Install

sparse-lm can be installed from PyPI or from source using pip.

PyPI

You can install sparse-lm using pip:

pip install sparse-lm

Install from source

To install sparse-lm from source, (fork and) clone the repository from github:

git clone https://github.com/CederGroupHub/sparse-lm
cd sparselm
pip install .

Installing MIQP solvers

Since cvxpy is used to specify and solve regression optimization problems, any of supported solvers can be used with sparse-lm estimators. cvxpy is shipped with open source solvers (OSQP, SCS, and ECOS) which are usually enough to solve most convex regression problems.

However, for the mixed integer quadratic programming (MIQP) formulations used in BestSubsetSelection and RegularizedL0 based classes we highly recommend installing an MIQP capable solver. ECOS_BB can be used to solve MIQP problems, but it can be very slow and more importantly has recurring correctness issues. See the mixed-integer program section in the cvxpy documentation for more details.

Gurobi

For using sparse-lm with MIQP solvers, we highly recommend installing Gurobi. It can be installed directly from PyPi:

pip install gurobipy

Without a license, a free trial Gurobi can be used to solve small problems. For larger problems a license is required. Gurobi grants free academic licenses to students and academic researchers.

SCIP

If installing a licensed solver is not an option, SCIP can be used as a free alternative. To use SCIP, the python interface PySCIPOpt must also be installed. PySCIPOpt can be installed from PyPi, however this requires building SCIP from source. See installation details here.

If you use conda, we recommend installing SCIP and PySCIPOpt using their conda-forge channel:

conda install -c conda-forge scipopt pyscipopt

The above command will install PySCIPOpt with a pre-built version of SCIP, and so you will not need to build it from source.

Testing

Unit tests can be run from the source folder using pytest. First, the requirements to run tests must be installed:

pip install .[tests]

Then run the tests using:

pytest tests