PyPSL is a new library for building PSL models in Python.
This is still a prototype.

Probabilistic soft logic (PSL) is a machine learning framework for developing probabilistic models. PSL models are easy to use and fast. You can define models using a straightforward logical syntax and solve them with fast convex optimization. PSL has produced state-of-the-art results in many areas spanning natural language processing, social-network analysis, knowledge graphs, recommender system, and computational biology.

To learn more about PSL, see this paper: Hinge-Loss Markov Random Fields and Probabilistic Soft Logic.

• User friendliness. PyPSL offers a consistent and user-friendly API.

• Easy extensibility. The modules composing the library are simple to extend.

• Work with Python. No separate models configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility.

git clone [email protected]:br-g/pypsl.git
cd pypsl
make install

To get started, please follow these examples.