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Ripe algorithm

Project description


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

Getting Started

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://

Another option is to clone the repository and install using python install or python 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 =,

ripe = RIPE.Learning(), y)





Inspect rules:

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 file for details

Project details

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ripe-algorithm-0.1.6.tar.gz (2.3 MB view hashes)

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