Metadata-Version: 1.1
Name: pygfl
Version: 1.0.0
Summary: A Fast and Flexible Graph-Fused Lasso Solver
Home-page: https://github.com/tansey/gfl
Author: Wesley Tansey
Author-email: wes.tansey@gmail.com
License: LGPL
Description: 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:
        
        ![GFL Convex Optimization Problem](https://raw.githubusercontent.com/tansey/gfl/master/img/eq1.png)
        
        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].
        
        Compiling the C solver lib
        ==========================
        To compile the C solver 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.
        
        Python requirements
        ===================
        The python wrapper requires `numpy`, `scipy`, and `networkx` to be able to run everything.
        
        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
        =======
        There are two steps in running the GFL solver (once installed). First, you need to decompose your graph into a set of trails then you need to run the C-based GFL solver.
        
        #### 1) Trail decomposition
        Suppose you have a graph file like the one in `example/edges.csv`, where each edge is specified on a new line, with a comma separating vertices:
        
        ```
        0,1
        1,2
        3,4
        2,4
        5,4
        6,0
        3,6
        ...
        ```
        
        You can then decompose this graph by running the command line `maketrails` script:
        
        ```
        maketrails file --infile example/edges.csv --savet example/trails.csv
        ```
        
        This will create a file in `example/trails.csv` containing a set of distinct, non-overlapping trails which minimally decomposes the original graph. Next you need to run the solver.
        
        #### 2) Solving via ADMM
        Given a set of trails in `example/trails.csv` and a vector of observations in `example/data.csv`, you can run the `graphfl` script to execute the GFL solver:
        
        ```
        graphfl example/data.csv example/trails.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 below.
        
        ![Example GFL Solution](https://raw.githubusercontent.com/tansey/gfl/master/img/example1.png)
        
        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 and J. G. Scott. "[A Fast and Flexible Algorithm for the Graph-Fused Lasso](http://arxiv.org/abs/1505.06475)," arXiv:1505.06475, May 2015.
        
        [2] [glmgen](https://github.com/statsmaths/glmgen)
Keywords: statistics machinelearning lasso fusedlasso
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: LGPL License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
