Implementation of a rule based prediction algorithm called RICE (Rule Induction Covering Estimator). RICE is a deterministic and interpretable algorithm, for regression problem.

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.

RICE is developed in Python version 3.5 or greater. 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/RICE.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

rice = RICE.Learning()
rice.fit(X, y)

rice.predict(X)

rice.score(X,y)

To have the Pandas DataFrame of the selected rules

rice.selected_rs.to_df()

Or, one can use

rice.make_selected_df()

To draw the distance between selected rules

rice.plot_dist()

To draw the count of occurrence of variables in the selected rules

rice.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