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Summary

An open API service for producing an overview of a list of open source projects.

JOSS: https://github.com/lmcinnes/umap

dimensionality-reduction machine-learning topological-data-analysis umap visualization

Score: 29.228933785411854

Last synced: about 22 hours ago
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Repository metadata:

Uniform Manifold Approximation and Projection

Owner metadata:

GitHub Events

Total
Last Year

Committers metadata

Last synced: about 1 month ago

Total Commits: 1,570
Total Committers: 144
Avg Commits per committer: 10.903
Development Distribution Score (DDS): 0.418

Commits in past year: 48
Committers in past year: 8
Avg Commits per committer in past year: 6.0
Development Distribution Score (DDS) in past year: 0.542

Name Email Commits
Leland McInnes l****s@g****m 914
leriomaggio v****o@g****m 63
paxtonfitzpatrick p****9@d****u 43
James Melville j****e@g****m 42
adalmia96 a****1@j****u 31
jacob golding t****1@a****o 25
timsainb t****b@g****m 18
Max Cembalest m****t@g****m 18
gclen g****g@u****t 17
Benoit Hamelin b****n@c****a 16
Nathaniel Saul n****t@s****m 14
AMS-Hippo 1****o 14
John Healy j****l@g****m 14
Benson Muite b****t 12
Hande Gözükan h****n@i****r 12
Joseph Courtney j****y@g****m 12
Sebastian Pujalte 3****s 11
parashardhapola p****a@g****m 11
Tom White t****e@g****m 10
Francois Chollet f****t@g****m 10
Hoon Cho h****o@m****u 10
sleighsoft j****r@g****m 9
gclendenning 6****g 9
Andrew Tritt a****t@l****v 8
Matthew Carrigan r****1@g****m 8
Vicram Rajagopalan 4****r 8
usul83 u****b@u****k 8
jc-healy j****y@g****m 8
Michael Louis Thaler m****r@d****m 7
markfraney m****y@o****m 6
and 114 more...

Issue and Pull Request metadata

Last synced: about 2 months ago

Total issues: 244
Total pull requests: 116
Average time to close issues: 4 months
Average time to close pull requests: 29 days
Total issue authors: 223
Total pull request authors: 68
Average comments per issue: 2.73
Average comments per pull request: 1.38
Merged pull request: 77
Bot issues: 0
Bot pull requests: 0

Past year issues: 37
Past year pull requests: 30
Past year average time to close issues: 6 days
Past year average time to close pull requests: 2 days
Past year issue authors: 36
Past year pull request authors: 16
Past year average comments per issue: 0.78
Past year average comments per pull request: 0.23
Past year merged pull request: 14
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/lmcinnes/umap

Top Issue Authors

  • gg4u (7)
  • idekany (3)
  • Rridley7 (2)
  • conchoecia (2)
  • KnSun99 (2)
  • Cristian-wp (2)
  • erenozcelik (2)
  • rhysnewell (2)
  • Dyn-Walker (2)
  • dbl001 (2)
  • dewball345 (2)
  • PierreGtch (2)
  • carlosgmartin (2)
  • bernstei (2)
  • abs51295 (2)

Top Pull Request Authors

  • lmcinnes (9)
  • gclendenning (6)
  • fchollet (5)
  • AMS-Hippo (4)
  • cakiki (4)
  • hamelin (4)
  • wilsonjr (2)
  • HairyFotr (2)
  • mcembalest (2)
  • NickCrews (2)
  • milesmcc (2)
  • Dhia-naouali (2)
  • kmkolasinski (2)
  • notgivenby (2)
  • TNTksals (2)

Top Issue Labels

  • Good Reads (4)
  • new feature (3)
  • documentation (2)
  • enhancement (2)
  • good first issue (1)
  • 0.4 (1)

Top Pull Request Labels

Package metadata

pypi.org: umap-learn

Uniform Manifold Approximation and Projection

  • Homepage: http://github.com/lmcinnes/umap
  • Documentation: https://umap-learn.readthedocs.io/
  • Licenses: BSD
  • Latest release: 0.5.8 (published 7 months ago)
  • Last Synced: 2025-12-30T18:32:18.259Z (about 1 month ago)
  • Versions: 43
  • Dependent Packages: 370
  • Dependent Repositories: 2,209
  • Downloads: 2,688,923 Last month
  • Docker Downloads: 1,318,322
  • Rankings:
    • Dependent packages count: 0.082%
    • Dependent repos count: 0.234%
    • Downloads: 0.327%
    • Stargazers count: 0.349%
    • Average: 0.558%
    • Docker downloads count: 0.798%
    • Forks count: 1.56%
  • Maintainers (3)
conda-forge.org: umap-learn

umap-learn provides the UMAP manifold based dimension reduction algorithm. The package provides an sklearn compatible interface to t-SNE like dimension reduction technique that has better runtime performance than t-SNE and often preserves more global structure than t-SNE.

