QLattice

{{Short description|Symbolic regression machine learning algorithm}}

{{Infobox software

| name = QLattice

| screenshot =

| caption =

| developer = Abzu

| released = {{Start date and age|2020|03|04}}

| operating system = Linux, macOS, Windows

| programming language = C, Python

| genre = Machine learning

| license = CC BY-NC-ND 4.0

| website = {{URL|https://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. Since its creation, the QLattice has attracted significant attention, mainly for the inherent explainability of the models it produces.

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.

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.

Scientific results

The QLattice mainly targets scientists, and integrates well with the scientific workflow. It has been used in research into many different areas, such as energy consumption in buildings, water potability, heart failure, pre-eclampsia, Alzheimer's disease, hepatocellular carcinoma, and breast cancer.

See also

References

{{reflist|refs=

{{cite web

|title = SRBench Competition 2022: Interpretable Symbolic Regression for Data Science

|author1 = Michael Kommenda

|author2 = William La Cava

|author3 = Maimuna Majumder

|author4 = Fabricio Olivetti de França

|author5 = Marco Virgolin

|url = https://cavalab.org/srbench/competition-2022/

|date = 2022-07-22

}}

{{cite web

|title = What is a QLattice?

|author = Abzu

|url = https://docs.abzu.ai/docs/guides/getting_started/qlattice.html

|date = 2022-07-22

}}

{{cite arXiv

| author1 = Kevin René Broløs

| author2 = Meera Vieira Machado

| author3 = Chris Cave

| author4 = Jaan Kasak

| author5 = Valdemar Stentoft-Hansen

| author6 = Victor Galindo Batanero

| author7 = Tom Jelen

| author8 = Casper Wilstrup

| date=2021-04-12

| title = An Approach to Symbolic Regression Using Feyn

| class = cs.LG

| eprint = 2104.05417

}}

{{cite journal

| last1=Wenninger

| first1=Simon

| last2=Kaymakci

| first2=Can

| last3=Wiethe

| first3=Christian

| title=Explainable long-term building energy consumption prediction using QLattice

| journal=Applied Energy

| publisher=Elsevier BV

| volume=308

| year=2022

| issn=0306-2619

| doi=10.1016/j.apenergy.2021.118300

| page=118300| bibcode=2022ApEn..30818300W

| s2cid=245428233

}}

{{citation

| last1=Wilstup

| first1=Casper

| last2=Cave

| first2=Chris

| title=Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths

| publisher=Cold Spring Harbor Laboratory

| date=2021-01-15

| doi=10.1101/2021.01.15.21249874 | s2cid=231609904

| doi-access=free

}}

{{cite journal

| last1=Christensen

| first1=Niels Johan

| last2=Demharter

| first2=Samuel

| last3=Machado

| first3=Meera

| last4=Pedersen

| first4=Lykke

| last5=Salvatore

| first5=Marco

| last6=Stentoft-Hansen

| first6=Valdemar

| last7=Iglesias

| first7=Miquel Triana

| title=Identifying interactions in omics data for clinical biomarker discovery using symbolic regression

| journal=Bioinformatics

| publisher=Oxford University Press (OUP)

| date=2022-06-22

| volume=38

| issue=15

| pages=3749–3758

| issn=1367-4803

| doi=10.1093/bioinformatics/btac405 | pmid=35731214

| pmc=9344843

}}

{{cite book

| last=Bharadi

| first=Vinayak

| title=Emerging Technologies for Healthcare

| chapter=QLattice Environment and Feyn QGraph Models—A New Perspective Toward Deep Learning

| publisher=Wiley

| date=2021-07-30

| doi=10.1002/9781119792345.ch3

| pages=69–92| isbn=9781119792345

| s2cid=238793347

}}

{{cite conference

| last1=Riyantoko

| first1=Prismahardi Aji

| last2=Diyasa

| first2=I Gede Susrama Mas

| title="F.Q.A.M" Feyn-QLattice Automation Modelling: Python Module of Machine Learning for Data Classification in Water Potability

| publisher=IEEE

| date=2021-10-28

| pages=135–141

| doi=10.1109/icimcis53775.2021.9699371

| isbn=978-1-6654-2733-3

}}

{{citation

| last1=Wilstrup

| first1=Casper

| last2=Hedley

| first2=Paula L.

| last3=Rode

| first3=Line

| last4=Placing

| first4=Sophie

| last5=Wøjdemann

| first5=Karen R.

| last6=Shalmi

| first6=Anne-Cathrine

| last7=Sundberg

| first7=Karin

| last8=Christiansen

| first8=Michael

| title=Symbolic regression analysis of interactions between first trimester maternal serum adipokines in pregnancies which develop pre-eclampsia

| publisher=Cold Spring Harbor Laboratory

| date=2022-06-30

| doi=10.1101/2022.06.29.22277072

| page=| s2cid=250331945

| doi-access=free

}}

}}

Category:Data mining and machine learning software

Category:Free data analysis software

Category:Big data products

Category:2020 software

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