TL-GPRSM: A python software for constructing transfer learning Gaussian process regression surrogate model with explainability

• Published in: Software impacts 2023-05, Vol.16, p.100515, Article 100515
• Main Authors: Saida, Taisei, Nishio, Mayuko
• Format: Article
• Language: English
• Subjects: Explainability; Gaussian process regression; Surrogate model; Transfer learning
• ISSN: 2665-9638; 2665-9638
• DOI: 10.1016/j.simpa.2023.100515

This paper presents a software for the Transfer Learning Gaussian Process Regression Surrogate Model (TL-GPRSM). This software implements sampling and regression, which are essential for constructing surrogate models. Transfer learning is also supported. The implementation supports estimating the degree of effect of transfer learning to detect any loss of accuracy due to transfer learning. Estimation of the contribution of each input factor to the prediction is also supported so that the validity of the surrogate model’s predictions can be known during training. The source code is available on GitHub, including implementation and how to use it. •Python implementation of transfer learning Gaussian process regression surrogate model (TL-GPRSM).•TL-GPRSM estimates the contribution of each input parameter to the target output.•The measure of effectiveness of transfer learning is estimated, which can prevent the negative transfer in transfer learning.•A software is published in GitHub and PyPI and can be installed with the pip.