Skip to content

[MRG] Fix parallelisation of kmeans clustering #12955

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 7 commits into from
Jan 16, 2019
Merged

[MRG] Fix parallelisation of kmeans clustering #12955

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
d0626e4
FIX Fix parallelisation of kmeans clustering
nixphix Jan 11, 2019
6ebf8c7
Use effective_n_jobs to find number of threads
nixphix Jan 15, 2019
ce1e952
Update docstring test to be more deterministic
nixphix Jan 15, 2019
a257c19
Update doctest expected result
nixphix Jan 15, 2019
6cb846c
Fix cluster centre result alignment
nixphix Jan 15, 2019
0fa1263
Add whats new entry
nixphix Jan 16, 2019
9966e20
Update whats new entry
nixphix Jan 16, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 7 additions & 0 deletions doc/whats_new/v0.20.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,13 @@ enhancements to features released in 0.20.0.
Changelog
---------

:mod:`sklearn.cluster`
......................

- |Fix| Fixed a bug in :class:`cluster.KMeans` where computation was single
threaded when `n_jobs > 1` or `n_jobs = -1`.
:issue:`12949` by :user:`Prabakaran Kumaresshan <nixphix>`.

:mod:`sklearn.linear_model`
...........................

Expand Down
14 changes: 7 additions & 7 deletions sklearn/cluster/k_means_.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -367,7 +367,7 @@ def k_means(X, n_clusters, sample_weight=None, init='k-means++',
else:
raise ValueError("Algorithm must be 'auto', 'full' or 'elkan', got"
" %s" % str(algorithm))
if effective_n_jobs(n_jobs):
if effective_n_jobs(n_jobs) == 1:
# For a single thread, less memory is needed if we just store one set
# of the best results (as opposed to one set per run per thread).
for it in range(n_init):
Expand Down Expand Up @@ -868,15 +868,15 @@ class KMeans(BaseEstimator, ClusterMixin, TransformerMixin):
>>> from sklearn.cluster import KMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
... [4, 2], [4, 4], [4, 0]])
... [10, 2], [10, 4], [10, 0]])
>>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
>>> kmeans.labels_
array([0, 0, 0, 1, 1, 1], dtype=int32)
>>> kmeans.predict([[0, 0], [4, 4]])
array([0, 1], dtype=int32)
array([1, 1, 1, 0, 0, 0], dtype=int32)
>>> kmeans.predict([[0, 0], [12, 3]])
array([1, 0], dtype=int32)
>>> kmeans.cluster_centers_
array([[1., 2.],
[4., 2.]])
array([[10., 2.],
[ 1., 2.]])

See also
--------
Expand Down