RICE
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RICE
Implementation of a rule based prediction algorithm called RICE (Rule Induction Covering Estimator). RICE is a deterministic and interpretable algorithm, for regression problem.
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
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
Installing
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.
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
rice = RICE.Learning()
rice.fit(X, y)
Predict
rice.predict(X)
Score
rice.score(X,y)
Inspect rules:
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()
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