  • Homepage: http://github.com/lmcinnes/umap
  • Licenses: BSD-2-Clause
  • Latest release: 0.5.3 (published almost 4 years ago)
  • Last Synced: 2025-12-01T12:31:48.980Z (2 months ago)
  • Versions: 23
  • Dependent Packages: 14
  • Dependent Repositories: 84
  • Rankings:
    • Dependent repos count: 3.803%
    • Stargazers count: 4.337%
    • Dependent packages count: 4.472%
    • Average: 4.664%
    • Forks count: 6.044%
proxy.golang.org: github.com/lmcinnes/umap

  • Homepage:
  • Documentation: https://pkg.go.dev/github.com/lmcinnes/umap#section-documentation
  • Licenses: bsd-3-clause
  • Latest release: v0.1.4 (published about 8 years ago)
  • Last Synced: 2025-12-27T10:11:10.901Z (about 1 month ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Stargazers count: 0.821%
    • Forks count: 0.995%
    • Average: 5.212%
    • Dependent packages count: 8.384%
    • Dependent repos count: 10.647%
spack.io: py-umap-learn

Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction.

  • Homepage: https://github.com/lmcinnes/umap
  • Licenses: []
  • Latest release: 0.5.3 (published about 3 years ago)
  • Last Synced: 2025-12-27T10:11:09.673Z (about 1 month ago)
  • Versions: 1
  • Dependent Packages: 2
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Stargazers count: 1.848%
    • Forks count: 3.418%
    • Average: 8.333%
    • Dependent packages count: 28.067%
  • Maintainers (1)
pypi.org: umap-learn-no-tf

Uniform Manifold Approximation and Projection

  • Homepage: http://github.com/lmcinnes/umap
  • Documentation: https://umap-learn-no-tf.readthedocs.io/
  • Licenses: BSD
  • Latest release: 0.5.7 (published 4 months ago)
  • Last Synced: 2025-12-27T10:11:09.304Z (about 1 month ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 66 Last month
  • Rankings:
    • Stargazers count: 0.848%
    • Forks count: 2.089%
    • Dependent packages count: 8.402%
    • Average: 15.92%
    • Downloads: 20.767%
    • Dependent repos count: 47.492%
  • Maintainers (1)
anaconda.org: umap-learn

umap-learn provides the UMAP manifold based dimension reduction algorithm. The package provides an sklearn compatible interface to t-SNE like dimension reduction technique that has better runtime performacne than t-SNE and often preserves more global structure than t-SNE.

  • Homepage: https://github.com/lmcinnes/umap
  • Licenses: BSD-3-Clause
  • Latest release: 0.5.4 (published over 2 years ago)
  • Last Synced: 2025-12-27T10:11:12.126Z (about 1 month ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 84
  • Rankings:
    • Stargazers count: 10.123%
    • Forks count: 12.699%
    • Dependent repos count: 20.184%
    • Average: 20.989%
    • Dependent packages count: 40.951%

Dependencies

doc/doc_requirements.txt pypi
  • bokeh >=0.13
  • datashader >=0.6
  • numba >=0.37
  • numpy >=1.13
  • scikit-learn >=0.19
  • scipy >=0.19
  • seaborn >=0.8
  • sphinx-gallery *
  • tqdm *
docs_requirements.txt pypi
  • matplotlib *
  • pillow *
  • sphinx *
  • sphinx_gallery *
  • sphinx_rtd_theme *
requirements.txt pypi
  • numba >=0.51.2
  • numpy >=1.17
  • pynndescent >=0.5
  • scikit-learn >=0.22
  • scipy >=1.3.1
  • tbb >=2019.0
  • tqdm *