ripe-algorithm 0.1.6

Description:

ripealgorithm 0.1.6

RIPE
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.
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.
Prerequisites
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

Installing
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.
Usage
RIPE has been developed to be used as a regressor from the package scikit-learn.
Training
from sklearn import datasets
iris = datasets.load_iris()
X, y = iris.data, iris.target

ripe = RIPE.Learning()
ripe.fit(X, y)

Predict
ripe.predict(X)

Score
ripe.score(X,y)

Inspect rules:
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()

Notes
This implementation is in progress. If you find a bug, or something witch could
be improve don't hesitate to contact me.
Authors

Vincent Margot

See also the list of contributors
who participated in this project.
License
This project is licensed under the GNU v3.0 - see the LICENSE.md
file for details