Google Summer of Code 2018

pracmln is a toolbox for statistical relational learning (SRL) and reasoning and as such also includes tools for standard probabilistic graphical models. pracmln is an implementation of Markov logic networks (MLN) that supports efficient learning and inference in relational domains. Its learning and inference engines are entirely written in the Python programming language. Markov logic networks generalize both first-order logic and probabilistic graphical models and have proven successful in many real-world applications such as natural-language understanding and robot perception. This makes MLNs one of the most general and most powerful representation formalisms for uncertain knowledge.

pracmln was designed with the particular needs of technical systems in mind. Our methods are geared towards practical applicability and can easily be integrated into other applications. The availability of graphical tools makes both learning or inference sessions a user-friendly experience. Scripting support enables automation, and a browser-based web application is intended for teaching and getting familiar with MLNs.

pracmln is an open-source project hosted on GitHub. It has its own project page (http://www.pracmln.org) that provides extensive documentation and tutorials that facilitate getting started with MLNs. It is provided as a pip package in the Python package index (PyPI).

Main Objective: The current implementation of pracmln is entirely written in pure Python. The main objective of this project is to port the computationally heavy parts of the learning and inference algorithms to Cython (cython.org), an extension to Python that allows static compilation of Python modules to C libraries. Cython allows to add static type declarations to Python, which can significantly speed up execution (up to a factor of 1000 compared to regular Python code).

As most of the Python libraries for machine learning and scientific computing (e.g. scikit-learn, numpy) make use of static compilation, it is expected that the practical applicability of Markov logic networks will substanially be pushed to more demanding real-world scenarios.

Task Difficulty: There is alreay a high-quality code base, however, solving this task properly requires understanding and experience in gradient-based optimization, machine learning and logics.

Requirements: Good programming skills in the Python programming language (CPython/Cython), experience in Artificial Intelligence and Machine Learning (ideally SRL technques and logic)

Expected Results: The core components of pracmln, i.e. the learning and inference modules are expected to be ported to the Cython language exploiting the means of implementational optimization provided by Cython.

Contact: Daniel Nyga

In the following, we list our proposals for the Google Summer of Code topics that contribute to the aforementioned open-source projects.