PyALAF - The Python Active Learning with Acquisition Functions package

PyALAF is a framework for using Active Learning in Python. It is specifically designed to use so-called acquisition functions for Active Learning, as discussed e.g. in [1] and [2]. The goal of this project is to enable sequential and batch-wise learning for pool and population data. It can be used for example together with packages like LECA (Liquid Electrolyte Composition Analysis package) to combine Machine Learning-based modeling directly with Active Learning.

The LECA package can be found here: https://github.com/Harrison-Teeg/LECA.

Requirements

Python 3.9+

With the following python libraries:

• Matplotlib 3.8.2+

• Scikit-Learn 1.3.2+

• Pandas 2.1.4+

• Scipy 1.11.4+

• Openpyxl 3.1.2+

• Pyswarms 1.3.0+

Installation

This package can be installed directly from the repository using the command:

pip install git+https://github.com/TibMont/PyALAF.git

References

[1]

Wu, D.; Lin, C.-T.; Huang, J. Information Sciences 2019, 474, 90–105., doi: https://doi.org/10.1016/j.ins.2018.09.060.

[2]

Bemporad, A. Information Sciences 2023, 626, 275–292.,doi: https://doi.org/10.1016/j.ins.2023.01.028.