causal-lasso 1.0.0

Causal Lasso

Causal Lasso

This repository implements paper "A Bregman Method for Structure Learning on Sparse Directed Acyclic Graphs" by Manon Romain and Alexandre d'Aspremont.

Requirements

This package requires numpy, scipy, tqdm, networkx and mosek, installation can be done with pip install -r docs/requirements.txt.

Solver

For now, the default solver used at each iteration is Mosek. We plan to provide an open source implementation in the near future.

MOSEK's license is free for academic use, first obtain your license here using institutional email and place the obtained file mosek.lic in a file called:

%USERPROFILE%\mosek\mosek.lic           (Windows)
$HOME/mosek/mosek.lic                   (Linux, MacOS)

Further information available here.

Use

Minimal testing code is:

import numpy as np
import networkx as nx
from causal_lasso.solver import CLSolver
X = np.random.random((1000, 30)) # Replace with your data
lasso = CLSolver()
W_est = lasso.fit(X)
nx.draw(nx.DiGraph(W_est))

A more detailed tutorial is available in examples/tutorial.ipynb.