Package: abess
Overviewabess(Adaptive BEst Subset Selection) library aims to solve general best subset selection, i.e.,
find a small subset of predictors such that the resulting model is expected to have the highest accuracy.
The selection for best subset shows great value in scientific researches and practical application.
For example, clinicians wants to know whether a patient is health or not
based on the expression level of a few of important genes.This library implements a generic algorithm framework to find the optimal solution in an extremely fast way[1].
This framework now supports the detection of best subset under:linear regression,(multi-class) classification,censored-response modeling[2],multi-response modeling (a.k.a. multi-tasks learning), etc.
It also supports the variants of best subset selection likegroup best subset selection[3]andnuisance best subset selection[4].
Especially, the time complexity of (group) best subset selection for linear regression is certifiably polynomial[1][3].Quick startInstall the stable abess Python package from Pypi:$pipinstallabessBest subset selection for linear regression on a simulated dataset in Python:fromabess.linearimportLinearRegressionfromabess.datasetsimportmake_glm_datasim_dat=make_glm_data(n=300,p=1000,k=10,family="gaussian")model=LinearRegression()model.fit(sim_dat.x,sim_dat.y)See more examples analyzed with Python in the tutorials; the notebooks are availablehere.Runtime PerformanceTo show the power of abess in computation,
we assess its timings of the CPU execution (seconds) on synthetic datasets, and compare to
state-of-the-art variable selection methods.
The variable selection and estimation results are deferred toperformance.We compare abess Python package with scikit-learn on linear and logistic regression.
Results are presented in the below figure, and can be reproduce by running the commands in shell:$python./simulation/Python/timings.pywe obtain the runtime comparison picture:abess reaches a high efficient performance especially in linear regression where it gives the fastest solution.Open source softwareabess is a free software and its source code are publicly available inGithub.
The core framework is programmed in C++, and user-friendly R and Python interfaces are offered.
You can redistribute it and/or modify it under the terms of theGPL-v3 License.
We welcome contributions for abess, especially stretching abess to
the other best subset selection problems.CitationIf you useabessor reference our tutorials in a presentation or publication, we would appreciate citations of our library[5].Jin Zhu, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu, Xueqin Wang (2022). “abess: A Fast Best Subset Selection Library in Python and R.” Journal of Machine Learning Research (Accepted).The corresponding BibteX entry:@article{zhu2022abess,author={JinZhuandLiyuanHuandJunhaoHuangandKangkangJiangandYanhangZhangandShiyunLinandJunxianZhuandXueqinWang},title={abess:AFastBestSubsetSelectionLibraryinPythonandR},journal={JournalofMachineLearningResearch(Accepted)},year={2022}}References[1](1,2)Junxian Zhu, Canhong Wen, Jin Zhu, Heping Zhang, and Xueqin Wang (2020). A polynomial algorithm for best-subset selection problem. Proceedings of the National Academy of Sciences, 117(52):33117-33123.[2]Pölsterl, S (2020). scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn. J. Mach. Learn. Res., 21(212), 1-6.[3](1,2)Yanhang Zhang, Junxian Zhu, Jin Zhu, and Xueqin Wang (2022). A Splicing Approach to Best Subset of Groups Selection. INFORMS Journal on Computing (Accepted). doi:10.1287/ijoc.2022.1241.[4]Qiang Sun and Heping Zhang (2020). Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression, Journal of the American Statistical Association, DOI: 10.1080/01621459.2020.1737079.[5]Zhu Jin, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, and Junxian Zhu. “abess: A Fast Best-Subset Selection Library in Python and R.” Journal of Machine Learning Research 23, no. 202 (2022): 1-7.