sparse-lm

sparse-lm: Sparse linear regression models in Python - Published in JOSS (2023)

https://github.com/cedergrouphub/sparse-lm

Science Score: 98.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 4 DOI reference(s) in README and JOSS metadata
✓
Academic publication links
Links to: joss.theoj.org
✓
Committers with academic emails
3 of 7 committers (42.9%) from academic institutions
✓
Institutional organization owner
Organization cedergrouphub has institutional domain (ceder.berkeley.edu)
✓
JOSS paper metadata
Published in Journal of Open Source Software

Keywords from Contributors

cluster-expansion ising-model lattice-gas materials-science monte-carlo statistical-mechanics mesh hydrology energy-system exoplanet

Scientific Fields

Economics Social Sciences - 40% confidence

Engineering Computer Science - 40% confidence

Last synced: 4 months ago · JSON representation

Repository

Sparse Linear Regression Models

Basic Info

Host: GitHub
Owner: CederGroupHub
License: other
Language: Python
Default Branch: main
Homepage: https://cedergrouphub.github.io/sparse-lm
Size: 11.9 MB

Statistics

Stars: 19
Watchers: 3
Forks: 6
Open Issues: 5
Releases: 9

Created over 3 years ago · Last pushed 5 months ago

Metadata Files

Readme Contributing License

Sparse Linear Regression Models

sparse-lm includes several (structured) sparse linear regression estimators that are absent in the sklearn.linear_model module. The estimators in sparse-lm are designed to fit right into scikit-learn, but the underlying optimization problem is expressed and solved by leveraging cvxpy.

Available regression models

Lasso, Group Lasso, Overlap Group Lasso, Sparse Group Lasso & Ridged Group Lasso.
Adaptive versions of Lasso, Group Lasso, Overlap Group Lasso, Sparse Group Lasso & Ridged Group Lasso.
Best Subset Selection, Ridged Best Subset, L0, L1L0 & L2L0 (all with optional grouping of parameters)

Installation

sparse-lm is available on PyPI, and can be installed via pip:

bash pip install sparse-lm

Additional information on installation can be found the documentation here.

Basic usage

If you already use scikit-learn, using sparse-lm will be very easy. Just use any model like you would any linear model in scikit-learn:

```python import numpy as np from sklearn.datasets import makeregression from sklearn.modelselection import GridSearchCV from sparselm.model import AdaptiveLasso

X, y = makeregression(nsamples=100, nfeatures=80, ninformative=10, randomstate=0) alasso = AdaptiveLasso(fitintercept=False) param_grid = {'alpha': np.logspace(-8, 2, 10)}

cvsearch = GridSearchCV(alasso, paramgrid) cvsearch.fit(X, y) print(cvsearch.bestparams_) ```

For more details on use and functionality have a look at the examples and API sections of the documentation.

Contributing

We welcome any contributions that you think may improve the package! Please have a look at the contribution guidelines in the documentation.

Owner

Name: Ceder Group
Login: CederGroupHub
Kind: organization

Website: http://ceder.berkeley.edu/
Repositories: 19
Profile: https://github.com/CederGroupHub

JOSS Publication

sparse-lm: Sparse linear regression models in Python

Published

December 21, 2023

DOI

10.21105/joss.05867

Volume 8, Issue 92, Page 5867

Authors

Luis Barroso-Luque

Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USA, Department of Materials Science and Engineering, University of California Berkeley, Berkeley CA, 94720, USA

Fengyu Xie

Editor

Mehmet Hakan Satman

GitHub Events

Total

Watch event: 6
Push event: 34
Pull request event: 2

Last Year

Watch event: 6
Push event: 34
Pull request event: 2

Committers

Last synced: 5 months ago

All Time

Total Commits: 497
Total Committers: 7
Avg Commits per committer: 71.0
Development Distribution Score (DDS): 0.201

Past Year

Commits: 1
Committers: 1
Avg Commits per committer: 1.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
lbluque	l**e@b**u	397
Izzy Xie	6**6@q**m	43
dependabot[bot]	4****]	33
pre-commit-ci[bot]	6****]	16
Ronald Kam	k**d@b**u	5
Peichen Zhong	z**c@b**u	2
tchen0965	t**5@g**m	1

Committer Domains (Top 20 + Academic)

berkeley.edu: 3 qq.com: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 13
Total pull requests: 102
Average time to close issues: about 2 months
Average time to close pull requests: 17 days
Total issue authors: 5
Total pull request authors: 6
Average comments per issue: 1.69
Average comments per pull request: 0.28
Merged pull requests: 78
Bot issues: 1
Bot pull requests: 71

Past Year

Issues: 0
Pull requests: 4
Average time to close issues: N/A
Average time to close pull requests: 7 days
Issue authors: 0
Pull request authors: 2
Average comments per issue: 0
Average comments per pull request: 0.25
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 4

View more stats

Top Authors

Issue Authors

qchempku2017 (7)
lbluque (3)
joannagiemza (1)
kamronald (1)
dependabot[bot] (1)

Pull Request Authors

dependabot[bot] (38)
github-actions[bot] (25)
lbluque (20)
pre-commit-ci[bot] (13)
qchempku2017 (10)
jbytecode (1)

Top Labels

Issue Labels

enhancement (6) bug (2) dependencies (1) github_actions (1) documentation (1) tests (1)

Pull Request Labels

dependencies (63) github_actions (34) python (5) tests (1) breaking (1)

Packages

Total packages: 1
Total downloads:
- pypi 64 last-month

Total dependent packages: 1
Total dependent repositories: 1
Total versions: 9
Total maintainers: 1

pypi.org: sparse-lm

Sparse linear regression models

Documentation: https://sparse-lm.readthedocs.io/
License: BSD 3-Clause License
Latest release: 0.5.2
published about 2 years ago

Versions: 9
Dependent Packages: 1
Dependent Repositories: 1
Downloads: 64 Last month

Rankings

Dependent packages count: 4.8%

Average: 15.5%

Downloads: 20.0%

Dependent repos count: 21.6%

Maintainers (1)

lbluque

Last synced: 4 months ago

Dependencies

.github/workflows/build.yml actions

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actions/upload-artifact v3 composite
pypa/gh-action-pypi-publish release/v1 composite

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.github/workflows/test.yml actions

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.github/workflows/update-precommit.yaml actions

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pyproject.toml pypi

cvxopt *
cvxpy >=1.2
joblib *
numpy >=1.23
scikit-learn >=1.2
scipy >=1.9

src/requirements.txt pypi

cvxpy *
joblib *
numpy *
scikit-learn *
scipy *

sparse-lm

Science Score: 98.0%

Keywords from Contributors

Scientific Fields

Repository

Basic Info

Statistics

Metadata Files

README.md

Sparse Linear Regression Models

Available regression models

Installation

Basic usage

Contributing

Owner

JOSS Publication

sparse-lm: Sparse linear regression models in Python

Authors

Editor

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GitHub Events

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Packages

pypi.org: sparse-lm

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Maintainers (1)

Dependencies