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DOC ensures DBSCAN docstring passes numpydoc validation #20375

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merged 6 commits into from
Jun 28, 2021
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DOC ensures DBSCAN docstring passes numpydoc validation #20375

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619fb1e
Fixing numpydoc for fit_predict method.
fbidu Jun 26, 2021
00ae6a2
Fixing numpydoc for fit method.
fbidu Jun 26, 2021
d24986d
DBSCAN complies with numpydoc style.
fbidu Jun 26, 2021
b7745e4
Better summary for method
ijpulidos Jun 26, 2021
812eac0
removing trailing space
ijpulidos Jun 26, 2021
1a198e1
Update sklearn/cluster/_dbscan.py
glemaitre Jun 28, 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 @@ -30,7 +30,6 @@
"ColumnTransformer",
"ComplementNB",
"CountVectorizer",
"DBSCAN",
"DecisionTreeClassifier",
"DecisionTreeRegressor",
"DictVectorizer",
Expand Down
45 changes: 22 additions & 23 deletions sklearn/cluster/_dbscan.py
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Expand Up @@ -183,7 +183,7 @@ class DBSCAN(ClusterMixin, BaseEstimator):
The number of samples (or total weight) in a neighborhood for a point
to be considered as a core point. This includes the point itself.

metric : string, or callable, default='euclidean'
metric : str, or callable, default='euclidean'
The metric to use when calculating distance between instances in a
feature array. If metric is a string or callable, it must be one of
the options allowed by :func:`sklearn.metrics.pairwise_distances` for
Expand Down Expand Up @@ -239,18 +239,6 @@ class DBSCAN(ClusterMixin, BaseEstimator):

.. versionadded:: 0.24

Examples
--------
>>> from sklearn.cluster import DBSCAN
>>> import numpy as np
>>> X = np.array([[1, 2], [2, 2], [2, 3],
... [8, 7], [8, 8], [25, 80]])
>>> clustering = DBSCAN(eps=3, min_samples=2).fit(X)
>>> clustering.labels_
array([ 0, 0, 0, 1, 1, -1])
>>> clustering
DBSCAN(eps=3, min_samples=2)

See Also
--------
OPTICS : A similar clustering at multiple values of eps. Our implementation
Expand Down Expand Up @@ -289,6 +277,18 @@ class DBSCAN(ClusterMixin, BaseEstimator):
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017).
DBSCAN revisited, revisited: why and how you should (still) use DBSCAN.
ACM Transactions on Database Systems (TODS), 42(3), 19.

Examples
--------
>>> from sklearn.cluster import DBSCAN
>>> import numpy as np
>>> X = np.array([[1, 2], [2, 2], [2, 3],
... [8, 7], [8, 8], [25, 80]])
>>> clustering = DBSCAN(eps=3, min_samples=2).fit(X)
>>> clustering.labels_
array([ 0, 0, 0, 1, 1, -1])
>>> clustering
DBSCAN(eps=3, min_samples=2)
"""

def __init__(
Expand Down Expand Up @@ -323,19 +323,19 @@ def fit(self, X, y=None, sample_weight=None):
``metric='precomputed'``. If a sparse matrix is provided, it will
be converted into a sparse ``csr_matrix``.

y : Ignored
Not used, present here for API consistency by convention.

sample_weight : array-like of shape (n_samples,), default=None
Weight of each sample, such that a sample with a weight of at least
``min_samples`` is by itself a core sample; a sample with a
negative weight may inhibit its eps-neighbor from being core.
Note that weights are absolute, and default to 1.

y : Ignored
Not used, present here for API consistency by convention.

Returns
-------
self

self : object
Returns a fitted instance of self.
"""
X = self._validate_data(X, accept_sparse="csr")

Expand Down Expand Up @@ -394,8 +394,7 @@ def fit(self, X, y=None, sample_weight=None):
return self

def fit_predict(self, X, y=None, sample_weight=None):
"""Perform DBSCAN clustering from features or distance matrix,
and return cluster labels.
"""Compute clusters from a data or distance matrix and predict labels.

Parameters
----------
Expand All @@ -405,15 +404,15 @@ def fit_predict(self, X, y=None, sample_weight=None):
``metric='precomputed'``. If a sparse matrix is provided, it will
be converted into a sparse ``csr_matrix``.

y : Ignored
Not used, present here for API consistency by convention.

sample_weight : array-like of shape (n_samples,), default=None
Weight of each sample, such that a sample with a weight of at least
``min_samples`` is by itself a core sample; a sample with a
negative weight may inhibit its eps-neighbor from being core.
Note that weights are absolute, and default to 1.

y : Ignored
Not used, present here for API consistency by convention.

Returns
-------
labels : ndarray of shape (n_samples,)
Expand Down