Implementation of a rule based prediction algorithm called RIPE (Rule Induction Partitioning Estimate). RIPE is a deterministic and interpretable algorithm, for regression problem. It has been presented at the International Conference on Machine Learning and Data Mining in Pattern Recognition 2018 (MLDM 18). The paper is available in arXiv https://arxiv.org/abs/1807.04602.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
RIPE is developed in Python version 2.7. It requires some usual packages
- NumPy (post 1.13.0)
- Scikit-Learn (post 0.19.0)
- Pandas (post 0.16.0)
- SciPy (post 1.0.0)
- Matplotlib (post 2.0.2)
- Seaborn (post 0.8.1)
sudo pip install package_name
To install a specific version
sudo pip install package_name==version
The latest version can be installed from the master branch using pip:
pip install git+git://github.com/VMargot/RIPE.git
Another option is to clone the repository and install using
python setup.py install or
python setup.py develop.
RIPE has been developed to be used as a regressor from the package scikit-learn.
from sklearn import datasets iris = datasets.load_iris() X, y = iris.data, iris.target ripe = RIPE.Learning() ripe.fit(X, y)
To have the Pandas DataFrame of the selected rules
Or, one can use
To draw the distance between selected rules
To draw the count of occurrence of variables in the selected rules
This implementation is in progress. If you find a bug, or something witch could be improve don't hesitate to contact me.
- Vincent Margot
See also the list of contributors who participated in this project.
This project is licensed under the GNU v3.0 - see the LICENSE.md file for details
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size ripe-algorithm-0.1.2.tar.gz (2.3 MB)||File type Source||Python version None||Upload date||Hashes View|