Science Score: 20.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: arxiv.org, zenodo.org -
✓Committers with academic emails
8 of 11 committers (72.7%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (17.7%) to scientific vocabulary
Repository
a feature engineering wrapper for sklearn
Basic Info
- Host: GitHub
- Owner: lacava
- License: gpl-3.0
- Language: Python
- Default Branch: master
- Homepage: https://lacava.github.io/few
- Size: 593 KB
Statistics
- Stars: 52
- Watchers: 8
- Forks: 18
- Open Issues: 9
- Releases: 0
Metadata Files
README.md
Few
Few is a Feature Engineering Wrapper for scikit-learn. Few looks for a set of feature transformations that work best with a specified machine learning algorithm in order to improve model estimation and prediction. In doing so, Few is able to provide the user with a set of concise, engineered features that describe their data.
Few uses genetic programming to generate, search and update engineered features. It incorporates feedback from the ML process to select important features, while also scoring them internally.
Install
You can use pip to install FEW from PyPi as:
pip install few
or you can clone the git repo and add it to your Python path. Then from the repo, run
python setup.py install
Mac users
Some Mac users have reported issues when installing with old versions of gcc (like gcc-4.2) because the random.h library is not included (basically this issue). I recommend installing gcc-4.8 or greater for use with Few. After updating the compiler, you can reinstall with
python
CC=gcc-4.8 python setupy.py install
Usage
Few uses the same nomenclature as sklearn supervised learning modules. Here is a simple example script:
```python
import few
from few import FEW
initialize
learner = FEW(generations=100, population_size=25, ml = LassoLarsCV())
fit model
learner.fit(X,y)
generate prediction
ypred = learner.predict(Xunseen)
get feature transformation
Phi = learner.transform(X_unseen) ```
You can also call Few from the terminal as
bash
python -m few.few data_file_name
try python -m few.few --help to see options.
Examples
Check out few_example.py to see how to apply FEW to a regression dataset.
Publications
If you use Few, please reference our publications:
La Cava, W., and Moore, J.H. A general feature engineering wrapper for machine learning using epsilon-lexicase survival. Proceedings of the 20th European Conference on Genetic Programming (EuroGP 2017), Amsterdam, Netherlands. preprint
La Cava, W., and Moore, J.H. Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods. GECCO '17: Proceedings of the 2017 Genetic and Evolutionary Computation Conference. Berlin, Germany. arxiv
Acknowledgments
This method is being developed to study the genetic causes of human disease in the Epistasis Lab at UPenn. Work is partially supported by the Warren Center for Network and Data Science. Thanks to Randy Olson and TPOT for Python guidance.
Owner
- Name: William La Cava
- Login: lacava
- Kind: user
- Location: Boston, MA
- Company: @cavalab
- Website: williamlacava.com
- Twitter: w_la_cava
- Repositories: 57
- Profile: https://github.com/lacava
Assistant Prof at Boston Children's Hospital / Harvard Medical School developing ML for applications in biomedical informatics. I run the @cavalab
GitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Committers
Last synced: almost 3 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| lacava | w****a@g****m | 265 |
| Rishabh Gupta | r****a@M****l | 7 |
| Rishabh Gupta | r****a@v****u | 2 |
| Rishabh Gupta | r****a@v****u | 1 |
| Rishabh Gupta | r****a@v****u | 1 |
| Rishabh Gupta | r****a@h****u | 1 |
| erp12 | E****e@M****m | 1 |
| Rishabh Gupta | r****a@h****u | 1 |
| Rishabh Gupta | r****a@m****u | 1 |
| mq | m****e@u****e | 1 |
| Rishabh Gupta | r****a@h****u | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 27
- Total pull requests: 14
- Average time to close issues: about 2 months
- Average time to close pull requests: 6 days
- Total issue authors: 7
- Total pull request authors: 5
- Average comments per issue: 2.07
- Average comments per pull request: 2.71
- Merged pull requests: 10
- 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
- lacava (17)
- echo66 (3)
- jay-reynolds (2)
- GinoWoz1 (2)
- Ohjeah (1)
- TheodoreGalanos (1)
- eyadsibai (1)
Pull Request Authors
- rgupta90 (8)
- lacava (3)
- Ohjeah (1)
- erp12 (1)
- fdion (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 100 last-month
- Total dependent packages: 0
- Total dependent repositories: 5
- Total versions: 48
- Total maintainers: 1
pypi.org: few
Feature Engineering Wrapper
- Homepage: https://github.com/lacava/few
- Documentation: https://few.readthedocs.io/
- License: GNU/GPLv3
-
Latest release: 0.0.51
published over 7 years ago
Rankings
Maintainers (1)
Dependencies
- Cython *
- DistanceClassifier *
- eigency *
- joblib *
- numpy *
- pandas *
- scikit-learn *
- scikit-mdr *
- scipy *
- tqdm *
- update_checker *