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https://github.com/lpfann/fri
Python implementation of the feature relevance interval (FRI) algorithm
https://github.com/lpfann/fri
bioinformatics biomarker-discovery data-science feature-relevance-interval feature-selection machine-learning-algorithms python scikit-learn
Last synced: 9 days ago
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Python implementation of the feature relevance interval (FRI) algorithm
- Host: GitHub
- URL: https://github.com/lpfann/fri
- Owner: lpfann
- License: mit
- Created: 2016-11-29T13:51:59.000Z (almost 8 years ago)
- Default Branch: dev
- Last Pushed: 2022-11-01T13:00:47.000Z (about 2 years ago)
- Last Synced: 2024-10-12T10:06:36.958Z (25 days ago)
- Topics: bioinformatics, biomarker-discovery, data-science, feature-relevance-interval, feature-selection, machine-learning-algorithms, python, scikit-learn
- Language: Python
- Homepage: https://fri.lpfann.me
- Size: 15.6 MB
- Stars: 8
- Watchers: 2
- Forks: 4
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Feature Relevance Intervals - FRI
[](https://doi.org/10.5281/zenodo.1188749)
[](https://colab.research.google.com/github/lpfann/fri/blob/master/doc/source/notebooks/Guide.ipynb)
__FRI__ is a Python 3 package for analytical feature selection
purposes. It allows superior feature selection in the sense that all
important features are conserved. At the moment we support multiple
linear models for solving Classification, Regression and Ordinal
Regression Problems. We also support LUPI paradigm where at learning
time, privileged information is available.
# Usage
Please refer to the [documentation](https://lpfann.github.io/fri/) for advice.
For a quick start we provide a simple guide which leads through the main functions.
## Installation
`FRI` requires __Python 3.6+__.
For a __stable__ version from `PyPI` use
```shell
$ pip install fri
```
or with new versions of `pip` (>=19?) you can clone the repository and run
```shell
$ pip install .
```
in the folder on the `master` or `dev` branch.
## Documentation
Check out our online documentation [here](https://lpfann.github.io/fri/).
There you can find a quick start guide and more background information.
You can also run the guide directly online without setup [here](https://colab.research.google.com/github/lpfann/fri/blob/master/doc/source/notebooks/Guide.ipynb).
# Development
For dependency management we use the newly released [poetry](https://python-poetry.org/) tool.
If you have `poetry` installed, use
```shell
$ poetry install
```
inside the project folder to create a new `venv` and to install all dependencies.
To enter the newly created `venv` use
```shell
$ poetry env
```
to open a new shell inside.
Or alternatively run commands inside the `venv` with `poetry run ...`.
#### Docs
The [documentation](https://lpfann.github.io/fri/) is compiled using [portray](https://github.com/timothycrosley/portray/).
If the dependencies are installed with `poetry install` you should be able to run
```shell
$ poetry run portray in_browser
```
to compile the files into html and launch a browser to preview changes.
(Be sure not to mix up `poetry` != `portray`.)
The documentation files are generated from `Python` docstrings inside the source files
and from Markdown located in the `docs` folder.
## References
[1] Göpfert C, Pfannschmidt L, Hammer B. Feature Relevance Bounds for Linear Classification. In: Proceedings of the ESANN. 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning;
[2] Göpfert C, Pfannschmidt L, Göpfert JP, Hammer B. Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing.
[3] Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tino, Barbara Hammer: Feature Relevance Bounds for Ordinal Regression. Proceedings of the ESANN. 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning; Accepted.
[4] Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B: FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.