Implementation of a rule based prediction algorithm called RIPE (Rule Induction Partitioning Estimate). RIPE is a deterministic and interpretable algorithm.
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)
See requirements.txt.
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)
ripe.predict(X)
ripe.score(X,y)
To have the Pandas DataFrame of the selected rules
ripe.selected_rs.to_df()
Or, one can use
ripe.make_selected_df()
To draw the distance between selected rules
ripe.plot_dist()
To draw the count of occurrence of variables in the selected rules
ripe.plot_counter_variables()
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