Science Score: 28.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
-
○.zenodo.json file
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○DOI references
-
○Academic publication links
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✓Committers with academic emails
1 of 4 committers (25.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (5.9%) to scientific vocabulary
Scientific Fields
Repository
python package
Basic Info
- Host: GitHub
- Owner: kpeng2019
- License: mit
- Language: Python
- Default Branch: master
- Size: 121 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
LAS
This package is a brief wrap-up toolkit built based on 2 explanation packages: LIME and SHAP. The package contains 2 explainers: LIMEBAG and SHAP. It takes data and fitted models as input and returns explanations about feature importance ranks and/or weights. (etc. what attributes matter most within the prediction model).
rq1.py
The demo runs LIMEBAG on a default dataset. It generates and presents explanations about feature importance ranks and weights for all testing data points. Can be called by LIMEBAG.demo1()
rq2.py
The demo uses the explanations returned from LIMEBAG to run an effect size test. A summary of feature importance ranks and weights will be generated and presented as output. Can be called by LIMEBAG.demo2()
Owner
- Login: kpeng2019
- Kind: user
- Repositories: 2
- Profile: https://github.com/kpeng2019
Citation (CITATION.md)
If you redistribute LASExplanation, please follow the terms established in
[the license](/LICENSE). If you wish to cite LASExplanation in an academic
publication, please use the following reference:
Formatted:
```
K. Peng, R. Agrawal, T. Menzies (2020).
LASExplanation - An explanation tool extended from LIME and SHAP
```
BibTeX:
```bibtex
@misc{Peng2020,
abstract = {LASExplanation is a brief wrap-up toolkit built based on 2 explanation packages: LIME and SHAP. The package contains 2 explainers: LIMEBAG and SHAP. It takes data and fitted models as input and returns explanations about feature importance ranks and/or weights. (etc. what attributes matter most within the prediction model).},
address = {Raleigh},
author = {K. Peng, R. Agrawal, T. Menzies},
publisher = {},
title = {{LASExplanation - An explanation tool extended from LIME and SHAP}},
url = {https://github.com/kpeng2019/LAS},
year = {2020}
}
```
GitHub Events
Total
Last Year
Committers
Last synced: about 3 years ago
All Time
- Total Commits: 27
- Total Committers: 4
- Avg Commits per committer: 6.75
- Development Distribution Score (DDS): 0.481
Top Committers
| Name | Commits | |
|---|---|---|
| kpeng2019 | 5****9@u****m | 14 |
| kpeng2019 | k****g@n****u | 10 |
| Tim Menzies | t****m@u****m | 2 |
| Rishabh Agrawal | o****4@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 8 months ago
All Time
- Total issues: 0
- Total pull requests: 3
- Average time to close issues: N/A
- Average time to close pull requests: 7 minutes
- Total issue authors: 0
- Total pull request authors: 2
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 3
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- 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
Issue Authors
Pull Request Authors
- timm (2)
- kpeng2019 (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 8 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 1
- Total maintainers: 1
pypi.org: lasexplanation
This package is a brief wrap-up toolkit built based on 2 explanation packages: LIME and SHAP. The package contains 2 explainers: LIMEBAG and SHAP. It takes data and fitted models as input and returns explanations about feature importance ranks and/or weights. (etc. what attributes matter most within the prediction model).
- Homepage: https://github.com/kpeng2019/LAS
- Documentation: https://lasexplanation.readthedocs.io/
- License: MIT License
-
Latest release: 0.0.1
published almost 6 years ago
Rankings
Maintainers (1)
Dependencies
- lime *
- numpy *
- pandas *
- shap *
- sklearn *
- ipython *
- lime *
- numpy *
- pandas *
- scikit-learn *
- setuptools *
- shap *