{{ message }}

# tansey / gfl

A fast and flexible algorithm for solving the graph-fused lasso

## Files

Failed to load latest commit information.
Type
Name
Commit time

## A Fast, Flexible Algorithm for the Graph-Fused Lasso

The goal in the graph-fused lasso (GFL) is to find a solution to the following convex optimization problem:

where l is a smooth, convex loss function. The problem assumes you are given a graph structure of edges and nodes, where each node corresponds to a variable and edges between nodes correspond to constraints on the first differences between the variables. The objective function then seeks to find a solution to the above problem that minimizes the loss function over the vertices plus the sum of the first differences defined by the set of edges E.

The solution implemented here is based on the graph-theoretic trail decomposition and ADMM algorithm implemented in [1]. The code relies on a slightly modified version of a linear-time dynamic programming solution to the 1-d (i.e. chain) GFL [2].

# Python Requirements

The python (Python version 2) wrapper requires `numpy`, `scipy`, and `networkx` to be able to run everything. Note that the `libgraphfl` library also depends on the Gnu Scientific Library `gsl` which should be available on your system.

# Installing

The package can be installed via Pip:

`pip install pygfl`

or directly from source:

``````python setup.py build
python setup.py install
``````

Note that the installation has not been tested on anything other than Mac OS X and Ubuntu. The underlying solver is implemented in pure C and should be cross-platform compatible.

# Running

The simplest way to run the script is via the command-line `graphfl` script. You just give it a CSV of your data that you wish to smooth and a CSV of your edges, one edge per row:

``````graphfl example/data.csv example/edges.csv --o example/smoothed.csv
``````

This will run a solution path to auto-tune the value of the penalty parameter (the λ in equation 1). The results will be saved in `example/smoothed.csv`. The results should look something like the image at the top of the readme.

# Calling within Python

To call the solver within a Python program, the simplest way is to use the `easy.solve_gfl` method:

``````import numpy as np
from pygfl.easy import solve_gfl

# Run the solver
beta = solve_gfl(y, edges)
``````

There are lots of other configuration options that affect the optimization procedure, but honestly they make little practical difference for most people.

# Compiling the C solver lib separately

To compile the C solver as a standalone library, you just need to run the make file from the `cpp` directory:

`make all`

Then you will need to make sure that you have the `cpp/lib` directory in your `LD_LIBRARY_PATH`:

`export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/my/path/to/gfl/cpp/lib/`

Note the above instructions are for *nix users.

# Licensing

This library / package is distributed under the GNU Lesser General Public License, version 3. Note that a subset of code from [2] was modified and is included in the C source.

# References

[1] W. Tansey, O. Koyejo, R. A. Poldrack, and J. G. Scott. "False Discovery Rate Smoothing," Supplementary material. Journal of the American Statistical Association (JASA): Theory and Methods, June, 2017.

[2] glmgen

A fast and flexible algorithm for solving the graph-fused lasso

## Releases

No releases published

## Packages 0

No packages published

## Languages

You can’t perform that action at this time.