Getting Started¶

Compatibility¶

This software suite works out-of-the-box on Linux and Windows 64-bit machines.

Source Code¶

The source code is publicly available under BSD License:

git clone https://github.com/danielnyga/pracmln.git

Prerequisites¶

Linux¶

Python 2.7/3 (tested with 3.5) with Tkinter installed.
Note

On Linux, the following packages should be installed (tested for Ubuntu).:
sudo apt-get install python-tk
pracmln is shipped with the open source WCSP solver toulbar2 for Linux and Windows 64-bit versions. For other architectures, it can be obtained from:
```
https://mulcyber.toulouse.inra.fr/projects/toulbar2
```
Its executable toulbar2 should then be included in the $PATH variable.

Windows¶

Python 2.7 with Tkinter installed.

Note

On Windows, Tkinter is usually shipped with Python.

You will also need the following python packages: pyparsing, tabulate, psutil and networkx. You can install them via
```
$ pip install pyparsing tabulate psutil==0.4.1 networkx
```
You will also need the python packages scipy and numpy+mkl. Installing with pip will probably not work, but you can obtain prebuilt versions (e.g. scipy‑0.XX.Y‑cp27‑cp27m‑win32.whl and numpy‑1.11.3+mkl‑cp27‑cp27m‑win32.wh) online from:
```
http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
```
and:
```
http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
```
You can install the files with pip:
```
$ pip install <filename>.whl
```
pracmln is shipped with the open source WCSP solver toulbar2 for Linux and Windows (Intel) 64-bit versions.
Install the toulbar2.exe in ./3rdparty/toulbar2-0.9.7.0/x86_64/Windows and make sure the path to the installed executable (most likely C:\Program Files (x86)\toulbar2.0.9.7.0-Release) is added to your PATH variable. For other architectures, toulbar2 can be obtained from:
https://mulcyber.toulouse.inra.fr/projects/toulbar2

Installation¶

As of Version 0.2.0, pracmln is shipped as a pip-compliant package. For installing it, just checkout the code from:

$> git clone https://github.com/danielnyga/pracmln.git

and install it with:

$> python setup.py install

C++ bindings¶

Requirements:

Linux OS (tested on Ubuntu 14.04, 16.04 with Python 2.7 and Python 3.5)

libboost-python

libpython-dev

Installation:

After the installation of pracmln, run:
libpracmln-build
It will compile the C++ sources in the current working directory, creating a folder libpracmln.

Usage:

Include the header pracmln/mln.h and link against libpracmln

A simple example program

#include <pracmln/mln.h>

int main(int argc, char **argv)
{
  // create a mln object
  MLN mln;

  // initialize the object (loading python packages, etc.)
  if(!mln.initialize()){
    // error
  }

  std::vector<std::string> query;
  query.push_back("some query");

  // change settings, give input files, etc.
  mln.setQuery(query);
  mln.setMLN("path to mln file");
  mln.setDB("path to db file");

  std::vector<std::string> results;
  std::vector<double> probabilities;

  // execute inference
  if(mln.infer(results, probabilities)){
    // error
  }

  // do something with the results

  return 0;
}

Examples¶

There are example models in the ./examples/ directory.

Simply run the mlnquery applications in one of the subdirectories to try out some inference tasks.

In the ./examples/meals/ directory, you can also try out learning. To train a MLN model run mlnlearn.