QLattice

QLattice
Developer(s)	Abzu
Initial release	March 4, 2020;5 years ago (2020-03-04)
Written in	C, Python
Operating system	Linux, macOS, Windows
Type	Machine learning
License	CC BY-NC-ND 4.0
Website	docs.abzu.ai

The QLattice is a software library which provides a framework for symbolic regression in Python. It works on Linux, Windows, and macOS. The QLattice algorithm is developed by the Danish/Spanish AI research company Abzu. ^[1] Since its creation, the QLattice has attracted significant attention, mainly for the inherent explainability of the models it produces. ^{[2] [3] [4]}

At the GECCO conference in Boston, MA in July 2022, the QLattice was announced as the winner of the synthetic track of the SRBench competition. ^[5]

Features

The QLattice works with data in categorical and numeric format. It allows the user to quickly generate, plot and inspect mathematical formulae that can potentially explain the generating process of the data. It is designed for easy interaction with the researcher, allowing the user to guide the search based on their preexisting knowledge. ^{[2] [6]}

Scientific results

The QLattice mainly targets scientists, and integrates well with the scientific workflow. ^{[2] [6]} It has been used in research into many different areas, such as energy consumption in buildings, ^[3] water potability, ^[7] heart failure, ^[8] pre-eclampsia, ^[4] Alzheimer's disease, ^[9] hepatocellular carcinoma, ^[9] and breast cancer. ^[9]

See also

• Symbolic regression
• Explainable artificial intelligence

References

1. Kevin René Broløs; Meera Vieira Machado; Chris Cave; Jaan Kasak; Valdemar Stentoft-Hansen; Victor Galindo Batanero; Tom Jelen; Casper Wilstrup (2021-04-12). "An Approach to Symbolic Regression Using Feyn". arXiv: 2104.05417 [cs.LG].
2. Abzu (2022-07-22). "What is a QLattice?".
3. Wenninger, Simon; Kaymakci, Can; Wiethe, Christian (2022). "Explainable long-term building energy consumption prediction using QLattice". Applied Energy. 308. Elsevier BV: 118300. Bibcode:2022ApEn..30818300W. doi:10.1016/j.apenergy.2021.118300. ISSN   0306-2619. S2CID   245428233.
4. Wilstrup, Casper; Hedley, Paula L.; Rode, Line; Placing, Sophie; Wøjdemann, Karen R.; Shalmi, Anne-Cathrine; Sundberg, Karin; Christiansen, Michael (2022-06-30), Symbolic regression analysis of interactions between first trimester maternal serum adipokines in pregnancies which develop pre-eclampsia, Cold Spring Harbor Laboratory, doi: 10.1101/2022.06.29.22277072 , S2CID   250331945
5. Michael Kommenda; William La Cava; Maimuna Majumder; Fabricio Olivetti de França; Marco Virgolin (2022-07-22). "SRBench Competition 2022: Interpretable Symbolic Regression for Data Science".
6. Bharadi, Vinayak (2021-07-30). "QLattice Environment and Feyn QGraph Models—A New Perspective Toward Deep Learning". Emerging Technologies for Healthcare. Wiley. pp. 69–92. doi:10.1002/9781119792345.ch3. ISBN   9781119792345. S2CID   238793347.
7. Riyantoko, Prismahardi Aji; Diyasa, I Gede Susrama Mas (2021-10-28). "F.Q.A.M" Feyn-QLattice Automation Modelling: Python Module of Machine Learning for Data Classification in Water Potability. IEEE. pp. 135–141. doi:10.1109/icimcis53775.2021.9699371. ISBN   978-1-6654-2733-3.
8. Wilstup, Casper; Cave, Chris (2021-01-15), Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths, Cold Spring Harbor Laboratory, doi: 10.1101/2021.01.15.21249874 , S2CID   231609904
9. Christensen, Niels Johan; Demharter, Samuel; Machado, Meera; Pedersen, Lykke; Salvatore, Marco; Stentoft-Hansen, Valdemar; Iglesias, Miquel Triana (2022-06-22). "Identifying interactions in omics data for clinical biomarker discovery using symbolic regression". Bioinformatics. 38 (15). Oxford University Press (OUP): 3749–3758. doi:10.1093/bioinformatics/btac405. ISSN   1367-4803. PMC   9344843 . PMID   35731214.