Accepted at the Ninth International Workshop on Statistical Relational AI - StarAI 2020.
Paper
Explore further on GitHub.
srlearn overview
srlearn has several aims. At a basic level it wraps the state-of-the-art
BoostSRL package
for learning the structure and parameters of statistical relational models.
At a higher level, it is an ongoing experiment in creating application programming
interfaces (APIs) that blend idea from logic programming with what is familiar to
the machine learning and data science communities built around the Python
programming language.
This example is presented as Figure 1 in the paper, and is presently featured
as one of the getting started examples in the srlearn documentation:
>>> from srlearn.rdn import BoostedRDN
>>> from srlearn import Background
>>> from srlearn import example_data
>>> bk = Background(
... modes=example_data.train.modes,
... use_std_logic_variables=True,
... )
>>> clf = BoostedRDN(
... background=bk,
... target='cancer',
... )
>>> clf.fit(example_data.train)
>>> clf.predict_proba(example_data.test)
array([0.88079619, 0.88079619, 0.88079619, 0.3075821 , 0.3075821 ])
>>> clf.classes_
array([1., 1., 1., 0., 0.])
Software
Results of the paper are based on srlearn==0.5.0
Please consider starring 🌟 the
srlearn GitHub Repository
repository. It’s an open-source project, so any feedback or recommendations are
appreciated.
Experiments
Scripts for reproducing Table 1 are contained in the
experiments/ directory on GitHub.
Citation
Please use the following citation when building on ideas of this work:
@misc{hayes2019srlearn,
title={srlearn: A Python Library for Gradient-Boosted Statistical Relational Models},
author={Alexander L. Hayes},
year={2019},
eprint={1912.08198},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Acknowledgments
ALH is sponsored through Indiana University’s “Precision Health Initiative” (PHI) Grand Challenge. ALH would like to thank Sriraam Natarajan, Travis LaGrone, and members of the StARLinG Lab at the University of Texas at Dallas.