RENT
RENT: A Python Package for Repeated Elastic Net Feature Selection - Published in JOSS (2021)
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Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
Feature selection method based on repeated elastic net.
Basic Info
Statistics
- Stars: 16
- Watchers: 5
- Forks: 6
- Open Issues: 3
- Releases: 1
Topics
Metadata Files
README.md
RENT
RENT (Repeated Elastic Net Technique) is a feature selection method for binary classification and regression problems. At its core
RENT trains an ensemble of generalized linear models using regularized elastic net to select features. Each model
in the ensemble is trained using a randomly, iid sampled subset of rows of the full training data. A single data point can appear at most once in each subset, but may appear in multiple subsets. From these
unique models one can acquire weight distributions for each
feature that contain rich information on the stability of feature selection and from which several adjustable classification criteria may be
defined.
More details are in the original paper published in IEEE Access: RENT - Repeated Elastic Net Technique for Feature Selection
Example
Below are links to Jupyter-notebooks that illustrate how to use RENT for
Requirements
Make sure that Python 3.5 or higher is installed. A convenient way to install Python and many useful packages for scientific computing is to use the Anaconda Distribution
- numpy >= 1.11.3
- pandas >= 1.2.3
- scikit-learn >= 0.22
- scipy >= 1.5.0
- hoggorm >= 0.13.3
- hoggormplot >= 0.13.2
- matplotlib >= 3.2.2
- seaborn >= 0.10
Installation
To install the package with the pip package manager, run the following command:
python3 -m pip install git+https://github.com/NMBU-Data-Science/RENT.git
Documentation
Documentation is available at ReadTheDocs. It provides detailed explanation of methods and their inputs.
Citing the RENT package
If you use RENT in a report or scientific publication, we would appreciate citations to the following paper:
Jenul et al., (2021). RENT: A Python Package for Repeated Elastic Net Feature Selection. Journal of Open Source Software, 6(63), 3323, https://doi.org/10.21105/joss.03323
Bibtex entry:
@article{RENT,
doi = {10.21105/joss.03323},
url = {https://doi.org/10.21105/joss.03323},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {63},
pages = {3323},
author = {Anna Jenul and Stefan Schrunner and Bao Ngoc Huynh and Oliver Tomic},
title = {RENT: A Python Package for Repeated Elastic Net Feature Selection},
journal = {Journal of Open Source Software}
}
Owner
- Name: NMBU-Data-Science
- Login: NMBU-Data-Science
- Kind: organization
- Repositories: 4
- Profile: https://github.com/NMBU-Data-Science
Data science related repositories developed at the Faculty of Science and Technology, Norwegian University of Life Sciences
JOSS Publication
RENT: A Python Package for Repeated Elastic Net Feature Selection
Authors
Department of Data Science, Norwegian University of Life Sciences
Department of Data Science, Norwegian University of Life Sciences
Department of Physics, Norwegian University of Life Sciences
Department of Data Science, Norwegian University of Life Sciences
Editor
Mikkel Meyer Andersen
Tags
feature selectionGitHub Events
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- Watch event: 3
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Last Year
- Watch event: 3
- Fork event: 1
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| annajenul | 6****l | 164 |
| Oliver Tomic | o****c@z****m | 76 |
| muaf | m****f@n****o | 11 |
| Ngoc Huynh | h****2@g****m | 5 |
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 7
- Total pull requests: 13
- Average time to close issues: 10 days
- Average time to close pull requests: about 1 hour
- Total issue authors: 5
- Total pull request authors: 4
- Average comments per issue: 1.86
- Average comments per pull request: 0.31
- Merged pull requests: 12
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
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- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
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- maximtrp (3)
- ditsiaou (1)
- alabamagan (1)
- AndreiRoibu (1)
- rajeshkalakoti (1)
Pull Request Authors
- olivertomic (8)
- Uzaaft (3)
- huynhngoc (1)
- annajenul (1)
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Dependencies
- coverage *
- hoggorm >=0.13.3
- hoggormplot >=0.13.2
- matplotlib >=3.2.2
- numpy >=1.11.3
- pandas >=1.2.3
- pip *
- pytest *
- pytest-cov *
- pytest-randomly *
- scikit-learn >=0.22
- scipy >=1.5.0
- seaborn >=0.10
- sphinx *
- sphinxbootstrap4theme *
- tox *
- twine *
- wheel *