Explore further on GitHub.
srlearn has several aims. At a basic level it wraps the state-of-the-art
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
This example is presented as Figure 1 in the paper, and is presently featured
as one of the getting started examples in the
>>> 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.])
Results of the paper are based on
Please consider starring 🌟 the
srlearn GitHub Repository
repository. It’s an open-source project, so any feedback or recommendations are
Scripts for reproducing Table 1 are contained in the
experiments/ directory on GitHub.
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.