mlsanityData to 14 Oct 2026

PyRelationAL: a python library for active learning research and development

arXiv 2205.11117 ↗First posted 23 May 2022cs.LG cs.AIcited by 2

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Abstract (arXiv)

Active learning (AL) is a sub-field of ML focused on the development of methods to iteratively and economically acquire data by strategically querying new data points that are the most useful for a particular task. Here, we introduce PyRelationAL, an open source library for AL research. We describe a modular toolkit based around a two step design methodology for composing pool-based active learning strategies applicable to both single-acquisition and batch-acquisition strategies. This framework allows for the mathematical and practical specification of a broad number of existing and novel strategies under a consistent programming model and abstraction. Furthermore, we incorporate datasets and active learning tasks applicable to them to simplify comparative evaluation and benchmarking, along with an initial group of benchmarks across datasets included in this library. The toolkit is compatible with existing ML frameworks. PyRelationAL is maintained using modern software engineering practices -- with an inclusive contributor code of conduct -- to promote long term library quality and utilisation. PyRelationAL is available under a permissive Apache licence on PyPi and at https://github.com/RelationRx/pyrelational.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 66 references that are in the map. First posted on arXiv 23 May 2022.

Builds on

23 of 66 references.

References · 23

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11 more
  1. 2019Bayesian Batch Active Learning as Sparse Subset Approximationcited by 68
  2. 2019Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisationcited by 51
  3. 2016A Benchmark and Comparison of Active Learning for Logistic Regressioncited by 36
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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2025No Foundations without Foundations -- Why semi-mechanistic models are essential for regulatory biologycited by 3
  2. 2024regAL: Python Package for Active Learning of Regression Problemscited by 0