse_output=False):
Expand All @@ -747,7 +747,8 @@ def fit(self, y):

Returns
-------
self : returns this MultiLabelBinarizer instance
self : object
Fitted estimator.
"""
self._cached_dict = None
if self.classes is None:
Expand Down Expand Up @@ -778,7 +779,7 @@ def fit_transform(self, y):
Returns
-------
y_indicator : {ndarray, sparse matrix} of shape (n_samples, n_classes)
A mat 72F6 rix such that `y_indicator[i, j] = 1` i.f.f. `classes_[j]`
A matrix such that `y_indicator[i, j] = 1` iff `classes_[j]`
is in `y[i]`, and 0 otherwise. Sparse matrix will be of CSR
format.
"""
Expand Down Expand Up @@ -846,6 +847,10 @@ def _transform(self, y, class_mapping):
Parameters
----------
y : iterable of iterables
A set of labels (any orderable and hashable object) for each
sample. If the `classes` parameter is set, `y` will not be
iterated.

class_mapping : Mapping
Maps from label to column index in label indicator matrix.

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