A Python package which implements the Elastic Net using the proximal gradient method. It is intended to be a drop-in replacement for the Elastic Net methods implemented in sci-kit learn (which are based on coordinate descent). For this reason, it makes heavy use of the infrastructure provided by sci-kit learn, and mirrors the coding style and syntax.

Just like the coordinate descent-based method shipped with sci-kit learn, prox_elasticnet uses a Cython extension to compute the iterates, so it should be performance-competitive.

To install from source, you must have the Python development headers, a C/C++ compiler, the ATLAS libraries and the following Python packages:

• NumPy
• SciPy
• sci-kit learn

To install these dependencies on a Debian-based system, run:

$ sudo apt-get install build-essential python-dev python-setuptools \ python-numpy python-scipy python-sklearn cython libatlas-dev libatlas3gf-base 

(replace python with python3 and cython with cython3 if using Python 3).

On RHEL/Fedora and derivatives, run:

$ sudo dnf install make automake gcc gcc-c++ python3-devel \ python3-setuptools python3-numpy python3-scipy \ python3-sklearn python3-Cython atlas-devel 

Once these dependencies are installed, change to the directory of the source code and run:

$ make install 

This will install the package into your Python user site directory.

To run the unit tests, change to the directory of the source code and run:

$ make test 

prox_elasticnet uses the same syntax as sci-kit learn. Below is a toy example, demonstrating how to fit a model to a one-dimensional training dataset and make predictions on a test set.

import numpy as np from prox_elasticnet import ElasticNet X_train = np.arange(100) y_train = X_train + np.random.normal(0,1,len(X)) X_train = X_train[:, np.newaxis] X_test = np.arange(0.5, 10.5) X_test = X_test[:, np.newaxis] model = ElasticNet(alpha = 0.1).fit(X_train,y_train) model.predict(X_test) 

Documentation can be accessed from within a Python session by entering:

help(prox_elasticnet.ElasticNet) 

or:

help(prox_elasticnet.ElasticNetCV) 

You can also check out the Jupyter notebook located at demo/demo.ipynb.