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DOC Minor updates to OPTICS docstring #31363

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May 23, 2025
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DOC Minor updates to OPTICS docstring #31363

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1f18373
doc updates
lucyleeow May 12, 2025
ee57017
add back sentence
lucyleeow May 14, 2025
c5d0ac3
Merge branch 'main' into doc_optics
lucyleeow May 21, 2025
e9c9410
amend docstring
lucyleeow May 21, 2025
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29 changes: 15 additions & 14 deletions sklearn/cluster/_optics.py
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Expand Up @@ -34,19 +34,21 @@ class OPTICS(ClusterMixin, BaseEstimator):
"""Estimate clustering structure from vector array.

OPTICS (Ordering Points To Identify the Clustering Structure), closely
related to DBSCAN, finds core sample of high density and expands clusters
from them [1]_. Unlike DBSCAN, keeps cluster hierarchy for a variable
related to DBSCAN, finds core samples of high density and expands clusters
from them [1]_. Unlike DBSCAN, it keeps cluster hierarchy for a variable
neighborhood radius. Better suited for usage on large datasets than the
current sklearn implementation of DBSCAN.
current scikit-learn implementation of DBSCAN.

Clusters are then extracted using a DBSCAN-like method
(cluster_method = 'dbscan') or an automatic
Clusters are then extracted from the cluster-order using a
DBSCAN-like method (cluster_method = 'dbscan') or an automatic
technique proposed in [1]_ (cluster_method = 'xi').

This implementation deviates from the original OPTICS by first performing
k-nearest-neighborhood searches on all points to identify core sizes, then
computing only the distances to unprocessed points when constructing the
cluster order. Note that we do not employ a heap to manage the expansion
k-nearest-neighborhood searches on all points to identify core sizes of
all points (instead of computing neighbors while looping through points).
Reachability distances to only unprocessed points are then computed, to
construct the cluster order, similar to the original OPTICS.
Note that we do not employ a heap to manage the expansion
candidates, so the time complexity will be O(n^2).

Read more in the :ref:`User Guide <optics>`.
Expand All @@ -68,9 +70,9 @@ class OPTICS(ClusterMixin, BaseEstimator):

metric : str or callable, default='minkowski'
Metric to use for distance computation. Any metric from scikit-learn
or scipy.spatial.distance can be used.
or :mod:`scipy.spatial.distance` can be used.

If metric is a callable function, it is called on each
If `metric` is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays as input and return one value indicating the
distance between them. This works for Scipy's metrics, but is less
Expand All @@ -90,8 +92,7 @@ class OPTICS(ClusterMixin, BaseEstimator):
'yule']

Sparse matrices are only supported by scikit-learn metrics.
See the documentation for scipy.spatial.distance for details on these
metrics.
See :mod:`scipy.spatial.distance` for details on these metrics.

.. note::
`'kulsinski'` is deprecated from SciPy 1.9 and will be removed in SciPy 1.11.
Expand All @@ -105,9 +106,9 @@ class OPTICS(ClusterMixin, BaseEstimator):
metric_params : dict, default=None
Additional keyword arguments for the metric function.

cluster_method : str, default='xi'
cluster_method : {'xi', 'dbscan'}, default='xi'
The extraction method used to extract clusters using the calculated
reachability and ordering. Possible values are "xi" and "dbscan".
reachability and ordering.

eps : float, default=None
The maximum distance between two samples for one to be considered as
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